diff --git a/lab-p10/.ipynb_checkpoints/practice-checkpoint.ipynb b/lab-p10/.ipynb_checkpoints/practice-checkpoint.ipynb
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@@ -0,0 +1,2940 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "95cfb3ce",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e16fd0f5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"practice.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c930d9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import practice_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39141561",
+   "metadata": {},
+   "source": [
+    "# Lab-P10:  File Handling and Namedtuples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ea7c0b",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this lab, you will practice how to...\n",
+    "* use the `os` module to handle files,\n",
+    "* load data from json files,\n",
+    "* combine data from different files to create data structures,\n",
+    "* create namedtuples,\n",
+    "* use `try/except` to handle malformed data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d92e3b3",
+   "metadata": {},
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: p10 and p11 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partner up with someone for lab-p10 and p10, you have to work on lab-p11 and p11 with the **same partner**. You must acknowledge to the lab TA to receive lab attendance credit.\n",
+    "\n",
+    "You may do these lab exercises with only with your project partner; you are not allowed to start working on lab-p10 with one person, then do the project with a different partner.  Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f22/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d17b31f",
+   "metadata": {},
+   "source": [
+    "## Setup:\n",
+    "\n",
+    "Before proceeding much further, download `small_data.zip` and extract it to a directory on your\n",
+    "computer (using [Mac directions](http://osxdaily.com/2017/11/05/how-open-zip-file-mac/) or\n",
+    "[Windows directions](https://support.microsoft.com/en-us/help/4028088/windows-zip-and-unzip-files)).\n",
+    "\n",
+    "You need to make sure that the project files are stored in the following structure:\n",
+    "\n",
+    "```\n",
+    "+-- practice.ipynb\n",
+    "+-- practice_test.py\n",
+    "+-- small_data\n",
+    "|   +-- .DS_Store\n",
+    "|   +-- .ipynb_checkpoints\n",
+    "|   +-- mapping_1.json\n",
+    "|   +-- mapping_2.json\n",
+    "|   +-- mapping_3.json\n",
+    "|   +-- planets_1.csv\n",
+    "|   +-- planets_2.csv\n",
+    "|   +-- planets_3.csv\n",
+    "|   +-- stars_1.csv\n",
+    "|   +-- stars_2.csv\n",
+    "|   +-- stars_3.csv\n",
+    "```\n",
+    "\n",
+    "Make sure that the files inside `small_data.zip` are inside the `small_data` directory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f8ef1a2",
+   "metadata": {},
+   "source": [
+    "## Introduction:\n",
+    "\n",
+    "In p10 and p11, we will be studying stars and planets outside our Solar System using this dataset from the [NASA Exoplanet Archive](https://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/nph-tblView?app=ExoTbls&config=PSCompPars). We will use Python to ask some interesting questions about the laws of the universe and explore the habitability of other planets in our universe.\n",
+    "\n",
+    "In lab-p10, you will work with a small subset of the full dataset. You can find these files inside `small_data.zip`. The full dataset used in p10 and p11 is stored in the same format, so you can then use this code to parse the dataset in p10 and p11."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "912690de",
+   "metadata": {},
+   "source": [
+    "## The Data:\n",
+    "\n",
+    "You can open each of the files inside the `small_data` directory using Microsoft Excel or some other Spreadsheet viewing software to see how the data is stored. For example, these are the contents of the file `stars_1.csv`:\n",
+    "\n",
+    "|Name|Spectral Type|Stellar Effective Temperature [K]|Stellar Radius [Solar Radius]|Stellar Mass [Solar mass]|Stellar Luminosity [log(Solar)]|Stellar Surface Gravity [log10(cm/s**2)]|Stellar Age [Gyr]|\n",
+    "|----|-------------|---------------------------------|-----------------------------|-------------------------|-------------------------------|----------------------------------------|-----------------|\n",
+    "|55 Cnc|G8V|5172.00|0.94|0.91|-0.197|4.43|10.200|\n",
+    "|DMPP-1|F8 V|6196.00|1.26|1.21|0.320|4.41|2.010|\n",
+    "|GJ 876|M2.5V|3271.00|0.30|0.32|-1.907|4.87|1.000|\n",
+    "\n",
+    "As you might have already guessed, this file contains data on a number of *stars* outside our solar system along with some important statistics about these stars. The columns here are as follows:\n",
+    "\n",
+    "- `Name`: The name given to the star by the International Astronomical Union,\n",
+    "- `Spectral Type`: The Spectral Classification of the star as per the Morgan–Keenan (MK) system,\n",
+    "- `Stellar Effective Temperature [K]`: The temperature of a black body (in units of Kelvin) that would emit the observed radiation of the star,\n",
+    "- `Stellar Radius [Solar Radius]`: The radius of the star (in units of the radius of the Sun),\n",
+    "- `Stellar Mass [Solar mass]`: The mass of the star (in units of the mass of the Sun),\n",
+    "- `Stellar Luminosity [log(Solar)]`: The total amount of energy radiated by the star each second (represented by the logarithm of the energy radiated by the Sun in each second),\n",
+    "- `Stellar Surface Gravity [log10(cm/s**2)]`: The acceleration due to the gravity of the Star at its surface (represented by the logarithm of the acceleration measured in centimeter per second squared),\n",
+    "- `Stellar Age [Gyr]`: The total age of the star (in units of Giga years, i.e., billions of years).\n",
+    "\n",
+    "The two other files `stars_2.csv`, and `stars_3.csv` also store similar data in the same format. At this stage, it is alright if you do not understand what these columns mean - they will be explained to you when they become necessary (in p10 and p11).\n",
+    "\n",
+    "On the other hand, here are the contents of the file `planets_1.csv`:\n",
+    "\n",
+    "|Planet Name|Discovery Method|Discovery Year|Controversial Flag|Orbital Period [days]|Planet Radius [Earth Radius]|Planet Mass [Earth Mass]|Orbit Semi-Major Axis [au]|Eccentricity|Equilibrium Temperature [K]|Insolation Flux [Earth Flux]|\n",
+    "|-----------|----------------|--------------|------------------|---------------------|----------------------------|------------------------|---------------------------|------------|---------------------------|----------------------------|\n",
+    "|55 Cnc b|Radial Velocity|1996|0|14.65160000|13.900|263.97850|0.113400|0.000000|700||\n",
+    "|55 Cnc c|Radial Velocity|2004|0|44.39890000|8.510|54.47380|0.237300|0.030000|||\n",
+    "|DMPP-1 b|Radial Velocity|2019|0|18.57000000|5.290|24.27000|0.146200|0.083000|877||\n",
+    "|GJ 876 b|Radial Velocity|1998|0|61.11660000|13.300|723.22350|0.208317|0.032400|||\n",
+    "|GJ 876 c|Radial Velocity|2000|0|30.08810000|14.000|226.98460|0.129590|0.255910|||\n",
+    "\n",
+    "\n",
+    "This file contains data on a number of *planets* outside our solar system along with some important statistics about these planets. The columns here are as follows:\n",
+    "\n",
+    "- `Planet Name`: The name given to the planet by the International Astronomical Union,\n",
+    "- `Discovery Method`: The method by which the planet was discovered,\n",
+    "- `Discovery Year`: The year in which the planet was discovered,\n",
+    "- `Controversial Flag`: Indicates whether the status of the discovered object as a planet was disputed at the time of discovery, \n",
+    "- `Orbital Period [days]`: The amount of time (in units of days) it takes for the planet to complete one orbit around its star,\n",
+    "- `Planet Radius [Earth Radius]`: The radius of the planet (in units of the radius of the Earth),\n",
+    "- `Planet Mass [Earth Mass]`: The mass of the planet (in units of the mass of the Earth),\n",
+    "- `Orbit Semi-Major Axis [au]`: The semi-major axis of the planet's elliptical orbit around its host star (in units of Astronomical Units),\n",
+    "- `Eccentricity`: The eccentricity of the planet's orbit around its host star,\n",
+    "- `Equilibrium Temperature [K]`: The temperature of the planet (in units of Kelvin) if it were a black body heated only by its host star,\n",
+    "- `Insolation Flux [Earth Flux]`:  The amount of radiation the planet received from its host star per unit of area (in units of the Insolation Flux of the Earth from the Sun).\n",
+    "\n",
+    "The two other files `planets_2.csv`, and `planets_3.csv` also store similar data in the same format.\n",
+    "\n",
+    "\n",
+    "Finally, if you take a look at `mapping_1.json` (you can open json files using any Text Editor), you will see that the file look like this:\n",
+    "\n",
+    "```\n",
+    "{\"55 Cnc b\": \"55 Cnc\", \"55 Cnc c\": \"55 Cnc\", \"DMPP-1 b\": \"DMPP-1\", \"GJ 876 b\": \"GJ 876\", \"GJ 876 c\": \"GJ 876\"}\n",
+    "```\n",
+    "\n",
+    "This file contains a *mapping* from each *planet* in `planets_1.csv` to the *star* in `stars_1.csv` that the planet orbits. Similarly, `mapping_2.json` contains a *mapping* from each *planet* in `planets_2.csv` to the *star* in `stars_2.csv` that the planet orbits, and `mapping_3.json` contains a *mapping* from each *planet* in `planets_3.csv` to the *star* in `stars_3.csv` that the planet orbits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4dcfabf4",
+   "metadata": {},
+   "source": [
+    "## Questions and Functions:\n",
+    "\n",
+    "Let us start by importing all the modules we will need for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ab88c43",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# place all your import statements in this cell if you need to import any more modules for this project\n",
+    "\n",
+    "# we have imported these modules for you\n",
+    "import os\n",
+    "from collections import namedtuple\n",
+    "import csv\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7aafe46c",
+   "metadata": {},
+   "source": [
+    "## Segment 2: File handling with the `os` module\n",
+    "\n",
+    "In this segment, you will learn how to use the `os` module effectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8b4c29",
+   "metadata": {},
+   "source": [
+    "**Question 1.1**: List **all** the files and directories in the directory `small_data` using the `os.listdir` function.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**. The order does **not** matter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cdbc60b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "all_files = os.listdir('small_data')\n",
+    "\n",
+    "all_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc4b9047",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c62ac649",
+   "metadata": {},
+   "source": [
+    "**Important Warning:** That appeared to work just fine, but you should be **very careful** when using the `os` module. You might have noticed that there are files and directories in the list returned by `os.listdir` that **begin** with the character `\".\"` (specifically in this case, the file `\".DS_Store\"` and the directory `\".ipynb_checkpoints\"`). Such files and directories are used by some operating systems to store metadata. These files are not actually a part of your dataset, and must be **ignored**. \n",
+    "\n",
+    "When you are processing the files in any directory, you **must** always **ignore** such files that begin with the character `\".\"`, as they are not actually files in the directory. You **must** do this every time you use `os.listdir`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7885a0a7",
+   "metadata": {},
+   "source": [
+    "**Question 1.2**: List **all** the files and directories in the directory `small_data` that do **not** **start with** the character`\".\"`.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**. The order does **not** matter.\n",
+    "\n",
+    "**Hint:** You can loop through `all_files` and add to a **new list**, only the files which do **not** start with `\".\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3753387c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'actual_files', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ab03d2c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "550918c6",
+   "metadata": {},
+   "source": [
+    "**Important Warning:** You are not done yet. Look at the order in which the files in the **list** `actual_files` are stored. The **ordering** of the files in the **list** returned by `os.listdir` **depends on whether you are using a Mac or a Windows computer**. This means that if you write your code on a Windows computer, and then run it on a Mac (or vice versa), the files **will** be sorted in a **different order**. This makes `os.listdir` a little dangerous because you could index or slice this list, and it will always work the same way on your computer, but will **behave differently on another computer**. To avoid these issues, you should **make sure** that you always **sort** the output of `os.listdir` before you use it. This will ensure that the ordering remains consistent across all computers.\n",
+    "\n",
+    "When you are processing the files in any directory, you **must** always **sort** the output of `os.listdir` first. You **must** do this every time you use `os.listdir`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "515812a4",
+   "metadata": {},
+   "source": [
+    "**Question 2**: List **all** the files and directories in the directory `small_data` that do **not** **start with** the character`\".\"`, sorted in **alphabetical order**.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**, sorted in **alphabetical** order. You must **explicitly** sort the list **even if it sorted by default** on your computer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bfe718f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'files_in_small_data', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49d0a69c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "789984fb",
+   "metadata": {},
+   "source": [
+    "**Important Warning:** Every time you use `os.listdir`, you **must** **ignore** files and directories that start with `\".\"`, and also **sort** the **list** returned by the function, before you do anything else. Otherwise, you are likely to write code that **works on your computer**, but **crashes on other computers**. Such errors are hard to debug, and you **must** be very careful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9113e36",
+   "metadata": {},
+   "source": [
+    "**Question 3.1**: What is the **path** of the file `stars_1.csv` in the directory `small_data`.\n",
+    "\n",
+    "You are **allowed** to \"hardcode\" the strings `'small_data'` and `'stars_1.csv'` to answer this question.\n",
+    "\n",
+    "**Warnings:**\n",
+    "\n",
+    "1. You **must not** hardcode the **absolute path** of any file in your code. For instance, the **absolute path** of this file `stars_1.csv` could be: `C:\\Users\\ms\\cs220\\lab-p10\\small_data\\stars_1.csv`. However, if you hardcode this path in your code, it will **only work on your computer**. In this case, since the notebook `practice.ipynb` is stored in the path `C:\\Users\\ms\\cs220\\lab-p10`, the **relative path** of the file is `small_data\\stars_1.csv`, and this is the path that **must** be used, if you want your code to work on all computers.\n",
+    "2. You **must not** hardcode either the character `\"\\\"` or the character `\"/\"` in your paths. If you do so, your code will **crash** when it runs on a **different operating system**. You **must** use the `os.path.join` function to create paths."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "276c8680",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "stars_1_path = os.path.join(\"small_data\", \"stars_1.csv\")\n",
+    "\n",
+    "stars_1_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecd10b0e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7163958",
+   "metadata": {},
+   "source": [
+    "**Question 3.2**: List the **relative paths** of **all** the files in the directory `small_data`.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**. You must **ignore** files that **start with** the character`\".\"`, and your output **must** be **explicitly** sorted in **alphabetical order**.\n",
+    "\n",
+    "You are **allowed** to \"hardcode\" the name of the directory `small_data` to answer this question.\n",
+    "\n",
+    "**Warnings:**\n",
+    "\n",
+    "1. You **must not** hardcode the **absolute path** of any file in your code. You must use the **relative path** of the files.\n",
+    "2. You **must not** hardcode either the character `\"\\\"` or the character `\"/\"` in your paths. You **must** use the `os.path.join` function to create paths.\n",
+    "3. You **must explicitly** sort your list of paths.\n",
+    "\n",
+    "**Hint:** You can start with the list `files_in_small_data`, which already contains the necessary files in the correct order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f0021ac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'paths_in_small_data', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdde4405",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "129e1121",
+   "metadata": {},
+   "source": [
+    "**Question 4.1**: List the **paths** of **all** the JSON files in the directory `small_data`.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**. You must **ignore** files that **start with** the character`\".\"`, and your output **must** be **explicitly** sorted in **alphabetical order**.\n",
+    "\n",
+    "**Hint:** You can identify the JSON files as the files which end with the string `\".json\"`. You can also start with the list `files_in_small_data`, which already contains the necessary files in the correct order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0158e761",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'json_paths', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e30a329",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70f67434",
+   "metadata": {},
+   "source": [
+    "**Question 4.2**: List the **paths** of **all** the files in the directory `small_data`, whose filename starts with `\"stars\"`.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**. You must **ignore** files that **start with** the character`\".\"`, and your output **must** sorted in **alphabetical order**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "613f3896",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'stars_paths', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c8e1f17",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "971cc33a",
+   "metadata": {},
+   "source": [
+    "## Segment 3: Creating Namedtuples\n",
+    "\n",
+    "In p10, you will be reading the data in files similar to `stars_1.csv`, `stars_2.csv`, and `stars_3.csv`, and storing the data as a **dictionary** of **namedtuples**. Now would be a great time to practice creating similar data structues."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51dfa98b",
+   "metadata": {},
+   "source": [
+    "### Data Structure 1: namedtuple `Star`\n",
+    "\n",
+    "We will now create a new `Star` type (using namedtuple). It **must** have the following attributes:\n",
+    "\n",
+    "* `spectral_type`,\n",
+    "* `stellar_effective_temperature`,\n",
+    "* `stellar_radius`,\n",
+    "* `stellar_mass`,\n",
+    "* `stellar_luminosity`,\n",
+    "* `stellar_surface_gravity`,\n",
+    "* `stellar_age`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b182b3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "# define the list of attributes we want in our namedtuple\n",
+    "star_attributes = ['spectral_type',\n",
+    "                  'stellar_effective_temperature',\n",
+    "                  'stellar_radius',\n",
+    "                  'stellar_mass',\n",
+    "                  'stellar_luminosity',\n",
+    "                  'stellar_surface_gravity',\n",
+    "                  'stellar_age']\n",
+    "\n",
+    "# create the namedtuple type 'Star' with the correct attributes\n",
+    "Star = namedtuple(\"Star\", star_attributes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b36a3bc",
+   "metadata": {},
+   "source": [
+    "If you are unsure of what happened in the previous cell, it is **strongly** recommended that you review the lecture slides from October 24 on [Objects & Tuples](https://cs220.cs.wisc.edu/f22/materials/lecture_ppts/lec_20_F22.pdf). Let us now test whether we have defined the namedtuple properly by creating a `Star` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5abdff33",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# run this following cell to initialize and test an example Star object\n",
+    "\n",
+    "sun = Star('G2 V', 5780.0, 1.0, 1.0, 0.0, 4.44, 4.6)\n",
+    "\n",
+    "sun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8605263",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"star_object\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7554317",
+   "metadata": {},
+   "source": [
+    "### Segment 3.1: Creating `Star` objects from `stars_1.csv`\n",
+    "\n",
+    "Now that we have created the `Star` namedtuple, our next objective will be to read the files `stars_1.csv`, `stars_2.csv`, and `stars_3.csv` and create `Star` objects out of all the stars in there. In order to process the CSV files, you will first need to copy/paste the `process_csv` function you have been using since p6."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ce96c25",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy & paste the process_csv file from previous projects here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0c7fb11",
+   "metadata": {},
+   "source": [
+    "You are now ready to read the data in `stars_1.csv` using `process_csv` and convert the data into `Star` objects. In the cell below, you **must** read the data in `stars_1.csv` and extract the **header** and the non-header **rows** of the file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b39d817c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "stars_1_csv = process_csv(os.path.join(\"small_data\", \"stars_1.csv\")) # read the data in 'stars_1.csv'\n",
+    "stars_header = ...\n",
+    "stars_1_rows = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b072bba3",
+   "metadata": {},
+   "source": [
+    "If you wish to **verify** that you have read the file and defined the variables correctly, you can check that `stars_header` has the value:\n",
+    "\n",
+    "```python\n",
+    "['Name', 'Spectral Type', 'Stellar Effective Temperature [K]', 'Stellar Radius [Solar Radius]', \n",
+    " 'Stellar Mass [Solar mass]', 'Stellar Luminosity [log(Solar)]', 'Stellar Surface Gravity [log10(cm/s**2)]',\n",
+    " 'Stellar Age [Gyr]']\n",
+    "```\n",
+    "\n",
+    "and that `stars_1_rows` has the value:\n",
+    "\n",
+    "```python\n",
+    "[['55 Cnc', 'G8V', '5172.00', '0.94', '0.91', '-0.197', '4.43', '10.200'],\n",
+    " ['DMPP-1', 'F8 V', '6196.00', '1.26', '1.21', '0.320', '4.41', '2.010'],\n",
+    " ['GJ 876', 'M2.5V', '3271.00', '0.30', '0.32', '-1.907', '4.87', '1.000']]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "743b8b48",
+   "metadata": {},
+   "source": [
+    "**Question 5**: Create a `Star` object for the **first** star in `\"stars_1.csv\"`.\n",
+    "\n",
+    "The **attribute** of the `Star` namedtuple object, the corresponding **column** of the `stars_1.csv` file where the value should be obtained from, and the correct **data type** for the value are listed in the table below:\n",
+    "\n",
+    "|Attribute of `Star` object|Column of `stars_1.csv`|Data Type|\n",
+    "|---------|------|---------|\n",
+    "|`spectral_type`|Spectral Type|**string**|\n",
+    "|`stellar_effective_temperature`|Stellar Effective Temperature [K]|**float**|\n",
+    "|`stellar_radius`|Stellar Radius [Solar Radius]|**float**|\n",
+    "|`stellar_mass`|Stellar Mass [Solar mass]|**float**|\n",
+    "|`stellar_luminosity`|Stellar Luminosity [log(Solar)]|**float**|\n",
+    "|`stellar_surface_gravity`|Stellar Surface Gravity [log10(cm/s**2)]|**float**|\n",
+    "|`stellar_age`|Stellar Age [Gyr]|**float**|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403e9a5d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "row_idx = 0 # the index of the star we want to convert into a Star object\n",
+    "\n",
+    "# extract the values from stars_1_rows\n",
+    "spectral_type = stars_1_rows[row_idx][stars_header.index('Spectral Type')]\n",
+    "stellar_effective_temperature = float(stars_1_rows[row_idx][stars_header.index('Stellar Effective Temperature [K]')])\n",
+    "stellar_radius = float(stars_1_rows[row_idx][stars_header.index('Stellar Radius [Solar Radius]')])\n",
+    "stellar_mass = float(stars_1_rows[row_idx][stars_header.index('Stellar Mass [Solar mass]')])\n",
+    "stellar_luminosity = float(stars_1_rows[row_idx][stars_header.index('Stellar Luminosity [log(Solar)]')])\n",
+    "stellar_surface_gravity = float(stars_1_rows[row_idx][stars_header.index('Stellar Surface Gravity [log10(cm/s**2)]')])\n",
+    "stellar_age = ... # extract the value in the column 'Stellar Age [Gyr]' and typecast\n",
+    "\n",
+    "# we have initialized 'first_star' for you\n",
+    "first_star = Star(spectral_type, stellar_effective_temperature, stellar_radius, \\\n",
+    "                  stellar_mass, stellar_luminosity, \\\n",
+    "                  stellar_surface_gravity, stellar_age)\n",
+    "\n",
+    "first_star"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7edf51cb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76c0bd4a",
+   "metadata": {},
+   "source": [
+    "**Question 6**: Create a `Star` object for the **second** star in `\"stars_1.csv\"`.\n",
+    "\n",
+    "You **must** create the `Star` object similarly to what you did in the previous question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d89ecde2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'second_star', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e619fa1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c299e0",
+   "metadata": {},
+   "source": [
+    "**Question 7.1**: What is the `spectral_type` of the **second** star in `\"stars_1.csv\"`?\n",
+    "\n",
+    "You **must** answer this question by accessing the correct **attribute** of the `Star` object `second_star`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f17662cb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "second_star_spectral_type = second_star.spectral_type\n",
+    "\n",
+    "second_star_spectral_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47290b27",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e362fc58",
+   "metadata": {},
+   "source": [
+    "**Question 7.2**: What is the `stellar_age` of the **first** star in `\"stars_1.csv\"`?\n",
+    "\n",
+    "You **must** answer this question by accessing the correct **attribute** of the `Star` object `first_star`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fda919e0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'first_star_stellar_age', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8559b368",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6ab4672",
+   "metadata": {},
+   "source": [
+    "**Question 8**: Create a **dictionary** mapping the `name` of each star in `\"stars_1.csv\"` to its `Star` object.\n",
+    "\n",
+    "Your output **must** look like this:\n",
+    "```python\n",
+    "{'55 Cnc': Star(spectral_type='G8V', stellar_effective_temperature=5172.0, stellar_radius=0.94, \n",
+    "                stellar_mass=0.91, stellar_luminosity=-0.197, stellar_surface_gravity=4.43, stellar_age=10.2),\n",
+    " 'DMPP-1': Star(spectral_type='F8 V', stellar_effective_temperature=6196.0, stellar_radius=1.26, \n",
+    "                stellar_mass=1.21, stellar_luminosity=0.32, stellar_surface_gravity=4.41, stellar_age=2.01),\n",
+    " 'GJ 876': Star(spectral_type='M2.5V', stellar_effective_temperature=3271.0, stellar_radius=0.3, \n",
+    "                stellar_mass=0.32, stellar_luminosity=-1.907, stellar_surface_gravity=4.87, stellar_age=1.0)}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5588463a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "stars_1_dict = {} # initialize empty dictionary to store all stars\n",
+    "\n",
+    "for row_idx in range(len(stars_1_rows)):\n",
+    "    star_name = stars_1_rows[row_idx][stars_header.index('Name')]\n",
+    "    spectral_type = stars_1_rows[row_idx][stars_header.index('Spectral Type')]\n",
+    "    stellar_effective_temperature = float(stars_1_rows[row_idx][stars_header.index('Stellar Effective Temperature [K]')])\n",
+    "    stellar_radius = ... # extract the value in the column 'Stellar Radius [Solar Radius]' and typecast\n",
+    "    stellar_mass = ... # extract the value in the column 'Stellar Mass [Solar mass]' and typecast\n",
+    "    stellar_luminosity = ... # extract the value in the column 'Stellar Luminosity [log(Solar)]' and typecast\n",
+    "    stellar_surface_gravity = ... # extract the value in the column 'Stellar Surface Gravity [log10(cm/s**2)]' and typecast\n",
+    "    stellar_age = ... # extract the value in the column 'Stellar Age [Gyr]' and typecast\n",
+    "    \n",
+    "    star = ... # initialize the 'Star' object using the variables defined above\n",
+    "    stars_1_dict[star_name] = star\n",
+    "\n",
+    "stars_1_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4166b010",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "stars_1_rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c668680",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1617c0d",
+   "metadata": {},
+   "source": [
+    "**Question 9.1**: What is the `Star` object of the star (in `stars_1.csv`) named *GJ 876*?\n",
+    "\n",
+    "You **must** access the `Star` object in `stars_1_dict` **dictionary** defined above to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3f44647",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'gj_876', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90a733b5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb95b63f",
+   "metadata": {},
+   "source": [
+    "**Question 9.2**: What is the `stellar_luminosity` of the star (in `stars_1.csv`) named *GJ 876*?\n",
+    "\n",
+    "You **must** access the `Star` object in `stars_1_dict` **dictionary** defined above to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fd3332c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'gj_876_luminosity', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c99211c0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4197140",
+   "metadata": {},
+   "source": [
+    "### Segment 3.2: Data Cleaning - missing data\n",
+    "\n",
+    "We have already parsed the data in `stars_1.csv`. We are now ready to parse the data in **all** the star files of the `small_data` directory. However, there is one minor inconvenience - there is some missing data in `stars_2.csv` and `stars_3.csv`. For example, this is the **first** row of `stars_2.csv`:\n",
+    "\n",
+    "```python\n",
+    "['HD 158259', 'G0', '5801.89', '1.21', '1.08', '0.212', '4.25', '']\n",
+    "```\n",
+    "\n",
+    "As you can see, the value of the last column (`Stellar Age [Gyr]`) is `''`, which means that the data is missing. When the data is missing, we will want the value of the corresponding attribute in the `Star` object to be `None`.\n",
+    "\n",
+    "So, for example, if we are to convert the row above to be a `Star` object, it should look like:\n",
+    "\n",
+    "```python\n",
+    "Star(spectral_type='G0', stellar_effective_temperature=5801.89, stellar_radius=1.21, stellar_mass=1.08,\n",
+    "     stellar_luminosity=0.212, stellar_surface_gravity=4.25, stellar_age=None)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cc944fd",
+   "metadata": {},
+   "source": [
+    "### Function 1: `star_cell(row_idx, col_name, stars_rows, header=stars_header)`\n",
+    "\n",
+    "Since we need to clean the values of the **list** of **lists** `stars_rows` before we can create our requried data structure (**dictionary** mapping **strings** to `Star` objects), now would be a good time to create a function that takes in a `row_idx`, a `col_name` and a **list** of **lists** `stars_rows` (as well as the optional argument `header`) and returns the value of the column `col_name` at the row `row_idx`.\n",
+    "\n",
+    "This function **must** typecast the values it returns based on the `col_name`. If the value in `stars_rows` is missing (i.e., it is `''`), then the value returned **must** be `None`.\n",
+    "\n",
+    "Recall that the **column** of `stars_rows` where the value should be obtained from, and the correct **data type** for the value are listed in the table below:\n",
+    "\n",
+    "|Column of `stars_rows`|Data Type|\n",
+    "|------|---------|\n",
+    "|Spectral Type|**string**|\n",
+    "|Stellar Effective Temperature [K]|**float**|\n",
+    "|Stellar Radius [Solar Radius]|**float**|\n",
+    "|Stellar Mass [Solar mass]|**float**|\n",
+    "|Stellar Luminosity [log(Solar)]|**float**|\n",
+    "|Stellar Surface Gravity [log10(cm/s**2)]|**float**|\n",
+    "|Stellar Age [Gyr]|**float**|\n",
+    "\n",
+    "**Hint:** You can use the `cell` function defined in p6 and p7 for inspiration here. Note that unlike previous `cell` functions, in this function, the **list** of **lists** is also included as one of the arguments to the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51e6ebdd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# the default argument to the parameter 'header' is the global variable 'stars_header' defined above\n",
+    "def star_cell(row_idx, col_name, stars_rows, header=stars_header):\n",
+    "    col_idx = header.index(...)\n",
+    "    val = stars_rows[row_idx][col_idx]\n",
+    "    # return None if value is missing\n",
+    "    # else typecast 'val' depending on 'col_name' and return it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c2807ef",
+   "metadata": {},
+   "source": [
+    "**Question 10.1**: Use the `star_cell` function to find the value of the column `\"Spectral Type\"` of the **first** star in `\"stars_2.csv\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa3fbef4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "# first read the data in 'stars_2.csv' as a list of lists\n",
+    "stars_2_data = process_csv(os.path.join(\"small_data\", \"stars_2.csv\"))\n",
+    "stars_2_rows = stars_2_data[1:]\n",
+    "\n",
+    "# use the 'star_cell' function to extract the correct value\n",
+    "first_star_type = star_cell(0, 'Spectral Type', stars_2_rows)\n",
+    "\n",
+    "first_star_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7f6dd0b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1685bf69",
+   "metadata": {},
+   "source": [
+    "**Question 10.2**: Use the `star_cell` function to find the value of the column `\"Stellar Age [Gyr]\"` of the **second** star in `\"stars_2.csv\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4ae8c43",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "# do not worry if there is no output, the variable is expected to hold the value None\n",
+    "\n",
+    "# use the 'star_cell' function to extract the correct value\n",
+    "second_star_age = star_cell(1, 'Stellar Age [Gyr]', stars_2_rows)\n",
+    "\n",
+    "second_star_age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccde8233",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfae45ad",
+   "metadata": {},
+   "source": [
+    "**Question 10.3**: Use the `star_cell` function to find the value of the column `\"Stellar Mass [Solar mass]\"` of the **third** star in `\"stars_2.csv\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d10c0757",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "# use the 'star_cell' function to extract the correct value\n",
+    "third_star_mass = star_cell(2, 'Stellar Mass [Solar mass]', stars_2_rows)\n",
+    "\n",
+    "third_star_mass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a079689d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a0dd0f1",
+   "metadata": {},
+   "source": [
+    "**Question 11**: Create a **dictionary** mapping the `name` of each star in `\"stars_2.csv\"` to its `Star` object.\n",
+    "\n",
+    "You **must** use the `star_cell` function to extract data from `stars_2.csv`.\n",
+    "\n",
+    "Your output **must** look like this:\n",
+    "```python\n",
+    "{'HD 158259': Star(spectral_type='G0', stellar_effective_temperature=5801.89, stellar_radius=1.21, \n",
+    "                   stellar_mass=1.08, stellar_luminosity=0.212, stellar_surface_gravity=4.25, stellar_age=None),\n",
+    " 'K2-187': Star(spectral_type=None, stellar_effective_temperature=5438.0, stellar_radius=0.83, \n",
+    "                stellar_mass=0.97, stellar_luminosity=-0.21, stellar_surface_gravity=4.6, stellar_age=None),\n",
+    " 'WASP-47': Star(spectral_type=None, stellar_effective_temperature=5552.0, stellar_radius=1.14, \n",
+    "                 stellar_mass=1.04, stellar_luminosity=0.032, stellar_surface_gravity=4.34, stellar_age=6.5)}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03375ba6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "stars_2_dict = {} # initialize empty dictionary to store all stars\n",
+    "\n",
+    "for row_idx in range(len(stars_2_rows)):\n",
+    "    star_name = star_cell(row_idx, 'Name', stars_2_rows)\n",
+    "    spectral_type = star_cell(row_idx, 'Spectral Type', stars_2_rows)\n",
+    "    stellar_effective_temperature = star_cell(row_idx, 'Stellar Effective Temperature [K]', stars_2_rows)\n",
+    "    stellar_radius = star_cell(row_idx, 'Stellar Radius [Solar Radius]', stars_2_rows)\n",
+    "    stellar_mass = star_cell(row_idx, 'Stellar Mass [Solar mass]', stars_2_rows)\n",
+    "    stellar_luminosity = star_cell(row_idx, 'Stellar Luminosity [log(Solar)]', stars_2_rows)\n",
+    "    stellar_surface_gravity = star_cell(row_idx, 'Stellar Surface Gravity [log10(cm/s**2)]', stars_2_rows)\n",
+    "    stellar_age = ... # extract the value in column 'Stellar Age [Gyr]' using 'star_cell'\n",
+    "    \n",
+    "    star = ... # initialize the 'Star' object using the variables defined above\n",
+    "    stars_2_dict[...] = star\n",
+    "\n",
+    "stars_2_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a5ff58e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f0cba96",
+   "metadata": {},
+   "source": [
+    "**Question 12.1**: Create a **dictionary** mapping the `name` of each star in `\"stars_3.csv\"` to its `Star` object.\n",
+    "\n",
+    "You **must** use the `star_cell` function to extract data from `stars_3.csv`.\n",
+    "\n",
+    "Your output **must** look like this:\n",
+    "```python\n",
+    "{'K2-133': Star(spectral_type='M1.5 V', stellar_effective_temperature=3655.0, stellar_radius=0.46, \n",
+    "                stellar_mass=0.46, stellar_luminosity=-1.479, stellar_surface_gravity=4.77, stellar_age=None),\n",
+    " 'K2-138': Star(spectral_type='G8 V', stellar_effective_temperature=5356.3, stellar_radius=0.86, \n",
+    "                stellar_mass=0.94, stellar_luminosity=-0.287, stellar_surface_gravity=4.54, stellar_age=2.8),\n",
+    " 'GJ 667 C': Star(spectral_type='M1.5 V', stellar_effective_temperature=3350.0, stellar_radius=None, \n",
+    "                  stellar_mass=0.33, stellar_luminosity=-1.863, stellar_surface_gravity=4.69, stellar_age=2.0)}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ecd8285",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'stars_3_dict', then display it\n",
+    "\n",
+    "# TODO: initialize stars_3_dict as an empty dictionary\n",
+    "# TODO: read the contents of 'stars_3.csv' and store in a list of lists\n",
+    "# TODO: loop through the rows and convert each row into a Star object\n",
+    "# TODO: add the Star object to stars_3_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f39ece39",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfa62c8b",
+   "metadata": {},
+   "source": [
+    "**Question 12.2**: Combine the three **dictionaries** `stars_1_dict`, `stars_2_dict`, and `stars_3_dict` into a single **dictionary** with all the stars in the `small_data` directory.\n",
+    "\n",
+    "**Hint:** The `dict.update` **dictionary method** takes in a second **dictionary** as its input, and adds the key/value pairs in this dictionary to the first **dictionary** `dict`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9dc4dfe8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "stars_dict = ... # initialize an empty dictionary\n",
+    "stars_dict.update(stars_1_dict) # add stars_1_dict to stars_dict\n",
+    "# similarly add stars_2_dict and stars_3_dict to stars_dict\n",
+    "\n",
+    "stars_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dba4e7c3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eee98cc",
+   "metadata": {},
+   "source": [
+    "### Data Structure 2: namedtuple `Planet`\n",
+    "\n",
+    "Just as you did with the stars, you will be using namedtuples to store the data about the planets in the `planets_1.csv`, `planets_2.csv`, and `planets_3.csv` files. Before you start reading these files however, you **must** create a new `Planet` type (using namedtuple). It **must** have the following attributes:\n",
+    "\n",
+    "* `planet_name`,\n",
+    "* `host_name`,\n",
+    "* `discovery_method`,\n",
+    "* `discovery_year`,\n",
+    "* `controversial_flag`,\n",
+    "* `orbital_period`,\n",
+    "* `planet_radius`,\n",
+    "* `planet_mass`,\n",
+    "* `semi_major_radius`,\n",
+    "* `eccentricity`,\n",
+    "* `equilibrium_temperature`\n",
+    "* `insolation_flux`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "258db0ec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "planet_attributes = ['planet_name', 'host_name', 'discovery_method', 'discovery_year', 'controversial_flag', \n",
+    "                     'orbital_period', 'planet_radius', 'planet_mass', 'semi_major_radius', 'eccentricity',\n",
+    "                     'equilibrium_temperature', 'insolation_flux']\n",
+    "\n",
+    "# define the namedtuple 'Planet'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e57afb8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# run this following cell to initialize and test an example Planet object\n",
+    "# if this cell fails to execute, you have likely not defined the namedtuple 'Star' correctly\n",
+    "jupiter = Planet('Jupiter', 'Sun', 'Imaging', 1610, False, 4333.0, 11.209, 317.828, 5.2038, 0.0489, 110, 0.0345)\n",
+    "\n",
+    "jupiter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f02c1886",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"planet_object\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28ff46bb",
+   "metadata": {},
+   "source": [
+    "### Segment 3.3: Creating `Planet` objects\n",
+    "\n",
+    "We are now ready to read the files in the `small_data` directory and create `Planet` objects. Creating `Planet` objects however, is going to be more difficult than creating `Star` objects, because the data required to create a single `Planet` object is split up into different files.\n",
+    "\n",
+    "The `planets_1.csv`, `planets_2.csv`, and `planets_3.csv` files contain all the data required to create `Planet` objects **except** for the `host_name`. The `host_name` for each planet is to be found in the `mapping_1.json`, `mapping_2.json`, and `mapping_3.json` files."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39b1de4a",
+   "metadata": {},
+   "source": [
+    "First, let us read the data in `planets_1.csv`. Since this is a CSV file, you can use the `process_csv` function from above to read this file. In the cell below, you **must** read the data in `planets_1.csv` and extract the **header** and the non-header **rows** of the file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91ee03e8",
+   "metadata": {},
+   "source": [
+    "**Question 13.1**: Read the contents of `'planets_1.csv'` into a **list** of **lists** using the `process_csv` function, and extract the **header** and the **rows** in the file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "421e8a37",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# read the data in 'planets_1.csv'; remember to use os.path.join to create relative paths\n",
+    "planets_1_csv = process_csv(...)\n",
+    "planets_header = ...\n",
+    "planets_1_rows = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62008b58",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb64dc06",
+   "metadata": {},
+   "source": [
+    "Now, you are ready to read the data in `mapping_1.json`. Since this is a JSON file, you will need a new function to read this file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2519a3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this function uses the 'load' function from the json module (already imported in this notebook) to read files\n",
+    "def read_json(path):\n",
+    "    with open(path, encoding=\"utf-8\") as f:\n",
+    "        return json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be468072",
+   "metadata": {},
+   "source": [
+    "**Question 13.2**: Read the contents of `'mapping_1.json'` into a **dictionary** using the `read_json` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35517846",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this for you\n",
+    "\n",
+    "mapping_1_json = read_json(os.path.join(\"small_data\", \"mapping_1.json\"))\n",
+    "\n",
+    "mapping_1_json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4563e321",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25210279",
+   "metadata": {},
+   "source": [
+    "### Segment 3.4: Combining data from CSV and JSON files\n",
+    "\n",
+    "We are now ready to combine the data from `planets_1_rows` and `mapping_1_json` to create `Planet` objects. Before we start, it might be useful to create a function similar to `star_cell` for preprocessing the values in the CSV files."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f5856e8",
+   "metadata": {},
+   "source": [
+    "### Function 2: `planet_cell(row_idx, col_name, planets_rows, header=planets_header)`\n",
+    "\n",
+    "Just like the data in `stars_1.csv`, `stars_2.csv`, and `stars_3.csv`, some of the data in `planets_1.csv`, `planets_2.csv`, and `planets_3.csv` is **missing**.  So, now would be a good time to create a function that takes in a `row_idx`, a `col_name` and a **list** of **lists** `planets_rows` (as well as the optional argument `header`) and returns the value of the column `col_name` at the row `row_idx`.\n",
+    "\n",
+    "This function **must** typecast the values it returns based on the `col_name`. If the value in `planets_rows` is missing (i.e., it is `''`), then the value returned **must** be `None`.\n",
+    "\n",
+    "The **column** of `planets_rows` where the value should be obtained from, and the correct **data type** for the value are listed in the table below:\n",
+    "\n",
+    "|Column of `planets_rows`|Data Type|\n",
+    "|------|---------|\n",
+    "|Planet Name|**string**|\n",
+    "|Discovery Year|**int**|\n",
+    "|Discovery Method|**string**|\n",
+    "|Controversial Flag|**bool**|\n",
+    "|Orbital Period [days]|**float**|\n",
+    "|Planet Radius [Earth Radius]|**float**|\n",
+    "|Planet Mass [Earth Mass]|**float**|\n",
+    "|Orbit Semi-Major Axis [au]|**float**|\n",
+    "|Eccentricity|**float**|\n",
+    "|Equilibrium Temperature [K]|**float**|\n",
+    "|Insolation Flux [Earth Flux]|**float**|\n",
+    "\n",
+    "**Important Warning:** Notice that the `Controversial Flag` column has to be converted into a **bool**. The data is stored in `planets_1.csv` (and consequently in `planets_rows`) as `\"0\"/\"1\"` values (with `\"0\"` representing `False` and `\"1\"` representing `True`). However typecasting **strings** to **bools** is not straightforward. Run the following cell and try to figure out what is happening:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a59c292f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "strings = [\"0\", \"1\", \"\", \" \", \"True\", \"False\"]\n",
+    "for string in strings:\n",
+    "    print(bool(string))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8410b858",
+   "metadata": {},
+   "source": [
+    "If you want to convert the **strings** into **bools**, you will have to explicitly use `if/else` statements to determine whether the value is `\"0\"` or `\"1\"`, as can be seen in th starter code below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de47fbe3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "def planet_cell(row_idx, col_name, planets_rows, header=planets_header):\n",
+    "    col_idx = ... # extract col_idx from col_name and header\n",
+    "    val = ... # extract the value at row_idx and col_idx\n",
+    "    if val == '':\n",
+    "        return None\n",
+    "    if col_name in [\"Controversial Flag\"]:\n",
+    "        if val == \"1\":\n",
+    "            return ...\n",
+    "        else:\n",
+    "            return ...\n",
+    "    # for all other columns typecast 'val' depending on col_name and return it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f86b9d78",
+   "metadata": {},
+   "source": [
+    "**Question 14.1**: Use the `planet_cell` function to find the value of the column `\"Planet Name\"` of the **first** planet in `\"planets_1.csv\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80801753",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "first_planet_name = planet_cell(0, 'Planet Name', planets_1_rows)\n",
+    "\n",
+    "first_planet_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3d62848",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4be9ebff",
+   "metadata": {},
+   "source": [
+    "**Question 14.2**: Use the `planet_cell` function to find the value of the column `\"Insolation Flux [Earth Flux]\"` of the **first** planet in `\"planets_1.csv\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f6c393d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "# do not worry if there is no output, the variable is expected to hold the value None\n",
+    "\n",
+    "first_planet_flux = planet_cell(0, 'Insolation Flux [Earth Flux]', planets_1_rows)\n",
+    "\n",
+    "first_planet_flux"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ce3d595",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53e78665",
+   "metadata": {},
+   "source": [
+    "**Question 14.3**: Use the `planet_cell` function to find the value of the column `\"Controversial Flag\"` of the **second** planet in `\"planets_1.csv\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbdee4d6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "second_planet_controversy = planet_cell(1, 'Controversial Flag', planets_1_rows)\n",
+    "\n",
+    "second_planet_controversy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c77f6ac4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86e7ee64",
+   "metadata": {},
+   "source": [
+    "**Question 15**: Create a `Planet` object for the **first** star in `\"planets_1.csv\"`.\n",
+    "\n",
+    "The **attribute** of the `Planet` namedtuple object, the corresponding **column** of the `planets_1.csv` file where the value should be obtained from, and the correct **data type** for the value are listed in the table below:\n",
+    "\n",
+    "|Attribute of `Planet` object|Column of `planets_1.csv`|Data Type|\n",
+    "|---------|------|---------|\n",
+    "|`planet_name`|Planet Name|**string**|\n",
+    "|`host_name`| - |**string**|\n",
+    "|`discovery_method`|Discovery Method|**string**|\n",
+    "|`discovery_year`|Discovery Year|**int**|\n",
+    "|`controversial_flag`|Controversial Flag|**bool**|\n",
+    "|`orbital_period`|Orbital Period [days]|**float**|\n",
+    "|`planet_radius`|Planet Radius [Earth Radius]|**float**|\n",
+    "|`planet_mass`|Planet Mass [Earth Mass]|**float**|\n",
+    "|`semi_major_radius`|Orbit Semi-Major Axis [au]|**float**|\n",
+    "|`eccentricity`|Eccentricity|**float**|\n",
+    "|`equilibrium_temperature`|Equilibrium Temperature [K]|**float**|\n",
+    "|`insolation_flux`|Insolation Flux [Earth Flux]|**float**|\n",
+    "\n",
+    "\n",
+    "The value of the `host_name` attribute is found in `mapping_1.json`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e446692",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "planets_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c9ba810",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "row_idx = 0 # the index of the planet we want to convert into a Planet object\n",
+    "\n",
+    "# extract the values from planets_1_rows\n",
+    "planet_name = planet_cell(row_idx, 'Planet Name', planets_1_rows)\n",
+    "host_name = mapping_1_json[...]\n",
+    "discovery_method = planet_cell(row_idx, 'Discovery Method', planets_1_rows)\n",
+    "discovery_year = planet_cell(row_idx, 'Discovery Year', planets_1_rows)\n",
+    "controversial_flag = planet_cell(row_idx, 'Controversial Flag', planets_1_rows)\n",
+    "orbital_period = planet_cell(row_idx, 'Orbital Period [days]', planets_1_rows)\n",
+    "planet_radius = planet_cell(row_idx, 'Planet Radius [Earth Radius]', planets_1_rows)\n",
+    "planet_mass = planet_cell(row_idx, 'Planet Mass [Earth Mass]', planets_1_rows)\n",
+    "semi_major_radius = planet_cell(row_idx, 'Orbit Semi-Major Axis [au]', planets_1_rows)\n",
+    "eccentricity = planet_cell(row_idx, 'Eccentricity', planets_1_rows)\n",
+    "equilibrium_temperature = planet_cell(row_idx, 'Equilibrium Temperature [K]', planets_1_rows)\n",
+    "insolation_flux = ... # extract the value in column 'Insolation Flux [Earth Flux]' using 'planet_cell'\n",
+    "\n",
+    "# initialize 'first_planet'\n",
+    "first_planet = Planet(planet_name, host_name, discovery_method, discovery_year,\\\n",
+    "                  controversial_flag, orbital_period, planet_radius, planet_mass,\\\n",
+    "                  semi_major_radius, eccentricity, equilibrium_temperature, insolation_flux)\n",
+    "\n",
+    "first_planet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07a07395",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e200fade",
+   "metadata": {},
+   "source": [
+    "**Question 16**: Create a **list** of `Planet` objects of each planet in `\"planets_1.csv\"`.\n",
+    "\n",
+    "Your output **must** look like this:\n",
+    "```python\n",
+    "[Planet(planet_name='55 Cnc b', host_name='55 Cnc', discovery_method='Radial Velocity', \n",
+    "        discovery_year=1996, controversial_flag=False, orbital_period=14.6516, \n",
+    "        planet_radius=13.9, planet_mass=263.9785, semi_major_radius=0.1134, eccentricity=0.0,\n",
+    "        equilibrium_temperature=700.0, insolation_flux=None),\n",
+    " Planet(planet_name='55 Cnc c', host_name='55 Cnc', discovery_method='Radial Velocity', \n",
+    "        discovery_year=2004, controversial_flag=False, orbital_period=44.3989, \n",
+    "        planet_radius=8.51, planet_mass=54.4738, semi_major_radius=0.2373, eccentricity=0.03, \n",
+    "        equilibrium_temperature=None, insolation_flux=None),\n",
+    " Planet(planet_name='DMPP-1 b', host_name='DMPP-1', discovery_method='Radial Velocity', \n",
+    "        discovery_year=2019, controversial_flag=False, orbital_period=18.57, \n",
+    "        planet_radius=5.29, planet_mass=24.27, semi_major_radius=0.1462, eccentricity=0.083, \n",
+    "        equilibrium_temperature=877.0, insolation_flux=None),\n",
+    " Planet(planet_name='GJ 876 b', host_name='GJ 876', discovery_method='Radial Velocity', \n",
+    "        discovery_year=1998, controversial_flag=False, orbital_period=61.1166, \n",
+    "        planet_radius=13.3, planet_mass=723.2235, semi_major_radius=0.208317, eccentricity=0.0324,\n",
+    "        equilibrium_temperature=None, insolation_flux=None),\n",
+    " Planet(planet_name='GJ 876 c', host_name='GJ 876', discovery_method='Radial Velocity', \n",
+    "        discovery_year=2000, controversial_flag=False, orbital_period=30.0881, \n",
+    "        planet_radius=14.0, planet_mass=226.9846, semi_major_radius=0.12959, eccentricity=0.25591, \n",
+    "        equilibrium_temperature=None, insolation_flux=None)]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed79205c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'planets_1_list', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "247bca81",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9159e70a",
+   "metadata": {},
+   "source": [
+    "**Question 17.1**: What is the **fifth** `Planet` object in `'planets_1.csv'`?\n",
+    "\n",
+    "You **must** access from the `planets_1_list` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4835c37",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'fifth_planet', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a06a49ae",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8dea1bd",
+   "metadata": {},
+   "source": [
+    "**Question 17.2**: What is the `planet_name` of the **fifth** `Planet` in `'planets_1.csv'`?\n",
+    "\n",
+    "You **must** access from the `planets_1_list` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83acb2f5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'fifth_planet_name', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a98cfdc3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0af0c90f",
+   "metadata": {},
+   "source": [
+    "### Segment 3.5: Data Cleaning - broken CSV rows\n",
+    "\n",
+    "The code you have written worked well for reading the data in `planets_1.csv` and `mapping_1.json`. However, it will likely **not** work for `planets_2.csv` and `mapping_2.json`. This is because the file `planets_2.csv` is **broken**. For some reason, a few rows in `planets_2.csv` have their data jumbled up. This is what `planets_2.csv` looks like:\n",
+    "\n",
+    "|Planet Name|Discovery Method|Discovery Year|Controversial Flag|Orbital Period [days]|Planet Radius [Earth Radius]|Planet Mass [Earth Mass]|Orbit Semi-Major Axis [au]|Eccentricity|Equilibrium Temperature [K]|Insolation Flux [Earth Flux]|\n",
+    "|-----------|----------------|--------------|------------------|---------------------|----------------------------|------------------------|--------------------------|------------|---------------------------|----------------------------|\n",
+    "|HD 158259 b|Radial Velocity|2020|0|2.17800000|1.292|2.22000|||1478|794.22|\n",
+    "|K2-187 b|Transit|2018|0|0.77401000|1.200|1.87000|0.016400||1815||\n",
+    "|K2-187 c|Transit|2018|0|2.87151200|1.400|2.54000|0.039200||1173||\n",
+    "|K2-187 d|K2-187|Transit|2018|0|7.14958400|2.400|6.35000|0.072000||865|\n",
+    "|WASP-47 b|2012|Transit|0|4.15914920|12.640|363.60000|0.052000|0.002800|1275|534.00|\n",
+    "\n",
+    "We can see that for some reason, in the **fourth** row, the value under the column `Discovery Method` is the name of the planet's host star. This is causing all the other columns in the row to also take meaningless values.\n",
+    "\n",
+    "Similarly, in the **fifth** row, we see that the values under the columns `Discovery Method` and `Discovery Year` are swapped.\n",
+    "\n",
+    "We will call such a **row** in a CSV file where the values under a column do not match the expected format to be a **broken row**. While it is possible to sometimes extract useful data from broken rows, in this lab and in p10, we will simply **skip** broken rows.\n",
+    "\n",
+    "In order to **skip** broken rows, you should first know how to recognize a **broken row**. In general, there is no general rule that helps you identify when a row is broken. This is because CSV rows can be **broken** in all sorts of different ways. Thankfully, we don't have to write code to catch all sorts of weird cases. It will suffice for us to manually **inspect** the file `planets_2.csv`, and identify **how** the rows are broken.\n",
+    "\n",
+    "The simplest way to recognize if a row is broken is if you run into any **RunTime Errors** when you execute your code. So, one simple way to skip bad rows would be to use `try/except` blocks to avoid processing any rows that cause the code to crash.\n",
+    "\n",
+    "**Important Note:** In this dataset, as you might have already noticed, it would be **significantly harder** to detect **broken rows** where some of the numerical values are swapped (for example, `Planet Radius [Earth Radius]` and `Planet Mass [Earth Mass]`). You may **assume** that the numerical values are **not** swapped in **any** row, and that **only the rows** in which the **data types** are not as expected are **broken**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32388f6c",
+   "metadata": {},
+   "source": [
+    "**Question 18**: Create a **list** of `Planet` objects of each planet in `\"planets_2.csv\"`.\n",
+    "\n",
+    "You **must** skip any broken rows in the CSV file. Your output **must** look like this:\n",
+    "```python\n",
+    "[Planet(planet_name='HD 158259 b', host_name='HD 158259', discovery_method='Radial Velocity', \n",
+    "        discovery_year=2020, controversial_flag=False, orbital_period=2.178, \n",
+    "        planet_radius=1.292, planet_mass=2.22, semi_major_radius=None, eccentricity=None, \n",
+    "        equilibrium_temperature=1478.0, insolation_flux=794.22),\n",
+    " Planet(planet_name='K2-187 b', host_name='K2-187', discovery_method='Transit', \n",
+    "        discovery_year=2018, controversial_flag=False, orbital_period=0.77401, \n",
+    "        planet_radius=1.2, planet_mass=1.87, semi_major_radius=0.0164, eccentricity=None, \n",
+    "        equilibrium_temperature=1815.0, insolation_flux=None),\n",
+    " Planet(planet_name='K2-187 c', host_name='K2-187', discovery_method='Transit', \n",
+    "        discovery_year=2018, controversial_flag=False, orbital_period=2.871512, \n",
+    "        planet_radius=1.4, planet_mass=2.54, semi_major_radius=0.0392, eccentricity=None, \n",
+    "        equilibrium_temperature=1173.0, insolation_flux=None)]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "052d544b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "planets_2_data = ... # read planets_2.csv\n",
+    "planets_2_rows = ... # extract the rows from planets_2_data\n",
+    "mapping_2_json = ... # read mapping_2.json\n",
+    "\n",
+    "planets_2_list = []\n",
+    "for row_idx in range(len(planets_2_rows)):\n",
+    "    try:\n",
+    "        pass # replace with your code\n",
+    "        # create a Planet object and append to 'planets_2_list'\n",
+    "    except ValueError: # do not change this line or the line below\n",
+    "        continue\n",
+    "\n",
+    "planets_2_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adfc7631",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64039907",
+   "metadata": {},
+   "source": [
+    "**Important Warning:** It is considered a bad coding practice to use *bare* `try/except` blocks. This means that you should never write code like this:\n",
+    "\n",
+    "```python\n",
+    "try:\n",
+    "    # some code\n",
+    "except:\n",
+    "    # some other code\n",
+    "```\n",
+    "\n",
+    "If you use *bare* `try/except` blocks, your code will seemingly work even if there are bugs in there, and it can get very hard to debug. You should always **explicitly** catch for specific errors like this:\n",
+    "\n",
+    "```python\n",
+    "try:\n",
+    "    # some code\n",
+    "except ValueError:\n",
+    "    # some other code\n",
+    "except IndexError:\n",
+    "    # some other code\n",
+    "```\n",
+    "\n",
+    "This way, your code will still crash if there is some other unexpected bug in your code that needs to be fixed, and will only go to the `except` block if it runs into a `ValueError` or an `IndexError`. The starter code above already catches specifically for `ValueError`. In p10, you will have to deal with broken CSV rows where you have to deal with a variety of different errors. You **must** explicitly catch these errors. You **must not** use *bare* `try/except` blocks in p10 either."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0b5c441",
+   "metadata": {},
+   "source": [
+    "### Segment 3.6: Data Cleaning - broken JSON files\n",
+    "\n",
+    "So far, we have written code that can read `planets_1.csv` and `mapping_1.json`, as well as `planets_2.csv` and `mapping_2.json`. However, if you try to read `mapping_3.json`, you are likely to run into some issues. This is because the file `mapping_3.json` is **broken**. Unlike **broken** CSV files, where we only had to skip the **broken rows**, it is much harder to parse **broken JSON files**. When a JSON file is **broken**, we often have no choice but to **skip the file entirely**.\n",
+    "\n",
+    "It is also not easy to detect if a JSON file is **broken** using `if` statements. The easiest is to simply try to read the file using the `read_json` function and check if the code crashes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24e9a933",
+   "metadata": {},
+   "source": [
+    "**Question 19**: Display the data in all `mappings` files as a **dictionary**.\n",
+    "\n",
+    "You **must** use `try/except` to determine if any of the JSON files are broken, and skip them if they are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77c2df33",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "mapping_data = {}\n",
+    "\n",
+    "# we loop through the list of mapping paths defined in q4.1\n",
+    "for mapping_path in json_paths:\n",
+    "    try:\n",
+    "        mapping_json = read_json(mapping_path)\n",
+    "    except json.JSONDecodeError:\n",
+    "        mapping_json = {}\n",
+    "    mapping_data.update(mapping_json)\n",
+    "    \n",
+    "mapping_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4c8807d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4921d314",
+   "metadata": {},
+   "source": [
+    "In the above cell, note that in the `try/except` block, we specifically checked for the `json.JSONDecodeError`. This is the error that is thrown when you try to call `json.load` on a **broken** JSON file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55596386",
+   "metadata": {},
+   "source": [
+    "## Segment 4: Data Analysis\n",
+    "\n",
+    "We have now managed to read all the data in the `small_data` directory. Now is the time to test if our data structures work!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19bad36d",
+   "metadata": {},
+   "source": [
+    "**Question 20.1**: What is the `host_name` of the **second** planet in `'planets_2.csv'`?\n",
+    "\n",
+    "You **must** skip any broken rows. So, you can directly access from the list `planets_2_list` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "495c9795",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'second_planet_host', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ec46afe",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d026ac61",
+   "metadata": {},
+   "source": [
+    "**Question 20.2**: What is the `Star` object of the **third** planet in `'planets_2.csv'`?\n",
+    "\n",
+    "You **must** skip any broken rows. So, you can directly access from the list `planets_2_list` to answer this question.\n",
+    "\n",
+    "**Hint:** You can use the `stars_dict` **dictionary** defined in q12.2 to find the `Star` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59a3ba60",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'third_planet_star', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f96d702",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f2c3768",
+   "metadata": {},
+   "source": [
+    "**Question 20.3**: What is the `stellar_radius` of the star around which the **first** planet in `'planets_1.csv'` orbits?\n",
+    "\n",
+    "You can directly access from the list `planets_1_list` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37786949",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'first_planet_star_radius', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cf1837f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20-3\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "planet_object": {
+     "name": "planet_object",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"qplanet_object\", jupiter)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1-1": {
+     "name": "q1-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1-1\", all_files)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1-2": {
+     "name": "q1-2",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1-2\", actual_files)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10-1": {
+     "name": "q10-1",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10-1\", first_star_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10-2": {
+     "name": "q10-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10-2\", second_star_age)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10-3": {
+     "name": "q10-3",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10-3\", third_star_mass)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q11\", stars_2_dict)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12-1": {
+     "name": "q12-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q12-1\", stars_3_dict)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12-2": {
+     "name": "q12-2",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q12-2\", stars_dict)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13-1": {
+     "name": "q13-1",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q13-1\", planets_1_rows)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13-2": {
+     "name": "q13-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q13-2\", mapping_1_json)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14-1": {
+     "name": "q14-1",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q14-1\", first_planet_name)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14-2": {
+     "name": "q14-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q14-2\", first_planet_flux)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14-3": {
+     "name": "q14-3",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q14-3\", second_planet_controversy)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q15\", first_planet)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q16\", planets_1_list)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17-1": {
+     "name": "q17-1",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17-1\", fifth_planet)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17-2": {
+     "name": "q17-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17-2\", fifth_planet_name)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q18\", planets_2_list)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q19\", mapping_data)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q2\", files_in_small_data)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20-1": {
+     "name": "q20-1",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q20-1\", second_planet_host)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20-2": {
+     "name": "q20-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q20-2\", third_planet_star)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20-3": {
+     "name": "q20-3",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q20-3\", first_planet_star_radius)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3-1": {
+     "name": "q3-1",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3-1\", stars_1_path)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3-2": {
+     "name": "q3-2",
+     "points": 4,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3-2\", paths_in_small_data)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4-1": {
+     "name": "q4-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q4-1\", json_paths)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4-2": {
+     "name": "q4-2",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q4-2\", stars_paths)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q5\", first_star)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q6\", second_star)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7-1": {
+     "name": "q7-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7-1\", second_star_spectral_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7-2": {
+     "name": "q7-2",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7-2\", first_star_stellar_age)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q8\", stars_1_dict)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9-1": {
+     "name": "q9-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q9-1\", gj_876)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9-2": {
+     "name": "q9-2",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q9-2\", gj_876_luminosity)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "star_object": {
+     "name": "star_object",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"qstar_object\", sun)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p10/practice.ipynb b/lab-p10/practice.ipynb
index 0be537d6153ec148addde0a34b0c3dd888c4fc57..c1b1e44168c21cc38efffa438349168a78a3909f 100644
--- a/lab-p10/practice.ipynb
+++ b/lab-p10/practice.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "95cfb3ce",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/lab-p11/.ipynb_checkpoints/practice-checkpoint.ipynb b/lab-p11/.ipynb_checkpoints/practice-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e74e743debc6069c2a24345316286d6d800565cc
--- /dev/null
+++ b/lab-p11/.ipynb_checkpoints/practice-checkpoint.ipynb
@@ -0,0 +1,2208 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6ccd3634",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2aad5dca",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"practice.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f553956",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import practice_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6158f7b",
+   "metadata": {},
+   "source": [
+    "# Lab-P11:  Scatter Plots and Recursion\n",
+    "\n",
+    "In this lab, you will practice...\n",
+    "* creating scatter plots,\n",
+    "* defining recursive functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30ab45ab",
+   "metadata": {},
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: p10 and p11 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partnered up with someone for lab-p10 and p10, you have to work on lab-p11 and p11 with the **same partner**. You must acknowledge to the lab TA to receive lab attendance credit.\n",
+    "\n",
+    "You may do these lab exercises with only with your project partner; you are not allowed to start working on lab-p11 with one person, then do the project with a different partner.  Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f22/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fea02f6",
+   "metadata": {},
+   "source": [
+    "## Setup:\n",
+    "\n",
+    "Before proceeding much further, download `practice_test.py`, `practice_plots.json`, and `planets_small.json` and store them in the same directory as `p11.ipynb`. You should also download `small_data.zip` and  extract the contents of the directory `sample_data` into the same directory which contains the files `practice.ipynb`, `practice_test.py`, `planets_small.json` and `practice_plots.json`.\n",
+    "\n",
+    "You need to make sure that the project files are stored in the following structure:\n",
+    "\n",
+    "```\n",
+    "+-- practice.ipynb\n",
+    "+-- practice_test.py\n",
+    "+-- practice_plots.json\n",
+    "+-- sample_data\n",
+    "|   +-- .DS_Store\n",
+    "|   +-- file_1.json\n",
+    "|   +-- sample_1\n",
+    "|   |   +-- .ipynb_checkpoints\n",
+    "|   |   +-- file_2.json\n",
+    "|   |   +-- file_3.json\n",
+    "|   +-- sample_2\n",
+    "|   |   +-- file_4.json\n",
+    "|   |   +-- sample_3\n",
+    "|   |   |   +-- .DS_Store\n",
+    "|   |   |   +-- file_5.json\n",
+    "```\n",
+    "\n",
+    "Make sure that **all** files are stored in this **exact** file structure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5f7cd7e",
+   "metadata": {},
+   "source": [
+    "## Introduction:\n",
+    "\n",
+    "In p10, you parsed the data in the `data` directory and created some useful data structures. In p11, you will be creating scatter plots, and using recursion to gather even more data.\n",
+    "\n",
+    "In lab-p11, you will practice creating scatter plots on a smaller dataset, and also practice writing some recursive functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a674b335",
+   "metadata": {},
+   "source": [
+    "## Questions and Functions:\n",
+    "\n",
+    "Let us start by importing all the modules we will need for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cf83736",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this lab\n",
+    "\n",
+    "# we have imported these modules for you\n",
+    "import os\n",
+    "import json\n",
+    "from collections import namedtuple\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a14851c5",
+   "metadata": {},
+   "source": [
+    "### Loading in data:\n",
+    "\n",
+    "Before proceeding further, we will need to load in some data. Recall that in p10, we created a **list** of `Planet` objects called `planets_list`. In p11, you will be analyzing this **list** and using the data in there to create scatter plots. If you have not finished p10, do not worry about it. For lab-p11, you will instead be working with a sample dataset. The data we will be analyzing in lab-p11 is stored in `planets_small.json`.\n",
+    "\n",
+    "**Warning:** Note that the data in `planets_small.json` is **not** stored in the same format as the files in the `data` directory that you parsed in p10. This file has been created solely for lab-p11, so it is easier to load in the data without having to create the functions that you need to create in p10 to read the actual dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a32bfa41",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have defined the namedtuple 'Planet' for you\n",
+    "\n",
+    "planets_attributes = ['planet_name',\n",
+    "                     'host_name', \n",
+    "                     'discovery_method',\n",
+    "                     'discovery_year',\n",
+    "                     'controversial_flag',\n",
+    "                     'orbital_period',\n",
+    "                     'planet_radius',\n",
+    "                     'planet_mass',\n",
+    "                     'semi_major_radius',\n",
+    "                     'eccentricity',\n",
+    "                     'equilibrium_temperature',\n",
+    "                     'insolation_flux']\n",
+    "\n",
+    "Planet = namedtuple(\"Planet\", planets_attributes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60973450",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have parsed the data in 'planets_small.json' and created a list of Planet objects for you\n",
+    "\n",
+    "def read_json(path):\n",
+    "    with open(path, encoding=\"utf-8\") as f:\n",
+    "        return json.load(f)\n",
+    "\n",
+    "# add the planets in 'planets_small.json' as Planet objects to 'small_planets_list'\n",
+    "small_planets_list = []\n",
+    "for planet in read_json('planets_small.json'):\n",
+    "    small_planets_list.append(Planet(**planet))\n",
+    "\n",
+    "# display the first three Planet objects in 'small_planets_list'\n",
+    "small_planets_list[:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c38ddba8",
+   "metadata": {},
+   "source": [
+    "## Segment 1: Scatter Plots\n",
+    "\n",
+    "We will first learn to create simple scatter plots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17e9af9e",
+   "metadata": {},
+   "source": [
+    "### Function 1: `plot_scatter(x_data, y_data, x_label, y_label, c, s)`\n",
+    "\n",
+    "You have been provided with a function `plot_scatter` which can create scatter plots. You do not need to worry about how it works just yet. The function will be explained to you over the course of the lab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aae598ce",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def plot_scatter(x_data, y_data, x_label='x axis', y_label='y axis', c=None, s=7):\n",
+    "    plt.scatter(x_data, y_data, c=c, s=s)\n",
+    "    plt.xlabel(x_label)\n",
+    "    plt.ylabel(y_label)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de76c356",
+   "metadata": {},
+   "source": [
+    "### Task 1.1: Simple plots with `scatter_plot`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24685bb7",
+   "metadata": {},
+   "source": [
+    "**Question 1.1:** Create a **scatter plot** with the points (1, 1), (2, 2), (3, 3), and (4, 4)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f56f3b8b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "x = [1, 2, 3, 4]\n",
+    "y = [1, 2, 3, 4]\n",
+    "plot_scatter(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82e87617",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8178394d",
+   "metadata": {},
+   "source": [
+    "As you can see, in order to create the **scatter plot**, we had to create **two** lists `x` and `y`. The **list** `x` contains the data in the **x axis** and the **list** `y` contains the data in the **y axis**. For example, the element at index *0* of `x` is *1*, and the element at index *0* of `y` is *1*. Therefore, a point is added to the **scatter plot** at the position *(1, 1)*. Similarly, the other points are also added to the **scatter plot** using the `x` and `y` lists."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73c237c5",
+   "metadata": {},
+   "source": [
+    "**Question 1.2:** Create a **scatter plot** with the points (1, 4), (2, 1), (3, 2), and (4, 3).\n",
+    "\n",
+    "You **must** create **two lists** and store the **x** and **y** values in these lists. In order to pass `grader.check` for scatter plots, you **must** define **both** **lists** properly.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "example_plot_scatter.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c6ed2267",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:example_plot_scatter.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1118ff0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "x = [1, 2, 3, 4]\n",
+    "y = ...\n",
+    "\n",
+    "plot_scatter(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f805a85f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f772fb4",
+   "metadata": {},
+   "source": [
+    "**Question 2.1:** Create a **scatter plot** having the numbers (1, 2, 3, .... 10) on the **x axis** and their **squares** on the **y axis**.\n",
+    "\n",
+    "You **must** create **two lists** and store the **x** and **y** values in these lists. In order to pass `grader.check` for scatter plots, you **must** define **both** **lists** properly.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "squares.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "03a1e6f3",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:squares.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d1b9d82",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "x = [i for i in range(1, 11)]\n",
+    "y = [... for j in x]\n",
+    "\n",
+    "plot_scatter(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2052f2fe",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7a80fe",
+   "metadata": {},
+   "source": [
+    "Note that the labels on the **x axis** and the **y axis** are not very descriptive. We can add in our own labels using the `plot_scatter` function by passing two optional arguments to the parameters `x_label` and `y_label` respectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c541321",
+   "metadata": {},
+   "source": [
+    "**Question 2.2:** Create a **scatter plot** having the numbers (1, 2, 3, .... 10) on the **x axis** and their **squares** on the **y axis**. Moreover, add the label `\"Numbers\"` on the **x axis** and the `\"Squares\"` on the **y axis**.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "squares_labelled.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "dd10ffb8",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:squares_labelled.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11a3800f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have already added in the label for the x axis\n",
+    "# complete the rest of the code and add the label for the y axis\n",
+    "\n",
+    "x = [i for i in range(1, 11)]\n",
+    "# TODO: define the list 'y' appropriately\n",
+    "\n",
+    "plot_scatter(..., ..., x_label=\"Numbers\", y_label=...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93648610",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a6539ae",
+   "metadata": {},
+   "source": [
+    "### Task 1.2: Plotting planets with `scatter_plot`\n",
+    "\n",
+    "We will now create more interesting plots using the data in the variable `small_planets_list`. However, let us first get acquainted with the dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16cc3a97",
+   "metadata": {},
+   "source": [
+    "**Question 3:** What is the **number** of `Planet` objects in `small_planets_list` that were discovered in the time period *2000* to *2010* (both, inclusive)?\n",
+    "\n",
+    "**Hint:** You can find the year of discovery of each `Planet` using the `discovery_year` attribute of the `Planet` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de8b9991",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'planet_count_2000_2010', then display it\n",
+    "\n",
+    "# TODO: initialize the variable 'planet_count_2000_2010'\n",
+    "# TODO: loop through the planets in 'small_planets_list'\n",
+    "    # TODO: update 'planet_count_2000_2010' if the 'discovery_year'\n",
+    "    #       of the planet is between 2000 and 2010 \n",
+    "        \n",
+    "# TODO: display 'planet_count_2000_2010'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc2f6628",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2e36c2e",
+   "metadata": {},
+   "source": [
+    "**Question 4:** What is the **percentage** of planets (in the range *0* to *100*) that have `insolation_flux` **greater** than *1000*?\n",
+    "\n",
+    "You **must** ignore the `Planet` objects with **missing** `insolation_flux` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0413e75",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'flux_1000_percentage', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dec45ed3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8517627",
+   "metadata": {},
+   "source": [
+    "**Question 5:** Create a **scatter plot** representing the `orbital_period` (on the **x-axis**) against the `semi_major_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list`.\n",
+    "\n",
+    "You **must** ignore any `Planet` objects for which **either** the `orbital_period` or the `semi_major_radius` data is **missing**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bffe498",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "orbital_periods = []\n",
+    "semi_major_radii = []\n",
+    "for planet in small_planets_list:\n",
+    "    orbital_period = planet.orbital_period\n",
+    "    semi_major_radius = planet.semi_major_radius\n",
+    "    if orbital_period == None or semi_major_radius == None:\n",
+    "        continue\n",
+    "    orbital_periods.append(orbital_period)\n",
+    "    semi_major_radii.append(semi_major_radius)\n",
+    "    \n",
+    "plot_scatter(orbital_periods, semi_major_radii, 'Orbital Period','Semi Major Radius')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97fd0f30",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d37b93d",
+   "metadata": {},
+   "source": [
+    "Note that almost all the points in the plot are clustered near the **y axis**. This is because there are a few outliers with **extremely high** `orbital_period`. To get a better idea of what the data looks like, it would be useful to create a **scatter plot** without the **outliers**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ffb1c42",
+   "metadata": {},
+   "source": [
+    "**Question 6:** Excluding planets with `orbital_period` **more than** *1000*, create a **scatter plot** representing the `orbital_period` (on the **x-axis**) against the `semi_major_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list`.\n",
+    "\n",
+    "You **must** ignore any `Planet` objects for which **either** the `orbital_period` or the `semi_major_radius` data is **missing**. You **must** also ignore any `Planet` object with `orbital_period` **greater than** *1000*.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "orbital_period_radius.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "11f1bcb8",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:orbital_period_radius.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72c6c74d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your own code\n",
+    "\n",
+    "orbital_periods_no_outliers = []\n",
+    "semi_major_radii_no_outliers = []\n",
+    "# TODO: loop through each planet in small_planets_list\n",
+    "# TODO: skip the planets with mssing 'orbital_period' or 'semi_major_radius' data\n",
+    "# TODO: skip the planets with 'orbital_period' greater than 1000\n",
+    "# TODO: add 'orbital_period' and 'semi_major_radius' data to the appropriate lists\n",
+    "    \n",
+    "plot_scatter(orbital_periods_no_outliers, semi_major_radii_no_outliers, 'Orbital Period','Semi Major Radius')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7818eb4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d140b8df",
+   "metadata": {},
+   "source": [
+    "**Question 7.1:** Create a **scatter plot** representing the `planet_mass` (on the **x-axis**) against the `planet_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list` whose `discovery_method` is *Transit*.\n",
+    "\n",
+    "You **must** ignore any `Planet` objects for which **either** the `planet_mass`, `planet_radius` or `discovery_method` data is **missing**. You **must** also ignore any `Planet` objects whose `discovery_method` is **not** *Transit*.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "mass_radius.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "7686f7c1",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:mass_radius.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee39e3f0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the lists 'transit_planet_masses', and 'transit_planet_radii'\n",
+    "# then create the scatter plot using the lists\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df327008",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a9fa33a",
+   "metadata": {},
+   "source": [
+    "**Question 7.2:** Excluding planets with `planet_mass` **more than** *250*, create a **scatter plot** representing the `planet_mass` (on the **x-axis**) against the `planet_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list` whose `discovery_method` is *Transit*.\n",
+    "\n",
+    "You **must** ignore any `Planet` objects for which **either** the `planet_mass`, `planet_radius` or `discovery_method` data is **missing**. You **must** also ignore any `Planet` objects whose `discovery_method` is **not** *Transit* or have a `planet_mass` greater than *250*.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "mass_radius_no_outliers.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "fa26761a",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:mass_radius_no_outliers.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f355ccf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the lists 'transit_planet_masses_no_outliers', and 'transit_planet_radii_no_outliers'\n",
+    "# then create the scatter plot using the lists\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e37c03ff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "824b1f83",
+   "metadata": {},
+   "source": [
+    "### Task 1.3: Changing the color and the size of points with `plot_scatter`\n",
+    "\n",
+    "So far, we have not used two of the **optional arguments** of the `plot_scatter` function: `c` and `s`. The parameter `c` can be used to change the **color** to the points on the **scatter plot**, while `s` can be used to change the **sizes** of the points on the **scatter plot**. These arguments can help in visualizing the data better, as in the examples below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02bdba41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell\n",
+    "\n",
+    "x = [10, 20, 30, 40]\n",
+    "y = [10, 20, 30, 40]\n",
+    "c = [10, 20, 30, 40]\n",
+    "\n",
+    "plot_scatter(x, y, c=c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f741ea5",
+   "metadata": {},
+   "source": [
+    "#### Understanding the `c` argument:\n",
+    "\n",
+    "In the example above, the argument passed to the `c` parameter was a **list** of numbers. The `plot_scatter` function used the values in this **list** to color each point in the plot. For example, the element at index *0* of `x` is *10*, the element at index *0* of `y` is *10*, and the element at index *0* of `c` is *10*. Therefore, the point at the position *(10, 10)* is colored using the number *10*. Similarly, the point at position *(20, 20)* is colored using the number *20*.\n",
+    "\n",
+    "A **high** color value means that the color will be **lighter** while a **low** color value means that the color will be **darker**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3dd3d25",
+   "metadata": {},
+   "source": [
+    "#### Understanding the `s` argument:\n",
+    "\n",
+    "In comparison to the `c` argument, the `s` argument is relatively simpler to understand. Run the cell below, and try to figure out what happened."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17538ce8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell\n",
+    "\n",
+    "x = [10, 20, 30, 40]\n",
+    "y = [10, 20, 30, 40]\n",
+    "s = [10, 20, 30, 40]\n",
+    "\n",
+    "plot_scatter(x, y, s=s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17b20ba9",
+   "metadata": {},
+   "source": [
+    "**Question 8**: Create a **scatter plot** representing the `planet_mass` (on the **x-axis**) against the `planet_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list` whose `discovery_method` is *Radial Velocity*. Additionally, **color** the points on the **scatter plot** depending on the `discovery_year`.\n",
+    "\n",
+    "You **must** ignore all `Planet` objects with **missing** `planet_mass`, `planet_radius`, or `discovery_year` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8785a2bf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "rad_vel_planet_masses = []\n",
+    "rad_vel_planet_radii = []\n",
+    "rad_vel_discovery_years = []\n",
+    "for planet in small_planets_list:\n",
+    "    mass = planet.planet_mass\n",
+    "    radius = planet.planet_radius\n",
+    "    year = planet.discovery_year\n",
+    "    if mass == None or radius == None or year == None:\n",
+    "        continue\n",
+    "    if planet.discovery_method == 'Radial Velocity':\n",
+    "        rad_vel_planet_masses.append(mass)\n",
+    "        rad_vel_planet_radii.append(radius)\n",
+    "        rad_vel_discovery_years.append(year)\n",
+    "\n",
+    "plot_scatter(rad_vel_planet_masses, rad_vel_planet_radii, 'Planet Mass','Planet Radius', c=rad_vel_discovery_years)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86521e6d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cbd2b58",
+   "metadata": {},
+   "source": [
+    "**Food for thought:** What do you think this plot signifies? Recall that a **lighter** color implies a **larger** value in the list `rad_vel_discovery_years`. Where are the lighter colors present in this plot? Can you guess why the colors are spread out the way they are?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f50c78a4",
+   "metadata": {},
+   "source": [
+    "**Question 9:** Excluding planets with `orbital_period` **more than** *1000*, create a **scatter plot** representing the `orbital_period` (on the **x-axis**) against the `semi_major_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list`. Additionally, **color** the points on the **scatter plot** depending on the `discovery_year`.\n",
+    "\n",
+    "You **must** ignore any `Planet` objects for which **either** the `orbital_period`, `semi_major_radius` or `discovery_year` data is **missing**. You **must** also ignore any `Planet` objects whose `orbital_period` is greater than *1000*. You **must** use the `discovery_year` to **color** the points in the **scatter plot**.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "orbital_period_radius_colors.PNG": {
+     "image/png": 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sMGXjxo1mcQmm0mTkXFlZ6dvTPkjIQjRdPHVelDtrEVdUgMj8XKSVlaLaZa9F6YXwE1Ahcx4y5x1zlSeWvLw88ztF7PHYa91JPxKyEE0fiplL8Nl1lR8hSECEzH7ioKAgM3jrxx9/xIIFC+otZ86c8b3DPkjIQggh7EBAhLxjxw7MmTPHZOnq3LkzXn755V+Vl156yfxcsmSJ7x32QUIWQghhBwLaZB0o2PTNOc1Op9MU/l4fnFLFpnDm0Pbvy/ddTfO4hCyEEMIOBETIFCIXkLhS4aCvhnDkyBGT4evZZ5/FG2+8YRKM1EdOTo5ZtOLxxx9H69at0bZtWwwcOBDbtm3z7XFlJGQhbh5urwfeuvor2EI0dwIi5F27dpllF/v06WN+vv/++6Z89NFHZhpUly5d8Mknn2D79u2+d1yeiIgI0+c8ZswYPPbYY2Zt5fpIT0/HokWL8Mwzzxgx8/jMDJacnOzb48pIyELceHJryrEq+TRGhm3FtxE7cSAnFg6PRjsLcTEBETIlOHr0aAwbNgwtW7Y0c48//vhjfP311/jiiy9MHutWrVo1uA+Z85kTEhJw6tQpPP/885cVMhet6NSpkxnVfS1IyELcWJweN04VpKDj4YV4YNtYPLZjAkaf247E8nzfHkIIEtA+ZDZL9+3b1+Su5hKMxJ/fmtHu5s2bzWsNgStHRUZG4sUXX7yskH/44QcjeyYd4f5c9pFN6JejtLQUSUlJCA8PN83hLVq0MFG+ECLwVLpqsTMz0sj4ni3f4P6to9Hr5E84khvv20MIQQIq5KKiIrz66qs4ePCgGVjFQVcsTKnJvt158+b59rwyDREypc/R3X/84x/x4IMPmpHcEydONElILsehQ4fQv39/PPnkk3jiiSfw97//HVu3bvVtFUIEEqfXjSN58Xhy10Tcv20MHt4+Dl+HbkJUSZZvDyEECaiQOe2JzccjRowwUs7KykJKSgqWL19u+pSvZtpTQ4TMec+UMiNdSphRLpvIBw0a5NujfthMzSZufrbo6GhTidi9e7dvqxAikHAN4kJHJXZlRmFE2FbMiDmAkwXJ6kMW4jcEVMhsKt67d6+JUilFRqEslCRlzAQiDaUhQvZPj2IEzkicFYJJkyaZudDMDnap6VIXoz5kIW48buteLKmtRmxpDlIqClHmrPFtEUL4CaiQKUWK9Pjx46Zvd8qUKZg+fTrWrl2L1NRUI8+G0hAh83gXT6Vi3/DkyZNNlE7RSshCCCEaCwEVMmG0yubg+Ph4REVFGan6C3NbNwT2RYeEhJjBYQ899BBmz55tlnLkcTn3mIUiZp5sTpHivhw4xmlPHNk9ZMgQUzloCBKyEEIIOxBQIXMgF2U5f/58dOvWzYx+5jQops5kaWgfMsXKRB+33XYb/uf//J/485//bI63dOlS82FZ2P/LvmP2V1Pajz76qJmPPGrUqKtqGpeQhRBC2IGACpnTnthnPG7cOJMUhJmzGOUyKchnn31mcl43BPb/crAWB2qdP3/ezEnOzs42TdL+4m+uZpRMObOwWZy/X2na08VIyEIIIexAQIXMpmZGtuwzHj9+vMnSxYj57Nmzphl51apVvj3tg4QshBDCDgRcyMzSxVzUbJ5mVMyolX3HTKk5d+5c3572QUIWQghhBwIqZDYlU8JMecnmaUbFCxcuNJExU2myD9huSMhCCCHsQECFzL5bZsFiVMwUlhs3bsQrr7xiBltx+hP7g+2GhCyEEMIOBFTInGrE7Fn+ZB0VFRVmcFZaWprpR6ao7YaELIQQwg4ERMj+LFlBQUEmfSXTUXLEdXl5OU6ePGkyd3FpxqtZXOJmISELIYSwAwERMqcnrVy5EkOHDjXLME6bNs38zjJ48GDzR8aOHWuEbTckZCHEtZBfXYWEkiKklpXA7fWgYamIhLg0ARHyunXr8O6775qc1VzRick6unbtaqZAde/e3SzcwBHYdkRCFuL68NbVobCmCudLCnC+tAD5NZW+LU0Tije3qgKbE2Iw5fRxzA07hbC8bNS4tViGuD4CImRm5urZs+eF3NHsQ2ZykDfeeMMk6mAGL7siIYvmAFdccllRHFdYYqFEAwGPUm2JaE1CBLofWI8PD67Hj/FhTTpi5EIZK6LC8NbGH3Hn99/hgWVz8M72tcioKPPtIcS1ERAhc2pT3759fb/93KdMSXMKlJ1lTCRk0RzgSktb089hWOhmjI/YZVZcqvW4fVuvHYd1jJjiPLy5YxnuWTUd963+Du/vX4vTeRkBOb4dYcVmxPEDeGLlAvx14VTcvmg6HvtxPpJKi317CHFtBETI7DN+8MEHTZO1vzCP9W9fs6P0JGTRHNiYFoqPTyzHc7unoNW+GRgXsRNJFQW+rdcOo+PQ/Cw8vXE+/rZiEm7/cTLa7FqBg5lJFzXheq1KOpdIvfLqa40Bj/U9NsZHo82mnywZT8PDy+fi8wM7kF1Z4dtDiGsjIELmyOoBAwaYhR1YRo4caQTsf42/8+fBgwd977APErJoDkyK2oPn90zFPVtG4f5tY9DhyEKEFqX7tl47TitaTC0vQa8jm/HEhnl40ioDg3YgvrTQRJIeTzZqa/agunIeqquWwO1OtMTcuNdCZgtgRnkZfowOx6igA5h65jiOZaaiyuX07SHEtREQIVNqTI95pcJFI+yGhCyaA0sST6Dd4QV4aPu3eHLXRAw4sxaxZTm+rQ2D/dBuK9KtdFfA4amxIsWfu6PYNL0nPR6jzxwwZUdq3IU+akf1FpQWdkJB9l9QmHuPEbPHk2G2NXYcbhdKHDUorW3cFQxhHwIi5MaMhCyaA1nVpZh//gg6WpHxRyeWI6w4HVXuhq+KRlxeJ/IdeThTfBLnK2JQ4iw2kr4cFaWDUZhzhyXk/7bKn1FS8BqczmDfViHExUjIErJoBrjrvCh11iCnpgx5NeUmqr2akdZeKxpOqIjDxLhRGBE1yJQt2etR7Lz8dMbK8vEoynv05wjZEnNZ8adwu875tgohLkZClpCFuCI1nhorMg5G//BP0Su0G3qHfoCFSbOsSDnWt0f9uFzhqKqYYURcXjIITsdheL0lvq1CiIsJqJAdDgf279+P3Nxc3yv2R0IW4srUeh0IKzmDwed6o09od1MWp8xDcmWib4/6qatzwu1OgrP2KFzO09bvVXz1541CiF8RMCH781lz7vGZM2fM740BCVmIK8O+4ozqNCxJmY9p58fhu/gJOJi/FxXu5j3Vh0+5KrcTMSW5OJ2fjsjinCafqUzcOAIaIZeUlOC9997D3r17zcISjJgvLlwBym5IyEI0DG+dFw6PA6lVSSiozTdRc3OHWbso4a6HV+KBTVPRbv9SrE0ON68LcbUEVMhc4WnQoEFo3749+vfvj7lz5+KHH364UEJCQnx72gcJWYiG83MKTpeZ8nSlEdbNgeyqMiyPP4OHNk3DHWvH418bpuDr0zuQXG7P3P3C3gRUyFz/ePz48WapRYp5zJgx+Pbbby+UI0eO+Pa0DxKyEOJayakux4qEs7hn/STcvnYc/rluAr44tRXplRq4Jq6egArZ6XTi+PHjOHDggGm2ZgYvFg704mtJSUm+Pe2DhCyEuFYqXU6czE/Dp8fWotWuBXjv0Er8mBiCcpea88XVE1AhcyAXBRcTE2OiYUqZMg4ODkZOTo4Rtt2QkIWoH85T5tzlxPJCxJc1/WUVr5VypwNHchIxPyYIO9JjFB2LayagQuayi2FhYfj000/x8MMP41//+hfuv/9+PPnkk1i8eDEyMzN9e9oHCVnYE49Vwa1GnbfcN1Xo5g8S4kpOB7LiMejkFvQ7sQlrksJMQhH1HAtxYwiokDnK+oMPPsB3332HkydPGgGnpKRg+/bt6N27N3788UffnvZBQhZ2xO2MRmX5BJQWdkBFyWB43OmWmG/eAvhcGCK1shhv71uM+zdOwb83TkXXwz9ZkWCSEbUQIvAEVMhFRUV45ZVXsHXrViM64vV6zfxkCpmjru2GhCzsSGX5tyjOe8qkmyzKexLVFbPg8dy8FiZGwnGl+Xh2+2wzWIkjiFvv/QFb0qLMkot+vN461NS6UFxejVqXRC3E9RBQIVO87dq1w6JFixAfH2+mQVHSp0+fNglDLveHbhUSsrAjZcUfoSD7r1b5gyXlO1FROhBud7xv642H82izqsrQ+8RGPL51Bh7Z/B16Bm3AuaIss+QioYhjU/Pww9aTmLv+OLYdi0JKtqb7CHGtBFTIXF5x8uTJGD58uImGN2/ejHXr1mHs2LHmNQ7wshsSsrAjVeXjUJz/LApz77V+Pu9btjDLt/XmwCh5b+Z5fBO6B1+f2WkSXjg9nH/8Mxl5JVhsyfjpT2biyY9m4J3hK7DlSORVLVohhPiFgAqZo6yZoYsDuLp27YoXXngBLVq0MHOSmU6T2brshoQs7IjHnYzqitkmUq4sG2vJOM+6v36OTO3C6eg0DJm1FY92m4ZHrPJ49+mYY0XKLre9PqcQjYWACpmwz5gJQgoKCsxUJy40wabs2tpaI2y7ISELW1LnMiOsvd5Cq5RaL9hPcgkZBZi15iie/niGiZBb9p2P5TsbTx57IexGQIQcHh6Offv2GbnNnj3bZOuqr0ybNg2rV682CULsktdaQhbi93D94yp3OeLKQ5BQEY7iWitC/82Ep6oaJyITc7BgYxAmLNuPdQfCkZRpVSCs91a6y3AkfwM2Z87D0fyNyKy+/KpQQogACZlzj5kEhH3IM2fO/FW6TH8ZN26cKUynSTlzwJcdkJCbJ26vxyyWIOqnxJlvRPpjygT8lDoJxwu2It/x+1HejloX0nKKEZ2ci8LSKqui7TUijyw9gdnxgzAm+j3MON8P+3NXm/zXQohLExAhs0k6OzvbRL1xcXGIjIyst5w9exYrVqzA+++/b5q07YCE3HxgfOfwOBFbnoZTRTEILY63IreC30V+AkiujMKk2E8wIrIDRkR0wPdJIxBWfNi39fIUO3OxJ+dHjIvuhmERba3SDkuTx8DldepcC3EZAt6H7PF4jOSYJIRTni4u1dXVKCwsxKFDh2wzwEtCbj4wKk6tzEG/0BloffwrdAkejUVJ21BjSfpiTXjq3Ja4q1DtrkCtt6ZZRnaJFecwOuodI9ShVpkdPxBBBdt8Wy9PmavQRNRT4npiVFRnEyWvTp8Kj5fdVBKyEJcioEKmjDMyMjBlyhSzBONzzz1n0mb6y8KFC82gL8rYLgM/JOTmQ4mzAvtyz6LjiZFodWQQXj/6hRUB/mAiZo+v+ZrN2OlV57EufQbmJgzG3tyVyK5JNtuaE/mODGzKmIuxlkxHRnbCxozZyKlJ8W29PKzAlLmKrCh5BZanfItd2UuRVhVnbZGMhbgcARUypzyNGDHCDN765JNP8NZbb2HJkiXo2bMnunfvjo0bN/r2tA8ScvOhwlVtRXmRaBs0DC2PDMArlpRHWkJOr8q90J9cVJuNI3kbMCn2Y4yyRDQtrhdOFu60IuZqs93OsDm41utAfEUsIsrCkFGdds0VX6d1nNyaNNMXHGWV7Jok81pDYStDoXUuM6sTrJ9ZpqVBCHF5AipkNku/+eabWLt2LSZMmGCkzCZqLsnIxCAbNmzw7WkfJOTmg8vrtiK1PMyIX4fB4XONjDdnHkOlu9rIjDAa3pG12ESFQyPamD5UDkjiQCW7QxkfKzyExSnzMDdxGjZmrkZKVSLclhyvjTrT7+vhFCwNgBPihhNwIb/22mtm6UUmB2G6zLS0NDMXuW/fvpg/f75vT/sgITcvKOWUyhwczg/D2eI45Dt+vVResTMPxwu2YXpcb4yN7oqZ8f1xpmifkZ3dKXeVYWLcKHwe9jF6hXbDqOgh2JWzxYruFZ0K0RgIqJA5kOvdd99FUFCQSZvZp08fMw2Kqzx17txZQha2h5FyniMd+3J/wvKUcThVtBtFtTm+rfXDVJEVrloUO6pRWlsDh9uKKH3bAgGbnZk3urSqBqWVNahyOOttii5zleIbS8K9Qz9Aj5D38FVkP6xJX46aRtDcLoQIsJCdTieOHTtmBnZlZWWZZRfZbP3ee++ZAV1ccMJuSMjit/hHWVe5y0zfJxNdXArKuMSS8OgzB/Da9qXoun8tNiRGmjzQgYICXn0oDB9PW4f3JvyEuVuDTFKO30q5xoqEd2RvwoiowUbK086PQ0JF3HU0WQshbiYBFTJHUDPhB0dRU87sP2bSEOaxpqRrauzXdCYhi+uh0uXEluRovLVzOf65ciruW/0duu5bi4KaKiPr68Xt8SI1twQfTFmDp/vPwZOfz0aP79bjWGQynK5fVxRYcSiozUNk2TmElJwyMmZ0rLm/QjQOAiLk6OhokxLzSoXJQRpCQkKC2Z+jtblqVHLypaedUPo7d+7EggULMG/ePBw4cMAkKmkoErK4HsqdtVgWF4KXt3yPv62YZMqLm79HblWFFWlfvwi5UENiViFafb0ID/X8Do/0noH3Jv6EnadjL7n+MCNit9dtCVoDsYRoTAREyEuXLjVzjlu1amWap3v16mUO/NtCcTaEo0ePon///nj11Vdx//33mzzZ9UGZsr/6ww8/xMcff2wygA0bNsyk8WwoErK4HmrcLhzPTkWPw5vwzKYFeHHLIgw4vh1ltYGZa+/11iG3uAIDF2xF6xFL8NbIJRi+dDciknPg8igVpRBNCXryuoXMyPTf//63WWpx5MiR+Omnn7Br1y4TrbJPOTg42BQ2WzcE9j+fPn3aTJei6C8lZPZJz5kzxyz1yJHcjKSZJ5sVAjafNwQJWVwPbCaucZdic3Iwxp7ditmRR5BYWmhFx4GLThklx2XkY8GOYMzbdgJB0SkBkb0Qwl4ERMhcbjEqKsqMpqYMOfXpgw8+MAO5YmJijPTYp8xMXg2B+zHJyLlz58yaypcS8v79+01EzL/Lz8Bc2my65khv5spuyN+TkMW1QhlXuDJxPGcoNiS3w/a0vogq3hbwyJXqdVvHrHQ4UVlTe8mmaiFE4yYgQiZc75hRakREhImK16xZg2+++QadOnUy/cHcdjVwABj7nF988cVLCnnTpk3mCxw+fPjCgDHKmU3YsbGxphJQHxQ9p2CxWZxTs9gszhHhoulSV+eCyxmByvLxqCjpD0f1BqvC9vvVi66GGnchksq3YUvq21iT9DQ2pLTC6fzxcLiLNZBKCHHVBEzIbEKjlDkgi33FEydONOkyO3ToYMR5tas7NUTI69atQ+/evXHy5MkLi1VQ/j169DBrNPPz1EdKSor5jIzguX7zE088gR07dvi2iqaI252Cqoo5KMp7HIU5d6C0sB1qa3Za12390SabnEucVYgty0JSRS5KrX//lip3LuJKV1nRcUusSnwcq5OexNGcIUbUddCAKiHE1REQITP6ZZ8vxbl8+XKz7vGQIUMwevRoI2P2CV8qWr0UDY2Q+/Xr97sI+aOPPjLLQDbkb6rJunngrD2B8pK+KMj+q1X+2yp/RrUl6Lq632fgMnOLLQFvzTyLyTFbMev8bhzKi0a1u/ZXcW+tpwxZVcexL/NTK0p+CzvT38G5ovlweZmIQxGyEOLqCIiQ169fb0ZEP/zww2YhiVWrVhmZ5uTk/Kqwn7ehNETI7EMeOnSo+pDFFXE5w1BZOsyKju80MubP6srvLSHXWu60Km51lkTrXNaeXjg8LpwrScOrh8bjiT3D8OSe4RgQsgyJVqT828FaLk8VUiv2IKxwFhLKNqHcmebbIoQQV0dAhMyRzf/jf/wP/Md//Af+8z//E3/605/wt7/9Df/4xz9Muf32202ZPn267x1XpiFCPn/+/O9GWTM6Z6VAo6zFxTASdjqOoazoE9NsXVb0EWodB1DnKbJ8HIq6qtWWj89ZPi5DmbMah3Kj8crBcXicQt47HJ+d/h5BBefNmsoXw75ij3Vsl7fS2nb5rF5CCHE5AiJkf7/x7t27L1u4X0NgnzAj39atW+O//uu/zHSqyZMnG2lyOhULBUyZcmoUR3SzmZpi5vuuRq4ScnOhzoqSQ1Fe3MuKju9DUe7j1r97wFE6FN6id+EtfAPe4vdQV7MZTnc5zpdno1vwHLywfzRePDAGI86tRVZ1sWnOFkKIG0HABnUFEka+K1euNJm6GPFOmTLFrKXMNJwhISGmcCELwkxdHCHNUdPM6sW5z1x1qqFIyM0HV+0JlBa0QUH2babZuij3IVTmPQ1v7n1W+Se8efehrmIavJ4cFFSW49s9W9B50Rz0WL0Yq88Fw+m1x3QjRuVMiXmuNBRBhUcQWx5lVnoSQjRubCnkm4mE3HxwOU+htKizJeO/WOWPKM57HFUFLS0RP2gJ+U7r5yOoq5yJ2poMJKcVYNx3O9G192IMGLEWm3eFweHgusC+g1lQjC5vLXJqUpBaFWNWieKiFDcayvh8eSzmJE7FN9FfYmHSLISWnDafRwjReJGQJeRmg8eKfGuqVloifgZFeY+ionQwnOWT4C18G978F+At6oK6mh0oLC7Djn2RePO9OXjurSl44e2p+PrbTcjMKTGpLP2461zId2Rgbfp3mB0/CJsz5yG5kvnab6wYcxzZWJO+AgPCe+CzkK5m7eOFybOg3NVCNG4kZAm5GeFBHQdfuZKsEg+vJ9/6vRx1nmzUOaOsnzlWBFyDgqIKbN1zzhLy7J+F3GYqvhq7EelZxb8SMtdJPpi3BuNjumNkZEeMjnoHGzNmw+29uil+V0t2TRZWpS9F//BPjZB7hr6PBUkzJWQhGjkSsoTczKizpMumZ0qTI6IpWOsn5yP7Rkg7nW4kphRg5MSt6PzJIvT+chXWbwtBzW+arAtqM7E7ZznGRL+HYRFtMTyyPValTYXrBgu52lNt+o253vHXkf0xO3EqThefUJO1EI2cgAiZg60otMrKSsyYMQNjx46ttzCBh92QkJs+HBmdVVaO0KxshGfnoNrpQm51BSKKc3CuKAsFjkpLtL+WGeV7JiwV26xI+XDQeaRl/n6gYKW7DJGlQZibMAQTYj7E/MQvEVS4DZ4bPPWJkXCVuwqhJWdwtOAgosrOocTZ8CVHhRD2JCBCPnjwIJYtW4bS0lKzuETnzp3RpUuXC4W/s6xdu9b3DvsgITd90kvLsCosAl/v2ocRew5gT0IClsadxciQ3Rh2difWJoejwlV71VOaKMYKVwmCCrZhR/ZinC7aa6JmIYS4Fm5IkzWjDWbNcrlcJn2lvzR0taebiYRsP+qsCNNb50ag8kH/GBqOdstX4a4pM3HvtFl4edliPLh2Ou5YOx7/sErLXQsQV5oPh0erKAkhbh03RMiUXGZmJqKjo83qT/5ytSs+3QwkZPtR5c5DuSsdNZ6Gzye/HJMOH8fz8xfj9onT8Y8p03Hf/O9w3+op+Oe6CUbKL++cj6O5ydbf/H1eayGEuFkEVMhMd8m81lzwoVu3bmbVpc8+++xC2bp1q29P+yAh2weXtwoZlYdxIm8EDmT1wpmCScip5vza64uUjySn4OP1m3HnlBl4aOY8fLFnF1rvXIwHNk7FA5um4oMjq62/W2r9ffu14Aghmg8BFTL7kJnCctiwYWaRBy764C/MvMV1iO2GhGwfqq3IOCh3BDamvIJViU9ga1pbhBZyOs/1NSUXVVVjb3wi5pw4hUWnzuJMZiY2p0RhYWwwFsSewO7MONR62EQuhBC3joAKmSkrX3vtNezdu7dBSx/aAQn5xsLxBA63G1nl5cirrESNiysq1U+lKwv7Mz/D+uSXjJA3pbyG4LxvLCFf+j0NxeP1wunxoNb6LMSs321JmCs7/XaEtRBC3AoCKuSysjIMHDjQrFOclJSE/Pz8XxXKz25IyDeWSqtiFp2fh0UhZ7A6MsL8m4KuD4enGJHFP2BPxgdmfeGDWb2RULbeDPISQoimTkCFzKj41KlTZqrTc889h44dO5q1if1l3bp1vj3tg4R8YzmWnorOG9bgkUXz8KhVvty/B6cyM3xbfw37itmPnF0VhJSKXcivCTNLGgohRHMg4H3IXIuYA7i4RvKSJUvM/GR/YQIRuyEh31i2no/DAwvn4PZZ00xpv24V1sVE+bbWRx2cnnIrWi4xcr4e3FZknecoxMbM3VicshYH8o6jwlVp/QU1UQsh7EfA+5Bff/11/PDDD4iJiUFaWhrS09MvFDZp2w0J+cZyKDUZb6xagTvmfId/zJ6OHtu34HBqim/rjaXcVYHVaVsxKHwsPjn7JcZEz8Th/JNG1EIIYTcCKmSuUfzhhx+apmnKuLi42ETN/uJw2G+ep4R8Y2DWK4fbhZjCfCwKPYNPtm9Gjx1bsCY6EjkVFb69biyMjgeHf4t3TvZFhxM9LSl/hXmJP8Lpvf5BYkIIEWgCKuTy8nIz5YkHnTVrFnbv3o39+/dfKBzoZTck5MBDGedUlSO8MAthhZlIqyhBenkpCqurL4xyvhkU1hZbUfEMdD89GF0sKfcLHYUVqRvhkpCFEDYkoEJmRMyc1S+//DJatWplpkD5C5uyV6xY4dvTPkjIgcVT50WZ04HeQRvx7PbZeH7HHPQ5sdF6rQZur/em9t4yEo6vSMa084vwVcRELEtdj1xHgfqQhRC2JKBCZu5qpsgMCQmpt2RnZ/v2tA8ScmCpdjtxMi8Nb+9bgrvXTzTlrb2LEV6UZW27OZFptbsCsWWnsCZtOpamjMe+3G2IKI1EjiPfio6Vr1oIYU8CKmQmWKDgjh07hp9++gn79u2D14qKCgsLTV7rrKws3572QUIOLDWWdEMLM9HakvCd6yaY8sae7xFTkosaz80RckJFuCXjaRgT9S6+iXoHq9KmILnyciO7hRDi1hNQIdfW1pq+4ilTpqBr16748ssvzQpPjIyZSpMZvOyGhBxY2CxdUFOJ0aF70eXgCqv8iG9C96Cktto0Z98MThbuwozz/TAsoi2GRrTB5NhPEVpyyLdVCCHsSUCFzFHW77zzjhnQNWjQIPTu3du8Tin37dvXvG43JOQbQ1ZVGY7lJpuSV1N+U3ttI0qPY3HyKIw2EXIXLEj8GjFlp3xbhRDCngRUyP55yAcPHsT8+fPRp08f3xagf//+mDNnju83+yAh3xg40pqrJ7Fc7cL/10u1pwJRZcFYmToBy1PGIrb8jOlXFkIIOxNQIXOUdZs2bUzT9OzZs42Q3W43EhMTTbS8cOFC3572QUJuenjrvJaAy5HrSLNKKmo8ldZrSgYihLA3ARUy5ca+4qlTp6JTp0544YUXTDP1F198gW+//RZBQUG+Pe2DhNz44TQmt9eNKncNPBKvEKKRElAhs684IyMDixYtQo8ePUx/cr9+/Uz/MZuxma3LbkjItwIv6rwVcLsirRIBryfPeu3apiMxDWZOTT6CCs9ib+4xhJZEoai2xLdVCCEaDwERMpuludIThUw41Ykjq0+fPm2iYkq6urraluvOSsg3mTqXJeB8OGuPoLykn1X6wFG9znot17fD1VHmKsfO7EPodmoQugT3NakyjxWcVqQshGh0BETIOTk5ZjEJzjemjElkZCSmTZtmlmLkGslclrGmxn5L6UnINxePOwVVFd+hKPchFOb8DQU5f0dJwSuoqVri2+PqiCtPxNzE5egc3AftT3xm8lavy9iBMqcGcQkhGhcBEfLGjRtNHzEHdVHIqampmDhxIjp06ICZM2ea/NZDhw41c5TthoR886irc1gyno2ivCdRkP1nq/zBlKK8h63Xp/r2ujoyqrPxY+omEx13PNEL7538HFuy9sHhqfXtIYQQjYOACJmjp3v16mUSg7BZevv27SYq/uqrrxAXF4fg4GCzTjKXZbQbEvLNw+stR0Xplz4Z/zfyTfkDcvJbo6RyjW+vq6PKXY3wkmgsSFqJCbFzsSx1g4mab1YSEiGECBQBETJHVlPI/j5iRseDBw/Gjh07zO+Ef4hToeyGhHzzqPNWWZHwdCsifgx52X9CeuZfcCb1bhzO+ADRJdssiV7bwC6Ork6pTMfZ4giTr7rW6/RtEUKIxkNAhLx48WITAXMNZA7m+uijj0zazPj4eLOdi05wpLWELFy1p1Ba8hVSsh/DiZT78V1MK4yMbId16TOUvEMI0awJiJCPHDlipje1bNkSHTt2xNNPP23ky1HXLOnp6SZJiBKDCE5vKnQk41DuMnwb1QnDI9pieGR7rEmfhnJXsbVdSyMKIZonARFyWVkZwsLCzHrHTJnJpmpGy4RCTklJwe7du83Ia7shId98ar01ZvWleQlDMDa6K+YnfolTRbvhrrs5q0EJIYQdCYiQCQd0sbk6KSnJCPriOcn8vaCgAJWVleY1OyEh/wIzXrm8Tjg81Sb95I2Cf4fN08w3fbpoj1n4obDWfmtlCyHEzSRgQm6sSMg/U2cJOKcmFedKj1mS3IvospOocnOVpusXM49NyadVxSGhIgzZNclweh2+rUIIIYiELCEbKMw9OT9iYuzHZsnCSbGfIqkywoqYr38+L+XLJurlKd9iZvzn2JQxF5nViSZSFkII8TMSsoRsyK5Jwqq0qRgZ2QnDItpZUn4HQQVbUeJknunro9iZi++ThmNM9Lvm2BNiPsKGjFlagUkIIS5CQpaQDaWuQqy3JEkhD49oj1GRnXGu5Khptv6ZOiPQqNJgHMpbh7PF+y3RNkzWRbU5WJD4lZH80Ii2GB/zAdalfychCyHERUjIErKBTdNRpSewJXOBFSlPwa7sZch3ZFwY+VzrKUd86W78lDIaM873MYLdn7sSbg8H8OWhzssBe/U3QXNt4uCCHWY09ZS4Hqbpmn3VgeifFkKIpoKELCFfgAv559SkILHinGlmvngaUrkzHfvSPsGiuFcxLboFJkW/jh8SuqGiYrHJT11TtQ4uV6xv71/DSLjCVWKmNh3IW4OI0uMXRd5CCCGIhCwhXxFGsqWOKOxNegD7ku/GtsR/YVX8Y1gf/wRys/6IvKw/ID/3AVSUTzIjqoUQQlw9ErKEfEVqPdXIqzqE8PS/4lzGHxGW/ier3IbY9H+YRSJ+XijiDygv+Rxeb7XvXUIIIa4G2wqZyUSYkvOzzz5D586dzRKOBw8e9G39Ba7BvHnzZrzzzjumdO/eHUOGDDGCra6+shwk5MvDBCFxZaexNrkXzlkijsz4AyIsKUdl3oHErH9b0fF/m5JjRcqlJYOsCFkDtYQQ4lqwpZC5alRUVBQ+/fRTs3IU03FyPeURI0YgPz//QhYwwuxgzJHdpk0bsybzypUrsXXrVsTGxsLpvPKqPxLy5eEc4pOFuzA15m3sTLoXZ9L+gvCM25GQ1x55peOQmvMMUrL+heyCDqioWud7lxBCiKvFlkKmJHfu3IlnnnnG5L9mpLt27Vr07t3bRMk1NTW+PX8WMtdZ5vrLoaGhvlcbTnMVco2nFvmOEmRU56PcVXXJ9YOd3lqcLT6AaXHdMSf2JWxMfBxnst9FTvkSVNUcQWpuC6RkP4Tcwg9QVb3Fqkxp6UMhhLgWbCnkjIwMLF++3DRV5+X9PNf11KlTJlqePHkySktLzWuEQuYXYDS9bds2pKammija7XabSLs+KOHc3Fyz6EV0dDReffVVs/hFc4BN0EW1hQgvicPmzKNYmrILxwrOodRVaW2r/3wx5SXnKDPLFheECCk+iGpXJhw121GQw37k26yft6OiZAC8nkLfu4QQQlwNthRyXFycaabmko7sIyZswuaSjuwfLioqMq8RCnnevHm49957TWnRogVGjhxpXqeU6+PQoUPo378/nnzySTzxxBP4+9//bpq5mzqcflTprsDMhO/Q9eQXePXIILx2dDDePj4Up4piUO2pP780JV7rqUGRM9dMV3J7XfC4U1FdMdcS8R2WkP9o/bwNZcWfwu2Mtt6hlJhCCHG12FbIlCw/3JWEzObs8+fPIzg4GGfOnMHGjRuNkClcrjBVH4ywuSpVeHi4eR8lvmvXLt/WpkuVu9IS7wl8FTEUbY73RovDn1tlAFoeGoTxJzYgOi8TlwiSLcXWwVPnNnI2v3krUOs4gMLcf5nomD8ry0bA6/2l9UIIIUTDsaWQ2ZS8ePFidOvWzTQ/k5CQEEydOtUM3CopKTGvES7v6HA4LjRRc3+uy/zggw8iPT3dt9elaU59yD8LOQhfRwxBu6CeaHmkH1oc6o+X9w1A28WzseL4aWQWF5uUmCcKt+NI/kYkVIRb7yvzHeEirGjb68lBTdUyVFVMQ3XlIrhqT1obNMpaCCGuBVsKudiSAiPdli1bmkjW5XKZQV6Mjjm46+J1lSlkDvLy9xdzutSGDRtwzz33IC0tzbx2OZqDkNlUzSxchbU5SKyMww/J89Av9At0OtET7Y99io6HP0GrecMwbqsl4YQInCrcjelxvc3KT6vTpiKu/KzvSL/FipTrnJaYmTqTmbckYyGEuFZsKWRKlk3JL7/8MjZt2oSwsDBMmzbNzEmmoDkgi83OtbW1Rs4cmJWQkGC2sX+Yg7+6du1q9rsSTV3IbGoupojLgxFdsh05VWcQWXISixKnYHBoNww42wFDQtrho53dMfvoWmyJ3oulyWPMqkxDI9pgdNQ72Jmz1Hc0IYQQNwpbCpmw6Xn16tV46qmncNddd6Ft27ZmjjH7jGfOnGmapdnXnJiYaCJnDtBiMzX379mzJ2JiYjQP2aLWW4Pd2QuxPP4tHEp5HEetklm+A+vTJ5uVnShdloEnO+JQxi7ElISaxR+44hNXZhoT9R525SzzHU0IIcSNwrZCZjM1B29xHjLnF1O+HODF6JkjqClsNlWz/5hTnSIiIswgLe6fnJxsXr/UtKeLaepCLnCkITp3ACLT7kRqxl+RYpWs/A44lj0K8xO/uiDkKdG9EFF8AhWuUkSXncSCxKFmmtOO7CVIrz7vO5oQQogbhW2FfLNoakJmRJxcGWVFxeux5PwWTAhdg5khPbEr5mFLyH9BfvZ/IzfnIWSWLsC50uPYk7PCku5iM7e4uDbPNzWq1PQbR5UFm9WfmMtaCCHEjUVCbgJC9tS5UOosQGpVDM4W78eq1JkYeKYXXto+DA+v+xYvbx6CL451wq54S8RZf0ZRQUc4HUdNWsxSZyEKHdlwe29chi1Olap2VyDXitYLa7PNADMhhBC/RkJuxEL2j57Oqk5CUME2LE8Zi/Ex3TEwpBM6HfoEf/txHP66fAJuWzEez20djJFn2yE64xGUV/4EjyfHd5QbCweVUcaRpSewI2sxDuatQVJlBNxKsSmEEL9CQm7EQub84LNF+zE3YTBGRHa4MDJ6QEhHdLSE/PdfCfkLDAv5BGdyv0WVK9t6983JpuWyIu+UymiMi+lmPuOoyE5YlTYFeVa0TFkLIYT4GQm5EQmZg9QcVbXITMhBRXElTuTuwPzELzEqqvOFwVksX51ri56n3sHz2wbh3tXf4LGNw/DO4b6YGz8UhY54uL21viPeeBjBc5DYmOj3TIWBUl6aMtokHJGQhRDiFyTkRiJkyjghNBkrRq/DpG6zMKv395i+ciTGnu3+KxmzjIrsjEnR/TA7ZiGWnT+MnxL3Y2fGLkSVnjQR680UIf9eelUcZscPwDdRXYyY12fMREFtpoQshBAXISE3EiHX1tRi5w8H8M5fe+LV/7sz3vh/3sWg3p9j3O7eF5qq/WXG+b7Yk70cKZVRcHiqUeOusiLVKtPnfPOpM6O2jxdsxebM+diW9T0iSo/f0EFkQgjRGJGQbS1kRpAeKzquQmlhPlZNWY/X/r930eL/7IBX/q9OGP3eJCzcNBXjoj/4lZCXJ3+NtIojlvTKrSPYI50lI3yurcyVohoyP1wIIZobErKtheyG11sMZ20QSivPYv36VWj7/Ed4/b/fxev/7ztYOmI1giIO4MfUCVaU3NbIeHjEW1gQ9wJOZr6O/MpdRspCCCHsj4RsUyHX1bngckagtKgzigteQWrOq9gQ/jY+39oRfdd0wKTv38SR4H5IK1yFqNLjWJDwJSZEd8S82OewMfHfOJL2AM5md0G1K9V3RCGEEHZGQrapkL3eEjhqtqIg5y6r/BUnUu7DgpgX8fW5NlZpi8ln3sCBxBbIKPoC+VVHEFd+BmuTu2BD4oPYk3wnDqTegwNpz6PCmeA7ohBCCDsjIdtQyBzwlFMVjqi8b5Cd9XfkZv0JuxIfwpSoVhhqCZlN06Mj38COpAeRnN8RpdXbUQcvdqZ/inWJj1oR8v3YlPQYtqd1QZlTEbIQQjQGJGQbCPnnFJYFyHWkoqA2CymVcdiWvhgzIlvjZMo9CE/9J9adfwKTIl/B8HNvYWREa3wX/TL2pzyI1IIPUFFzxAh5ZcpQzIx9DbNin8f8829iTepIlDhvTkYuIYQQ14eEbAMhp1bFYm36dJPgY2Z8f4yJ+hx9zn6K7qe6os+Zzhge1gaDQzrgq9C3MTmqBWbGvIT9yfciIvNx5JZOhcdbg7o6LxYlDjOJN0wEHdXF/F5UKyELIURjQEK+xULm/OAThdtNDmqOlP4561YnDA7viAFh7fFFeDt8Hd4Gvc92wdfW7z/GPY+g1DsQmv4XRGbegbTCj1HpOGEi5PUZszAx5iNLyh0xKfYTs4pTmavI95eEEELYGQn5FgvZYQn5aP4mjLQk6p9H/LVVvjr3c+G/WfqHdsTU2IE4kjkMURmWjDP+aJW/ICm/HUqrNllCrkNSZST25f6EDRmzsTfnJ2RUx5u5v0IIIeyPhHyLhcxEGTFlp/B90jcYEfERPjn9Abqd/AA9z7yDL8/9PLeYZXjk+1iTthBp5YeRmPsGojP/aZW7rAi5h4mQiafOg3xHJtKqYpHnyFACDiGEaERIyLe8D7kOZbVpOJG3Dl+EDseLB77Co7u/xOuHe6P32Z8XjeBUp6lxg3G8YD/cniIUV65CakE3M6CrqHIFPN4q37GEEEI0ViTkWyzkujonSio3YFdiGzy7bxAe2/MVHtn9NR7f8yVePdTHCPmr8DZYlDgD50pifO8ijH4VAQshRFNBQr6FQmYT89nCE1iW+A2GhXfC8/sHWkL+0hLyV5aQh+DVw33xRXh7fHqmK+Ynfo/YskTfO4UQQjQ1JORbJGRvnRdnimMw9/xsTIp8D7OjXsaQM+3xysG+eMaKlF880B9vHeuJD093Q9dTH2Nj5nYU1Zb43i2EEKKpISHfNCFz1SanJWInqtyVSKpMwJyERZge1Q1r457A8eR7sDvhQQwPaYMvTnfA8DNtMSqkLb4K6YzpsSMRVRpuImohhBBNEwn5hgvZa4m4Gm5XNJzOkyiqOYuwkmAsTVmA/qGfYXnsswhN/ScS0/+KrMw/4kjyfZacH0dIagucTWuPA6kfIqNsGxzufN/xhBBCNEUk5BssZK5l7KwNQUn+SyjIvg17kx7FkPBO+PTse/jw9PsYFNIRU8+9hs3nH0de1n8jPeNPiMz6BCnF6+ByRsLjzrSO4fIdTQghRFNFQr7BQvZ40lFRNgJFufdZQv4DdiY8gr4hnY2Qu5z8GO8Ef4zuJ7tjaGg77E98AOFpDyGnbIUVERdZImZKTKd1FI2mFkKIpo6EfMOFnGkJeRziMx7H7rhHMCn0NXx4qis+sYTcKfgTtD/xGTqc6IHeZ/tgc+pYpJeugcOVZilYEhZCiOaEhHzDhVyKsspd2BDbEdNDWmFQcHt8FNQVX4W0QfdT7xspdw7ugSHhw3CmMBjV7mrfO4UQQjQnJOQbLGSHuwLJJUGYEvoRZoU9j+mhL2H4qbcwI6wFhoe+ZVZxGhraCUvO90N5TTA8XglZCCGaIxLyDRQyc0nnV0VibUwrnEj+B8LT/2jKvsS7MPpMa6yLewLhaXciK+svKMy9B6XF71sRdYbv3UIIIZoTEvINFHJ6VTJ2ZMzH8pjncCLldpwzQv4TDibdiZnnXsahxH8jKeM25Gf/AYU5d6C0sA087jTfu4UQQjQnJOQbJGSvtwbhhTuxMPZ9LI19BlsT/o3jlpRPpt6G/Yl3Y1XsU4jIbI3snMctGd+F4vyXUVO51HqfsnEJIURzREIOuJDrUOctQ2nlLgRnWic3+lnMjXkRc2JewLK4J7Am7mFsiH0QB5IeQFbJVJSXz0BV+QRLxj/A48nWnGMhhGimSMgBF7IH1Y6TiM7uhgOJ92Jd/KOYGt0CoyPfwPCItzAu9BUsC38K5zIfQVVtqG+esRBCiOaOhBxgITNXdXLuSGxNfA3L4h/HT4mPYEXCY5ge8xK+iXgDsyJewD4rUk4r+BBOV6IVULt97xRCCNGckZADLGSPtxY7o7tjZcxTWJnwqCXkR7HSKvPinsW34a9hbfRzyM+8D468R+Ep6oQ6x15LyjW+dwshhGiuSMgBFHJ5bQmOpWzH5yuGYNi29pgb8gJWnH8cy61IeXJkC6yJex2xac/BlXsvvLl3wpv3AOoqpltBcrLvCEIIIZorEnLAhFyH1LJUDDs6Ai+MnIC3JgxBr+XdMXxHe4w41Aajzr6J4MzBqC76yBLxvywh/9Mq96KufCLqXPG+YwghhGiuSMgBEnKZFR0fzDqIjvs+wTMjxuDhPlPw5IDxaDl2GFovG4DvwnohoWQfvNXr4C1qC2/BS/AWtkad9bv1gu8oQgghmisScoCEfChrLwYGv4MJEa+gy/y+eO5rS8r9JuGJkWPRfufniMw/hFp3sSXfctQ5z6Guainqak9av2vesRBCCAk5YEI+nrcNI0LfxJToFhh35nUM2dcBH2/tju47P8CEs62QXLoTTg8jYc/Pg7i8lpzrmLfa+l0IIUSzR0IOgJC9Xi+O52/CqIjXMS32ZUyPfQmTIlphyOk2GHSyDTbFP4TEkvmodqX73iGEEEL8GtsK2eFwICkpCevXr8ePP/6IXbt2ISEhwbf1F7iAQ21tLY4ePYq1a9di1apVOHjwICoqKowor0QghJxYGomfkiZjRERrjI9+FROjW2Fc1Kv49lwrzIp8FkfSHkB62RI43Fm+dwghhBC/xrZCTk9Px4wZM9C5c2e0bdsWH374IebPn4+amhojYT8Ud3x8PPr06YNOnTqZffv27YugoCCz75UIhJC3JM/HqJBO+PpcGwyLeAsjLTF/Ff42ZkW/jIOpTyEs9wOUOUK1tKIQQohLYkshM7I9fPgwHnjgAZw8eRJlZWVYtGgRunbtitjYWDidv6SbzMjIMOLmFwkNDUVcXBxmz56Nbt26IS8vz7fXpQmEkHekTsOYsLeMkIdGtDE/B4a0x8LYdkgo+AJOTwnq1FcshBDiMthSyBTp6tWr0bp1ayNcj8eDQ4cOYdSoUebDlpeX+/YEIiIi8M4775jm6pycHBMVs3n73nvvRVralZcyDISQz+Xvx5K4LzDKiowp46/C22LkmXbYmTwTte5cn4x/ieqFEEKI32JLIbPvmB/qk08+QUFBgXktLCwM06dPN1IuLi42r5HTp0/j+eefx/Hjx02/MZuzjx07hrvvvhsxMTFwu3+fK5qvr1ixwhxr6NCheOihh7B9+3bf1qunrLYAR7O3Y1LYEPQ79j6GnuyLbcnLkV3x+z5vIYQQoj5sKWQ2O7O/mB+usLDQvBYVFWWaoocMGYKiol8SaQQHB+Oxxx7D2bNnL/QZs//4/vvvx7lz58yAr9/CPud169ZhypQpGDduHB555BHs2LHDt/XayKnKwaHMQ1hzfjV2JO1EVkXWr/q6hRBCiMthSyFzNPXChQvx2WefXRAy5cq+4uHDh/8qQmYf89NPP40TJ06gsrLSvMZomU3WlLjLdfn1hQPRZC2EEEJcL7YUcnZ2tpnq1K5dO+Tm5prXGPUymp05c6YZ5OWHTdlt2rQxTc6UN/ubDxw4gNtvvx0pKSm+vS6NhCyEEMIO2FLIHEW9b98+05QcEhJiIt/ly5fjo48+Mk3TFzdDp6amYvz48fj666/NAK/k5GQTXXfo0MEM8roSErIQQgg7YEshs++VzdYjRoww84v79euH3r17Y/LkySYK3rlzp2mWZiRNWVPS3M7Sq1cvDBo0CFu2bDGyvRISshBCCDtgSyETijY8PBzfffcdxo4di8WLF5t5xuwT5hxljq7m9CjKm1JlRq9p06Zh4sSJJlsX+5nZfH0lJGQhhBB2wLZCvllIyEIIIeyAhCwhCyGEsAESsoQshBDCBkjIErIQQggb0OyFzMFjLVq0MNOmfvrpp+sqnDv9ww8/YOXKlfVuV6m/LFu2DEuXLq13m8rvC9O+cpCjrrOrK7zGeK3Vt02l/uJ/pvFnfdtVfl94nXGabn3brlRef/11zJkzx2en39PkhVxdXY0ePXrgvffew8cff3xdhXOf77jjDrPYRX3bVeovzz33nMm2Vt82ld8Xtujcc889ZkWz+rar1F+eeuopvPDCC/VuU6m/dOnSBf/4xz/QsWPHerer/L48+uijJsirb9uVSvv27c3CSpeiyQs5kDBrGG/6hmQIE7/A+eXffPON7zdxJTjt79VXX/1VCllxZYYNG2YWqBENh/kguFZAZGSk7xVxJZg7gy1YNwIJ+SqQkK8NCfnqkJCvDQn56pGQrx4J2SZIyNeGhHx1SMjXhoR89UjIV4+EbBO4rCP7o5nSUzQcDk7iwBHRMFjxGzhwIMrLy32viIawYMGCy/bPid+TkZFh+ja5Rr1oGMwYeT3r618OCfkqYMTCPNsVFRW+V0RD4DKZTJMqGgZXQNu/f3+9a32LS8PUuzExMb7fRENgpY9rxZeUlPheEVfi1KlTpmXhRiAhCyGEEDZAQhZCCCFsgIQshBBC2AAJuYGwr4X9oBxdxwFKTMWZlpbm29r8YMKVkydPmgFbS5YsMYVrWBcUFJhlMfPz87FhwwaT1Yb7cF+n0+l7988D5LimNc8lsyvFxcWZYzYHuKQo+4jXrFljzovX6zXX1759+8y5YNYkbq+pqTH7cjsHEvJ88fpjliCuEd5czheXZD1//ry5nvj9ea2FhISYMR1ZWVlYt26deY2ZkNiPfPF1Fh0djU2bNl3IRpWcnAyHw+Hb2nThtcFzxGuM9yAHux09ehRut9uMgeG9yuuI19u2bdtQVFRkrjWW0tJS06/MbSx8HzMeNjX4DOI1NW/ePHP9+Afr8vnFa4v3m/9+5PnimA6eH8JBcDxvvK54fnmdMU0zufh65faNGzeav8VzfyUk5AbCka9ct5nZupjd5rPPPjMPguZwc9cHhbtw4UJ8+OGHeP/999G1a1f079/fTNnJycnB3r170blzZ5PVjD957niR8oIuKyszN8AHH3xgtjN7zezZs81F29ThTcmRrT179sRLL72Eb7/91lxDfOgNGDAAnTp1wrvvvmu28yanlPmA3LVrlzlX3MZrkOuCR0RE+I7atOHDjw+2jz766MK1RtGcO3fOPDR5PnheeO6mTp2KxMTEC2JhmkJeZ7xnuZ3XbGpqqu/ITRMKhVMzBw0aZO5Pfn+eM15f/O6Uy8iRI012Lp43Xld8jaKmeFlJ5P7cxn2GDh1qBjKxYtiUYFDF5zizu9133304ceKEeZ3XDZ9jvNb8z6/hw4ebAYO8f1l5ZuWve/fuZjuvP67rz/uV1x1lPmXKFHO98prjKHaOzOZx/UK/FBJyA+D/BCYEb9OmjYmKWfvknMevvvqq2U4XYI2aIw0pF96ovJEpVl6IjEiGDBliHo6sdR48eBBjxowx85F5Lnnz89zxIvbX5HnRsxZ/pQu2scPKyKxZs9CrVy9zvijkwsJC89Dk67GxsUa0rNxwTi0foHxAjhs3zjwU+DAICgrCqFGjMGHCBN9Rmy68HlhZoxT8C8QwAuH1tnv3bnMd8bxxlDBnQHC+OyVMKfG6o4R47/rP25tvvmn2a8rXGSO1Q4cO4bbbbjMi5T3Hc0FBMJqjpMePH2/uO7YwcMGDSZMmmeuO0uF92rdvXyN1tgryd55HnvemBJ/lPD8MHpgO0y9knofBgweb7+2vwPA+ZVDBc8v7ccSIEeaeZNTMYI33L1tvWLnmrBIezz96fevWrXjyySdNQHJx6019SMgNgP9TZsyYYS5SXpS8mRkd83/S+vXrfXs1L/jA48XFm51QypQIJczo7a233jI3NyM8RtOLFi0yNUm+hw9Q7sNaKM8lL2JGMGw64oOzqcLvyXPC70rJsFLHa4iVln//+99mbiP34fliMxcjFDbBsqLCB+KRI0fMA4APBT4MWENvymLhd2OFrV+/fhcehv7Xeb2xyZXniBVDXldsmWElhhEJf2eFhRXE4OBgsz8jFCZcYRNkU2yC9eMX8p///GccO3bM3IOszFAavMaeeeYZM2eb55ZRMbtKGCmyFYbi5vnjv3m++F4227788stNbhoen+UUJq8PJkfxC/nAgQNo2bKlqbDwXLLCzGvmtddeM+eErSys0FDkvBZ5XljZ4bOP0uX9ynNIUfO6Y/fSJ598Ylp18vLyzN+4FBJyA2AtkSd79OjRvldgLnjWKufOnet7pflCKbPvmM0zjE4om2effdbIlRckL3xe0Fxkgg9KRnp8cLIm6YcJV2bOnGlq7E0V3qyUBCM2RmmUDCM6NtVTyLymeIP7mw3ZpM2ojg9Pnh/2f/Jccx9ej2xS4778vSnCa4dRDJvv2YTKZj9GxLy+2JLAn6z4sRLDc8Bri69xcQ4+JBn58f7keSfcjy0xbP7mXO+mCq8RNtuzFYYVFn8zPys2PBeUDSszfpnw+uN5YZDBftG2bduaiqP/vK5duxYvvviiaRVjRbwpQeEySn788ccvCJmR7UMPPWQqeLwGeT43b96Mhx9+2DRHsxLNZ//FuRVYYeZrrDTzOcZnnP+647nk7wxErjTuSEJuAKdPnzY1bUZ1fvigZJNGc0/VxwuWFy4vQj4A2IzIZrEnnnjCPCD9sM+Fr/EBwOYgNldf3GfMhwVbIZpqWlLeyIxO2MrCG5XXFIXMpi/2PfFm581MGJWwds0VshjhUDKsgfM8+x+IPFdcDYrH5f+Dpgi/K88N++nYXcRrhlLmtcJrjMLlcnYXQwHzNcqElRhWZliRIbz2GCWytYZdLU0Vnjd2pfFaY6sVl57lOevdu7eRT6tWrcxAS8LKcmZmJtq1a2ciO3Y3vfHGG6ZV0H//cnASVzfi9edvEWsq1CdkNjGzT5n3lh9WVPgaKyW8ZylfVgr9sKLI1/j8oxcobVaKCO933ut0iP9avBQScgNgsyFPJvtB/bC5lf8DLre2ZVOHtWc2wbD2yJuYTTJsfl21apVpFvNHb7yJGSEzauZNzgcEKzJ82PphFESp8+HQFPH3xbH/jiOoGaGwGevTTz+9cLP7I2T/Q4JRCSt+8+fPN/vxIcmHLfehmBj5sMmRvzdF+F1ZaWMUTLmmp6cbqbJrg2MUKJbWrVuba8p/nfF+5P7cjzmHua+/4kdJU+6MkCmXpgpbpli5Y8WEzy5W8DhokIJmlEb5cM1tnjOeJ4rDHyFTRmxh4KwHf4TM19law6Zbf4WwqVCfkFlxfuCBB8yzjZVdfmcOHnzwwQeNpOmB3w6q5AA6+oD3MCuOlLb/uqO4+Tufb7yHL4eE3ABYq6FAWNvk/yBepKxJsh+P8mmu8EbnhcqRmIzi/P0jFDQfBqyl84bnRcyHIB+UfHhSJrx4OViC55IXPPtV+TDloKemCPvmKOC7777byJdr0P7v//2/cdddd5m1utnfx347Rix88PGhwD539j8xmmE0yGZsnj+eU/ZhsRmS56+pwu/GiITnjRGHv/mQ54GtLKz0USSMdv3njQ9DthzwPPHByWvtzJkz5lisILIPmTJqyulv+dDnACOeB39ERjnwHmWUzGuOrQQ8RzwnlDXPMZtqWVlkKwKvV4qd55Xn6/nnnzfXXVOjPiGzT52VYXap8RnHfmM+59lKwD5ntsKw64nyJXx+sc+YFW4GGTxf7JOnsHndsbWLrYe8j69UEZSQGwAvRHbks1mHERz/J7Hvj5Hexc0WzQleaBx4RFGwb53S9deeOUiCfSp8ALCvjs2ufKCy8IHKm53njgLmueT0FfY/s1m7qTa/cgANKyzsQ2LhIBvWqr/88ksTjbBCwhudA5R4UzMCZC2cD1RWXNjkxUKRsLmbo6wvbrFpqvCa4uA3fnc+zHgvUiY8b4yAKWbem3xo8kHKa5FS5nXG5kOeYw5Konh43tiSw8pOU73OCK8ztlaxH5QRMs8FW1rYbM2xB7zWeG3xvmOLFs8lgwuKg6s+8d88r7xOKSW+hyKnnJsa9QmZ54HPNUa0bJVhBZACZj+wvwLDiJcCZqWG54iVH16XPB7fz1HVvP4YYPB6Y+sgzy1bHS6HhNwAKB/+T+P/EPblsXmVNSA2w/J/QHOEtXA+EBnt8SHHG/iLL74wDwJGJGz6YuTHc8VmWj4o2WzLByEjGfbtsVbJmiNr5OwSuHiQV1OHtWdKhpUUVkoYHVMePCdsnuX1xVGefABwwBxbIng++WDk6GpGyLwmmwPsHuL3ZZMrV8Hi9cQohJLhgCM2Q/uvI7a88DriPcvR6v5R1/7rkH3vN2phALvASgsf/nxW8bywZY8/eX2xskdRcB4yzwdf58Avjixm9MeKDZu72frCc8ZjsELESnRTq8Tw+uF54PiE//zP/zRN9awU83rj/cj7jPchzwGf/bwfWSlhqw3HMPB1//3ICg4rPzxHvF9ZYeY23sv8ycoz33elcyghNxBerKxBsVbEm579Kk39xr4cjFb4UGRLASMSPuhYOBWArQisebOZmk2GjJR5w19cO6SQ2I/K88mHJmVN+TQXeP74kPM/6Hh9scWB54I1c/6b54Ni4XZGKzxfjFZ4nvnQbMrNrhfDc8OHJM8LvzuTynCgDFsdOAiQETLPC1tc2JpwcdMqm/y5P68zvp8tWnxfU4fXBpufKRjeg6wAU8QUCqM2ttDwvuX1xsDC31/Kwgozm2i5nYUzAppiVxJbCBjVUpZsseNPVvB4bbEyx/uN54f97jx3/j51wpHTPG90Affhdea/Hxk183nG5x+38/rj864hLQwSshBCCGEDJGQhhBDCBkjIQgghhA2QkIUQQggbICELIYQQNkBCFkIIIWyAhCyEDeB0E07H4fQSzgVl4Rx3f7KV+uAUDE6x4HQLJn+oD04B4nG5r3//S+1bH5yqwWlH/Hz1wb/tn7/Kwt+vNYEE/wbnZF88belq4Hv5XS93zoSwMxKyELcYCoTzHjlnkUkKuCoW0/RxDWBm8bqUYChCzmNm4gHOqawPJhRh0hWmQWTh3NRL7VsfnNf71FNPXTLlH5NHcGGMV155xRSmYGTCkquVMisLnP/KucScg30tcL4o54ReKYG/EHZFQhbiFkOxUr7MGsQMQExCQCkxMxUTM9QnUAqMGYGYlo+5xJkWsj6Y55nZmRh1UqrMOMQUpQ2F6Sj/8pe/XHJ1JOaS5iIXFD0/C4VMMTJxQkPhd2Glg4kXWAFhusJrgZnPmNLQv+ydEI0NCVmIWwibWJmSjysXMfOZPxJlczUX6WBaPmYMYq5wpuZjxMsscZTq+vXrjQSZ8o+Zqvh+7suo2Z8VjWlM+R6+n8vrcdUepprk+5ljnNnAmAWL+Z65TjMrBEy76M+adiUh81hME0j4XRjls3LBSgXzmHMlKx6bWYuYHYpZ3ChfipdZ3fgd+LlYmN2N72OETUnzO/C78HsxoxIrKZQ1o28eg+JlZiV+bh7Hn+ZRQhaNFQlZiFsIBUw5tm/f3ojZD4XEPlEKj/mumZ+ZaRDffPNN00zM9VeZEpGy5brJFDcXD2B+YjZhU0oUGlNNMgc2RUZZcsEBrpLE9zP1H5uJKUsm02feXv/KSv6l465WyIyOKWRWDPwLaPBz8dhcMIPi5ffiGrsUKHMmM20hI1u2BHA/Vi54LFYMuEgJPxPzUTN3OpeiZOWC8qbo/TmXeeyXX37Z5G2WkEVjRUIW4hbCJSopVoqrvpXDKCHm2GU0S9lxMQ8u3EHZUqaMGv/0pz8ZObHpmyvTUO5cfi8rK+uCkNm8zXzYlCBzi/vhAC8KjtvZj81KwaOPPmoiVVYKGiJkrhbEY3DBEYqTqwUxdzQly4ibUTE/CxPwcyEDJtlnlM8lOpkLndE4B3RxP7+Q+Vl5HH5/5gnmd2MUfM8995gmeH5vip99xnwv1/Tl8p5sPpeQRWNFQhbiFuIXMlfLqk/I/pViKGRGh1zmjdKkLClkypmRIReboJi42AIlzKiT/bgNETIjZUbYHEjGdW//1//6XxdWMruSkLlm8+23326a3LnWMIXMz8oFHzgY7MknnzTHbdmypYnO2bzO78lImdEs14jl5/6tkCldfm62HjAa5nb2k7/wwgtmHVqeE8rcv2Qem7F5DlmxkZBFY0VCFuIWQtlQfhyYdaUma0qOA73YlEsoZPbRvvbaaxfERHFyP8qJ77mckDkFiqOoGWkyoqV82W999913m35bTsG6kpD9g7rY18u+aK4Fy2iWETrly+/GvmJu58AvRvCMphkh8+/6R1T/Vsj8jjwn3M6ViAj70Fn54HE4OI1LevLv+WElhE3XErJorEjIQtxCGIUy8mMEySXv2GxM2Ie6Y8cOs8Yvm30Z+XJgE6NK/7rRFDIFysiTYmYzNiNLrktNwTL6vljI7K/m8fgaofApTK4xTPHxs1Bwd955pxEqxXo1fcgXs2nTJrPWLsXrX5aOx+cxWRFgHzKlys9Pfitk9mGzX9v/b343LuH52GOPmfM1b948Myqdn5uVF0qelQ32J0vIorEiIQtxi6FMKEWKjU20bH6mjDkYimJl5MxBWfUJmeK76667MGHCBCMsypv9tOwD5nH9Qma/LechswmcA7ooNR6HQqTAuR8jTzaLs0+aA72uR8hc/5VTtvh5udg7m7H5k9+tIUJmxYTN2uxD5gAxfh+uZ8wmckb6wcHBZpAavwuPzfPFlgIOAJOQRWNFQhbiFsMpPJwixH5R9sNyUBVHTrM/lBEvt3NQFJuRKTFOGSIULpuDOdKYfctsIn7jjTdMUy6zZjFyZPM1I1XKldJjZNmuXTuTgISyp+h5TPYB8zUKkP20jDzZZE2BPvjgg6ZpvT4oUB7nt7BvmsJnlMs+5GeffdYM4ho/frwRMqdrcZAXJU342RjBs3LAz8jf+ff5XVq3bm36thkBs8mbkT37jLlwPisb/Lzsu2YTNweCcYCXEI0RCVkIG0DpUkDsL6WYKGhK1J/xits5Gpmi9b9GaVFOjJRZGFWycB9uI2wuZnR88e/+EdX8exQn9+ff5WuUPP82m8z5HjYV8/Pw79eHP/KuD35OHo/H5jH9f9PfP85/86cf/g1+Z38TN/8+Pxvfx/fzbzHBCd/vPwa/t38bv5f/OwnRGJGQhRBCCBsgIQshhBA2QEIWQgghbICELIQQQtgACVkIIYSwARKyEEIIYQMkZCGEEMIGSMhCCCGEDZCQhRBCCBsgIQshhBA2QEIWQgghbICELIQQQtgACVkIIYSwARKyEEIIYQMkZCGEEMIGSMhCCCGEDZCQhRBCCBsgIQshhBA2QEIWQgghbICELIQQQtxygP8flS5G8GiWRiIAAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "df147d7c",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:orbital_period_radius_colors.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cfcdd3c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the lists 'orbital_periods', 'semi_major_radii', and 'discovery_years'\n",
+    "# then create the scatter plot using the lists\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9260870",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "272027fb",
+   "metadata": {},
+   "source": [
+    "**Question 10:** Excluding planets with `orbital_period` **more than** *1000*, create a **scatter plot** representing the `orbital_period` (on the **x-axis**) against the `semi_major_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list`. Additionally, **color** the points on the **scatter plot** depending on the `discovery_year`, and change the **size** of the points depending on the `planet_mass`.\n",
+    "\n",
+    "You **must** ignore any `Planet` objects for which **either** the `orbital_period`, `semi_major_radius`, `discovery_year` or `planet_mass` data is **missing**. You **must** also ignore any `Planet` objects whose `orbital_period` is greater than *1000*. You **must** use the `discovery_year` to **color** the points in the **scatter plot** and you **must** use the `planet_mass` to change the **size** of the points.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "orbital_period_radius_colors_size.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "d1e706c6",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:orbital_period_radius_colors_size.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01d4fe45",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the lists 'orbital_periods', 'semi_major_radii', 'discovery_years', and 'planet_masses'\n",
+    "# then create the scatter plot using the lists\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fe9599a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b05f542f",
+   "metadata": {},
+   "source": [
+    "### Task 1.4: Plotting multiple scatter plots with `plot_scatter_multiple`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11468908",
+   "metadata": {},
+   "source": [
+    "### Function 2: `plot_scatter_multiple(x_data_dict, y_data_dict, x_label, y_label)`\n",
+    "\n",
+    "So far, we have used `plot_scatter` to create **scatter plots** using two **lists**, and we have seen how to use the `c` argument to color the **scatter plots** based on another **list** containing *numeric* data. It would be good if we could also **color** the points using *non-numeric* data. For example, we might want to create a **scatter plot** of the `planet_mass` against the `planet_radius`, and **color** the points differently depending on the `discovery_method`. However, since the `discovery_method` is **non-numeric**, the `plot_scatter` function will not be of help (you can try!)\n",
+    "\n",
+    "You have been provided with another function `plot_scatter_multiple` which can create **multiple** scatter plots on the same plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "718fb51d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_scatter_multiple(x_data_dict, y_data_dict, x_label='x axis', y_label='y axis'):\n",
+    "    legend_values = list(x_data_dict.keys())\n",
+    "    for key in x_data_dict:\n",
+    "        plt.scatter(x_data_dict[key], y_data_dict[key], s=7)\n",
+    "    plt.xlabel(x_label)\n",
+    "    plt.ylabel(y_label)\n",
+    "    plt.legend(legend_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36dd2f7c",
+   "metadata": {},
+   "source": [
+    "Here is a quick example to see how this function works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06816797",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell\n",
+    "\n",
+    "x_dict = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}\n",
+    "y_dict = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}\n",
+    "plot_scatter_multiple(x_dict, y_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccdd4c6f",
+   "metadata": {},
+   "source": [
+    "As you can see, in order to create the **scatter plot**, we had to create **two** dictionaries `x_dict` and `y_dict`. Both **dictionaries** contain the same **keys** (which correspond to the **different colors** on the plot). Corresponding to each **key**, the value in the **dictionary** `x_dict` contains the data in the **x axis** and the value in the **dictionary** `y_dict` contains the data in the **y axis**. For example, the element at index *0* of `x_dict['A']` is *1*, and the element at index *0* of `y_dict['A']` is *1*. Therefore, a point is added to the **scatter plot** at the position *(1, 1)* in the **color** corresponding to the **key** `A`. On the other hand, the element at index *0* of `x_dict['B']` is *5*, and the element at index *0* of `y_dict['B']` is *5*. Therefore, a point is added to the **scatter plot** at the position *(5, 5)* in the **color** corresponding to the **key** `B`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18cbe97c",
+   "metadata": {},
+   "source": [
+    "**Question 11:** Create a **scatter plot** representing the `planet_mass` (on the **x-axis**) against the `planet_radius` (on the **y-axis**) of each `Planet` object of **each** `discovery_method`.\n",
+    "\n",
+    "You **must** ignore any `Planet` objects for which **either** the `planet_mass`, `planet_radius` or `discovery_method` data is **missing**. You **must** color the points depending on the `discovery_method`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9128deb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "planet_masses = {}\n",
+    "planet_radii = {}\n",
+    "for planet in small_planets_list:\n",
+    "    method = planet.discovery_method\n",
+    "    mass = planet.planet_mass\n",
+    "    radius = planet.planet_radius\n",
+    "    if method == None or mass == None or radius == None:\n",
+    "        continue\n",
+    "    if method not in planet_masses:\n",
+    "        planet_masses[method] = []\n",
+    "        planet_radii[method] = []\n",
+    "    planet_masses[method].append(mass)\n",
+    "    planet_radii[method].append(radius)\n",
+    "\n",
+    "plot_scatter_multiple(planet_masses, planet_radii, 'Planet Mass','Planet Radius')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a3fe13f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a74969b",
+   "metadata": {},
+   "source": [
+    "**Question 12:** Create a **scatter plot** representing the `planet_mass` (on the **x-axis**) against the `planet_radius` (on the **y-axis**) of each `Planet` object discovered in **each decade**.\n",
+    "\n",
+    "You **must** ignore any `Planet` objects for which **either** the `planet_mass`, `planet_radius` or `discovery_year` data is **missing**. You **must** color the points depending on the **decade** of the `discovery_year`.\n",
+    "\n",
+    "**Hint:** You can use the `year_to_decade` function defined in the next cell to determine the **decade** that a given year belongs to.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "mass_radius_colors.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "2956c4ea",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:mass_radius_colors.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06031a16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have defined this function for you\n",
+    "\n",
+    "def year_to_decade(year):\n",
+    "    if year % 10 == 0:\n",
+    "        decade = str(year - 9) + ' to ' + str(year)\n",
+    "    else:\n",
+    "        decade_start = year - (year % 10) + 1\n",
+    "        decade = str(decade_start) + ' to ' + str(decade_start + 9)\n",
+    "    return decade"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "296c9cff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the dictionaries 'planet_masses', and 'planet_radii'\n",
+    "# then create the scatter plot using the dictionaries\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46ef834b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6fdb6f0",
+   "metadata": {},
+   "source": [
+    "## Segment 2: Recursion\n",
+    "\n",
+    "In addition to **scatter plots**, you will also have to write some **recursive** functions in p11. You can now practice creating some **recursive** functions here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64fed746",
+   "metadata": {},
+   "source": [
+    "### Task 2.1: Flattening a Nested List\n",
+    "\n",
+    "A **nested list** is a **list** which contains **lists** as its elements, and these **lists** can themselves contain other **lists** (and so on). **Flattening** a **nested list** involves extracting all the elements in a **nested list**. For example, if a **nested list** is as follows:\n",
+    "\n",
+    "```python\n",
+    "[[1, 2], 3, [4, [5, 6], [[7], 8, [9, 10]]]]\n",
+    "```\n",
+    "\n",
+    "then the **flattened** version of this list would be:\n",
+    "\n",
+    "```python\n",
+    "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a45de11",
+   "metadata": {},
+   "source": [
+    "### Function 3: `flatten(some_list)`\n",
+    "\n",
+    "We are now going to write a **recursive** function to **flatten** a **nested list** and print out all the elements in list.\n",
+    "\n",
+    "Let's first consider a **simple case**: when there are no **nested lists** inside the input **list** (i.e., there could be **lists** inside the input **list** but these **lists** have no additional **lists** inside them)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "641c9999",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this for you\n",
+    "\n",
+    "def flatten(some_list):\n",
+    "    # initialize an empty list into a variable called 'flattened_list'   \n",
+    "    flattened_list = []\n",
+    "    # iterate over every item inside some_list\n",
+    "    for item in some_list:\n",
+    "        # if item is a list, extend new list by item\n",
+    "        if type(item) == list:\n",
+    "            flattened_list.extend(item)\n",
+    "        # otherwise, append item to the new list\n",
+    "        else:\n",
+    "            flattened_list.append(item)\n",
+    "    return flattened_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a136e295",
+   "metadata": {},
+   "source": [
+    "**Question 13:** What is the output of `flatten(['rabbit', ['cat'], ['lion', 'chimpanzee']])`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf5736ff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "animals = flatten(['rabbit', ['cat'], ['lion', 'chimpanzee']])\n",
+    "\n",
+    "animals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b15a546",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dda290e6",
+   "metadata": {},
+   "source": [
+    "The function `flatten` works when the input is a **list** which does not have any **nested lists** inside it. While this is a good start, we are not finished yet, as this next example shows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7b1c3f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell\n",
+    "\n",
+    "nested_animals = [\"turtle\", [\"skunk\", [\"raccoon\"], \"crocodile\"]]\n",
+    "\n",
+    "print(\"flatten does this: \", flatten(nested_animals))\n",
+    "print(\"flatten should do this: \", ['turtle', 'skunk', 'raccoon', 'crocodile'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c38c0839",
+   "metadata": {},
+   "source": [
+    "Go back and **update** the function so that it works even if the **lists** in the input `some_list` have additional **nested lists** inside them.\n",
+    "\n",
+    "Here is some hints for how your function **must** look:\n",
+    "\n",
+    "```python\n",
+    "def flatten(some_list):\n",
+    "    # TODO: initialize an empty list into a variable called 'flattened_list'\n",
+    "    # TODO: iterate over every 'item' inside 'some_list'\n",
+    "        # TODO: check if type of 'item' is list\n",
+    "            # TODO: call 'flatten' function on 'item'\n",
+    "            # TODO: add the elements of the returned list to 'flattened_list'\n",
+    "        # TODO: if item is not a list, just add to 'flattened_list'\n",
+    "    # TODO: return 'flattened_list'\n",
+    "```\n",
+    "\n",
+    "**Hint:** Now would be a good time for you to review `append` vs `extend` list methods. Run the following cell to see that these list methods do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79a0551b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell\n",
+    "\n",
+    "list1 = [1, 2, 3, 4]\n",
+    "list1.append([4, 5, 6])\n",
+    "print(\"Append does this:\", list1)\n",
+    "\n",
+    "list2 = [1, 2, 3, 4]\n",
+    "list2.extend([4, 5, 6])\n",
+    "print(\"Extend does this:\", list2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca55bcf4",
+   "metadata": {},
+   "source": [
+    "**Warning:** `flatten` is quite a complicated function, so do **not** try to implement it in one sitting. Practice incremental coding here. First, try to **print** the outcome the recursive call to `flatten(item)` before you figure out how to add it to your list `flattened_list`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6d438af",
+   "metadata": {},
+   "source": [
+    "**Question 14:** What is the output of the `flatten` function when run against the below **nested list** `nested_names`?\n",
+    "\n",
+    "```python\n",
+    "nested_names = [\"Adam\", [\"Bob\", [\"Chet\"]], \"Bea\", [\"Treasure\", \"Andy\", [\"Ann\"]]]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3ef49a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'flattened_names', then display it\n",
+    "\n",
+    "nested_names = [\"Adam\", [\"Bob\", [\"Chet\"]], \"Bea\", [\"Treasure\", \"Andy\", [\"Ann\"]]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e03b55f1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b759fb9",
+   "metadata": {},
+   "source": [
+    "**Question 15:** What is the output of the `flatten` function when run against the below **nested list** `more_nested_names`?\n",
+    "\n",
+    "```python\n",
+    "more_nested_names = [\"Adam\", [\"Bob\", [\"Chet\", \"Cat\"], \"Barb\", \"Bert\"], \"Alex\", [\"Bea\", [\"Gold\", \\\n",
+    "                     [\"Andy\"]], \"Ann\"]]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8aef34b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'more_flattened_names', then display it\n",
+    "\n",
+    "more_nested_names = [\"Adam\", [\"Bob\", [\"Chet\", \"Cat\"], \"Barb\", \"Bert\"], \"Alex\", [\"Bea\", [\"Gold\", \\\n",
+    "                     [\"Andy\"]], \"Ann\"]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6ae32f2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b413891",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Explore a directory recursively\n",
+    "\n",
+    "In Task 2.1, we practiced how to **recursively** collect elements in a **nested list**. In Task 2.2, we are going to **recursively** collect all the **paths** of files in a **given directory**. After collecting them, your function `get_all_paths_in` will return a **sorted list of paths** to files inside this directory. We will practice our function on the `sample_data` directory inside the `sample_data.zip`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b996fd6f",
+   "metadata": {},
+   "source": [
+    "**Question 16:** What are the **files** and **sub-directories** that are inside the `sample_data` directory?\n",
+    "\n",
+    "Your output **must** be a **list** containing the **names** of the **files** and **sub-directories**. You **must** ignore all files and sub-directories whose names begin `\".\"`. You **must** **explicitly** sort the **list** in **ascending order**.\n",
+    "\n",
+    "**Hint:** Use `os.listdir` to get the **files** and **sub-directories** in a **directory**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1c27d5e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "directory = \"sample_data\"\n",
+    "sample_data_contents = sorted([content for content in os.listdir(directory) if not content.startswith(\".\")])\n",
+    "\n",
+    "sample_data_contents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96fd2328",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51a60f76",
+   "metadata": {},
+   "source": [
+    "**Question 17:** How **many** **files** (i.e., **not** **sub-directories**) are in the `sample_data` directory?\n",
+    "\n",
+    "Your output **must** be an **int**. You **must** ignore all files whose names begin `\".\"`.\n",
+    "\n",
+    "**Hints:**\n",
+    "\n",
+    "1. Use the `os.path.isfile` function to recognize **files** from **directories**.\n",
+    "2. You **must** pass the **relative path** of the **file** to determine if it is a file. For example, if you want to determine if the file `file_1.json` inside the directory `sample_data` is a **file**, you must use\n",
+    "\n",
+    "```python\n",
+    "os.path.isfile(os.path.join(\"sample_data\", \"file_1.json\"))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b33f4378",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'num_sample_data_files', then display it\n",
+    "\n",
+    "directory = \"sample_data\"\n",
+    "# TODO: find the number of files in 'directory'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09e6d1e7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7c1ea64",
+   "metadata": {},
+   "source": [
+    "**Question 18:** What are the **relative paths** of the **sub-directories** (i.e., **not files**) that are inside the `sample_data` directory?\n",
+    "\n",
+    "Your output **must** be a **list** containing the **relative paths** of the **sub-directories**. You **must** ignore all sub-directories whose names begin `\".\"`. You **must** **explicitly** sort the **list** in **ascending order**.\n",
+    "\n",
+    "**Hint:** Use the `os.path.isdir` function to recognize **files** from **directories**.\n",
+    "\n",
+    "**Warning:** Use `os.path.join` to get the **relative paths** of the **sub-directories**. Remember you **must not** hardcode `'\\\\'` or `'/'` in your paths."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a794e05c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'sample_data_directories', then display it\n",
+    "\n",
+    "directory = \"sample_data\"\n",
+    "# TODO: list the sub-directories in 'directory'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d484efea",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68889480",
+   "metadata": {},
+   "source": [
+    "We are now ready to define our recursive function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bce7aa25",
+   "metadata": {},
+   "source": [
+    "### Function 4:  `get_all_paths_in(directory)`\n",
+    "\n",
+    "You **must** write this function that takes in the **relative path** of a `directory` as its input, and returns a **list** of **relative paths** of all the **files** inside `directory` and its subdirectories.\n",
+    "\n",
+    "You **must** **ignore** all files that start with `\".\"`, and your output **must** be **explicitly** sorted in **alphabetical** order.\n",
+    "\n",
+    "In other words, if a directory `small_data` looks like this:\n",
+    "```\n",
+    "+-- sample_data\n",
+    "|   +-- .DS_Store\n",
+    "|   +-- file_1.json\n",
+    "|   +-- sample_1\n",
+    "|   |   +-- .ipynb_checkpoints\n",
+    "|   |   +-- file_2.json\n",
+    "|   |   +-- file_3.json\n",
+    "|   +-- sample_2\n",
+    "|   |   +-- file_4.json\n",
+    "|   |   +-- sample_3\n",
+    "|   |   |   +-- .DS_Store\n",
+    "|   |   |   +-- file_5.json\n",
+    "```\n",
+    "\n",
+    "then the output of the function call `get_all_paths_in(\"sample_data\")` (depending on whether the code is run on a Windows computer or a Mac) **must** either be the **list**\n",
+    "```python\n",
+    "['sample_data\\\\file_1.json',\n",
+    " 'sample_data\\\\sample_1\\\\file_2.json',\n",
+    " 'sample_data\\\\sample_1\\\\file_3.json',\n",
+    " 'sample_data\\\\sample_2\\\\file_4.json',\n",
+    " 'sample_data\\\\sample_2\\\\sample_3\\\\file_5.json']\n",
+    "```\n",
+    "\n",
+    "or the **list**\n",
+    "```python\n",
+    "['sample_data/file_1.json',\n",
+    " 'sample_data/sample_1/file_2.json',\n",
+    " 'sample_data/sample_1/file_3.json',\n",
+    " 'sample_data/sample_2/file_4.json',\n",
+    " 'sample_data/sample_2/sample_3/file_5.json']\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34dc7ca6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the 'get_all_paths_in' function here\n",
+    "\n",
+    "def get_all_paths_in(directory):\n",
+    "    pass # replace with your code\n",
+    "    # TODO: initialize an empty list\n",
+    "    # TODO: loop through all files and directories in 'directory' that do not start with \".\"\n",
+    "        # TODO: check if it is a directory\n",
+    "            # TODO: call 'get_all_paths_in' and add returned paths to your list\n",
+    "        # TODO: if it is a file, directly add to your list\n",
+    "    # TODO: sort the list of paths and then return the list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8146ffa7",
+   "metadata": {},
+   "source": [
+    "**Question 19:** How **many** files are there in total, in the `sample_2` directory of the `sample_data` directory?\n",
+    "\n",
+    "You **must** **ignore** all files that start with `\".\"`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc1ed64f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "num_files_sample_2 = len(get_all_paths_in(os.path.join(\"sample_data\", \"sample_2\")))\n",
+    "\n",
+    "num_files_sample_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b2ae0b6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50349a0e",
+   "metadata": {},
+   "source": [
+    "**Question 20:** What are the **relative paths** of all the **files** that are inside the `sample_data` directory?\n",
+    "\n",
+    "Your output **must** be a **list** containing the **relative paths** of **files**. You **must** ignore all files whose names begin `\".\"`. You **must** use the `get_all_paths_in` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc2f5de2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'sample_data_all_files', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "162dbdd0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ec5d482",
+   "metadata": {},
+   "source": [
+    "## Great work! You are now ready to start p11!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1-1": {
+     "name": "q1-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1-1\", {'x': x, 'y': y})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1-2": {
+     "name": "q1-2",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1-2\", {'x': x, 'y': y})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10\", {'orbital_periods': orbital_periods, 'semi_major_radii': semi_major_radii, 'discovery_years': discovery_years, 'planet_masses': planet_masses})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q11\", {'planet_masses': planet_masses, 'planet_radii': planet_radii})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q12\", {'planet_masses': planet_masses, 'planet_radii': planet_radii})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q13\", animals)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q14\", flattened_names)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q15\", more_flattened_names)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q16\", sample_data_contents)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17\", num_sample_data_files)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q18\", sample_data_directories)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q19\", num_files_sample_2)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2-1": {
+     "name": "q2-1",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q2-1\", {'x': x, 'y': y})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2-2": {
+     "name": "q2-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q2-2\", {'x': x, 'y': y})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q20\", sample_data_all_files)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3\", planet_count_2000_2010)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q4\", flux_1000_percentage)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q5\", {'orbital_periods': orbital_periods, 'semi_major_radii': semi_major_radii})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q6\", {'orbital_periods_no_outliers': orbital_periods_no_outliers, 'semi_major_radii_no_outliers': semi_major_radii_no_outliers})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7-1": {
+     "name": "q7-1",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7-1\", {'transit_planet_masses': transit_planet_masses, 'transit_planet_radii': transit_planet_radii})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7-2": {
+     "name": "q7-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7-2\", {'transit_planet_masses_no_outliers': transit_planet_masses_no_outliers, 'transit_planet_radii_no_outliers': transit_planet_radii_no_outliers})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q8\", {'rad_vel_planet_masses': rad_vel_planet_masses, 'rad_vel_planet_radii': rad_vel_planet_radii, 'rad_vel_discovery_years': rad_vel_discovery_years})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q9\", {'orbital_periods': orbital_periods, 'semi_major_radii': semi_major_radii, 'discovery_years': discovery_years})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p11/practice.ipynb b/lab-p11/practice.ipynb
index 6ed49cb79ca44cbbc96e3e94b09401cef35bf80b..e74e743debc6069c2a24345316286d6d800565cc 100644
--- a/lab-p11/practice.ipynb
+++ b/lab-p11/practice.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6ccd3634",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/lab-p12/.ipynb_checkpoints/practice-checkpoint.ipynb b/lab-p12/.ipynb_checkpoints/practice-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c3237c86d051292ed8972aaed069d1657e5d6b8e
--- /dev/null
+++ b/lab-p12/.ipynb_checkpoints/practice-checkpoint.ipynb
@@ -0,0 +1,1066 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "229fb9f4",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1e8226b",
+   "metadata": {},
+   "source": [
+    "# Lab-P12:  Web Requests, Caching, DataFrames and Scraping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "851f4fe3",
+   "metadata": {},
+   "source": [
+    "In this lab, you will practice how to:\n",
+    "\n",
+    "* use HTTP requests to download content from the internet,\n",
+    "* cache data onto your computer,\n",
+    "* construct and modify DataFrames to analyze datasets,\n",
+    "* use `BeautifulSoup` to parse web pages and extract useful information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a21135b",
+   "metadata": {},
+   "source": [
+    "## Segment 1: Web Requests and File Downloads"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8316db82",
+   "metadata": {},
+   "source": [
+    "We will first import some important modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bb3595f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this lab\n",
+    "\n",
+    "# we have imported these modules for you\n",
+    "import requests\n",
+    "import os\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "from bs4 import BeautifulSoup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "952a3bea",
+   "metadata": {},
+   "source": [
+    "### Task 1.1: Fetch `rankings.json` from an internet URL\n",
+    "\n",
+    "Use the `requests` library to fetch the file at this URL: `https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/rankings.json`. Make sure to call the appropriate function to **raise** an HTTPError if status code is not `200`.\n",
+    "\n",
+    "Then create a variable called `file_text` that saves the text of the response.\n",
+    "\n",
+    "**Hint:** You can review the relevant lecture code [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-29)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c239ddf1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'file_text', but do NOT display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "954c89dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert file_text[:30] == '[\\n    {\\n        \"World Rank\": '"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5814286",
+   "metadata": {},
+   "source": [
+    "### Task 1.2: Save `rankings.json` as a file\n",
+    "\n",
+    "Open a file in write mode called `rankings.json`, and write the contents of the variable `file_text` into it. Make sure to **close** the file afterwards (unless you used a `with` block to open the file).\n",
+    "\n",
+    "**Hint:** You can review the relevant lecture code [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-26)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ee0965b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "399c19b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your file has been created\n",
+    "\n",
+    "assert(os.path.exists(\"rankings.json\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7b02de",
+   "metadata": {},
+   "source": [
+    "Note that the cell above only checked if the file had been created, and **not** whether it contains the correct data. You must check that yourself. Check your `lab-p12` directory in Finder (Mac) / Explorer (Windows). It should now have a file called `rankings.json`. **Manually open** this file and confirm that it contains the contents of the page [`rankings.json`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/rankings.json)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c87c927b",
+   "metadata": {},
+   "source": [
+    "### Task 1.3: Implement the `download` function\n",
+    "\n",
+    "Now, you will implement a function `download` to download data from the internet and save it to a file. \n",
+    "\n",
+    "This function takes in two arguments `filename` and `url`. The contents at the address pointed to by the `url` field should be saved into the file whose path is specified by `filename`. Remember that you can reuse the code you wrote above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "970b97a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def download(filename, url):\n",
+    "    # TODO: make the request\n",
+    "    # TODO: raise an HTTPError if status code is not 200\n",
+    "    # TODO: get the text\n",
+    "    # TODO: open the file (with 'utf-8' encoding)\n",
+    "    # TODO: write to the file\n",
+    "    # TODO: close the file\n",
+    "    return (str(filename) + \" created!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c15ee7eb",
+   "metadata": {},
+   "source": [
+    "### Task 1.4: Implement caching in the `download` function\n",
+    "\n",
+    "The function `download` above is however, not quite what we want. One big disadvantage is that it **downloads** the file even if it has already been created. Fetching data from webpages takes both time and resources, and **must** be avoided as much as possible. In particular, repeatedly downloading files that have been already downloaded is a **very bad** coding practices, and **must** be avoided. \n",
+    "\n",
+    "Your `download` function above is particularly troublesome to use in a Jupyter Notebook, as it will try to fetch data over the internet **every time** your notebook is restarted (or the cell with the call to `download` is run).\n",
+    "\n",
+    "Now go back and **modify** `download` to implement *caching*. This means that **before** downloading the file from the internet, the function **must** check if the file already exists. If the file already exists, the function should return the message `\"<filename> already exists!\"` where `filename` is the argument. It should **not** make a request.\n",
+    "\n",
+    "**Hint:** You can use the `os.path.exists` function to check if the `filename` already exists."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70603939",
+   "metadata": {},
+   "source": [
+    "### Task 1.5: Test the `download` function\n",
+    "\n",
+    "Run the two cells below to test your function. Think about why the test code is written in this way. Ask a TA if you're not sure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1738cc49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# delete the file if it already exists and download the file\n",
+    "\n",
+    "rankings_url = \"https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/rankings.json\"\n",
+    "\n",
+    "if os.path.exists(\"rankings.json\"):\n",
+    "    os.remove(\"rankings.json\") # delete the existing file\n",
+    "\n",
+    "assert download(\"rankings.json\", rankings_url) == \"rankings.json created!\"\n",
+    "assert(os.path.exists(\"rankings.json\"))\n",
+    "assert(os.path.getsize(\"rankings.json\") > 1600000 and os.path.getsize(\"rankings.json\") < 2500000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26928b02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# verify that the function does not download again if file already exists\n",
+    "\n",
+    "f = open(\"rankings.json\", \"w\") # rewrite the contents of the file\n",
+    "f.close()\n",
+    "assert (download(\"rankings.json\",rankings_url) == \"rankings.json already exists!\" )\n",
+    "assert(os.path.getsize(\"rankings.json\") == 0) # check that the file was not redownloaded\n",
+    "os.remove(\"rankings.json\")\n",
+    "assert download(\"rankings.json\", rankings_url) == \"rankings.json created!\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56498bf7",
+   "metadata": {},
+   "source": [
+    "You **must** use this `download` function to download files during p12. This will ensure that you do not download the files each time you 'Restart & Run All'."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3ce745f",
+   "metadata": {},
+   "source": [
+    "## Segment 2:  Creating DataFrames\n",
+    "\n",
+    "For this project, we will be analyzing statistics about world university rankings adapted from\n",
+    "[here](https://cwur.org/). The `rankings.json` file was created by scraping content from pages on the linked website. \n",
+    "\n",
+    "We are going to use `pandas` throughout the lab and project to analyze this dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "470c2330",
+   "metadata": {},
+   "source": [
+    "### Task 2.1: Load data from `rankings.json` into a dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "740cc6f8",
+   "metadata": {},
+   "source": [
+    "In lecture, you reviewed different ways to create pandas DataFrames. For this task, create a DataFrame `rankings` by reading the JSON data saved in `rankings.json`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2e910b5",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "rankings = pd.read_json('rankings.json')\n",
+    "rankings.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed09f03d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to test whether 'rankings' has been defined properly\n",
+    "\n",
+    "assert(type(rankings) == pd.DataFrame)\n",
+    "assert(rankings.iloc[0][\"Institution\"] == 'Harvard University')\n",
+    "assert(rankings.iloc[1][\"Score\"]== 96.7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c8470ec",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Find the unique universities in the dataset\n",
+    "\n",
+    "As the dataset contains rankings for three different years, the same university may have featured multiple times. Find the names of the unique universities that are represented in the dataset.\n",
+    "\n",
+    "First, extract just the names of the institutions as a `pandas` **Series**. Then, make a **list** of unique names called `institutions_list`. **Series** can be easily typecast just like any other data type in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "649ab7cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: create a pandas `Series` of just the institution names in the dataset. \n",
+    "institutions = ...\n",
+    "# TODO: typecast the Series into a set and then into a list, to remove duplicates\n",
+    "institutions_list = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "251516a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to test whether 'institutions_list' has been defined properly\n",
+    "\n",
+    "assert(type(institutions) == pd.Series)\n",
+    "assert(len(institutions_list) == 2156)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b971679d",
+   "metadata": {},
+   "source": [
+    "### Task 2.3: Use `value_counts` to count instances in a dataframe\n",
+    "\n",
+    "Now, let's find the country that is the 5th most represented in the dataframe, and the number of times it features. Recall that `value_counts` enables us to count number of occurrences of unique values in a pandas **Series**.\n",
+    "\n",
+    "#### Task 2.3.1: Obtain the counts for all countries\n",
+    "\n",
+    "First, use the `value_counts` function on the `Country` column of `rankings`, and then typecast to a pandas **Series** called `country_counts`. This **Series** should contain each country in the dataset and the number of times it occurs.\n",
+    "\n",
+    "**Hint:** You can review the relevant lecture code [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-27)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46df6bf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'country_counts', and display it\n",
+    "\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a032ebfd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to test whether 'country_counts' has been defined properly\n",
+    "\n",
+    "assert(type(country_counts) == pd.Series)\n",
+    "assert(country_counts[\"USA\"] == 1062)\n",
+    "assert(len(country_counts) == 103)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d6b13f9",
+   "metadata": {},
+   "source": [
+    "Note that in the **Series** `country_counts`, the countries are already **sorted** in *decreasing* order of the number of times they appear in `rankings`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e80dea20",
+   "metadata": {},
+   "source": [
+    "#### Task 2.3.2: Find the 5th most represented country\n",
+    "\n",
+    "Use the `.index` attribute of the **Series** `country_counts` to fetch the name of the 5th most represented country. Note that `country_counts` is **sorted** in *decreasing* order of the number of times each country appears in `rankings`. You **must** use `iloc` to fetch the count of this country. Make sure to use the **Series** `country_counts` defined in Task 2.3.1.\n",
+    "\n",
+    "**Hint**: The pandas `Series.index` works differently from the `.index` method you are familiar with for **lists**. `Series.index` takes in the numerical **index** of the element you want to access, and returns the **label** you can pass to `.loc` to access it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51935d37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: find the 5th most represented country\n",
+    "fifth_country = country_counts.index[...]\n",
+    "# TODO: find the count of the 5th most represented country\n",
+    "fifth_count = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bb7bbba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to test whether 'fifth_country' and 'fifth_count' have been defined properly\n",
+    "\n",
+    "assert(fifth_country == \"France\")\n",
+    "assert(fifth_count == 256)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4708406b",
+   "metadata": {},
+   "source": [
+    "### Task 2.4: `loc` vs `iloc`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4448b40",
+   "metadata": {},
+   "source": [
+    "In this lab and project, you **must** only use `iloc`. Using `loc` will be considered **hardcording**. This is because `iloc` selects rows and columns at the given **integer position** while `loc` selects rows at the given **pandas index**. \n",
+    "\n",
+    "Recall that **row index** can be given meaningful names like string indices. Consider a scenario where you add rows to the beginning of the DataFrame - if you use `.loc` indexing, your answer will become **incorrect** if the data changes. Whereas if you use `.iloc`, you will always get the correct answer.\n",
+    "\n",
+    "This distinction may not be as intuitive for the current `rankings` **DataFrame**. As an example, use both `loc` and `iloc` to fetch the first row in `rankings`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9481efa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use iloc to extract the first row from 'rankings'\n",
+    "\n",
+    "first_row_iloc = ???\n",
+    "first_row_iloc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d16458e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use loc to extract the first row from 'rankings'\n",
+    "\n",
+    "first_row_loc = ???\n",
+    "first_row_loc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "409581bb",
+   "metadata": {},
+   "source": [
+    "The results are exactly the same! This happens since the integer positions correspond to the pandas indices in the `rankings` dataframe. However, this will not always hold true - as we see in the next task."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0625ab34",
+   "metadata": {},
+   "source": [
+    "### Task 2.5: Use boolean indexing to filter data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73fc0f85",
+   "metadata": {},
+   "source": [
+    "Now, use **boolean indexing** to extract data from the **DataFrame**. You can find the relevant lecture code [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-28).\n",
+    "\n",
+    "Create a **DataFrame** `rankings_arg_bra` that **only** consists of rankings of universities from *Argentina* and *Brazil*. \n",
+    "\n",
+    "**Hints**:When implementing **boolean indexing** in `pandas`, the `or` operator is represented by `|` and the `and` operator is represented by `&`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08166b72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rankings_arg_bra', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50ea4838",
+   "metadata": {},
+   "source": [
+    "Now, we will try to extract the **first** value in this new **DataFrame** using `iloc` and `loc`. As you'll see, using `loc` will not work the same way it did before. In fact, it will throw an **error**. To verify, run the two cells below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfab6e71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_row_iloc = rankings_arg_bra.iloc[0]\n",
+    "first_row_iloc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39cfbc55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_row_loc = rankings_arg_bra.loc[0]\n",
+    "first_row_loc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "811dd8ef",
+   "metadata": {},
+   "source": [
+    "We see that using `.loc` now causes a **KeyError**.\n",
+    "\n",
+    "`.loc[0]` tries to find the row with the *labeled* **index** 0. Run the cell below and notice how `rankings_arg_bra` starts at the labeled **index** 127. There is no 0. Hence the **KeyError**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9f26433",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rankings_arg_bra.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "292f2828",
+   "metadata": {},
+   "source": [
+    "### Task 2.6: Sort the DataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96eeaaeb",
+   "metadata": {},
+   "source": [
+    "The **DataFrame** in Task 2.5 is sorted by `World Rank`, with the result that universities from *Argentina* and *Brazil* are interleaved throughout the data. **Re-sort** the data to sort by `Country` so that all universities from *Argentina* appear **first** followed by universities from *Brazil*. Within each country, the universities should be **sorted** by their `National Rank`. \n",
+    "\n",
+    "Use the `sort_values` function of `pandas`. You can find the relevant lecture code [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-28). Remember - by default, `pandas` returns a **new** sorted **DataFrame** and does **not** modify the existing one.\n",
+    "\n",
+    "Recall that `sort_values` takes an argument for the parameter `by` as the column name, based on which you want to do the sorting. If you want to use one column for primary sorting and another for secondary sorting, you can specify a **list** of column names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bc78818",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'sorted_rankings_arg_bra', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5a21a9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to test whether 'sorted_rankings_arg_bra' has been defined properly\n",
+    "\n",
+    "assert(sorted_rankings_arg_bra.iloc[0][\"Institution\"] == \"University of Buenos Aires\")\n",
+    "assert(sorted_rankings_arg_bra.iloc[-1][\"World Rank\"] == 1997)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "557b328d",
+   "metadata": {},
+   "source": [
+    "### Task 2.7: Create a new, simplified DataFrame to track changes in rankings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "714acb5b",
+   "metadata": {},
+   "source": [
+    "As we have seen, universities that have featured in rankings of multiple years are featured repeatedly. To simplify comparisons, we want to feature each university once and remove all other metrics. \n",
+    "\n",
+    "This time - instead of simply ranking universities, we want to find the **absolute change** in universities' rankings between the years *2019-2020* and *2020-2021*. We are only interested in the absolute change and **not** whether the rank improved or declined.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75fba1df",
+   "metadata": {},
+   "source": [
+    "#### Task 2.7.1: Find the absolute difference in World Rank for \"University of Madras\" between 2019-2020 and 2020-2021\n",
+    "\n",
+    "First, let's attempt to measure the change for one particular university."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4353ab3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'absolute_diff_madras', then display it\n",
+    "\n",
+    "# TODO: first find the ranking of \"University of Madras\" in the year \"2019-2020\"\n",
+    "# TODO: then find the ranking of \"University of Madras\" in the year \"2020-2021\n",
+    "# TODO: Remember to use .iloc[0] to extract the value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6da84f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to test whether 'absolute_diff_madras' has been defined properly\n",
+    "\n",
+    "assert(absolute_diff_madras == 108)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9c5dfd4",
+   "metadata": {},
+   "source": [
+    "#### Task 2.7.2: Create a Series with the absolute difference in ranks for \"University of Madras\" between 2019-2020 and 2020-2021\n",
+    "\n",
+    "Now, create a **dictionary** with the keys as `Institution` and `Absolute Change`. The values should be the relevant values for *University of Madras*. Then, convert this **dictionary** to a **Series**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "813a7351",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'madras_series', then display it\n",
+    "\n",
+    "# TODO: first define the dictionary 'madras_dict'\n",
+    "\n",
+    "# TODO: then you can convert to a Series\n",
+    "madras_series = pd.Series(madras_dict)\n",
+    "\n",
+    "madras_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c0fb02a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to test whether 'madras_series' has been defined properly\n",
+    "\n",
+    "assert(madras_series[\"Institution\"] == \"University of Madras\")\n",
+    "assert(madras_series[\"Absolute Change\"] == 108)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c069767a",
+   "metadata": {},
+   "source": [
+    "#### Task 2.7.3: Create the `change_in_rankings` DataFrame\n",
+    "\n",
+    "Now, create a **DataFrame** `change_in_rankings` with just 2 columns, `Institution` and `Absolute Change` where **each** university is only featured once. If the institution is **not** present in the rankings of **both** years, we will just ignore it.\n",
+    "\n",
+    "The institutions should be **sorted** in *increasing* order of their **absolute change**. For institutions with the **same** absolute change, sort them *alphabetically* by their **names**.\n",
+    "\n",
+    "**Warning:** Even if your code is optimal, this cell may take a few seconds to run. However, if it takes much longer than that (say, if it takes 30 seconds or longer), then you will **need** to optimize your code so it runs faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92a57fe2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# suggested approach (you may do it some other way if you wish)\n",
+    "\n",
+    "# TODO: initialize an empty list\n",
+    "# TODO: create a list of unique institutions in the DataFrame 'rankings'\n",
+    "# TODO: loop through these institutions\n",
+    "    # TODO: create a new dataframe that has rankings for only this institution\n",
+    "    #       (hint: Use boolean indexing for the \"Institution\" column)\n",
+    "    # TODO: create a list of years by casting the \"Year\" column of this dataframe to a list\n",
+    "    # TODO: skip institution if \"2019-2020\" or \"2020-2021\" are *not* in this list\n",
+    "        \n",
+    "    # TODO: extract the \"World Rank\" for both years from the new DataFrame \n",
+    "    #       (remember to use .iloc[0] to extract the actual value)\n",
+    "    # TODO: find their absolute difference\n",
+    "       \n",
+    "    # TODO: make a dictionary where the keys are “Institution” and “Absolute Change”\n",
+    "    #       and the values are the corresponding values you just found for this institution\n",
+    "    \n",
+    "    # TODO: append this dictionary to the list initialized in the first step\n",
+    "\n",
+    "# TODO: finally, convert the list of dicts to a pandas DataFrame called change_in_rankings\n",
+    "# TODO: sort this DataFrame using sort_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd49ebb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to test whether 'change_in_rankings' has been defined properly\n",
+    "\n",
+    "assert(change_in_rankings.iloc[100][\"Institution\"] == \"Vrije Universiteit Brussel\")\n",
+    "assert(change_in_rankings.iloc[-1][\"Absolute Change\"] == 1081)\n",
+    "assert(change_in_rankings.shape[1] == 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54c815e5",
+   "metadata": {},
+   "source": [
+    "# Segment 3: BeautifulSoup\n",
+    "\n",
+    "As mentioned in Segment 2, the `rankings.json` file was created by parsing HTML content on the Web, specifically the web pages listed below.\n",
+    "\n",
+    "* https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2019-2020.html\n",
+    "* https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2020-2021.html\n",
+    "* https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2021-2022.html\n",
+    "\n",
+    "Now, let's write a function to do this ourselves. We will use the `BeautifulSoup` module to scrape web pages and extract information. It is recommended that you review the relevant lecture code [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-31) if you get stuck at any point in this segment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae12fb9f",
+   "metadata": {},
+   "source": [
+    "### Task 3.1: Download the HTML files\n",
+    "\n",
+    "Use the `download` function you previously created to download the contents of each of the URLs above and save them into files. Name the files `2019-2020.html`, `2020-2021.html` and `2021-2022.html` based on the respective URL."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50cb15ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2019-2020.html'\n",
+    "# to the file '2019-2020.html'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6578b33b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2020-2021.html'\n",
+    "# to the file '2020-2021.html'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb54466f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2021-2022.html'\n",
+    "# to the file '2021-2022.html'\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59510a31",
+   "metadata": {},
+   "source": [
+    "### Task 3.2: Read `2019-2020.html`\n",
+    "\n",
+    "First, read the contents of the file `2019-2020.html`.\n",
+    "\n",
+    "**Hint:** If you get a `UnicodeDecodeError`, make sure all your calls to `open` have the keyword argument `encoding=\"utf-8\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e09ba60f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here - you may use any variable names you want to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7198846c",
+   "metadata": {},
+   "source": [
+    "### Task 3.3: Initialize `BeautifulSoup` object instance\n",
+    "\n",
+    "Use the variable defined in Task 3.2. You can review the relevant lecture code [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-31)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7707a22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here - you may use any variable names you want to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "874a8204",
+   "metadata": {},
+   "source": [
+    "### Task 3.4: Find the `table` element\n",
+    "\n",
+    "The webpage has a `table` containing all the data we're trying to extract. Write the code to **find** this element and store it in a variable. Use the `BeautifulSoup` object instance defined in Task 3.3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73e2fc79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here - you may use any variable names you want to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0659d77c",
+   "metadata": {},
+   "source": [
+    "### Task 3.5: Find all `th` tags, to parse the table header\n",
+    "\n",
+    "Use the variable defined in Task 3.4.\n",
+    "\n",
+    "**Hint**: The **header** should be a **list** of elements, that can be obtained by using the `get_text` method for each `th` element in the table. You may also find list comprehension useful here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10082cbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'header', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2abf9e15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to test whether 'header' has been defined properly\n",
+    "\n",
+    "assert(len(header) == 9)\n",
+    "assert(type(header) == list)\n",
+    "assert(header[0] == \"World Rank\")\n",
+    "assert(header[-1] == \"Score\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b86fca71",
+   "metadata": {},
+   "source": [
+    "### Task 3.6: Build row dictionary for one row\n",
+    "\n",
+    "Scrape the **second** row (the first one is the **header**!) of the `table`, convert the data to the appropriate **data types**, and populate the data into a **dictionary**. The **keys** of the dictionary **must** be the columns in the **DataFrame**. **Avoid** *hardcoding* these **keys** - instead, use the variable `header` obtained in the previous task.\n",
+    "\n",
+    "**Hint**: Rows can be found by locating the `tr` elements in the table. After identifying the second row of the table, try to figure out which tag separates the different columns.\n",
+    "\n",
+    "The required data types for each column is:\n",
+    "\n",
+    "|**Column Name**|**Data Type**|\n",
+    "|---------------|-------------|\n",
+    "|`World Rank`|**int**|\n",
+    "|`Institution`|**str**|\n",
+    "|`Country`|**str**|\n",
+    "|`National Rank`|**int**|\n",
+    "|`Quality of Education Rank`|**int**|\n",
+    "|`Alumni Employment Rank`|**int**|\n",
+    "|`Quality of Faculty Rank`|**int**|\n",
+    "|`Research Performance Rank`|**int**|\n",
+    "|`Score`|**float**|\n",
+    "\n",
+    "You can **compare** your output with the data in `rankings.json`, to confirm whether you have parsed the file correctly (note that you do **not** yet have to implement the `Year` column in your **dicitonary**)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8affe622",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here - you may use any variable names you want to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fff288f",
+   "metadata": {},
+   "source": [
+    "### Task 3.7: Build list of all row dictionaries\n",
+    "\n",
+    "Scrape **all** rows, **convert** data to appropriate types, and populate data into a row **dictionary** and append row all dictionaries to a **list**.\n",
+    "\n",
+    "This is a natural extension of Task 3.6. You can use a loop to extract all rows and populate the list.\n",
+    "\n",
+    "**Important**:\n",
+    "* Some fields in the dataset have **missing** values, represented simply as `\"-\"`. Such **missing** values should be replaced by `None` in your **dictionary**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "401e5a99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here - you may use any variable names you want to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83ed2994",
+   "metadata": {},
+   "source": [
+    "### Task 3.8: Write the `parse_html` function\n",
+    "\n",
+    "Convert tasks 3.2 to 3.7 to a **function**. This function `parse_html` should take in a `filename` as **input** and **return** a **list** of **dictionaries**, with each **dictionary** representing a **row** in the dataset.\n",
+    "\n",
+    "Additionally, we **also** want to include the **key** `Year` to all our **dictionaries**. The `Year` value is **not** present in the dataset. You can extract this value from the `filename`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a855afdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_html(filename):\n",
+    "    '''parse_html(filename) parses an HTML file and \n",
+    "    returns a list of dictionaries containing the tabular data'''\n",
+    "    pass # replace with your code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c39ca4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to test whether 'parse_html' has been defined properly\n",
+    "\n",
+    "parsed_2019_2020 = parse_html(\"2019-2020.html\")\n",
+    "parsed_2020_2021 = parse_html(\"2020-2021.html\")\n",
+    "parsed_2021_2022 = parse_html(\"2021-2022.html\")\n",
+    "assert(parsed_2019_2020[-1][\"Institution\"] == 'Government College University Faisalabad')\n",
+    "assert(parsed_2020_2021[15][\"Score\"] == 89.0)\n",
+    "assert(parsed_2021_2022[100][\"Country\"] == 'United Kingdom')\n",
+    "assert(parsed_2021_2022[25][\"World Rank\"] == 26)\n",
+    "assert(parsed_2020_2021[-5][\"National Rank\"] == 15)\n",
+    "assert(parsed_2019_2020[50][\"Quality of Faculty Rank\"] == 78)\n",
+    "assert(parsed_2021_2022[87][\"Alumni Employment Rank\"] == 464)\n",
+    "assert(parsed_2020_2021[40][\"Research Performance Rank\"] == 398)\n",
+    "assert(parsed_2019_2020[0][\"Year\"] == \"2019-2020\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3c48aa9",
+   "metadata": {},
+   "source": [
+    "If you fail any of the checks above, you can open `rankings.json` **manually** and compare with the output of your `parse_html` function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f2b7c36",
+   "metadata": {},
+   "source": [
+    "### Congratulations, you are now ready to start p12!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p12/practice.ipynb b/lab-p12/practice.ipynb
index f8cf839edf3f2bb6339036021e4b53f6a2134e4c..c3237c86d051292ed8972aaed069d1657e5d6b8e 100644
--- a/lab-p12/practice.ipynb
+++ b/lab-p12/practice.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "229fb9f4",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a1e8226b",
diff --git a/lab-p13/.ipynb_checkpoints/practice-checkpoint.ipynb b/lab-p13/.ipynb_checkpoints/practice-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7487dc057f4c8418c557ff0e63e94facf46b85be
--- /dev/null
+++ b/lab-p13/.ipynb_checkpoints/practice-checkpoint.ipynb
@@ -0,0 +1,825 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7252973f",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "115889c5",
+   "metadata": {},
+   "source": [
+    "# Lab-P13: Analyzing World Data with SQL\n",
+    "\n",
+    "In this lab, you will practice how to:\n",
+    "\n",
+    "* write SQL queries,\n",
+    "* create your own plots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daed65a3",
+   "metadata": {},
+   "source": [
+    "# Segment 1: Setup\n",
+    "\n",
+    "### Task 1.1: Import the required modules\n",
+    "\n",
+    "We will first import some important modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e59b7bdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n",
+    "\n",
+    "import sqlite3\n",
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "import math\n",
+    "import numpy as np # this is *only* for the function get_regression_coeff - do NOT use this module elsewhere"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97a3f1e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this ensures that font.size setting remains uniform\n",
+    "%matplotlib inline \n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "matplotlib.rcParams[\"font.size\"] = 13 # don't use value > 13! Otherwise your y-axis tick labels will be different."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75adca21",
+   "metadata": {},
+   "source": [
+    "### Task 1.2: Use the `download` function to download `QSranking.json`\n",
+    "\n",
+    "Warning: For the lab and the project, do **not** download the dataset `QSranking.json` manually (you **must** write Python code to download this, as in p12). When we run the autograder, this file `QSranking.json` will not be in the directory. So, unless your `p13.ipynb` downloads this file, you will get a **zero score** on the project. Also, make sure your `download` function includes code to check if the file already exists. The TAs will **manually deduct points** otherwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bb742ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# copy the definition of your 'download' function from p12 here - remember to import the necessary modules\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe96e53b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p13/QSranking.json'\n",
+    "# to the file 'QSranking.json'\n",
+    "\n",
+    "download(\"QSranking.json\", \"https://raw.githubusercontent.com/msyamkumar/cs220-f21-projects/main/p13/QSranking.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0023581a",
+   "metadata": {},
+   "source": [
+    "### Task 1.3: Create a database called 'rankings.db' out of 'QSRankings.json'\n",
+    "\n",
+    "You can review the relevant lecture code [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-32)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "270d8da5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a database called 'rankings.db' out of 'QSranking.json'\n",
+    "\n",
+    "# TODO: load the data from 'QSranking.json' into a variable called 'qs_ranking' using pandas' 'read_json' function\n",
+    "# TODO: connect to 'rankings.db' and save it to a variable called 'conn'\n",
+    "\n",
+    "# write the contents of 'qs_ranking' to the table 'rankings' in the database\n",
+    "qs_ranking.to_sql(\"rankings\", conn, if_exists=\"replace\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84a77c79",
+   "metadata": {},
+   "source": [
+    "### Task 1.4: Read all the rows in rankings (the database table)\n",
+    "\n",
+    "You'll have to use pandas's `read_sql` function to make a query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a300adde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rankings', but do NOT display it\n",
+    "# replace the ... with your code\n",
+    "\n",
+    "rankings = pd.read_sql(\"SELECT ... FROM ...\", conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e4d16ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert len(rankings) == 1201\n",
+    "assert rankings.iloc[0][\"country\"] == \"United States\"\n",
+    "assert rankings.iloc[-1][\"institution_name\"] == \"Wake Forest University\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b09ee5a",
+   "metadata": {},
+   "source": [
+    "# Segment 2: SQL Practice\n",
+    "\n",
+    "In practice, we often are more interested in writing more specific queries about our data. For example, we might be interested in finding institutions in the *United States*, or data collected in the `year` *2018*, or both. With **SQL**, **WHERE** and **AND** clauses can help filter the data accordingly.\n",
+    "\n",
+    "Before proceeding with this segment, it is **recommended** that you **review** the relevant lecture code [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-32) and [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-33)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cebe083",
+   "metadata": {},
+   "source": [
+    "### Task 2.1: Use WHERE to find institutions in the United States\n",
+    "\n",
+    "* Write a query to select the rows from the database with *United States* as the `country`.\n",
+    "* Keep only the `institution_name` column.\n",
+    "* Save these institution names to a **list**.\n",
+    "\n",
+    "**Hint:** You will need to use **quotes** (`'`) around the **strings** in your query and **backticks** (``` ` ```) around **column names** as in the example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64012949",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "us_institutions_df = pd.read_sql(\"SELECT `institution_name` FROM rankings WHERE `country` = 'United States'\", conn)\n",
+    "us_institutions = list(us_institutions_df['institution_name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c035f899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert \"University Of Wisconsin-Madison\" in us_institutions\n",
+    "assert \"Tampere University\" in list(rankings[\"institution_name\"])\n",
+    "assert \"Tampere University\" not in us_institutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fe4da4e",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Add an AND clause to find institutions in the United States with at least 70 overall score\n",
+    "\n",
+    "* Copy your query from Task 2.1.\n",
+    "* Update it to only select rows with `overall_score` of **at least** *70*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12f341ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'good_us_institutions', but do NOT display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25e2d3cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert \"Massachusetts Institute Of Technology\" in good_us_institutions\n",
+    "assert \"University Of Wisconsin-Madison\" in good_us_institutions\n",
+    "assert \"Wake Forest University\" not in good_us_institutions\n",
+    "assert \"University of Connecticut\" not in good_us_institutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf715227",
+   "metadata": {},
+   "source": [
+    "### Task 2.3: Use an ORDER BY clause to display the top 5 institutions by academic reputation in 2019\n",
+    "\n",
+    "In addition to **WHERE** and **AND**, the **ORDER BY** keyword helps organize data even further. Much like the `sort_values()` function in `pandas`, the **ORDER BY** clause can be used to organize the result of the query in *increasing* (**ASC**) or *decreasing* (**DESC**) order based on a column's values.\n",
+    "\n",
+    "* Write a new query to select rows in rankings where the `year` is *2019*.\n",
+    "* Use **ORDER BY** and **LIMIT** to select the top 5 rows with the **highest** `academic_reputation`.\n",
+    "* Save these institution names to a **list**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "763304e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_5_institutions', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "404fa832",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert len(top_5_institutions) == 5\n",
+    "assert top_5_institutions[0] == \"Massachusetts Institute Of Technology\"\n",
+    "assert top_5_institutions[-1] == \"University Of Cambridge\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13e1803b",
+   "metadata": {},
+   "source": [
+    "### Task 2.4: Order by multiple columns\n",
+    "\n",
+    "If you print out the resulting dataframe from your query, you might notice that all 5 rows have the same academic reputation. This makes it hard to compare the universities, so we will add some **tiebreaking** rules. If two universities have the same `academic_reputation`, then we should order them by their `citations_per_faculty` (with the **highest** appearing first). You can do this by ordering by multiple columns.\n",
+    "\n",
+    "* Copy your query from Task 2.3.\n",
+    "* Update the **ORDER BY** clause to add this tiebreaking behavior.\n",
+    "* Save these institution names to a **list**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26f5a433",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_5_with_tiebreak', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5b2382b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert top_5_with_tiebreak[0] == \"University Of California, Berkeley\"\n",
+    "assert top_5_with_tiebreak[-1] == \"University Of California, Los Angeles\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b991dcf",
+   "metadata": {},
+   "source": [
+    "### Task 2.5: Use GROUP BY clause and SUM aggregate function to get the total number of international_students for each country in 2019\n",
+    "\n",
+    "The **GROUP BY** keyword groups rows that have the same value. It is often used with aggregate functions, such as **COUNT**, **SUM**, **AVG**, etc. to obtain a summary about groups in the data.\n",
+    "\n",
+    "For example, to answer the question \"What is the average rank of each country's institutions?\", we could **GROUP BY** the `country` and use the **AVG** aggregate function to get the average rank of each country.\n",
+    "\n",
+    "* Write a new query that uses **GROUP BY** and **SUM** to get the total number of international students in each country, using **WHERE** to filter by the `year`.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `country` and the **sum** of the `international_students` for that country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f31786c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'inter_students_by_country', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c84f12c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert math.isclose(inter_students_by_country[inter_students_by_country[\"country\"] == \"Japan\"].iloc[0][1], 280.9)\n",
+    "assert math.isclose(inter_students_by_country[inter_students_by_country[\"country\"] == \"Australia\"].iloc[0][1], 1895.5)\n",
+    "assert math.isclose(inter_students_by_country[inter_students_by_country[\"country\"] == \"United States\"].iloc[0][1], 3675.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06ecba29",
+   "metadata": {},
+   "source": [
+    "### Task 2.6: Use the AS keyword to rename the new column from Task 2.5 to total_international_students\n",
+    "\n",
+    "Although the dataframe does have a column for the sum of international students for each country, the name of the column looks strange:\n",
+    "\n",
+    "```sql\n",
+    "SUM(`international_students`)\n",
+    "```\n",
+    "\n",
+    "In SQL, the **AS** keyword allows us to rename the columns we create with our queries to make the resulting **DataFrame** easier to understand.\n",
+    "\n",
+    "* Paste your query from Task 2.5 and modify it so the **SUM** column has the name `total_international_students`.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `country` and `total_international_students`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3947be0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'inter_students_by_country_renamed', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e114959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert \"total_international_students\" in inter_students_by_country_renamed.columns\n",
+    "assert math.isclose(inter_students_by_country_renamed[inter_students_by_country_renamed[\"country\"] == \"Japan\"][\"total_international_students\"], 280.9)\n",
+    "assert math.isclose(inter_students_by_country_renamed[inter_students_by_country_renamed[\"country\"] == \"Australia\"][\"total_international_students\"], 1895.5)\n",
+    "assert math.isclose(inter_students_by_country_renamed[inter_students_by_country_renamed[\"country\"] == \"United States\"][\"total_international_students\"], 3675.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79fdda0c",
+   "metadata": {},
+   "source": [
+    "### Task 2.7: Use the HAVING keyword to only keep countries with more than 1000 international students\n",
+    "\n",
+    "In addition to **WHERE**, the **HAVING** keyword is useful for filtering **GROUP BY** queries. Whereas **WHERE** filters the number of rows, **HAVING** filters the number of groups.\n",
+    "\n",
+    "* Paste your query from Task 2.6 and modify it so that it only returns countries (`country`) and `total_international_students` with **more than** *1000* international students.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `country` and `total_international_students`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bc00cf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'inter_students_by_country_more_than_1000', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1c5be56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert len(inter_students_by_country_more_than_1000) == 4\n",
+    "assert \"Australia\" in list(inter_students_by_country_more_than_1000[\"country\"])\n",
+    "assert \"Germany\" in list(inter_students_by_country_more_than_1000[\"country\"])\n",
+    "assert \"United Kingdom\" in list(inter_students_by_country_more_than_1000[\"country\"])\n",
+    "assert \"United States\" in list(inter_students_by_country_more_than_1000[\"country\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d83309db",
+   "metadata": {},
+   "source": [
+    "# Segment 3: Plotting\n",
+    "\n",
+    "SQL provides powerful tools to manipulate and organize data. Now we might be interested in plotting the data to engage in data exploration and visualize our results.\n",
+    "\n",
+    "Before starting this segment, it is recommended that you go through the readings assigned during lecture. You can review the relevant lecture readings [here](https://cs220.cs.wisc.edu/f22/materials/readings/line-and-bar.html) and [here](https://cs220.cs.wisc.edu/f22/materials/readings/matplotlib-intro.html)."
+   ]
+  },
+  {
+   "attachments": {
+    "bar_plot.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "d27b7c2c",
+   "metadata": {},
+   "source": [
+    "### Task 3.1: Use a bar plot to plot the data from Task 2.7\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:bar_plot.png\" width=\"400\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e4dc5d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instead of specifically plotting just the DataFrame 'inter_students_by_country_more_than_1000',\n",
+    "# create a general function to create bar plots\n",
+    "\n",
+    "def bar_plot(df, x, y):\n",
+    "    \"\"\"bar_plot(df, x, y) takes in a DataFrame 'df' and displays \n",
+    "    a bar plot with the column 'x' as the x-axis, and the column\n",
+    "    'y' as the y-axis\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: set dataframe index to 'x'\n",
+    "    # TODO: use df.plot.bar to plot the data in black with no legend\n",
+    "    # TODO: set 'x' as the x label \n",
+    "    # TODO: set 'y' as the y label\n",
+    "    # TODO: set the color to black"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e21ed94a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to plot the data from Task 2.7\n",
+    "# verify that this plot matches exactly with the image shown above\n",
+    "\n",
+    "bar_plot(inter_students_by_country_more_than_1000, 'country', 'total_international_students')"
+   ]
+  },
+  {
+   "attachments": {
+    "scatter_plot.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "0adf3bdd",
+   "metadata": {},
+   "source": [
+    "### Task 3.2: Use a scatter plot to plot the relationship between employer_reputation and academic_reputation in 2019\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:scatter_plot.png\" width=\"500\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8eb6036d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a general function to create scatter plots\n",
+    "\n",
+    "def scatter_plot(df, x, y):\n",
+    "    \"\"\"scatter_plot(df, x, y) takes in a DataFrame 'df' and displays \n",
+    "    a scatter plot with the column 'x' as the x-axis, and the column\n",
+    "    'y' as the y-axis\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: use df.plot.scatter to plot the data in black with no legend\n",
+    "    # TODO: set x as the x label \n",
+    "    # TODO: set y as the y label\n",
+    "    # TODO: set the color to black"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d77b0f09",
+   "metadata": {},
+   "source": [
+    "With the `scatter_plot` function defined, you are ready to create the required plot.\n",
+    "\n",
+    "* Write a SQL query to select rows from the database where the `year` is *2019*.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `employer_reputation` and `academic_reputation`.\n",
+    "* Call `scatter_plot`, passing in `employer_reputation` and `academic_reputation` as the `x` and `y` arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ef617ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame\n",
+    "# then create the scatter plot using the DataFrame\n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "attachments": {
+    "horizontal_bar_plot.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "d144417b",
+   "metadata": {},
+   "source": [
+    "### Task 3.3: Make a Horizontal Bar plot of average employer_reputation and average faculty_student_score across all years\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:horizontal_bar_plot.png\" width=\"600\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78e21b0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "def horizontal_bar_plot(df, x):\n",
+    "    \"\"\"horizontal_bar_plot(df, x) takes in a DataFrame 'df' and displays \n",
+    "    a horizontal bar plot with the column 'x' as the x-axis, and all\n",
+    "    other columns of 'df' on the y-axis\"\"\"\n",
+    "    df = df.set_index(x)\n",
+    "    ax = df.plot.barh()\n",
+    "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.9))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cbdaa9f",
+   "metadata": {},
+   "source": [
+    "Use the `horizontal_bar_plot` function to create the required plot.\n",
+    "\n",
+    "* Write a SQL query to select `year`, **average** `employer_reputation`, and **average** `faculty_student_score` grouped by `year`.\n",
+    "* Save the resulting **DataFrame** with **three** columns: `year`, the **average** of the `employer_reputation` and the **average** of the `faculty_student_score`.\n",
+    "* Call `horizontal_bar_plot`, passing in `year` as the `x` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc779e0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame\n",
+    "# then create the horizontal bar plot using the DataFrame\n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "attachments": {
+    "pie_plot.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "aaeeebe7",
+   "metadata": {},
+   "source": [
+    "### Task 3.4 Display a Pie Chart of the average overall score of the top 10 countries in descending order\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:pie_plot.png\" width=\"400\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here. Your plot must also have the **title** `Countries with top 10 overall scores` as in the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aedb58d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "def pie_plot(df, x, y, title=None):\n",
+    "    \"\"\"pie_plot(df, x, y, title) takes in a DataFrame 'df' and displays \n",
+    "    a pie plot with the column 'x' as the x-axis, the (numeric) column\n",
+    "    'y' as the y-axis, and the 'title' as the title of the plot\"\"\"\n",
+    "    df = df.set_index(x)\n",
+    "    ax = df.plot.pie(y=y, legend=False)\n",
+    "    ax.set_ylabel(None)\n",
+    "    ax.set_title(title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "805c89c1",
+   "metadata": {},
+   "source": [
+    "Use the `pie_plot` function to create the required plot.\n",
+    "\n",
+    "* Write a SQL query to select the **top** *10* countries based on **average** `overall_score`.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `country`, and the **average** of the `overall_score`.\n",
+    "* Call `pie_plot`, passing in `country` as the `x` argument, and the **average** of the `overall_score` as the `y` argument.\n",
+    "\n",
+    "**Hint:** If you are having trouble writing the SQL query, take a look at Task 2.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "777d3b49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame\n",
+    "# then create the pie plot using the DataFrame\n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "attachments": {
+    "regression_line_plot.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "de3777de",
+   "metadata": {},
+   "source": [
+    "### Task 3.5: Fit a regression line to the data from Task 3.2\n",
+    "\n",
+    "Your line of best fit should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:regression_line_plot.png\" width=\"500\"/></div>\n",
+    "    \n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68941bde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have defined this function for you\n",
+    "\n",
+    "def get_regression_coeff(df, x, y):\n",
+    "    \"\"\"get_regression_coeff(df, x, y) takes in a DataFrame 'df' and returns \n",
+    "    the slope (m) and the y-intercept (b) of the line of best fit in the\n",
+    "    plot with the column 'x' as the x-axis, and the column 'y' as the y-axis\"\"\"\n",
+    "    df[\"1\"] = 1\n",
+    "    res = np.linalg.lstsq(df[[x, \"1\"]], df[y], rcond=None)\n",
+    "    coefficients = res[0]\n",
+    "    m = coefficients[0]\n",
+    "    b = coefficients[1]\n",
+    "    return (m, b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a70404d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you must define this function to plot the best fit line on the scatter plot\n",
+    "\n",
+    "def regression_line_plot(df, x, y):\n",
+    "    \"\"\"regression_line_plot(df, x, y) takes in a DataFrame 'df' and displays\n",
+    "    a scatter plot with the column 'x' as the x-axis, and the column\n",
+    "    'y' as the y-axis, as well as the best fit line for the plot\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: use the 'get_regression_coeff' function to get the slope and\n",
+    "    #       intercept of the line of best fit\n",
+    "    # TODO: save them into variables m and b respectively\n",
+    "    \n",
+    "    # TODO: use df.plot.scatter (not scatter_plot) to plot the x and y columns\n",
+    "    #       of 'df' in black color.\n",
+    "    # TODO: save the return value of df.plot.scatter to a variable called 'ax'\n",
+    "    \n",
+    "    # TODO: create a new column in the dataframe called 'fit', which is\n",
+    "    #       is calculated as df['fit'] = m * df[x] + b\n",
+    "    \n",
+    "    # TODO: use df.plot.line to plot the fitted line in red,\n",
+    "    #       using ax=ax as a keyword argument.\n",
+    "    #       this ensures that both the scatter plot and line end up on the same plot\n",
+    "    #       play careful attention to what the 'x' and 'y' arguments ought to be"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef4b46de",
+   "metadata": {},
+   "source": [
+    "Now, use the `regression_line_plot` function to create the required plot.\n",
+    "\n",
+    "* Call `regression_line_plot` on your data from Task 3.2 to show the correlation between `employer_reputation` and `academic_reputation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "065d0ef5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the scatter plot with the best fit line using the DataFrame from Task 3.2 \n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f20a99c",
+   "metadata": {},
+   "source": [
+    "### Congratulations, you are now ready to start p13!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p13/practice.ipynb b/lab-p13/practice.ipynb
index f8aa22a8bbab0aa31908f292b9621f5b70dbac42..7487dc057f4c8418c557ff0e63e94facf46b85be 100644
--- a/lab-p13/practice.ipynb
+++ b/lab-p13/practice.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7252973f",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "115889c5",
diff --git a/lab-p5/.ipynb_checkpoints/practice-checkpoint.ipynb b/lab-p5/.ipynb_checkpoints/practice-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..11a118176748d6819a1c4803735a4ea69c110689
--- /dev/null
+++ b/lab-p5/.ipynb_checkpoints/practice-checkpoint.ipynb
@@ -0,0 +1,2343 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dace73af",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fd97833",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"practice.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccfcf1e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import practice_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "794deac1",
+   "metadata": {},
+   "source": [
+    "# Lab-P5: Looping Patterns and Hurricane API\n",
+    "\n",
+    "**WARNING:** Please go through Segment 1 of [lab-p5](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p5) **before** you start to solve this notebook."
+   ]
+  },
+  {
+   "attachments": {
+    "table.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "370ac454",
+   "metadata": {},
+   "source": [
+    "## Segment 2: Learning the API\n",
+    "\n",
+    "### Task 2.1: Examine the `hurricanes` CSV file\n",
+    "\n",
+    "The `project.py` file will allow you to access the dataset you'll use this week, `hurricanes.csv`. We generated this data file by writing a Python program to extract data from several lists of hurricanes over the Atlantic Ocean on Wikipedia (here is an [example](https://en.wikipedia.org/wiki/2022_Atlantic_hurricane_season)). You can take a look at the script `gen_csv.ipynb` yourself. At the end of the semester, you will be able to write it yourself.\n",
+    "\n",
+    "Open `hurricanes.csv` with Microsoft Excel or some other Spreadsheet viewer and look at the hurricanes in the dataset. The data shows:\n",
+    "\n",
+    "* name\n",
+    "* the date of formation\n",
+    "* the date of dissipation\n",
+    "* max wind speed (in mph)\n",
+    "* damage (in US dollars)\n",
+    "* deaths\n",
+    "\n",
+    "Often, we'll organize data by assigning numbers (called **indexes**) to different parts of the data (e.g., rows or columns in a table). In Computer Science, indexing typically starts with the number `0`; i.e., when you have a sequence of things, you'll start counting them from `0` instead of `1`. Thus, you should **ignore the numbers shown by your Spreadsheet Viewer to the left of the rows**. From the perspective of `project.py`, the indexes of `1804 New England hurricane`, `1806 Great Coastal hurricane`, and `1812 Louisiana hurricane` are `0`, `1`, and `2` respectively (and so on).\n",
+    "\n",
+    "For example, consider this example from `hurricanes.csv` as viewed from Microsoft Excel:\n",
+    "\n",
+    "![table.PNG](attachment:table.PNG)\n",
+    "\n",
+    "The **index** for the `1812 Louisiana hurricane` is `2` but its actual **location** is `3`, and it is on **row** `4` of the table. Therefore, you must follow this convention for all the questions\n",
+    "asking for the value at a particular **index**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f205700",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Explore the API\n",
+    "Use the inspection process we learned in [lab-p3](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p3) and [lab-p4](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p3) to know more details of the 'project' API. In lab-p3, we saw how to use `dir`, and `help` to learn the API. Run the following cells to explore the API:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a580a5e5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8827151a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# use the 'dir' function to learn more about the project API."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cdc482b",
+   "metadata": {},
+   "source": [
+    "Spend some time reading about each of the seven functions that don't begin with two underscores. For example, run this to learn about `count`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7795d95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(project.count) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29fc81cb",
+   "metadata": {},
+   "source": [
+    "Alternatively, you could run the following to just see the function's documentation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0137157b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(project.count.__doc__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba650835",
+   "metadata": {},
+   "source": [
+    "You may also open up the `project.py` file directly to learn about the functions provided. E.g., you might see this:\n",
+    "\n",
+    "```python\n",
+    "def count():\n",
+    "    \"\"\"This function will return the number of records in the dataset\"\"\"\n",
+    "    return len(__hurricane__)\n",
+    "```\n",
+    "\n",
+    "You don't need to understand the code in the functions, but the strings in triple quotes (called *docstrings*) explain what each function does. As it turns out, all `project.count.__doc__` is providing you is the docstring of the `count` function.\n",
+    "\n",
+    "Try to learn other functions in `project.py`, by using `help` function. For example, you may try: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33648ffb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(project.get_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cf33b37",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# now try getting help for the other functions in the `project` module\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69107f87",
+   "metadata": {},
+   "source": [
+    "### Task 2.2.1: Getting familiar with `project.py`\n",
+    "\n",
+    "You will now demonstrate your familiarity with the functions inside the `project` module by answering a few simple questions. You must have already imported the `project` module to this notebook. Make sure you placed the `import` statememnt at the **top** of the notebook in the designated cell.\n",
+    "\n",
+    "**Remember:** In Computer Science, we start indexing at `0`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0468307",
+   "metadata": {},
+   "source": [
+    "**Question 1.1:** What is the `name` of the hurricane at **index** `0`? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87a454af",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this for you!\n",
+    "name_idx0 = project.get_name(0)\n",
+    "\n",
+    "name_idx0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9599ed5b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "460a10de",
+   "metadata": {},
+   "source": [
+    "**Question 1.2:** What is the `name` of the hurricane at **index** `1`? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fdcd88a",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "name_idx1 = ...\n",
+    "name_idx1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c701b3ca",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31169275",
+   "metadata": {},
+   "source": [
+    "**Question 1.3:** What is the speed in `mph` of the hurricane at **index** `7`? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88a848d1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "mph_idx7 = ...\n",
+    "mph_idx7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f95cd1e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b04e1e77",
+   "metadata": {},
+   "source": [
+    "**Question 1.4:** What is the `damage` in dollars caused of the hurricane at **index** `5`? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9340bf3f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "damage_idx5 = ...\n",
+    "damage_idx5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb12a906",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1-4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2431aa4",
+   "metadata": {},
+   "source": [
+    "Notice that the damage amount ends with a \"M\". In this dataset, \"K\" represents one thousand, \"M\" represents one million, and \"B\" represents one billion. For p5, you'll need to convert these strings to integers (e.g., `\"1.5K\"` will become `1500`, `\"2.55M\"` will become `2550000`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "581fd4cb",
+   "metadata": {},
+   "source": [
+    "**Question 2:** What is the `name` of the **last** hurricane in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7ba7233",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this for you!\n",
+    "name_idx_last = project.get_name(project.count() - 1)\n",
+    "name_idx_last"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e05a03c6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce5a9f74",
+   "metadata": {},
+   "source": [
+    "Now, let us try to get the `name` at index `project.count()` instead. What happens? Why? Feel free to reach out to your TA/PM, if you are not sure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "faf4ba8c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# execute this cell without changing anything\n",
+    "project.get_name(project.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bda1e187",
+   "metadata": {},
+   "source": [
+    "## Segment 3: Working with strings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a755004",
+   "metadata": {},
+   "source": [
+    "### Task 3.1: Indexing / slicing Strings\n",
+    "\n",
+    "Stepping back from the Hurricane data, Tasks 3.1 and 3.2 introduce us to performing operations with strings. While this will be covered in more detail during Friday's lecture, we will cover the essentials now.\n",
+    "\n",
+    "We can think of a string as a sequence of characters. For example, the string `my_str = 'hello_world!'` can be written as...\n",
+    "\n",
+    "| index  | 0    | 1    | 2    | 3    | 4    | 5    | 6    | 7    | 8    | 9    | 10   | 11   |\n",
+    "| ------ | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n",
+    "| string | h    | e    | l    | l    | o    | _    | w    | o    | r    | l    | d    | !    |\n",
+    "\n",
+    "... where we can then access specific characters of the string by an index, e.g. `my_str[0]` which returns `'h'` or `my_str[8]` which returns `'r'`.\n",
+    "\n",
+    "Furthermore, we can \"slice\" strings -- that is, get a particular section of characters. For example,\n",
+    "\n",
+    "- `my_str[1:5]` returns `'ello'`\n",
+    "- `my_str[:8]` returns `'hello_wo'`\n",
+    "- `my_str[5:]` returns `'_world!'`\n",
+    "- `my_str[:]` returns `'hello_world!'`\n",
+    "\n",
+    "Try running this in the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea50ae49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_str = 'hello_world!'\n",
+    "print(\"my_str[0] returns\", my_str[0])\n",
+    "print(\"my_str[8] returns\", my_str[8])\n",
+    "print(\"my_str[1:5] returns\", my_str[1:5])\n",
+    "print(\"my_str[:8] returns\", my_str[:8])\n",
+    "print(\"my_str[5:] returns\", my_str[5:])\n",
+    "print(\"my_str[:] returns\", my_str[:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad6bd97b",
+   "metadata": {},
+   "source": [
+    "Notice that slicing is *inclusive* on the lower bound and *exclusive* on the upper bound. We can also leave out a bound to start from the beginning (e.g. `my_str[:6]`) or the end (e.g. `my_str[8:]`). Lastly, a negative index will count *backwards* from the *end* of the string.\n",
+    "\n",
+    "Try running the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2aa43fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"my_str[-1] returns\", my_str[-1])\n",
+    "print(\"my_str[-4:-1] returns\", my_str[-4:-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b91dadbd",
+   "metadata": {},
+   "source": [
+    "**Your Turn!** Try slicing the below phone number! Can you extract the area code (first 3 digits), exchange code (middle 3 digits), and line number (last 4 digits) of the given phone number?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23d637e7",
+   "metadata": {},
+   "source": [
+    "**Question 3.1:** What is the **last digit** of the phone number: `608-867-5309`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "268ef563",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "phone_number = \"608-867-5309\"\n",
+    "last_digit = phone_number[...]\n",
+    "\n",
+    "last_digit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a1827e6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85e81772",
+   "metadata": {},
+   "source": [
+    "**Question 3.2:** What is the **area code** (i.e., the first three characters) of the phone number: `608-867-5309`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb530940",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "phone_number = \"608-867-5309\"\n",
+    "area_code = phone_number[:...]\n",
+    "\n",
+    "area_code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e96b8b24",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a315496a",
+   "metadata": {},
+   "source": [
+    "**Question 3.3:** What is the **line number** (i.e., the last four characters) of the phone number: `608-867-5309`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f16daa1e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "phone_number = \"608-867-5309\"\n",
+    "line_number = phone_number[...:]\n",
+    "\n",
+    "line_number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44989b68",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "573faf27",
+   "metadata": {},
+   "source": [
+    "**Question 3.4:** What is the **exchange code** (i.e., middle three characters) of the phone number: `608-867-5309`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a7b68a5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "phone_number = \"608-867-5309\"\n",
+    "exchange_code = phone_number[...:...]\n",
+    "\n",
+    "exchange_code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4e7a3ae",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3-4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5f066fb",
+   "metadata": {},
+   "source": [
+    "**Question 4.1:** What is the **department code** (i.e., the letters at the start) of the course: `CS220`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3943d514",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "course = 'CS220'\n",
+    "dept_code = course[...]\n",
+    "\n",
+    "dept_code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3a574ee",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5ccdc76",
+   "metadata": {},
+   "source": [
+    "**Question 4.2:** What is the **course code** (i.e., the numbers at the end) of the course: `CS220`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51b104c2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "course = 'CS220'\n",
+    "course_code = course[...]\n",
+    "\n",
+    "course_code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51a9ec37",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5960ec9",
+   "metadata": {},
+   "source": [
+    "After that short detour, we will now go back to working on the hurricane dataset.\n",
+    "\n",
+    "### Task 3.2: Calculating Damage Costs\n",
+    "\n",
+    "`Q1.4` showed us that damage costs are represented as strings with suffixes for thousands, millions, and billions.\n",
+    "\n",
+    "We can **index** the last character of these damages to find the suffix. We can then potentially use it to determine whether the suffix represents a thousand, million, or a billion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "434202b9",
+   "metadata": {},
+   "source": [
+    "**Question 5.1:** What is the **suffix** (i.e., the last character) of the cost `\"3.19B\"`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49c62b08",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "cost = \"3.19B\"\n",
+    "suffix = cost[...]\n",
+    "\n",
+    "suffix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ef02418",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93cebe72",
+   "metadata": {},
+   "source": [
+    "**Question 5.2:** How many billions are there in the cost `\"3.19B\"`?\n",
+    "\n",
+    "Just as we found the suffix by **indexing**, we can also find the number by **slicing**. Answer the question by slicing the string to obtain the number of billions, and typecasting the string into a float."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5fa301b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "cost = \"3.19B\"\n",
+    "billions = float(cost[...])\n",
+    "\n",
+    "billions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39c5db16",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3073bd6",
+   "metadata": {},
+   "source": [
+    "### Task 3.3: Slicing dates\n",
+    "\n",
+    "Run the below cell which prints the formation and dissipation date of the first hurricane."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47ef5b05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(project.get_formed(0))\n",
+    "print(project.get_dissipated(0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de24845a",
+   "metadata": {},
+   "source": [
+    "The dates are represented as a string in `mm/dd/yyyy` notation. Two digits are used to represent the month and day even when they can be represented with a single digit, that is, `'9/4/1804'` is represented as `'09/04/1804'`.\n",
+    "\n",
+    "To extract the month, we could run the following code..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60934995",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.get_formed(0)[:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f140cbb",
+   "metadata": {},
+   "source": [
+    "Notice, however, that this is the *string* `'09'`.\n",
+    "\n",
+    "Write the code to get this as the *int* (e.g. `9`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fda6fd2",
+   "metadata": {},
+   "source": [
+    "**Question 6:** In which `month` did the hurricane at **index** `0` form?\n",
+    "\n",
+    "Your answer **must** be an `int` between `1` and `12`. You **must not** hardcode the answer, but use the appropriate function from the `project` module to find the date of formation of the hurricane."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32b38883",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "month_idx0 = ...\n",
+    "month_idx0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26dbdaaf",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ba646b7",
+   "metadata": {},
+   "source": [
+    "### Task 3.4: Helper Functions for Month, Day, and Year\n",
+    "\n",
+    "The below functions will be useful in p5. Solve the below questions for getting the day, and year as an int. The function to get the month has already been done for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "147321ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_month(date):\n",
+    "    \"\"\"get_month(date) returns the month when the date is the in the 'mm/dd/yyyy' format\"\"\"\n",
+    "    return int(date[:2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec811c71",
+   "metadata": {},
+   "source": [
+    "You can confirm that `get_month` works by running the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d5cba56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "month = get_month(\"10/05/2022\")\n",
+    "month"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb1cfbbc",
+   "metadata": {},
+   "source": [
+    "### Task 3.4.1: Define `get_year(date)`\n",
+    "\n",
+    "You must now define this function, which will take in the `date` as a `str` and return the `year` as an `int`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf457247",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_year(date):\n",
+    "    pass # replace with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cad3cd3d",
+   "metadata": {},
+   "source": [
+    "**Question 7:** What is the `year` in the date `\"10/05/2022\"`?\n",
+    "\n",
+    "You **must** answer this question by calling the `get_year` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f1a82c2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "year = ...\n",
+    "year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d8d072c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "614e039d",
+   "metadata": {},
+   "source": [
+    "### Task 3.4.2: Define `get_day(date)`\n",
+    "\n",
+    "You must now define this function, which will take in the `date` as a `str` and return the `day` as an `int`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26079a79",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_day(date):\n",
+    "    \"\"\"get_day(date) returns the day when the date is the in the 'mm/dd/yyyy' format\"\"\"\n",
+    "    pass # replace with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ba10faf",
+   "metadata": {},
+   "source": [
+    "**Question 8:** What is the `day` in the date `\"10/05/2022\"`?\n",
+    "\n",
+    "You **must** answer this question by calling the `get_day` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3319da66",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "day = ...\n",
+    "day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eab11beb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a088adc2",
+   "metadata": {},
+   "source": [
+    "### Task 3.5: Using Helper Functions\n",
+    "\n",
+    "Using the helper functions you made above, complete the following questions.\n",
+    "\n",
+    "**Hint:** You'll use these helper functions in combination with `project.get_formed(idx)` and `project.get_dissipated(idx)`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeb27630",
+   "metadata": {},
+   "source": [
+    "**Question 9:** On what `day` did the hurricane at **index** `100` **form**?\n",
+    "\n",
+    "You **must** answer this question by calling the `get_day` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dde28812",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "day_formed_idx100 = ...\n",
+    "day_formed_idx100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b1032d2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f912f89",
+   "metadata": {},
+   "source": [
+    "**Question 10:** On what `year` did the hurricane at **index** `200` **form**?\n",
+    "\n",
+    "You **must** answer this question by calling the `get_year` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f11e986",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace with your code\n",
+    "year_formed_idx200 = ...\n",
+    "year_formed_idx200"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bc3b091",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3527891d",
+   "metadata": {},
+   "source": [
+    "**Question 11:** On what `month` did the hurricane at **index** `300` **dissipate**?\n",
+    "\n",
+    "You **must** answer this question by calling the `get_month` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "922f9d98",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "month_diss_idx300 = ...\n",
+    "month_diss_idx300"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52217eb2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6ddf2fb",
+   "metadata": {},
+   "source": [
+    "## Segment 4: Looping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2360b944",
+   "metadata": {},
+   "source": [
+    "### Task 4.1: `while` and `for` loops\n",
+    "\n",
+    "Run the below code and observe the output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3a7ecd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = 0\n",
+    "while i < 5:\n",
+    "    print(i)\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0be699ba",
+   "metadata": {},
+   "source": [
+    "Equivalently, we can use `for` and `range(n)`. The `range(n)` function returns a sequence of numbers, from `0` to `n` but not including `n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2187ff3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(5):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6b0a6cb",
+   "metadata": {},
+   "source": [
+    "Now, we will try to use `while` and `for` loops to answer a few simple questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3be0b6c1",
+   "metadata": {},
+   "source": [
+    "**Question 12:** What is the sum of the numbers *0 to 25*, both inclusive?\n",
+    "\n",
+    "You **must** answer this with a `while` loop. Ask your TA/PM if you are not sure what to do."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "997235fa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "i = 0\n",
+    "sum_while = ... # replace the ... with the correct initial value for the sum\n",
+    "while i ... 25: # replace the ... with the correct comparison operator\n",
+    "    sum_while += i \n",
+    "    i += 1\n",
+    "    \n",
+    "sum_while"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db38be0d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8a1ce6d",
+   "metadata": {},
+   "source": [
+    "**Question 13:** What is the sum of the numbers *0 to 25*, both inclusive?\n",
+    "\n",
+    "You **must** answer this with a `for` loop. Ask your TA/PM if you are not sure what to do."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ecbb697",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "sum_for = ...\n",
+    "for i in range(...):\n",
+    "    sum_for += ...\n",
+    "sum_for"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "830abb17",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caa42eb3",
+   "metadata": {},
+   "source": [
+    "### Task 4.2: Looping through hurricanes\n",
+    "\n",
+    "You have had some practice with simple looping structures. You will now loop through the hurricanes dataset.\n",
+    "\n",
+    "Run the below code and observe the output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65ce5250",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for idx in range(10):\n",
+    "    print(project.get_name(idx))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16a1b09d",
+   "metadata": {},
+   "source": [
+    "Can you make the code above display the year of the formation of the first 10 hurricanes? How about the first 15 hurricanes? Please feel free to reach out to your TA/PM and ask them for help, if you face any issues.\n",
+    "\n",
+    "You are now ready to answer some interesting questions with loops."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b02409fb",
+   "metadata": {},
+   "source": [
+    "**Question 14:** What is the **total** `deaths` caused by the **first** `10` hurricanes in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3f4774d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "total_deaths_first10 = ...\n",
+    "for idx in range(...):\n",
+    "    total_deaths_first10 += ...\n",
+    "\n",
+    "total_deaths_first10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77da3770",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfa23d34",
+   "metadata": {},
+   "source": [
+    "**Question 15:** What is the **average** speed (in `mph`) of **all** the hurricanes in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c904c16",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "sum_wind_speed = ...\n",
+    "for idx in range(project.count()):\n",
+    "    sum_wind_speed += ...\n",
+    "average_wind_speed = sum_wind_speed/project.count()\n",
+    "\n",
+    "average_wind_speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d6c85d4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "740316b6",
+   "metadata": {},
+   "source": [
+    "### Task 4.3: Filtering\n",
+    "\n",
+    "You will now *filter* the data using an `if` condition as you loop through the dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de7ba8a2",
+   "metadata": {},
+   "source": [
+    "**Question 16:** How many hurricanes caused **more than** `1000` deaths in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41afb676",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "num_hurr_1000_deaths = ...\n",
+    "for idx in range(...): # loop through ALL hurricanes in the dataset; do NOT hardcode the number here\n",
+    "    if ...: # replace ... with a Boolean expression\n",
+    "        num_hurr_1000_deaths += 1\n",
+    "\n",
+    "num_hurr_1000_deaths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "361e8b2d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f6e860a",
+   "metadata": {},
+   "source": [
+    "**Question 17:** How many hurricane `names` **start** with the letter *D* in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef23511b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'num_hurr_D'\n",
+    "# TODO: initialize the variable 'num_hurr_D'\n",
+    "# TODO: loop through all hurricanes in the dataset\n",
+    "# TODO: update the value of 'num_hurr_D' only if\n",
+    "#       the name of the hurricane at the current idx starts with 'D'\n",
+    "        \n",
+    "# display the variable 'num_hurr_D' here\n",
+    "num_hurr_D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a82e0de9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "657a9d81",
+   "metadata": {},
+   "source": [
+    "### Task 4.4: Maximization/Minimization\n",
+    "\n",
+    "You will now find the maximum/minimum using loops. Run the following two cells and observe the output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2676f02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(n):\n",
+    "    return 3 + (n % 7)\n",
+    "\n",
+    "for n in range(11):\n",
+    "    print('f(' + str(n) + ') = ' + str(f(n)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d30522e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_n = 0\n",
+    "for n in range(11):\n",
+    "    if f(n) > f(best_n):\n",
+    "        best_n = n\n",
+    "\n",
+    "best_n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "602f5707",
+   "metadata": {},
+   "source": [
+    "Can you figure out what the code above is doing? It is using the variable `best_n` to keep track of the value of `n` with the maximum value of `f(n)`. At each iteration of the loop, `best_n` stores the **best** value of `n` observed **so far**. At each iteration of the loop, the code checks if `f(n) > f(best_n)`. If this is the case, then the **new** value `n` has a higher value of `f(n)` than the **previous best** value `best_n`, so `best_n` is **updated** to take the value of `n`, which is now the **new best** seen **so far**. At the end of the loop, `best_n` is the value of `n` for which `f(n)` is maximum.\n",
+    "\n",
+    "Notice that this code does **not** find the maximum value of `f(n)`, it finds the value of `n` for which `f(n)` is maximum. This is far more useful than simply finding the maximum value of `f(n)`, as you shall see when you solve p5.\n",
+    "\n",
+    "### If the above explanation is not clear, reach out to your TA/PM. You will have to find maximums in p5 and in future projects. It is very important that you understand how this code works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51487414",
+   "metadata": {},
+   "source": [
+    "**Question 18:** What is the `name` of the hurricane which has the **fastest** wind speed (in `mph`)?."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4663b80",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "fastest_idx = 0\n",
+    "for idx in range(...):\n",
+    "    if ...:\n",
+    "        fastest_idx = idx\n",
+    "fastest_name = project.get_name(fastest_idx)\n",
+    "        \n",
+    "fastest_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "404679c2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e85c6563",
+   "metadata": {},
+   "source": [
+    "**Question 19:** What is the `name` of the hurricane which has the **slowest** wind speed (in `mph`)?.\n",
+    "\n",
+    "You **must** break ties in favor of the hurricanes that appear **first** in the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3461cb1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "slowest_idx = 0\n",
+    "for idx in range(...):\n",
+    "    if project.get_mph(idx) ... project.get_mph(slowest_idx): # replace the ... with a comparison operator\n",
+    "        ...\n",
+    "slowest_name = ...\n",
+    "        \n",
+    "slowest_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6553440",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32d4500f",
+   "metadata": {},
+   "source": [
+    "### Task 4.5: More Filtering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b3d1736",
+   "metadata": {},
+   "source": [
+    "You will now create a function that takes in two years `start_year` and `end_year`, and return the number of hurricanes that were formed between these two years (both years inclusive).\n",
+    "\n",
+    "You **must** use the `get_year` function you defined above to find the year of formation of each hurricane. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e730e28c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def count_hurricanes_between(start_year, end_year):\n",
+    "    # replace the ... with your code\n",
+    "    num_hurricanes = 0\n",
+    "    for idx in range(...):\n",
+    "        year_formed = ... # TODO: find the year of formation of the hurricane at idx\n",
+    "        # hint: to find year_formed, you first find the date of formation and pass that value to\n",
+    "        #       another function to find the year from that date.\n",
+    "        #       note that you can perform both computations in a single line by passing the\n",
+    "        #       value returned by one function as an argument to another function.\n",
+    "        if ...: # TODO: evaluate if hurricane at idx was formed between start_year and end_year\n",
+    "            num_hurricanes += 1\n",
+    "    return num_hurricanes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bae0ec4",
+   "metadata": {},
+   "source": [
+    "**Question 20.1:** How many hurricanes were `formed` between *1980 and 2002*, both inclusive?\n",
+    "\n",
+    "You **must** answer this question by calling the `count_hurricanes_between` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "749dcb89",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "hurr_between_1980_2002 = ...\n",
+    "hurr_between_1980_2002"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc75f162",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d06a8d27",
+   "metadata": {},
+   "source": [
+    "**Question 20.2:** How many hurricanes were `formed` between *1901 and 2000*, both inclusive?\n",
+    "\n",
+    "You **must** answer this question by calling the `count_hurricanes_between` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "639638bd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "hurr_between_1901_2000 = ...\n",
+    "hurr_between_1901_2000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfb5e1e7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af9fc6b0",
+   "metadata": {},
+   "source": [
+    "## Great work! You are now ready to start [p5](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1-1": {
+     "name": "q1-1",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1-1\", name_idx0)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1-2": {
+     "name": "q1-2",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1-2\", name_idx1)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1-3": {
+     "name": "q1-3",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1-3\", mph_idx7)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1-4": {
+     "name": "q1-4",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1-4\", damage_idx5)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10\", year_formed_idx200)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q11\", month_diss_idx300)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q12\", sum_while)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q13\", sum_for)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q14\", total_deaths_first10)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q15\", average_wind_speed)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q16\", num_hurr_1000_deaths)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17\", num_hurr_D)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q18\", fastest_name)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q19\", slowest_name)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q2\", name_idx_last)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20-1": {
+     "name": "q20-1",
+     "points": 2.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q20-1\", hurr_between_1980_2002)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20-2": {
+     "name": "q20-2",
+     "points": 2.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q20-2\", hurr_between_1901_2000)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3-1": {
+     "name": "q3-1",
+     "points": 1.25,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3-1\", last_digit)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3-2": {
+     "name": "q3-2",
+     "points": 1.25,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3-2\", area_code)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3-3": {
+     "name": "q3-3",
+     "points": 1.25,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3-3\", line_number)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3-4": {
+     "name": "q3-4",
+     "points": 1.25,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3-4\", exchange_code)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4-1": {
+     "name": "q4-1",
+     "points": 2.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q4-1\", dept_code)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4-2": {
+     "name": "q4-2",
+     "points": 2.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q4-2\", course_code)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5-1": {
+     "name": "q5-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q5-1\", suffix)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5-2": {
+     "name": "q5-2",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q5-2\", billions)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q6\", month_idx0)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7\", year)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q8\", day)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q9\", day_formed_idx100)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p5/practice.ipynb b/lab-p5/practice.ipynb
index 8f9ade96ff2b9053784c502722a559af7e955c5c..11a118176748d6819a1c4803735a4ea69c110689 100644
--- a/lab-p5/practice.ipynb
+++ b/lab-p5/practice.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dace73af",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/lab-p6/.ipynb_checkpoints/practice-checkpoint.ipynb b/lab-p6/.ipynb_checkpoints/practice-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..700726a4d1399302ef97d608ea8b047953bfcceb
--- /dev/null
+++ b/lab-p6/.ipynb_checkpoints/practice-checkpoint.ipynb
@@ -0,0 +1,2246 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cb977960",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8e47436",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"practice.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "624f33a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import practice_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ea8895e",
+   "metadata": {},
+   "source": [
+    "# Lab-P6: Real-world Datasets (Airbnb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55b71f24",
+   "metadata": {},
+   "source": [
+    "## Segment 2: Loading Data from CSVs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea7beaec",
+   "metadata": {},
+   "source": [
+    "### About the dataset\n",
+    "\n",
+    "Now would be a good time to open `airbnb.csv` with Microsoft Excel (or some other Spreadsheet viewing software) and have a look at the data. The first few rows of the dataset are reproduced here:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c85a848",
+   "metadata": {},
+   "source": [
+    "room_id|name|host_id|host_name|neighborhood_group|neighborhood|latitude|longitude|room_type|price|minimum_nights|number_of_reviews|last_review|reviews_per_month|calculated_host_listings_count|availability_365\n",
+    "------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|\n",
+    "2539|Clean & quiet apt home by the park|2787|John|Brooklyn|Kensington|40.64749000000001|-73.97237|Private room|149|1|9|2018-10-19|0.21|6|365\n",
+    "2595|Skylit Midtown Castle|2845|Jennifer|Manhattan|Midtown|40.75362|-73.98376999999998|Entire home/apt|225|1|45|2019-05-21|0.38|2|355\n",
+    "3647|THE VILLAGE OF HARLEM....NEW YORK !|4632|Elisabeth|Manhattan|Harlem|40.80902|-73.9419|Private room|150|3|0|||1|365\n",
+    "3831|Cozy Entire Floor of Brownstone|4869|LisaRoxanne|Brooklyn|Clinton Hill|40.68514|-73.95976|Entire home/apt|89|1|270|2019-07-05|4.64|1|194\n",
+    "5022|Entire Apt: Spacious Studio/Loft by central park|7192|Laura|Manhattan|East Harlem|40.79851|-73.94399|Entire home/apt|80|10|9|2018-11-19|0.1|1|0\n",
+    "5099|Large Cozy 1 BR Apartment In Midtown East|7322|Chris|Manhattan|Murray Hill|40.74767|-73.975|Entire home/apt|200|3|74|2019-06-22|0.59|1|129"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d664f566",
+   "metadata": {},
+   "source": [
+    "The `airbnb.csv` file has data about nearly 50,000 listings on Airbnb from New York City, NY from the year 2019. Each row in the file contains data about a *single listing*. The columns contain the following data about each listing (along with the correct data type you **must** represent it as):\n",
+    "\n",
+    "1. `room_id` - the ID of the room listing (`str`)\n",
+    "2. `name` - the name of the room listing (`str`)\n",
+    "3. `host_id` - the ID of the host for the room listing (`str`)\n",
+    "4. `host_name` - the name of the host for the room listing (`str`)\n",
+    "5. `neighborhood_group` - the group of neighborhoods the room is in (`str`)\n",
+    "6. `neighborhood` - the neighborhood the room is in (`str`)\n",
+    "7. `latitude` - the latitude where the room is located (`float`)\n",
+    "8. `longitude` - the longitude where the room is located (`float`)\n",
+    "9. `room_type` - the type of room (`str`)\n",
+    "10. `price` - the price per night for the room in US dollars (`int`)\n",
+    "11. `minimum_nights` - the minimum amount of nights the room must be booked for (`int`)\n",
+    "12. `number_of_reviews` - the total number of reviews the room has received (`int`)\n",
+    "13. `last_review` - the date of the most recent review in the form yyyy-mm-dd (`str`)\n",
+    "14. `reviews_per_month` - how many reviews per month the room receives (`float`)\n",
+    "15. `calculated_host_listings_count` - how many listings the host of the room has (`int`)\n",
+    "16. `availability_365` - how many days per year the listing is available for (`int`)\n",
+    "\n",
+    "**Warning:** Keep in mind while writing your project, some entries may be **missing data** for specific columns. Sadly, data in real life is often messy, and in p6, we will have to deal with missing data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a403dd1",
+   "metadata": {},
+   "source": [
+    "### Task 2.1: Processing the CSV file\n",
+    "\n",
+    "You will now read this dataset with Python. [Chapter 14](https://automatetheboringstuff.com/chapter14/) of Automate the Boring Stuff introduces CSV files and provides a code snippet you can reuse. You can use the same code snippet for p6. Run the next few cells and see their outputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95a2e907",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this lab\n",
+    "\n",
+    "import csv # we have imported this module for you; it is required by the process_csv function below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80e07c48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# modified from https://automatetheboringstuff.com/chapter14/\n",
+    "def process_csv(filename):\n",
+    "    example_file = open(filename, encoding=\"utf-8\")\n",
+    "    example_reader = csv.reader(example_file)\n",
+    "    example_data = list(example_reader)\n",
+    "    example_file.close()\n",
+    "    return example_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c920ee88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this call to process_csv reads the data in \"airbnb.csv\"\n",
+    "csv_data = process_csv(\"airbnb.csv\")\n",
+    "\n",
+    "# this will display the first three items in the list `csv_data`\n",
+    "csv_data[:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3690669d",
+   "metadata": {},
+   "source": [
+    "The variable `csv_data` stores the contents of the file `airbnb.csv` as a **list of lists** (i.e., `csv_data` is a **list**, and the elements of this list are **lists** themselves). In the next subsection, you will learn to access data stored within this data structure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c81f538",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Accessing the contents of the dataset\n",
+    "\n",
+    "You will now index the data to extract the correct answers for the questions listed below. Some have been done for you. To understand the results better, locate the values in the `airbnb.csv` file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ec72709",
+   "metadata": {},
+   "source": [
+    "**Question 1:** What are the `names` of the **columns** in the dataset?\n",
+    "\n",
+    "**Hint:** Take a look at the output of the cell above and see where the header data is stored. Use **indexing** to extract the csv header."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2c0a93c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "csv_header = csv_data[...] # A list of the column headers\n",
+    "\n",
+    "csv_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e30c05fb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c79c4656",
+   "metadata": {},
+   "source": [
+    "It would be a good idea to use **slicing** to extract all the inner lists (except the header), and store them in a variable you can use later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d77b835f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "csv_rows = csv_data[...] # extract the entire CSV data set besides the header\n",
+    "# do NOT attempt to display csv_data(i.e., do not print out the variable \n",
+    "# by adding the variable name to the end of the cell, do not add an addtional line to this cell);\n",
+    "# it has over 50,000 lists, and will take up unnecessary space\n",
+    "# you can confirm that you extracted the data correctly by answering the next few questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b63c5262",
+   "metadata": {},
+   "source": [
+    "**Question 2.1:** How many **rows** are in the dataset (excluding the **header**)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa23d8ee",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "num_rows = len(csv_rows)\n",
+    "\n",
+    "num_rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2235cbc0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41669ff3",
+   "metadata": {},
+   "source": [
+    "**Question 2.2:** What are the **first** ten listings in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9004eda",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "first_ten_rows = csv_rows[:10]\n",
+    "\n",
+    "first_ten_rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf8705b0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e56b5401",
+   "metadata": {},
+   "source": [
+    "**Question 2.3:** What are the **last** ten listings in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bec5596f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "last_ten_rows = csv_rows[-10:]\n",
+    "\n",
+    "last_ten_rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc6267da",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cd7d6f4",
+   "metadata": {},
+   "source": [
+    "**In general, when you want to confirm that you are reading a very large file correctly, it is a good idea to check that you have the correct number of rows, and that the first and last few rows are correct. Here, you were given access to `practice_test.py`, which has the correct answers, so it was easy for you to check. Otherwise, you would have to manually open `airbnb.csv` and confirm that you have not made any mistakes. It is recommended that you manually open `airbnb.csv` in any case to verify that the data matches your answers for the previous three questions.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38257c3d",
+   "metadata": {},
+   "source": [
+    "**Question 3:** What values are present in the **first row** of the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e18947d6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "first_row = ...\n",
+    "\n",
+    "first_row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecab2464",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6154777a",
+   "metadata": {},
+   "source": [
+    "You now know how to extract a single **row** (or a list of rows) from the file `airbnb.csv`. You will now extract data from a single **cell** of the file.\n",
+    "\n",
+    "To extract data from a single cell of the csv file, we need two things:\n",
+    "  1. row index\n",
+    "  2. column index\n",
+    "    \n",
+    "You already know to extract a row of data with `csv_rows[row_idx]`. Given this list, can you now extract the data in a particular cell using the **column index**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7852e646",
+   "metadata": {},
+   "source": [
+    "**Question 4:** What is the `host_name` of the **first** listing?\n",
+    "\n",
+    "**Hint:** The **column index** for the `host_name` column is *3*. You may **hardcode** the **column index** as *3* **just for this question**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0836dbc5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "first_row = csv_rows[...] # extract the first row of the dataset\n",
+    "first_host_name = first_row[...] # extract the host name\n",
+    "\n",
+    "first_host_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a58d3186",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1d7c0ab",
+   "metadata": {},
+   "source": [
+    "You solved the previous question by **hardcoding** the **column index**, when you were just given the **column name**. This is however a **bad practice**, and you **must not** do it in your project. It would be much safer to somehow **extract** the **column index** **from** the **column name**, and then use the **column index**. The following (built-in) list method helps us with that:\n",
+    "\n",
+    "### List method: `index`\n",
+    "\n",
+    "**Syntax:** `list.index(column_name)`\n",
+    "\n",
+    "This function will return the index of the item `column_name` in the `list`. You can see this function in action in the question below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ea3f7c8",
+   "metadata": {},
+   "source": [
+    "**Question 5:** What is the **index** of the column `neighborhood_group`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4864c6ed",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "nbhd_group_index = csv_header.index('neighborhood_group')\n",
+    "\n",
+    "nbhd_group_index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de31b94f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02dc4138",
+   "metadata": {},
+   "source": [
+    "Can you make sense of the code above? If not, please request your TA/PM to help you before proceeding further."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1469998",
+   "metadata": {},
+   "source": [
+    "**Question 6:** What is the **value** in the `neighborhood_group` column of the **first** row?\n",
+    "\n",
+    "You **must** use the `index` method to extract the **column index** from the given **column name**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ff23dc0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "nbh_group_row1 = csv_rows[0][...] # do NOT hardcode the number; use the index method to find the relevant column index\n",
+    "\n",
+    "nbh_group_row1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2d49810",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "508412d6",
+   "metadata": {},
+   "source": [
+    "### Task 2.3: Build a helper function for quick data access\n",
+    "\n",
+    "It is quite cumbersome to extract data from `airbnb.csv` by indexing `csv_rows` and using the `index` method each time. To save yourself some time and effort, fill in the details of the following helper function.\n",
+    "\n",
+    "After finishing the definition of the `cell` function below, you **must only** use this function to extract data from `airbnb.csv`. In p6, you will **lose points** if you attempt to extract data from `airbnb.csv` without **explicitly** calling the `cell` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cd7953a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def cell(row_idx, col_name):\n",
+    "    \"\"\"\n",
+    "    cell(row_idx, col_name) returns the data value (cell) \n",
+    "    corresponding to the row index `row_idx`\n",
+    "    and the column name `col_name` of a CSV file\n",
+    "    \"\"\"\n",
+    "    # replace the ... with your code\n",
+    "    col_idx = csv_header.index(...)\n",
+    "    val = csv_rows[...][col_idx]\n",
+    "    if val == \"\": # when we come across missing data, we return None instead\n",
+    "        return None\n",
+    "    return val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adc668d0",
+   "metadata": {},
+   "source": [
+    "**Question 7.1:** What is the `neighborhood` of the **first** listing?\n",
+    "\n",
+    "You **must** answer this question by calling the `cell` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4d94868",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "first_neighborhood = cell(..., ...)\n",
+    "\n",
+    "first_neighborhood"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45cfdd60",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb7de0d2",
+   "metadata": {},
+   "source": [
+    "**Question 7.2:** What is the `name` of the **second** listing?\n",
+    "\n",
+    "You **must** answer this question by calling the `cell` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16a87308",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "second_name = cell(..., ...)\n",
+    "\n",
+    "second_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3a13d11",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73cb241a",
+   "metadata": {},
+   "source": [
+    "**Question 7.3:** What is the `price` of the **third** listing?\n",
+    "\n",
+    "You **must** answer this question by calling the `cell` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9be683da",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "third_price = cell(..., ...)\n",
+    "\n",
+    "third_price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07e8abba",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7da91653",
+   "metadata": {},
+   "source": [
+    "**Question 8:** How many rooms are listed in the `neighborhood_group` *Bronx*?\n",
+    "\n",
+    "You **must** use `cell` if you want to extract any data from the csv file.\n",
+    "\n",
+    "**Hint:** You must loop through the entire dataset. Use `cell` to extract the `neighborhood_group` of each room."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a57863df",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "bronx_num_rooms = ... # initialize with the correct value\n",
+    "for idx in range(...): # loop through all the indices \n",
+    "    if ...: # use `cell` to determine if the listing at `idx` is from the correct neighborhood_group\n",
+    "        bronx_num_rooms += 1 # update the variable appropriately\n",
+    "\n",
+    "bronx_num_rooms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5fc6acc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b25c4d5",
+   "metadata": {},
+   "source": [
+    "**Question 9:** List the `host_names` of all the rooms in the `neighborhood` *University Heights*.\n",
+    "\n",
+    "Your output **must** be a *list*. You **must** use `cell` if you want to extract any data from the csv file.\n",
+    "\n",
+    "**Hint:** Loop through the entire dataset and use `cell` to determine if each room is in the correct `neighborhood`. Use `cell` once again to extract the `host_name` of each listing, and use the `append` list method to add the `host_name` to your list. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29c801f3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "univ_heights_names = ... # initialize as an empty list\n",
+    "for idx in range(...): # loop through all the indices \n",
+    "    if ...: # use `cell` to determine if the listing at `idx` is from the correct neighborhood\n",
+    "        univ_heights_names.append(...) # use `cell` to append the host_name to the list\n",
+    "\n",
+    "univ_heights_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2741d1f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87b748c5",
+   "metadata": {},
+   "source": [
+    "**Important**: Raise your hand and confirm your implementation with a TA. We'll use the `cell` function for all remaining tasks in this lab and throughout the project.\n",
+    "\n",
+    "## Segment 3: Sorting Data\n",
+    "\n",
+    "There are two major ways to sort lists in Python: (1) with the `sorted` function and (2) with the `.sort` method. For each method, let's examine (a) how it modifies existing structures, and (b) what new values it returns, if any.\n",
+    "\n",
+    "The default sorting order is ascending. You can change that to descending, by passing keyword argument `reverse = True`. Same parameter / argument pair applicable for both `sort` method and `sorted` function.\n",
+    "\n",
+    "### Task 3.1: Sort lists using `.sort()`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43e4e81e",
+   "metadata": {},
+   "source": [
+    "**Question 10.1:** What is the list of `neighborhood` names of the **first three** listings in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e4049c7",
+   "metadata": {
+    "scrolled": false,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# fetch the neighborhood names for the first three rows in the dataset\n",
+    "neighborhood1 = cell(0,\"neighborhood\") # we have done this one for you\n",
+    "# replace the ... with your code\n",
+    "neighborhood2 = ...\n",
+    "neighborhood3 = ...\n",
+    "# initialize a list with the three neighborhood names as elements\n",
+    "first_three_nbhds = ...\n",
+    "\n",
+    "first_three_nbhds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72952257",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb685262",
+   "metadata": {},
+   "source": [
+    "**Question 10.2:** What does the function call `first_three_nbhds.sort()` do?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2ef2d49",
+   "metadata": {},
+   "source": [
+    "_Type your answer here, replacing this text._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "408c7b60",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "result = first_three_nbhds.sort()\n",
+    "\n",
+    "print(\"Returned value:\", result)\n",
+    "print(\"List after sorting:\", first_three_nbhds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee63ace8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10.2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae743cbd",
+   "metadata": {},
+   "source": [
+    "Now run the below code. Can you explain the output?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c948546c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sort in descending order\n",
+    "first_three_nbhds.sort(reverse=True)\n",
+    "\n",
+    "first_three_nbhds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d0ca3f9",
+   "metadata": {},
+   "source": [
+    "### Task 3.2: Sort lists using `sorted()`\n",
+    "\n",
+    "Now, use the `sorted` function to complete the same task as above. That is, fetch the names of the neighborhoods in the **first three** rows of the dataset. This time, use the `append` list method and a `for` loop to add entries into the list."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f7c61a5",
+   "metadata": {},
+   "source": [
+    "**Question 11.1:** What is the list of `neighborhood` names of the **first three** listings in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49828faa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "first_three_nbhds = [] # this creates an empty list\n",
+    "# replace the ... with your code\n",
+    "for row_idx in range(...): # iterate over the indices of the first 3 rows in the dataset\n",
+    "    first_three_nbhds.append(cell(..., ...))\n",
+    "\n",
+    "first_three_nbhds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82655298",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39a0956d",
+   "metadata": {},
+   "source": [
+    "**Question 11.2:** What does the function call `sorted(first_three_nbhds)` do?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3d53a0c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# sort the list `first_three_nbhds` and assign the sorted list to the variable `sorted_nbhds`.\n",
+    "# replace the ... with your code\n",
+    "sorted_nbhds = sorted(...)\n",
+    "\n",
+    "print(\"Returned value:\", sorted_nbhds)\n",
+    "print(\"List after sorting:\", first_three_nbhds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66be4ae0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7547dff1",
+   "metadata": {},
+   "source": [
+    "Now run the below code to sort the list in reverse (descending) order using the `sorted()` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "003edfb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sort in descending order\n",
+    "reverse_sorted_nbhds = sorted(first_three_nbhds, reverse=True) \n",
+    "reverse_sorted_nbhds "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e46a8782",
+   "metadata": {},
+   "source": [
+    "**Can you compare the outcome of calling `list.sort()` and `sorted(list)`**? What is returned by these functions, and what happens to the original lists? If you are not able to spot the difference, reach out to a TA/PM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "897f8baa",
+   "metadata": {},
+   "source": [
+    "### Task 3.4: Sorting to find the median"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0fe7706",
+   "metadata": {},
+   "source": [
+    "Now, let's try using sorting to solve a common problem - that of finding the median of a given distribution of values. Recall that the median is the **middle number** in a sorted (ascending or descending) list of numbers.   \n",
+    "  \n",
+    "In a sorted list, if the list has an **odd** number of elements, the median is the middle number:\n",
+    "\n",
+    "For example, for the list `[10, 20, 30, 40, 50]` --> median is `30`.\n",
+    "\n",
+    "If a sorted list has an **even** number of elements, the median is the **average** of the **two middle numbers**:\n",
+    "\n",
+    "For example, for the list `[10, 20, 30, 40]` --> median is `25`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ff46df3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def median(items):\n",
+    "    \"\"\"\n",
+    "    median(items) returns the median of the list `items`\n",
+    "    \"\"\"\n",
+    "    # sort the list\n",
+    "    sorted_list = ...\n",
+    "    # determine the length of the list\n",
+    "    list_len = ...\n",
+    "    if ...: # determine whether length of the list is odd\n",
+    "        # return item in the middle using indexing\n",
+    "        return sorted_list[...]\n",
+    "    else:\n",
+    "        first_middle = ... # use appropriate indexing\n",
+    "        second_middle = ... # use appropriate indexing\n",
+    "        return (first_middle + second_middle) / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2a2e724",
+   "metadata": {},
+   "source": [
+    "**Question 12.1:** What is the median of the list `list1 = [5, 3, 1, 2, 4]`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5470de6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "list1 = [5, 3, 1, 2, 4]\n",
+    "# replace the ... with your code\n",
+    "median1 = ...\n",
+    "\n",
+    "median1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ca8f530",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81bbbd3c",
+   "metadata": {},
+   "source": [
+    "**Question 12.2:** What is the median of the `list2 = [5, 3, 1, 2, 4, 6]`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7de2c876",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "list2 = [5, 3, 1, 2, 4, 6]\n",
+    "# replace the ... with your code\n",
+    "median2 = ...\n",
+    "\n",
+    "median2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "812e1fc1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9119c204",
+   "metadata": {},
+   "source": [
+    "After that short detour, let us dive back into the Airbnb dataset.\n",
+    "\n",
+    "**Question 13:** What is the median `price` of all rooms in the `neighborhood` *Harlem*?\n",
+    "\n",
+    "**Hint:** First create a *list* of all the `prices` of all the rooms in the given `neighborhood`, and then use the `median` function to find the **median** of that list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43c617d0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create a list of prices of all rooms in Harlem and store it in a suitably named variable\n",
+    "\n",
+    "# find the median of the list of prices and store it in the variable 'harlem_median_price', then display it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18097d0d",
+   "metadata": {},
+   "source": [
+    "**Troubleshooting your function:**\n",
+    "\n",
+    "Beware of type errors.\n",
+    "\n",
+    "We expect the price to be an `int` value, but what type does the `cell` function actually return? Use the `type` function to find out. Think about how to solve this and test your result below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac1d5b09",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f36f95c",
+   "metadata": {},
+   "source": [
+    "## Segment 4: Building a better helper function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "364e0383",
+   "metadata": {},
+   "source": [
+    "Our helper function `cell` could use some improvement. As you have seen, we had have to **manually** convert the type returned by the function to represent the `price` as an `int` instead of a `str`. Let us ensure that the function returns the required type on its own. \n",
+    "\n",
+    "We will define a new function `cell_v2` to test our new implementation. Once the function is tested and works correctly, you **must** replace the original function with the new version.\n",
+    "\n",
+    "First, define `cell_v2` running the code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7a57a50",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we did this one for you\n",
+    "def cell_v2(row_idx, col_name):\n",
+    "    col_idx = csv_header.index(col_name)\n",
+    "    val = csv_rows[row_idx][col_idx]\n",
+    "    if val == \"\":\n",
+    "        return None\n",
+    "    elif col_name == 'price':\n",
+    "        val = int(val)\n",
+    "    elif col_name == 'reviews_per_month':\n",
+    "        val = float(val)\n",
+    "    return val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d058037",
+   "metadata": {},
+   "source": [
+    "### Task 4.1 Return the correct data type for price\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e5e2921",
+   "metadata": {},
+   "source": [
+    "**Question 14**: What is the `price` of the **fifth** listing?\n",
+    "\n",
+    "Your output **must** be an `int`. You **must** call the `cell_v2` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64af7540",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this for you\n",
+    "fifth_price = cell_v2(4, 'price')\n",
+    "\n",
+    "fifth_price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47589ea0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28144976",
+   "metadata": {},
+   "source": [
+    "### Task 4.2 Return the correct data type for `minimum_nights` column\n",
+    "\n",
+    "Update `cell_v2` so it can handle the column `minimum_nights` as well. Your function **must** return an `int` when the `col_name` is `minimum_nights`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35a58d08",
+   "metadata": {},
+   "source": [
+    "**Question 15**: How many `minimum_nights` does the **last** listing require?\n",
+    "\n",
+    "You **must** answer this question by calling the **`cell_v2`** function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb300750",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "last_minimum_nights = ...\n",
+    "\n",
+    "last_minimum_nights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "750c64f6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "285c13c4",
+   "metadata": {},
+   "source": [
+    "### Task 4.3 Return the correct data types for all other columns\n",
+    "\n",
+    "- Recall that the **correct** datatypes for each of the columns are as follows:\n",
+    "    1. `room_id` - the ID of the room listing (`str`)\n",
+    "    2. `name` - the name of the room listing (`str`)\n",
+    "    3. `host_id` - the ID of the host for the room listing (`str`)\n",
+    "    4. `host_name` - the name of the host for the room listing (`str`)\n",
+    "    5. `neighborhood_group` - the group of neighborhoods the room is in (`str`)\n",
+    "    6. `neighborhood` - the neighborhood the room is in (`str`)\n",
+    "    7. `latitude` - the latitude where the room is located (`float`)\n",
+    "    8. `longitude` - the longitude where the room is located (`float`)\n",
+    "    9. `room_type` - the type of room (`str`)\n",
+    "    10. `price` - the price per night for the room in US dollars (`int`)\n",
+    "    11. `minimum_nights` - the minimum amount of nights the room must be booked for (`int`)\n",
+    "    12. `number_of_reviews` - the total number of reviews the room has received (`int`)\n",
+    "    13. `last_review` - the date of the most recent review in the form yyyy-mm-dd (`str`)\n",
+    "    14. `reviews_per_month` - how many reviews per month the room receives (`float`)\n",
+    "    15. `calculated_host_listings_count` - how many listings the host of the room has (`int`)\n",
+    "    16. `availability_365` - how many days per year the listing is available for (`int`)\n",
+    "    \n",
+    "- Update your `cell_v2` function so it can handle **all** columns.\n",
+    "- The `if` condition will become very long if you keep using `or` to separate each column comparison operation.\n",
+    "- It is easier to make a list of all the column names whose values require `int` conversion and use `in` operator to check \n",
+    "```python\n",
+    "if col_name in [..., ..., ...]\n",
+    "```\n",
+    "\n",
+    "**Important:** Using `cell_v2` function is recommended but **optional** for the p6. You may choose to use the basic version and convert the return types manually when needed. Even if you use the `cell_v2` function defined here in p6, you **must** name that function `cell` in your p6 notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48390fe1",
+   "metadata": {},
+   "source": [
+    "## Segment 5: Sets\n",
+    "In class, we learned about the Python `list` sequence. Another simpler structure you'll sometimes find useful is the `set`. A set is **not** a sequence because it does not keep all the values in any particular order. \n",
+    "\n",
+    "### Task 5.1: Create a set\n",
+    "You can create sets the same way as lists, just **replacing** the *square brackets*(`[]`) with *curly braces*(`{}`). In the cell below, create a set with the same elements as the example list provided."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc50a944",
+   "metadata": {},
+   "source": [
+    "**Question 16:** What is the set having the same contents as `example_list` below?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb5b90f0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "example_list = [\"Kensington\", \"Harlem\", \"Midtown\"]\n",
+    "print(example_list)\n",
+    "# replace the ... with your code\n",
+    "example_set = {..., ..., ...}\n",
+    "\n",
+    "example_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a74e53e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f24b55df",
+   "metadata": {},
+   "source": [
+    "### Task 5.2: Check if an element is present in a list or set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26e38eea",
+   "metadata": {},
+   "source": [
+    "The `in` operator is used to check if an element is present in a list or set. Try it below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d3d0789",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"Harlem\" in example_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37bf5cce",
+   "metadata": {},
+   "source": [
+    "**Question 17:** Check if `neighborhood` *Midtown* is present in the set `example_set`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b28cb6c7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "midtown_check = ... in ...\n",
+    "\n",
+    "midtown_check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af386acf",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e81de47",
+   "metadata": {},
+   "source": [
+    "### Task 5.3: Check the ordering of elements in a list or set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "098d2db8",
+   "metadata": {},
+   "source": [
+    "Sets have no inherent ordering, so they don't support indexing. Try the code in the cells below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "447be3ea",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "example_list[0]  # this works"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db9aa9da",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "example_set[1] # but this does not work"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc3bd066",
+   "metadata": {},
+   "source": [
+    "The lack of order also matters for comparisons. Try evaluating this boolean expression:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7972f632",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[\"Harlem\", \"Midtown\", \"Kensington\"] == [\"Kensington\", \"Harlem\", \"Midtown\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbc38428",
+   "metadata": {},
+   "source": [
+    "And now try this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94097189",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "{\"Harlem\", \"Midtown\", \"Kensington\"} == {\"Kensington\", \"Harlem\", \"Midtown\"}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08af36e3",
+   "metadata": {},
+   "source": [
+    "### Task 5.4 Convert between lists and sets\n",
+    "You can switch back and forth between lists and sets with ease. Let's try it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "732c971c",
+   "metadata": {},
+   "source": [
+    "**Question 18**: What is the **list** of all `host_names` in the `neighborhood` *Throgs Neck*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "540a44b9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'throgs_neck_hosts', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "710cd5b4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1c587b4",
+   "metadata": {},
+   "source": [
+    "Now, let us convert the *list* `throgs_neck_hosts` into a *set*. Compare the the number of elements in the list and the set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ef3408",
+   "metadata": {},
+   "source": [
+    "**Question 19**: What is the **set** of all `host names` in the `neighborhood` *Throgs Neck*?\n",
+    "\n",
+    "**Hint:** You can convert a *list* into a *set* by typecasting. For example, to convert a *list* `example_list` into a *set*, you can use `set(example_list)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5ba34d5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "throgs_neck_hosts_set = ...\n",
+    "\n",
+    "print('Length of list:',  len(throgs_neck_hosts))\n",
+    "print('Length of set:', len(throgs_neck_hosts_set))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1f3b829",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e6af4ff",
+   "metadata": {},
+   "source": [
+    "As you can see, the number of elements is different! This is because a set is a collection of **unique** elements. Therefore, there can be no duplicates in a **set**.\n",
+    "\n",
+    "**Be careful!** When going from a set to a list, Python has to choose how to order the previously unordered values. If you run the same code, there's no guarantee Python will always choose the same way to order the set values in the new list."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15a73958",
+   "metadata": {},
+   "source": [
+    "### Task 5.5 Remove Duplicates\n",
+    "Let's use the uniqueness property of sets above to remove duplicates from a list by converting from a list to a set and back to a list again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bbd00a1",
+   "metadata": {},
+   "source": [
+    "**Question 20**: Convert `list_1 = [\"Brooklyn\", \"Brooklyn\", \"Manhattan\", \"Midtown\", \"Kensington\", \"Kensington\", \"Manhattan\"]` to a set and then back to a list.\n",
+    "\n",
+    "**Hint:** Just as you can convert a *list* into a *set* by typecasting, you can convert a *set* into a *list*. For example, to convert a *set* `example_set` into a *list*, you can use `list(example_set)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7afc9110",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# try playing with different values here\n",
+    "# the backslash enables us to split a long line of code into two lines\n",
+    "list_1 = [\"Brooklyn\", \"Brooklyn\", \"Manhattan\", \"Midtown\", \\\n",
+    "          \"Kensington\", \"Kensington\", \"Manhattan\"] \n",
+    "# convert list_1 to a set and back to a list\n",
+    "# replace the ... with your code\n",
+    "list_2 = ...\n",
+    "\n",
+    "list_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a212bc4a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7a261ee",
+   "metadata": {},
+   "source": [
+    "## Great work! You are now ready to start [p6](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p6)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1\", csv_header)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10-1": {
+     "name": "q10-1",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10-1\", first_three_nbhds)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10.2": {
+     "name": "q10.2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10-2\", first_three_nbhds)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11-1": {
+     "name": "q11-1",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q11-1\", first_three_nbhds)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11-2": {
+     "name": "q11-2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q11-2\", sorted_nbhds)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12-1": {
+     "name": "q12-1",
+     "points": 2.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q12-1\", median1)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12-2": {
+     "name": "q12-2",
+     "points": 2.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q12-2\", median2)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q13\", harlem_median_price)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q14\", fifth_price)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q15\", last_minimum_nights)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q16\", example_set)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17\", midtown_check)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q18\", throgs_neck_hosts)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q19\", throgs_neck_hosts_set)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2-1": {
+     "name": "q2-1",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q2-1\", num_rows)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2-2": {
+     "name": "q2-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q2-2\", first_ten_rows)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2-3": {
+     "name": "q2-3",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q2-3\", last_ten_rows)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q20\", list_2)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3\", first_row)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q4\", first_host_name)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q5\", nbhd_group_index)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q6\", nbh_group_row1)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7-1": {
+     "name": "q7-1",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7-1\", first_neighborhood)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7-2": {
+     "name": "q7-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7-2\", second_name)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7-3": {
+     "name": "q7-3",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7-3\", third_price)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q8\", bronx_num_rooms)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q9\", univ_heights_names)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p6/practice.ipynb b/lab-p6/practice.ipynb
index 49ab07418b46ddb7221313771191eeb6eb35a8e2..700726a4d1399302ef97d608ea8b047953bfcceb 100644
--- a/lab-p6/practice.ipynb
+++ b/lab-p6/practice.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cb977960",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/lab-p7/.ipynb_checkpoints/practice-checkpoint.ipynb b/lab-p7/.ipynb_checkpoints/practice-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6abec6a517d591b903af0aa88eb2325bdf6aa824
--- /dev/null
+++ b/lab-p7/.ipynb_checkpoints/practice-checkpoint.ipynb
@@ -0,0 +1,2359 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "78106d41",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef1d537b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"practice.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "878c24a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import practice_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "274d4b64",
+   "metadata": {},
+   "source": [
+    "# Lab-P7: Dictionaries (Drinking Water Accessibility)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b10b5bdc",
+   "metadata": {},
+   "source": [
+    "## Segment 2: Loading Data from CSVs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e94458d",
+   "metadata": {},
+   "source": [
+    "## Dataset:\n",
+    "\n",
+    "In p7, you will be analyzing the data in `water_accessibility.csv`. This dataset has information on the **percentage of population** of each country who had **at least basic** (alb) water supply in the years **2015** and **2020**. You will learn more about the dataset in p7. In lab-p7, you will practice creating dictionaries using the data in `water_accessibliity.csv` and using dictionaries to answer simple questions. \n",
+    "\n",
+    "For now, open `water_accessibility.csv` with Microsoft Excel or some other Spreadsheet viewer and look at the list of countries in the dataset. Data for each country appears twice, one row for the year `2015` and the other row for year `2020`. Countries which had incomplete data have been **omitted** from the dataset, and we will **ignore** those countries in this project. You do **not** have to deal with any **missing data** in the dataset.\n",
+    "\n",
+    "The data shows:\n",
+    "- `country_code` : the unique country code that consists of three alphabet letters\n",
+    "- `country_name` : the name of the country\n",
+    "- `region` : the geographical location of the country (does not equal to its corresponding continents, but follows the administrative groupings from [The World Bank](https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups)\n",
+    "- `year` : the year in which it was subject to data collection\n",
+    "- `income_level` : the classification of income level based on GNI per capita in US dollars ([The World Bank Atlas Method](https://datahelpdesk.worldbank.org/knowledgebase/articles/378834-how-does-the-world-bank-classify-countries))\n",
+    "- `pop` : population of the country in a specific year (in thousands)\n",
+    "- `urban_percent` : the percentage of population in a given country that is urban\n",
+    "- `national_alb` : the percentage of a country's population that has access to at least basic water supply\n",
+    "- `urban_alb` : the percentage of a country's urban population that has access to at least basic water supply"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6142e780",
+   "metadata": {},
+   "source": [
+    "### Task 2.1: Processing the CSV file\n",
+    "\n",
+    "You should first copy/paste the `process_csv` function from your lab-p6 notebook so you can read the data in `water_accessibility.csv`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95ec00df",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this lab\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fee50a9b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the 'process_csv' function here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cb308d9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# call process_csv here to read 'water_accessibility.csv'\n",
+    "csv_data = ...\n",
+    "\n",
+    "# split the header and data into appropriate variables\n",
+    "csv_header = ...\n",
+    "csv_rows = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4069d642",
+   "metadata": {},
+   "source": [
+    "### Function 1: `cell(row_idx, col_name)`\n",
+    "\n",
+    "This function must take in a row index, `row_idx` and a column name, `col_name` as its inputs, and return the value in `water_accessibility.csv` stored there. There is **no missing data** in this dataset.\n",
+    "\n",
+    "Your `cell` function **must** also typecast the values based on column names (similar to `cell_v2` from [lab-p6](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p6)). These are the expected data types for each of the columns:\n",
+    "\n",
+    "- `country_code` : **str**\n",
+    "- `country_name` : **str**\n",
+    "- `region` : **str**\n",
+    "- `year` : **int**\n",
+    "- `income_level` : **str**\n",
+    "- `pop` : **int** (handle conversion to thousands)\n",
+    "- `urban_percent` : **int**\n",
+    "- `national_alb` : **int**\n",
+    "- `urban_alb` : **int**\n",
+    "\n",
+    "**Important:** You **must** only use the `cell` function to extract data from the dataset in p7. If you extract any data without explicitly using this function, you will **lose points** during manual review."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22e15a6c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the cell function here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f687ebae",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Verifying the `cell` function\n",
+    "\n",
+    "It is recommended that you verify that you have defined this function properly. Open `water_accessibility.csv` with Microsoft Excel or some other Spreadsheet viewer, then call the `cell` function on different inputs and confirm that the output matches the values in the table.\n",
+    "\n",
+    "Move on to the next section after you have verified that `cell` function works as it should for the below test cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "247d7bdb",
+   "metadata": {},
+   "source": [
+    "**Question 0.1:** What is the data in the `year` column in the *4*th row index?\n",
+    "\n",
+    "You **must** use the `cell` function to answer this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62a69705",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "year_idx4 = cell(4, \"year\")\n",
+    "\n",
+    "year_idx4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f5f73b1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"cell_test1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fb0fde1",
+   "metadata": {},
+   "source": [
+    "**Question 0.2:** What is the data in the `pop` column in the *5*th row index?\n",
+    "\n",
+    "You **must** use the `cell` function to answer this.\n",
+    "\n",
+    "Remember that in the dataset, the `pop` represents the population in thousands, so your `cell` function **must** multiply the value by *1000* if the column name is `pop`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cad23ff0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "pop_idx5 = cell(5, \"pop\")\n",
+    "\n",
+    "pop_idx5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6211e134",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"cell_test2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df15b1bf",
+   "metadata": {},
+   "source": [
+    "**Question 0.3:** What is the data in the `urban_percent` column in the *57*th row index?\n",
+    "\n",
+    "You **must** use the `cell` function to answer this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0104331",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# call cell function to retrive the urban_percent data in the 57th row index\n",
+    "urban_percent_idx57 = ...\n",
+    "\n",
+    "urban_percent_idx57"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13758ec9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"cell_test3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0805c83",
+   "metadata": {},
+   "source": [
+    "**Question 0.4:** What is the data in the `national_alb` column in the *58*th row index?\n",
+    "You **must** use the `cell` function to answer this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d21a35a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# call cell function to retrive the national_alb data in the 58th row index\n",
+    "national_alb_idx58 = ...\n",
+    "\n",
+    "national_alb_idx58"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3564a3b8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"cell_test4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65b249e1",
+   "metadata": {},
+   "source": [
+    "**Question 0.5:** What is the data in the `urban_alb` column in the *58*th row index?\n",
+    "\n",
+    "You **must** use the `cell` function to answer this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da695eca",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# call cell function to retrive the urban_alb data in the 58th row index\n",
+    "urban_alb_idx58 = ...\n",
+    "\n",
+    "urban_alb_idx58"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7017e78",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"cell_test5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ab1caa0",
+   "metadata": {},
+   "source": [
+    "**Question 0.6:** What is the data in the `country_code` column in the *59*th row index?\n",
+    "\n",
+    "You **must** use the `cell` function to answer this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d00eccf7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# call cell function to retrive the country_code data in the 59th row index\n",
+    "country_code_idx59 = ...\n",
+    "\n",
+    "country_code_idx59"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96707d8f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"cell_test6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee793d26",
+   "metadata": {},
+   "source": [
+    "**Question 0.7:** What is the data in the `income_level` column in the *59*th row index?\n",
+    "\n",
+    "You **must** use the `cell` function to answer this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96065a23",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# call cell function to retrive the income_level data in the 59th row index\n",
+    "income_level_idx59 = ...\n",
+    "\n",
+    "income_level_idx59"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b15a6eee",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"cell_test7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d06c357",
+   "metadata": {},
+   "source": [
+    "## Segment 3: Dictionaries and Looping through Time series data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98a0d379",
+   "metadata": {},
+   "source": [
+    "### Task 3.1: Creating and Accessing Dictionaries"
+   ]
+  },
+  {
+   "attachments": {
+    "rows_example-2.png": {
+     "image/png": 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+    }
+   },
+   "cell_type": "markdown",
+   "id": "25370e84",
+   "metadata": {},
+   "source": [
+    "**Question 1:** Create a dictionary mapping each `country_name` to its `country_code`.\n",
+    "\n",
+    "Your output **must** be a **dict** where each key is a `country_name` and the value is the `country_code`. The first five key-value pairs within this **dict** would be these:\n",
+    "```\n",
+    "{'Afghanistan': 'AFG',\n",
+    " 'Albania': 'ALB',\n",
+    " 'Algeria': 'DZA',\n",
+    " 'Andorra': 'AND',\n",
+    " 'Angola': 'AGO',\n",
+    " ...\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "**Important:** This dataset is different from the previous datasets you have worked with because every country in the dataset has **two** rows: one for the `year` *2015* and one for *2020*. \n",
+    "This means that when working with data, you need to make sure to set up your `for` loop in a way that will let you process two rows at once. \n",
+    "The data is organized in such a way that for each country, the data from *2015* appears in the **even** indexed rows (0, 2, 4, ...), and the data from *2020* appears in the **odd** indexed rows (1, 3, 5, ...).\n",
+    "We can use this to extract data from just the year *2015* or *2020* if necessary.\n",
+    "\n",
+    "![rows_example-2.png](attachment:rows_example-2.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d02c7b74",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# Initialize an empty dictionary\n",
+    "country_codes = ...\n",
+    "\n",
+    "# this will loop through from 0 to len(csv_rows) and increase by 2 numbers each loop\n",
+    "# i.e. it will loop through even indices corresponding to just the data from 2015\n",
+    "for idx in range(0, len(csv_rows), 2): \n",
+    "    # Before you populate the dictionary with the key-value pairs,\n",
+    "    # use print function calls to print the key and the value.\n",
+    "    # Once you have verified the output for the first five lines of data, \n",
+    "    # use the dictionary key-value pair insert syntax to update the dictionary.\n",
+    "    # Don't forget to delete the print function call line of code.\n",
+    "    country_codes[...] = ...\n",
+    "\n",
+    "\n",
+    "country_codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86f0f443",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5491394b",
+   "metadata": {},
+   "source": [
+    "**Question 2:** What is the country code of *Ireland*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e77ad60",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "ireland_country_code = ...\n",
+    "\n",
+    "ireland_country_code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e60cc1c5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfdecfb7",
+   "metadata": {},
+   "source": [
+    "**Question 3:** What is the country code of *Sudan*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "517926bb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "sudan_country_code = ...\n",
+    "\n",
+    "sudan_country_code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e6e98e4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fb379f8",
+   "metadata": {},
+   "source": [
+    "**Question 4:** Create a dictionary mapping each `country_name` to `income_level` for *2015*.\n",
+    "\n",
+    "The first five key-value pairs within this **dict** would be these:\n",
+    "```\n",
+    "{'Afghanistan': 'Low income',\n",
+    " 'Albania': 'Upper middle income',\n",
+    " 'Algeria': 'Upper middle income',\n",
+    " 'Andorra': 'High income',\n",
+    " 'Angola': 'Upper middle income',\n",
+    " ...\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "**Hint:** The data for *2015* is found on rows at **even** indices. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a22e392",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# Initialize an empty dictionary\n",
+    "income_levels_2015 = ...\n",
+    "\n",
+    "# you need to loop through from 0 to len(csv_rows) and increase by 2 numbers each loop\n",
+    "for idx in range(..., ..., ...):\n",
+    "    # Before you populate the dictionary with the key-value pairs,\n",
+    "    # use print function calls to print the key and the value.\n",
+    "    # Once you have verified the output for the first five lines of data, \n",
+    "    # use the dictionary key-value pair insert syntax to update the dictionary.\n",
+    "    # Don't forget to delete the print function call line of code.\n",
+    "    income_levels_2015[...] = ...\n",
+    "\n",
+    "\n",
+    "income_levels_2015"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4795f393",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08db305d",
+   "metadata": {},
+   "source": [
+    "**Question 5:** Create a dictionary mapping each `country_name` to `income_level` for *2020*.\n",
+    "\n",
+    "The first five key-value pairs within this **dict** would be these:\n",
+    "```\n",
+    "{'Afghanistan': 'Low income',\n",
+    " 'Albania': 'Upper middle income',\n",
+    " 'Algeria': 'Lower middle income',\n",
+    " 'Andorra': 'High income',\n",
+    " 'Angola': 'Lower middle income',\n",
+    " ...\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "**Hint:** The data for *2020* is found on rows at **odd** indices. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27e96924",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# Initialize an empty dictionary\n",
+    "income_levels_2020 = ...\n",
+    "\n",
+    "# you need to loop through from 1 to len(csv_rows) and increase by 2 numbers each loop\n",
+    "for idx in range(..., ..., ...):\n",
+    "    # Before you populate the dictionary with the key-value pairs,\n",
+    "    # use print function calls to print the key and the value.\n",
+    "    # Once you have verified the output for the first five lines of data, \n",
+    "    # use the dictionary key-value pair insert syntax to update the dictionary\n",
+    "    # Don't forget to delete the print function call line of code.\n",
+    "    income_levels_2020[...] = ...\n",
+    "\n",
+    "\n",
+    "income_levels_2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "235c2e07",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d042c7d2",
+   "metadata": {},
+   "source": [
+    "**Question 6.1:** What is the `income_level` of *Zimbabwe* in *2015*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1582e55d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "zimbabwe_income_level_2015 = ...\n",
+    "\n",
+    "zimbabwe_income_level_2015"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81fa2887",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d2b09af",
+   "metadata": {},
+   "source": [
+    "**Question 6.2:** What is the `income_level` of *Zimbabwe* in *2020*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf62c3f4",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "zimbabwe_income_level_2020 = ...\n",
+    "\n",
+    "zimbabwe_income_level_2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8456840b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb6ca277",
+   "metadata": {},
+   "source": [
+    "### Task 3.2: Comparing data in odd and even indices\n",
+    "\n",
+    "We have already seen that the data from the year *2015* is stored in **even** indices, and the data from the year *2020* is stored in **odd** indices. Moreover, the data from each country is stored in consecutive rows. This allows us to easily compare data from the years *2015* and *2020* for a single country. Run the following cell, and figure out what it is doing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fc034b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for idx in range(0, 10, 2):\n",
+    "    idx_2015 = idx\n",
+    "    idx_2020 = idx + 1\n",
+    "    print('At row idx ' + str(idx_2015) + ' the year is ' + str(cell(idx_2015, 'year')))\n",
+    "    print('At row idx ' + str(idx_2020) + ' the year is ' + str(cell(idx_2020, 'year')))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c754f145",
+   "metadata": {},
+   "source": [
+    "See if you can edit the code above to make it display the `pop` of the years *2015* and *2020* from the first 10 rows of the dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53f9a2d6",
+   "metadata": {},
+   "source": [
+    "**Question 7:** Create a **list** of all the countries (`country_name`) whose populations (`pop`) have **decreased** from *2015* to *2020*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9ae5c38",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initalize as an empty list\n",
+    "pop_decrease_countries = ...\n",
+    "\n",
+    "# you need to loop through only even indices to avoid duplicate counting of the rows\n",
+    "for idx in range(..., ..., ...):\n",
+    "    idx_2015 = ...\n",
+    "    idx_2020 = ...\n",
+    "    if ...: # check if population in 2020 is less than population in 2015\n",
+    "        pop_decrease_countries.append(...)\n",
+    "        \n",
+    "pop_decrease_countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c50df33c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58fb5e00",
+   "metadata": {},
+   "source": [
+    "**Question 8:** Create a list of all the countries whose urban population percentage (`urban_percent`) **increased** from *2015* to *2020*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6815bcd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initialize as an empty list\n",
+    "more_urban_countries = []\n",
+    "\n",
+    "# you need to loop through only even (or only odd) indices to avoid duplicate counting of the rows\n",
+    "# and add the country_name to the list if urban_percent is higher in 2020 than in 2015\n",
+    "        \n",
+    "more_urban_countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "441a6154",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99daeb5e",
+   "metadata": {},
+   "source": [
+    "### Task 3.3: Working with Nested Dictionaries\n",
+    "\n",
+    "You have defined two dictionaries `income_levels_2015` and `income_levels_2020`. You will now create a *nested* dictionary that combines both these dictionaries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "957238a2",
+   "metadata": {},
+   "source": [
+    "**Question 9:** Create a dictionary mapping each `country_name` to another **dictionary** that maps each `year` to `income_level`. You may hardcode the years *2015* and *2020* for this question.\n",
+    "\n",
+    "The first five key-value pairs within this **dict** would be these:\n",
+    "```\n",
+    "{'Afghanistan': {2015: 'Low income', 2020: 'Low income'},\n",
+    " 'Albania': {2015: 'Upper middle income', 2020: 'Upper middle income'},\n",
+    " 'Algeria': {2015: 'Upper middle income', 2020: 'Lower middle income'},\n",
+    " 'Andorra': {2015: 'High income', 2020: 'High income'},\n",
+    " 'Angola': {2015: 'Upper middle income', 2020: 'Lower middle income'},\n",
+    " ...\n",
+    "}\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "996eb5de",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# Initialize an empty dictionary\n",
+    "income_levels = ...\n",
+    "\n",
+    "# you need to loop through only even (or only odd) indices to avoid duplicate counting of the rows\n",
+    "for idx in range(..., ..., ...):\n",
+    "    country_name = cell(idx, \"country_name\")\n",
+    "    # initialize an empty nested dictionary as the value\n",
+    "    income_levels[country_name] = ...\n",
+    "    # First, use print function call to display the 2015 income_level for\n",
+    "    # the current country. Once you verify that, delete the print function call line.\n",
+    "    # add the income_level for 2015 as key-value pair to inner dictionary\n",
+    "    income_levels[...][...] = income_levels_2015[...]\n",
+    "    # add the income_level for 2020 as key-value pair to inner dictionary\n",
+    "    income_levels[...][...] = income_levels_2020[...]\n",
+    "\n",
+    "income_levels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fa82e61",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ada0cd58",
+   "metadata": {},
+   "source": [
+    "**Question 10.1:** Use the nested dictionary you created to find the income level of *Cuba* in *2015*.\n",
+    "\n",
+    "You **must** use the dictionary `income_levels` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ab0c99f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "cuba_income_level_2015 = income_levels[...][...]\n",
+    "\n",
+    "cuba_income_level_2015"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05633ffe",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57097128",
+   "metadata": {},
+   "source": [
+    "**Question 10.2:** Use the nested dictionary you created to find the income level of *Gibraltar* in *2020*.\n",
+    "\n",
+    "You **must** use the dictionary `income_levels` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2766eda5",
+   "metadata": {
+    "scrolled": false,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "gibraltar_income_level_2020 = income_levels[...][...]\n",
+    "\n",
+    "gibraltar_income_level_2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81c1bdd5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0700c958",
+   "metadata": {},
+   "source": [
+    "**Question 11**: Create a dictionary mapping each `country_name` to a **boolean** representing whether or not the `income_level` of a country is different in *2020* and *2015*.\n",
+    "\n",
+    "The first five key-value pairs within this **dict** would be these:\n",
+    "```\n",
+    "{'Afghanistan': False,\n",
+    " 'Albania': False,\n",
+    " 'Algeria': True,\n",
+    " 'Andorra': False,\n",
+    " 'Angola': True,\n",
+    " ...\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "You have already created a nested dictionary called `income_levels` which contains all the relevant data. Loop through the keys of that dictionary and combine the data from the inner dictionary to create your dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53ea9fe8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initialize an empty dictionary\n",
+    "income_level_differences = ...\n",
+    "\n",
+    "# loop over the keys of income_levels\n",
+    "for ... in ...:\n",
+    "    if ...: \n",
+    "        # use the dictionary income_levels to compare 2015 and 2020 income_level\n",
+    "        income_change_boolean = True\n",
+    "    else:\n",
+    "        income_change_boolean = False\n",
+    "    # map the country name to the boolean\n",
+    "    income_level_differences[...] = income_change_boolean\n",
+    "\n",
+    "income_level_differences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a14359df",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a38af5a",
+   "metadata": {},
+   "source": [
+    "### Task 3.4: Binning\n",
+    "\n",
+    "We created the dictionary `income_levels_2015` a little while ago which maps each `country_name` to its `income_level`. We will now use this dictionary to create a **new** dictionary where each key is an `income_level`, and the corresponding value is a **list** of all the countries with that `income_level`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99fc4fcb",
+   "metadata": {},
+   "source": [
+    "**Question 12:** Create a new dictionary using the `income_levels_2015` dictionary by mapping each `income_level` to a **list** of all the countries (`country_name`) with that `income_level`.\n",
+    "\n",
+    "The first three country names for each key within this **dict** would be these:\n",
+    "```\n",
+    "{'Low income': ['Afghanistan',\n",
+    "  'Benin',\n",
+    "  'Burkina Faso', ...],\n",
+    " 'Upper middle income': ['Albania',\n",
+    "  'Algeria',\n",
+    "  'Angola', ...],\n",
+    " 'High income': ['Andorra',\n",
+    "  'Australia',\n",
+    "  'Austria', ...],\n",
+    " 'Lower middle income': ['Armenia',\n",
+    "  'Bangladesh',\n",
+    "  'Bhutan', ...]\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e218509",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initialize an empty dictionary\n",
+    "income_levels_to_country_names_2015 = ...\n",
+    "\n",
+    "# loop over the keys of income_levels_2015\n",
+    "for ... in ...:\n",
+    "    # find the income_level of the country\n",
+    "    income_level = ...\n",
+    "    # if the income_level doesn't already exist in the new dict, map it to an empty list \n",
+    "    if income_level not in income_levels_to_country_names_2015:\n",
+    "        income_levels_to_country_names_2015[income_level] = ...\n",
+    "    # append the country name to the list\n",
+    "    income_levels_to_country_names_2015[income_level].append(...)\n",
+    "\n",
+    "income_levels_to_country_names_2015"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc9a56d3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed2fd0f6",
+   "metadata": {},
+   "source": [
+    "**Question 13:** Create a new dictionary using the `income_levels_2020` dictionary by mapping each `income_level` to a **list** of all the countries (`country_name`) with that `income_level`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b78d568",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initialize an empty dictionary\n",
+    "income_levels_to_country_names_2020 = ...\n",
+    "\n",
+    "# loop over the keys of income_levels_2020\n",
+    "# and update income_levels_to_country_names_2020 by appending each country to the correct list\n",
+    "\n",
+    "income_levels_to_country_names_2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "578ae9ba",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17eb7f34",
+   "metadata": {},
+   "source": [
+    "### Task 3.5: Summarizing Data in Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e119acb3",
+   "metadata": {},
+   "source": [
+    "**Question 14.1:** Create a new dictionary that maps each `income_level` to the **number** of countries with that `income_level` in *2015*.\n",
+    "\n",
+    "The key-value pairs within this **dict** would be these:\n",
+    "```\n",
+    "{'Low income': 25,\n",
+    " 'Upper middle income': 44,\n",
+    " 'High income': 41,\n",
+    " 'Lower middle income': 41}\n",
+    "```\n",
+    "\n",
+    "**Hint:** Start with the dictionary `income_levels_to_country_names_2015`. Your new dictionary must have the same keys, but the values must be the **length** of the corresponding values in `income_levels_to_country_names_2015`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1573958f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initialize an empty dictionary\n",
+    "income_level_country_counts_2015 = ...\n",
+    "\n",
+    "# loop over the keys of income_levels_to_country_names_2015\n",
+    "for ... in ...:\n",
+    "    # map the income level to the length of the value in income_levels_to_country_names_2015\n",
+    "    income_level_country_counts_2015[...] = len(...)\n",
+    "\n",
+    "income_level_country_counts_2015"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37f8c9a5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20b81859",
+   "metadata": {},
+   "source": [
+    "**Question 14.2**: Create a new dictionary that maps each `income_level` to the **number** of countries with that `income_level` in *2020*.\n",
+    "\n",
+    "The key-value pairs within this **dict** would be these:\n",
+    "```\n",
+    "{'Low income': 22,\n",
+    " 'Upper middle income': 43,\n",
+    " 'Lower middle income': 44,\n",
+    " 'High income': 42}\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de02f1a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initialize an empty dictionary\n",
+    "income_level_country_counts_2020 = ...\n",
+    "\n",
+    "# loop over the keys of income_levels_to_country_names_2020\n",
+    "# and map the income_level to the length\n",
+    "\n",
+    "income_level_country_counts_2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb047f1b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78e0e669",
+   "metadata": {},
+   "source": [
+    "### Task 3.6: Combining data from different dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11017dfb",
+   "metadata": {},
+   "source": [
+    "**Question 15.1:** Create a dictionary that maps each `country_name` to the population (`pop`) of that country in *2020*.\n",
+    "\n",
+    "The first five key-value pairs within this **dict** would be these:\n",
+    "```\n",
+    "{'Afghanistan': 38928000,\n",
+    " 'Albania': 2878000,\n",
+    " 'Algeria': 43851000,\n",
+    " 'Andorra': 77000,\n",
+    " 'Angola': 32866000,\n",
+    " ...\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "\n",
+    "**Reminder:** The `pop` column represents the population in **thousands**. Your `cell` function definition should already have handled this conversion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c13f8fa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initialize an empty dictionary\n",
+    "pops = ...\n",
+    "\n",
+    "# loop over the odd indices of csv_rows to get the data for 2020\n",
+    "# and map the country_name to the pop\n",
+    "\n",
+    "pops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fab346a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e691461e",
+   "metadata": {},
+   "source": [
+    "**Question 15.2:** Create a dictionary that maps each `country_name` to the urban population percentage (`urban_percent`) of that country in *2020*.\n",
+    "\n",
+    "The first five key-value pairs within this **dict** would be these:\n",
+    "```\n",
+    "{'Afghanistan': 26,\n",
+    " 'Albania': 62,\n",
+    " 'Algeria': 74,\n",
+    " 'Andorra': 88,\n",
+    " 'Angola': 67,\n",
+    " ...\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05052939",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'urban_percents', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e86cdde4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fd3a644",
+   "metadata": {},
+   "source": [
+    "**Question 16:** Using the two dictionaries `pops` and `urban_percents`, create a new dictionary that maps each `country_name` to the size of the urban population of that country in *2020*.\n",
+    "\n",
+    "You **must** round the numbers to the nearest integer using the built-in function `round`.  To learn more about the `round` function, check out this [link](https://docs.python.org/3/library/functions.html#round).\n",
+    "\n",
+    "The first five key-value pairs within this **dict** would be these:\n",
+    "```\n",
+    "{'Afghanistan': 10121280,\n",
+    " 'Albania': 1784360,\n",
+    " 'Algeria': 32449740,\n",
+    " 'Andorra': 67760,\n",
+    " 'Angola': 22020220,\n",
+    " ...\n",
+    " }\n",
+    "```\n",
+    "\n",
+    "**Hint:** For each country, the size of the urban population is just the `pop * urban_percent / 100`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30ada55a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initialize an empty dictionary\n",
+    "urban_populations = ...\n",
+    "\n",
+    "# loop over the keys of either pops/urban_percents (it doesn't matter as they are the same)\n",
+    "for ... in ...:\n",
+    "    # map the country name to its urban population using data from pops and urban_percents\n",
+    "    # Remember that both pops and urban_percents have the same keys (country names)\n",
+    "    # So a single for loop can be sufficient to access data from both of these dictionaries\n",
+    "    urban_populations[...] = ...\n",
+    "\n",
+    "urban_populations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7899c8a0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ffe560b",
+   "metadata": {},
+   "source": [
+    "### Task 3.7: Finding Minimum/Maximum Values in Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41c8fd6f",
+   "metadata": {},
+   "source": [
+    "**Question 17**: What is the name of the country with the **smallest** urban population? Do not worry about ties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a26ff11",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initialize a new variable as None\n",
+    "smallest_urban_country = ...\n",
+    "\n",
+    "# loop over the keys of urban_populations\n",
+    "for ... in ...:\n",
+    "    # check if the variable smallest_urban_country should be updated\n",
+    "    if ...:\n",
+    "        # correctly update the variable\n",
+    "        smallest_urban_country = ...\n",
+    "\n",
+    "smallest_urban_country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b787e06",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a65d7dbf",
+   "metadata": {},
+   "source": [
+    "**Question 18**: What is the name of the country with the **largest** urban population? Do not worry about ties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf65200d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'largest_urban_country', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf91e415",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8de5ec57",
+   "metadata": {},
+   "source": [
+    "### Task 3.8: Adding additional data to nested dictionaries\n",
+    "\n",
+    "You have already defined a nested dictionary `income_levels` in Q9. This is a dictionary where each `country_name` maps to an inner dictionary with the keys *2015* and *2020*. The corresponding values for this inner dictionary are the `income_levels` of `country_name` in *2015* and *2020* respectively.\n",
+    "\n",
+    "You will now add an additional column to this nested dictionary."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "979f1bae",
+   "metadata": {},
+   "source": [
+    "**Question 19**: Add an additional key-value pair mapping the string `\"region\"` to the `region` of each country to each of the inner dictionaries of `income_levels`.\n",
+    "\n",
+    "The value of the variable `income_levels` should look like this:\n",
+    "```python\n",
+    "{'Afghanistan': {2015: 'Low income',\n",
+    "  2020: 'Low income',\n",
+    "  'region': 'South Asia'},\n",
+    " 'Albania': {2015: 'Upper middle income',\n",
+    "  2020: 'Upper middle income',\n",
+    "  'region': 'Europe & Central Asia'},\n",
+    " 'Algeria': {2015: 'Upper middle income',\n",
+    "  2020: 'Lower middle income',\n",
+    "  'region': 'Middle East & North Africa'},\n",
+    "  ...\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af41313e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# update the value of the variable 'income_levels', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89c473f1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e983216",
+   "metadata": {},
+   "source": [
+    "### Task 3.9: More binning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "069662f1",
+   "metadata": {},
+   "source": [
+    "**Question 20**: Create a new dictionary that maps each `region` to the **list** of countries from that region whose `income_level` was *Lower middle income* in *2020*.\n",
+    "\n",
+    "You **must** create a bin for each `income_level` even if there is no country in that `income_level`. In this instance, there was no *Lower middle income* country in *2020* from the `region` of *North America*. However, we still **require** that *North America* be included as a key in our dictionary, but with the value being the empty list `[]`.\n",
+    "\n",
+    "You can accomplish this by first initializing all keys to the dictionary with the value as `[]`, and then adding the values to these bins in a separate loop, as in the code snippet below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f44e990",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "# initialize as an empty dictionary\n",
+    "lower_mid_countries_by_region = ...\n",
+    "# we make a list of all the regions\n",
+    "regions_list = []\n",
+    "for country_name in income_levels:\n",
+    "    regions_list.append(income_levels[country_name]['region'])\n",
+    "\n",
+    "# we then add the unique regions as keys to the dictionary\n",
+    "for region in list(set(regions_list)):\n",
+    "    lower_mid_countries_by_region[region] = []\n",
+    "    \n",
+    "# loop over the keys of income_levels\n",
+    "for ... in ...:\n",
+    "    # find the region of the country\n",
+    "    region = ...\n",
+    "    # if the income level of the country was 'Lower middle income' in 2020,\n",
+    "    # add country to the correct bin\n",
+    "\n",
+    "lower_mid_countries_by_region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a83ffe8d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3265a43",
+   "metadata": {},
+   "source": [
+    "## Great work! You are now ready to start [p7](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p7)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "cell_test1": {
+     "name": "cell_test1",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q0-1\", year_idx4)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "cell_test2": {
+     "name": "cell_test2",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q0-2\", pop_idx5)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "cell_test3": {
+     "name": "cell_test3",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q0-3\", urban_percent_idx57)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "cell_test4": {
+     "name": "cell_test4",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q0-4\", national_alb_idx58)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "cell_test5": {
+     "name": "cell_test5",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q0-5\", urban_alb_idx58)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "cell_test6": {
+     "name": "cell_test6",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q0-6\", country_code_idx59)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "cell_test7": {
+     "name": "cell_test7",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q0-7\", income_level_idx59)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1\", country_codes)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10-1": {
+     "name": "q10-1",
+     "points": 2.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10-1\", cuba_income_level_2015)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10-2": {
+     "name": "q10-2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10-2\", gibraltar_income_level_2020)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q11\", income_level_differences)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q12\", income_levels_to_country_names_2015)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q13\", income_levels_to_country_names_2020)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14-1": {
+     "name": "q14-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q14-1\", income_level_country_counts_2015)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14-2": {
+     "name": "q14-2",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q14-2\", income_level_country_counts_2020)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15-1": {
+     "name": "q15-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q15-1\", pops)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15-2": {
+     "name": "q15-2",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q15-2\", urban_percents)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q16\", urban_populations)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17\", smallest_urban_country)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q18\", largest_urban_country)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q19\", income_levels)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q2\", ireland_country_code)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q20\", lower_mid_countries_by_region)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3\", sudan_country_code)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q4\", income_levels_2015)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q5\", income_levels_2020)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6-1": {
+     "name": "q6-1",
+     "points": 2.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q6-1\", zimbabwe_income_level_2015)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6-2": {
+     "name": "q6-2",
+     "points": 2.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q6-2\", zimbabwe_income_level_2020)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7\", pop_decrease_countries)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q8\", more_urban_countries)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q9\", income_levels)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p7/practice.ipynb b/lab-p7/practice.ipynb
index 6a2b01854b43f229db8cb960dec16766e0b40f33..6abec6a517d591b903af0aa88eb2325bdf6aa824 100644
--- a/lab-p7/practice.ipynb
+++ b/lab-p7/practice.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "78106d41",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/lab-p8/.ipynb_checkpoints/practice-checkpoint.ipynb b/lab-p8/.ipynb_checkpoints/practice-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e4f42163c6ae072b0352bfa2a727964754a19eed
--- /dev/null
+++ b/lab-p8/.ipynb_checkpoints/practice-checkpoint.ipynb
@@ -0,0 +1,1961 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "80375cd4",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e03f2b2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"practice.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "689fd8eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import practice_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86384bbe",
+   "metadata": {},
+   "source": [
+    "# Lab-P8: Lists and Dictionaries\n",
+    "\n",
+    "This lab is designed to help you prepare for p8. We will focus on dictionaries, mutating lists, binning, and copying."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "165e4b6f",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this lab, you will practice how to...\n",
+    "* Integrate relevant information from various sources (e.g. multiple csv files)\n",
+    "* Build appropriate data structures for organization and informative presentation (e.g. list of dictionaries)\n",
+    "* Practice good coding style"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e732eae",
+   "metadata": {},
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: p8 and p9 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partner up with someone for p8, you have to sustain that partnership until end of p9. You must acknowledge to the lab TA to receive lab attendance credit.\n",
+    "\n",
+    "You may do these lab exercises with only with your project partner; you are not allowed to start working on lab-p8 with one person, then do the project with a different partner.  Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f22/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83274533",
+   "metadata": {},
+   "source": [
+    "#### Please make sure `small_movies.csv` and `small_mapping.csv` are in your `lab-p8` folder before continuing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7a1e9ca",
+   "metadata": {},
+   "source": [
+    "## Introduction:\n",
+    "\n",
+    "In p8 and p9, we will be working on the [IMDb Movies Dataset](https://www.imdb.com/interfaces/). We will use Python to discover some cool facts about our favorite movies, cast, and directors.\n",
+    "\n",
+    "In lab-p8, you will work with a small subset of movies, and practice writing some code to parse the data stored in `small_mapping.csv` and `small_movies.csv`. You can then use this code to parse the much larger datasets in p8 and p9."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f074fa11",
+   "metadata": {},
+   "source": [
+    "## The Data:\n",
+    "\n",
+    "Open `small_movies.csv` and `small_mapping.csv` in any spreadsheet viewer, and see what the data looks like. When seen with a good spreadsheet viewer, this is what some of `small_movies.csv` wil look like:\n",
+    "\n",
+    "|title|year|duration|genres|rating|directors|cast|\n",
+    "| ----  |-----------------|-----------------------------|----------|---------------------------------------------------------------------|----------------------------------------------|--------|\n",
+    "|tt3104988|2018|120|\"Comedy, Drama, Romance\"|6.9|nm0160840|\"nm2090422, nm6525901, nm0000706, nm2110418, nm0523734\"|\n",
+    "|tt4846340|2016|127|\"Biography, Drama, History\"|7.8|nm0577647|\"nm0378245, nm0818055, nm1847117\"|\n",
+    "\n",
+    "However, if you open the raw version of `small_movies.csv`, you are more likely to see something like this. It's the same data, but is sometimes a little harder to read:\n",
+    "```\n",
+    "title,year,duration,genres,rating,directors,cast\n",
+    "tt3104988,2018,120,\"Comedy, Drama, Romance\",6.9,nm0160840,\"nm2090422, nm6525901, nm0000706, nm2110418, nm0523734\"\n",
+    "tt4846340,2016,127,\"Biography, Drama, History\",7.8,nm0577647,\"nm0378245, nm0818055, nm1847117\"\n",
+    "```\n",
+    "\n",
+    "The `title`, `directors`, and `cast` members are represented by their unique *IMDb ID* instead of their actual *names*. Now would be a good time to open `small_mapping.csv` to observe the data stored there. It should look like this:\n",
+    "\n",
+    "|         |           |\n",
+    "| --------|-----------|\n",
+    "|tt3104988|Crazy Rich Asians|\n",
+    "|nm0160840|Jon M. Chu|\n",
+    "|nm2090422|Constance Wu|\n",
+    "|nm6525901|Henry Golding|\n",
+    "|nm0000706|Michelle Yeoh|\n",
+    "|nm2110418|Gemma Chan|\n",
+    "|nm0523734|Lisa Lu|\n",
+    "|tt4846340|Hidden Figures|\n",
+    "|nm0577647|Theodore Melfi|\n",
+    "|nm0378245|Taraji P. Henson|\n",
+    "|nm0818055|Octavia Spencer|\n",
+    "|nm1847117|Janelle Monáe|\n",
+    "\n",
+    "Note that this file does **not** have a header. This file maps the *IMDB IDs* to the actual *names* (so for example, the *name* of the movie with the *IMDB ID* of *tt3104988* is *Crazy Rich Asians*)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3445e68",
+   "metadata": {},
+   "source": [
+    "## Segment 2: Loading data from csv file\n",
+    "\n",
+    "In this segment, you will learn to parse the data in `small_movies.csv` and `small_mapping.csv`, and convert them into useful data structures.\n",
+    "\n",
+    "However, before we do any of that, **open `small_movies.csv` and `small_mapping.csv` with Excel or some other spreadsheet viewer** and look at the format in which the data is stored. Inspecting this data will be extremely useful when you try to read it in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78b72587",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# place all your import statements in this cell if you need to import any more modules for this project\n",
+    "\n",
+    "# we have imported the csv module for you\n",
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "226de883",
+   "metadata": {},
+   "source": [
+    "### Task 2.1: Process `small_movies.csv`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e382491b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the 'process_csv' function from lab-p6 or lab-p7 here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8be61a5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# read data from \"small_movies.csv\"\n",
+    "file_data = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8a7fb48",
+   "metadata": {},
+   "source": [
+    "**Question 1:** What is the **header** of the file `small_movies.csv`?\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1390d83",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# extract the header into csv_header variable\n",
+    "csv_header = ...\n",
+    "\n",
+    "csv_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb746fc5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb97b353",
+   "metadata": {},
+   "source": [
+    "**Question 2:** What is the **data** (without the header) in the file `small_movies.csv`?\n",
+    "\n",
+    "Your output **must** be a **list** of **lists**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95b5b01c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# extract just the data rows into csv_rows variable\n",
+    "csv_rows = ...\n",
+    "\n",
+    "csv_rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca829ed0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b15ed4bf",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Convert your `small_mapping` to a `dict`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a09e987",
+   "metadata": {},
+   "source": [
+    "**Question 3:** What is the **data** in the file `small_mapping.csv`?\n",
+    "\n",
+    "Your output **must** be a **list** of **lists**. There is no header in `small_mapping.csv`, so you should **not** slice off the first row here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea5f35ce",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# use process_csv to read `small_mapping.csv` into a list of lists data structure\n",
+    "mapping_rows = ...\n",
+    "\n",
+    "mapping_rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6739a07",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "245b38e3",
+   "metadata": {},
+   "source": [
+    "Currently `mapping_rows` is a **list** of **lists**.  To make it more useful, let us convert it to a **dict** with the *ID* as the **key** and the *name* as the **value**, like this:\n",
+    "\n",
+    "```python\n",
+    "{'tt3104988':'Crazy Rich Asians',\n",
+    "'nm0160840': 'Jon M. Chu',\n",
+    "'nm2090422': 'Constance Wu',\n",
+    "'nm6525901': 'Henry Golding',\n",
+    "'nm0000706': 'Michelle Yeoh',\n",
+    "'nm2110418': 'Gemma Chan',\n",
+    "'nm0523734': 'Lisa Lu',\n",
+    "'tt4846340': 'Hidden Figures',\n",
+    "'nm0577647': 'Theodore Melfi',\n",
+    "'nm0378245': 'Taraji P. Henson',\n",
+    "'nm0818055': 'Octavia Spencer',\n",
+    "'nm1847117': 'Janelle Monáe'}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f401c03e",
+   "metadata": {},
+   "source": [
+    "**Question 4:** Display the **data** in the file `small_mapping.csv` as a **dictionary**.\n",
+    "\n",
+    "Your output **must** be a **dictionary**.\n",
+    "\n",
+    "It is acceptable for you to *hardcode* **column indices** here. This is because this csv file does not have a header, and it is **implicit** that the first string is the ID and that the second string is the name associated with the ID. For csv files which have headers, you should **never** hardcode **column indices**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9ab7fc6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "mapping_dict = ... # initialize an empty dictionary into the variable mapping_dict\n",
+    "# TODO: iterate over each row of the small_mapping dataset\n",
+    "for ... in ...:\n",
+    "    pass # replace with your code\n",
+    "    # TODO: insert the correct key-value pair into mapping_dict\n",
+    "\n",
+    "mapping_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f7b2968",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a0ec48f",
+   "metadata": {},
+   "source": [
+    "**Question 5:** What is the **value** associated with the key *nm0160840*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d07a24c8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "nm0160840_value = mapping_dict[\"nm0160840\"]\n",
+    "\n",
+    "nm0160840_value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ceebb130",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63a7c668",
+   "metadata": {},
+   "source": [
+    "**Question 6:** What is the **value** associated with the key *tt4846340*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7797058",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "tt4846340_value = ...\n",
+    "\n",
+    "tt4846340_value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c057e52e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a371a93",
+   "metadata": {},
+   "source": [
+    "### Task 2.3: Convert your `small_movies` into a `list` of `dicts`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57def164",
+   "metadata": {},
+   "source": [
+    "### Task 2.3.1: Convert a list of lists to a list of dictionaries\n",
+    "\n",
+    "Now, let's go back to `small_movies.csv`. Your variable `csv_rows` (defined above in q2) should look like this:\n",
+    "\n",
+    "```python\n",
+    "[['tt3104988',\n",
+    "  '2018',\n",
+    "  '120',\n",
+    "  'Comedy, Drama, Romance',\n",
+    "  '6.9',\n",
+    "  'nm0160840',\n",
+    "  'nm2090422, nm6525901, nm0000706, nm2110418, nm0523734'],\n",
+    " ['tt4846340',\n",
+    "  '2016',\n",
+    "  '127',\n",
+    "  'Biography, Drama, History',\n",
+    "  '7.8',\n",
+    "  'nm0577647',\n",
+    "  'nm0378245, nm0818055, nm1847117']]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1d1aaae",
+   "metadata": {},
+   "source": [
+    "It's a list of lists without its header. To make it easier to access data, let us convert it to a **list** of **dictionaries**. The data structure should look like:\n",
+    "\n",
+    "```python\n",
+    "[{'title': 'tt3104988',\n",
+    "  'year': '2018',\n",
+    "  'duration': '120',\n",
+    "  'genres': 'Comedy, Drama, Romance',\n",
+    "  'rating': '6.9',\n",
+    "  'directors': 'nm0160840',\n",
+    "  'cast': 'nm2090422, nm6525901, nm0000706, nm2110418, nm0523734'},\n",
+    " {'title': 'tt4846340',\n",
+    "  'year': '2016',\n",
+    "  'duration': '127',\n",
+    "  'genres': 'Biography, Drama, History',\n",
+    "  'rating': '7.8',\n",
+    "  'directors': 'nm0577647',\n",
+    "  'cast': 'nm0378245, nm0818055, nm1847117'}]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efc2ff33",
+   "metadata": {},
+   "source": [
+    "**Question 7.1:** Display the **first** movie in the file `small_movies.csv` as a **dictionary**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e28a4812",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "first_movie = {} # initialize an empty dictionary\n",
+    "first_movie[\"title\"] = csv_rows[0][csv_header.index(...)] # extract the title of the movie\n",
+    "first_movie[\"year\"] = ...\n",
+    "# TODO: add the other columns to first_movie\n",
+    "\n",
+    "first_movie"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ceae82e3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e85e218e",
+   "metadata": {},
+   "source": [
+    "**Question 7.2:** Display the **data** in the file `small_movies.csv` as a **list** of **dictionaries**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6636196b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "raw_movies_list = [] # use this empty list to append your dictionary\n",
+    "# TODO: loop through all the rows of csv_rows\n",
+    "# TODO: create a dictionary similar to the one in q7.1 for each movie\n",
+    "# TODO: add each movie dictionary to raw_movies_list\n",
+    "\n",
+    "raw_movies_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bd7c457",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0de12d53",
+   "metadata": {},
+   "source": [
+    "**Question 8:** What is the `title` *ID* of the **first** movie in your list?\n",
+    "\n",
+    "Your output **must** be a **string**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef559a92",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "first_movie_title = raw_movies_list[0]['title']\n",
+    "\n",
+    "first_movie_title"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0743627",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb12052c",
+   "metadata": {},
+   "source": [
+    "**Question 9:** What is the `duration` of the **second** movie in your list?\n",
+    "\n",
+    "Your output **must** be a **string**. You **must** answer this question by querying the value from the `raw_movies_list` data structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de7b8414",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'second_movie_duration', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8214e789",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a0cb932",
+   "metadata": {},
+   "source": [
+    "**Question 10:** What are the `genres` of the **second** movie in your list?\n",
+    "\n",
+    "Your output **must** be a **string**. You **must** answer this question by querying the value from the `raw_movies_list` data structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3838e0c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'second_movie_genres', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23fdb92e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bcb68bc",
+   "metadata": {},
+   "source": [
+    "### Task 2.3.2: Convert the `int` and `float` values to the correct type\n",
+    "\n",
+    "Did you notice that currently all the values in the dictionaries are **strings**? We should convert them into correct types. In particular, the `year` and `duration` should be data type **int** and the `rating` should be a **float**. \n",
+    "\n",
+    "After converting the **int** and **float** values to the right types, your list of dictionaries should look like: \n",
+    "\n",
+    "```python\n",
+    "[{'title': 'tt3104988',\n",
+    "  'year': 2018,\n",
+    "  'duration': 120,\n",
+    "  'genres': 'Comedy, Drama, Romance',\n",
+    "  'rating': 6.9,\n",
+    "  'directors': 'nm0160840',\n",
+    "  'cast': 'nm2090422, nm6525901, nm0000706, nm2110418, nm0523734'},\n",
+    " {'title': 'tt4846340',\n",
+    "  'year': 2016,\n",
+    "  'duration': 127,\n",
+    "  'genres': 'Biography, Drama, History',\n",
+    "  'rating': 7.8,\n",
+    "  'directors': 'nm0577647',\n",
+    "  'cast': 'nm0378245, nm0818055, nm1847117'}]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9429f732",
+   "metadata": {},
+   "source": [
+    "**Question 11:** Display the **data** in the file `small_movies.csv` as a **list** of **dictionaries**.\n",
+    "\n",
+    "The `year` and `duration` values **must** be of data type **int** and the `rating` value **must** be a **float**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "799db204",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "mostly_raw_movies_list = [] # use this empty list to append your dictionary\n",
+    "for movie in raw_movies_list: # loop (directly) through movies in raw_movies_list\n",
+    "    new_movie = ... # create an empty dictionary to insert values for each movie\n",
+    "    new_movie['title'] = ... # extract the title of movie \n",
+    "    new_movie['year'] = ... # convert the year of movie into an int\n",
+    "    # TODO: add the other columns (with the required data types) to new_movie\n",
+    "    # TODO: add new_movie to mostly_raw_movies_list\n",
+    "\n",
+    "mostly_raw_movies_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8c679bb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8fa69eb",
+   "metadata": {},
+   "source": [
+    "**Question 12:** What is the `type` of the `duration` of the **first** movie in your list?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c7d7f8f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "first_movie_duration_type = type(mostly_raw_movies_list[0][\"duration\"])\n",
+    "\n",
+    "first_movie_duration_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55b0faff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccd65ef8",
+   "metadata": {},
+   "source": [
+    "**Question 12:** What is the `type` of the `rating` of the **first** movie in your list?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d638a098",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "first_movie_rating_type = type(mostly_raw_movies_list[0][\"rating\"])\n",
+    "\n",
+    "first_movie_rating_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1768441",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aab3ecd",
+   "metadata": {},
+   "source": [
+    "### Task 2.3.3: Convert the `genres`, `directors`, and `cast` to list of strings\n",
+    "\n",
+    "Run the next cell and observe its output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cfa72ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# these are the 'genres' of the first movie\n",
+    "mostly_raw_movies_list[0][\"genres\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23eeb901",
+   "metadata": {},
+   "source": [
+    "Notice that the `genres` are stored as a *single* **string**. It would be much more useful to store this value as **list** of *three different* **strings**, with each **string** representing a *single* **string**: `['Comedy', 'Drama', 'Romance']`. Unfortunately, the CSV file format cannot represent **lists**, so **lists** of **strings** are often represented as a *single* **string** with the values separated by a comma. So, we will have to convert the **string** into a **list** of **strings** ourselves.\n",
+    "\n",
+    "In the `small_movies.csv` and `movies.csv` datasets, the `directors` and `cast` are similarly stored as a *single* **string** with the values separated by a comma(`, `). We are now going to convert the value corresponding to the keys `genres`, `casts`, and `directors` to a **list** of **strings**. The output **must** be a **list** of **dictionaries** in the following format:\n",
+    "\n",
+    "```python\n",
+    "   {\n",
+    "        'title': <title-id>,\n",
+    "        'year': <the year as an integer>,\n",
+    "        'duration': <the duration as an integer>,\n",
+    "        'genres': [<genre1>, <genre2>, ...],\n",
+    "        'rating': <the rating as a float>,\n",
+    "        'directors': [<director-id1>, <director-id2>, ...],\n",
+    "        'cast': [<actor-id1>, <actor-id2>, ....]\n",
+    "    }\n",
+    "```\n",
+    "\n",
+    "After converting the strings to list of strings, the **list** of **dictionaries** should look like: \n",
+    "\n",
+    "```python\n",
+    "    [{'title': 'tt3104988',\n",
+    "      'year': 2018,\n",
+    "      'duration': 120,\n",
+    "      'genres': ['Comedy', 'Drama', 'Romance'],\n",
+    "      'rating': 6.9,\n",
+    "      'directors': ['nm0160840'],\n",
+    "      'cast': ['nm2090422', 'nm6525901', 'nm0000706', 'nm2110418', 'nm0523734']},\n",
+    "     {'title': 'tt4846340',\n",
+    "      'year': 2016,\n",
+    "      'duration': 127,\n",
+    "      'genres': ['Biography', 'Drama', 'History'],\n",
+    "      'rating': 7.8,\n",
+    "      'directors': ['nm0577647'],\n",
+    "      'cast': ['nm0378245', 'nm0818055', 'nm1847117']}]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48dbda0d",
+   "metadata": {},
+   "source": [
+    "**Question 14:** Display the **data** in the file `small_movies.csv` as a **list** of **dictionaries**.\n",
+    "\n",
+    "The `genres`, `directors`, and `cast` values **must** be **lists** of **strings**.\n",
+    "\n",
+    "**Hint:** Recall that there is a **string method** that enables you to perform this. If you don't know where to start, please review the [lecture slides](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-14) from the October 10 lecture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac358b5a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "semi_raw_movies_list = [] # use this empty list to append your dictionary\n",
+    "for movie in mostly_raw_movies_list: # loop through movies in mostly_raw_movies_list\n",
+    "    new_movie = ... # create an empty dictionary to insert values for each movie\n",
+    "    new_movie['title'] = ... # extract the title of movie \n",
+    "    new_movie['year'] = ... # extract the year of movie\n",
+    "    new_movie['genres'] = ... # split the genres into a list of strings\n",
+    "    # TODO: add the other columns (with the required data types) to new_movie\n",
+    "    # TODO: add new_movie to semi_raw_movies_list\n",
+    "\n",
+    "semi_raw_movies_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f282dcfe",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76b20c2e",
+   "metadata": {},
+   "source": [
+    "**Question 15:** What are the `genres` of the **second** movie in your list?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f0ec317",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "second_movie_genres_list = semi_raw_movies_list[1][\"genres\"]\n",
+    "\n",
+    "second_movie_genres_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a81a81c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a88bfe",
+   "metadata": {},
+   "source": [
+    "**Question 16:** How **many** `cast` members are there in the **second** movie?\n",
+    "\n",
+    "You **must** answer this question by querying the value from the `semi_raw_movies_list` data structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aefb94e9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "second_movie_num_cast = ...\n",
+    "\n",
+    "second_movie_num_cast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49711bec",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a3df880",
+   "metadata": {},
+   "source": [
+    "# Segment 3: Mapping IDs to Actual Names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "140e62ba",
+   "metadata": {},
+   "source": [
+    "You may have noticed that `title`, `directors`, and `cast` are represented by *IDs* rather than actual *names*. To make our data more intuitive, we next need to **convert** these *IDs* to actual *names*. The output **must** be a **list** of **dictionaries** in the following format:\n",
+    "\n",
+    "```python\n",
+    "   {\n",
+    "        'title': \"the movie name\",\n",
+    "        'year': <the year as an integer>,\n",
+    "        'duration': <the duration as an integer>,\n",
+    "        'genres': [<genre1>, <genre2>, ...],\n",
+    "        'rating': <the rating as a float>,\n",
+    "        'directors': [\"director-name1\", \"director-name2\", ...],\n",
+    "        'cast': [\"actor-name1\", \"actor-name2\", ....]\n",
+    "    }\n",
+    "```\n",
+    "\n",
+    "After converting the IDs to actual names, your **list** of **dictionaries** should look like:\n",
+    "\n",
+    "```python\n",
+    "        [{'title': 'Crazy Rich Asians',\n",
+    "      'year': 2018,\n",
+    "      'duration': 120,\n",
+    "      'genres': ['Comedy', 'Drama', 'Romance'],\n",
+    "      'rating': 6.9,\n",
+    "      'directors': ['Jon M. Chu'],\n",
+    "      'cast': ['Constance Wu',\n",
+    "       'Henry Golding',\n",
+    "       'Michelle Yeoh',\n",
+    "       'Gemma Chan',\n",
+    "       'Lisa Lu']},\n",
+    "     {'title': 'Hidden Figures',\n",
+    "      'year': 2016,\n",
+    "      'duration': 127,\n",
+    "      'genres': ['Biography', 'Drama', 'History'],\n",
+    "      'rating': 7.8,\n",
+    "      'directors': ['Theodore Melfi'],\n",
+    "      'cast': ['Taraji P. Henson', 'Octavia Spencer', 'Janelle Monáe']}]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b77ad5e",
+   "metadata": {},
+   "source": [
+    "### Task 3.1: Find the Actual Names\n",
+    "\n",
+    "Run the cell below and try to explain its output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03aa8cf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "title_id = semi_raw_movies_list[0][\"title\"] # extract the title of the first movie\n",
+    "title = mapping_dict[title_id] # recall the dictionary mapping_dict from q4\n",
+    "print(title_id + \": \" + title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cb9b8e9",
+   "metadata": {},
+   "source": [
+    "**Question 17.1:** List the `title` of the **second** movie in your list.\n",
+    "\n",
+    "Your output **must** be a **string** of the *name* and **not** the *ID*. You **must** answer this question by querying values from the `semi_raw_movies_list` and `mapping_dict` data structures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9c00e6e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'title_second', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83ddfbf7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fdef441",
+   "metadata": {},
+   "source": [
+    "**Question 17.2:** List the **names** of the `cast` of the **first** movie in your list.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "111cdc20",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cast_names = ... # create an empty list to store the names of the cast members\n",
+    "first_movie = semi_raw_movies_list[...] # extract the dictionary of the first movie\n",
+    "# TODO: iterate over the IDs of the cast members of first_movie\n",
+    "    # take each cast member's ID and lookup the value from the mapping_dict dict\n",
+    "    # add each cast member's name to the list 'cast_names'\n",
+    "\n",
+    "cast_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41e7ade7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0820d836",
+   "metadata": {},
+   "source": [
+    "**Question 17.3:** List the **names** of all the `directors` of **both** movies in your list.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abcd3dd8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'directors', then display it\n",
+    "# create an empty list to store the names of the directors\n",
+    "# TODO: iterate over each movie in semi_raw_movies_list\n",
+    "    # TODO: for each movie, iterate over the IDs of the directors\n",
+    "        # take each director's ID and lookup the value from the mapping_dict dict\n",
+    "        # add each director's name to the list 'directors'\n",
+    "        \n",
+    "# display the variable 'directors'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cd1570f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d08760",
+   "metadata": {},
+   "source": [
+    "### Task 3.2: Convert to Actual Names\n",
+    "\n",
+    "Use your `mapping_dict` and `semi_raw_movies_list` to finish this task.\n",
+    "There are three columns (`title`, `directors`, and `cast`) which need to be converted.\n",
+    "\n",
+    "We will use convert these columns *incrementally*. First, we will convert the `title` values to be the **names** instead of the **IDs**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "337f5e44",
+   "metadata": {},
+   "source": [
+    "**Question 18.1:** Display the **data** in the file `small_movies.csv` as a **list** of **dictionaries**.\n",
+    "\n",
+    "The `title` values **must** be **actual names** instead of **IDs**. The `directors` and `cast` values **must** be the **IDs**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cda9910",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "movies_list_v1 = [] # use this empty list to append your dictionary\n",
+    "# TODO: iterate over the movies in semi_raw_movies_list\n",
+    "    # TODO: create a new empty dictionary for each movie\n",
+    "    # TODO: find the actual title from the title ID and add to the new dictionary\n",
+    "    # TODO: add the other columns to the new dictionary\n",
+    "    # TODO: add the new dictionary to movies_list_v1\n",
+    "\n",
+    "movies_list_v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8b2977b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e9506af",
+   "metadata": {},
+   "source": [
+    " We will now convert the `directors` values to be the **names** instead of the **IDs**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "707a6d3d",
+   "metadata": {},
+   "source": [
+    "**Question 18.2:** Display the **data** in the file `small_movies.csv` as a **list** of **dictionaries**.\n",
+    "\n",
+    "The `title` and `directors` values **must** be **actual names** instead of **IDs**. The `cast` values **must** be the **IDs**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41ee1196",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "movies_list_v2 = [] # use this empty list to append your dictionary\n",
+    "# TODO: copy/paste your loop for q18.1 where you defined movies_list_v1\n",
+    "# TODO: inside the loop, for each movie define a new list for the names of the directors\n",
+    "# TODO: loop through the director IDs of the movie and add their names to list\n",
+    "# TODO: assign the value of the key 'directors' to be this new list\n",
+    "# TODO: add the new dictionary to movies_list_v2\n",
+    "\n",
+    "movies_list_v2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "095291a2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "551530c5",
+   "metadata": {},
+   "source": [
+    " Finally, we will now convert the `cast` values to be the **names** instead of the **IDs**, to finish our definition of `movies_list`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a642d29e",
+   "metadata": {},
+   "source": [
+    "**Question 18.3:** Display the **data** in the file `small_movies.csv` as a **list** of **dictionaries**.\n",
+    "\n",
+    "The `title`, `directors` and `cast` values **must** be **actual names** instead of **IDs**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57a690ac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "movies_list = [] # use this empty list to append your dictionary\n",
+    "# TODO: copy/paste your loop for q18.1 where you defined movies_list_v1\n",
+    "# TODO: replace the IDs of the cast members with their names\n",
+    "\n",
+    "movies_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9d58f35",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51acbfca",
+   "metadata": {},
+   "source": [
+    "We are now ready to test your data structure `movies_list`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d295d18",
+   "metadata": {},
+   "source": [
+    "**Question 19:** What is the movie `title` of the **first** movie in your list?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1cd08ec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "first_movie_title = movies_list[0][\"title\"]\n",
+    "\n",
+    "first_movie_title"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc07a8ce",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40eee857",
+   "metadata": {},
+   "source": [
+    "**Question 20:** Who are the `directors` of the **second movie** in your list?\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**. You **must** answer this question by querying the value from the `movies_list` data structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee06954e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'second_movie_directors', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4c94a33",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f6ff2d4",
+   "metadata": {},
+   "source": [
+    "## Great work! You are now ready to start [p8](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p8)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1\", csv_header)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10\", second_movie_genres)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q11\", mostly_raw_movies_list)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q12\", first_movie_duration_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q13\", first_movie_rating_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q14\", semi_raw_movies_list)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q15\", second_movie_genres_list)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q16\", second_movie_num_cast)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17-1": {
+     "name": "q17-1",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17-1\", title_second)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17-2": {
+     "name": "q17-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17-2\", cast_names)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17-3": {
+     "name": "q17-3",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17-3\", directors)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18-1": {
+     "name": "q18-1",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q18-1\", movies_list_v1)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18-2": {
+     "name": "q18-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q18-2\", movies_list_v2)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18-3": {
+     "name": "q18-3",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q18-3\", movies_list)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q19\", first_movie_title)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q2\", csv_rows)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q20\", second_movie_directors)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3\", mapping_rows)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q4\", mapping_dict)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q5\", nm0160840_value)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q6\", tt4846340_value)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7-1": {
+     "name": "q7-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7-1\", first_movie)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7-2": {
+     "name": "q7-2",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7-2\", raw_movies_list)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q8\", first_movie_title)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q9\", second_movie_duration)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p8/practice.ipynb b/lab-p8/practice.ipynb
index eac47080791a2764c516d94c4860cf4168349a66..e4f42163c6ae072b0352bfa2a727964754a19eed 100644
--- a/lab-p8/practice.ipynb
+++ b/lab-p8/practice.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "80375cd4",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/lab-p9/.ipynb_checkpoints/practice-checkpoint.ipynb b/lab-p9/.ipynb_checkpoints/practice-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c32fa70f29ba02912f14cea015f3102838410bd0
--- /dev/null
+++ b/lab-p9/.ipynb_checkpoints/practice-checkpoint.ipynb
@@ -0,0 +1,1838 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a63c3861",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d3547b2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"practice.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e845c38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import practice_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0450f490",
+   "metadata": {},
+   "source": [
+    "# Lab-P9:  Plotting and Sorting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4270f044",
+   "metadata": {},
+   "source": [
+    "# Learning Objectives:\n",
+    "\n",
+    "In this lab, you will practice how to...\n",
+    "* Create simple plots from dictionaries\n",
+    "* Sort dictionaries by keys and values\n",
+    "* Use bucketizing to obtain useful data structures\n",
+    "* Use comprehensions to create lists and dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4bf8a1b",
+   "metadata": {},
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: p8 and p9 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partnered up with someone for p8, you have to work on lab-p9 and p9 with the **same partner**. You must acknowledge to the lab TA to receive lab attendance credit.\n",
+    "\n",
+    "You may do these lab exercises with only with your project partner; you are not allowed to start working on lab-p9 with one person, then do the project with a different partner.  Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f22/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "841de06b",
+   "metadata": {},
+   "source": [
+    "## Introduction:\n",
+    "\n",
+    "In lab-p8 and p8, you learnt to parse the `mapping.csv` and `movies.csv` datasets. In p9, you will be analyzing the movies dataset using the data structures you created in p8. In lab-p9, you will create some useful helper functions that will be needed in p9, and you will also learn to create bar plots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f3fd8c4",
+   "metadata": {},
+   "source": [
+    "In order to create bar plots, you will use two new packages: `matplotlib` and `pandas`. Import them below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08cd500c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53efa067",
+   "metadata": {},
+   "source": [
+    "## Segment 2: Plotting Dictionaries\n",
+    "\n",
+    "### Task 2.1: Understand how `plot_dict` works\n",
+    "\n",
+    "**Creating a dictionary** will often be the first step towards creating a bar plot in Python. In particular, each dictionary **key** might correspond to a **category** (along the *x-axis*), and each **value** might correspond to an **amount** (along the *y-axis*). To setup plotting in Python, run the following cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e39d09a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_dict(d, label=\"Please Label Me!\"):\n",
+    "    \"\"\"plot_dict(d, label) creates a bar plot using the \n",
+    "    dictionary 'd' and labels the y-axis as 'label'\"\"\"\n",
+    "    ax = pandas.Series(d).sort_index().plot.bar(color=\"black\", fontsize=16)\n",
+    "    ax.set_ylabel(label, fontsize=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17cd09e8",
+   "metadata": {},
+   "source": [
+    "Let's try creating a simple dictionary and using it to create a bar plot with the `plot_dict` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb9ae52c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a dictionary mapping different players to their goals scored\n",
+    "goals = {\"Ada Lovelace\": 5, \"Rose Lavelle\": 22, \"Marta Vieira da Silva\": 113}\n",
+    "goals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98fae914",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a bar plot from the data in the dictionary 'goals'\n",
+    "plot_dict(goals)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c4b75da",
+   "metadata": {},
+   "source": [
+    "Since p9 is all about the movies dataset, we will now work on a **list** of movie **dictionaries**, similar to the one you created in p8. If you haven't completed p8, don't worry! The variable has been defined below and you do not need to copy any p8 code over. Run the cell below to define the variable `movies_data`. Note that this is a **list** of **dictionaries** in the same format the **list** `movies` that you defined in p8 (and will work with, in p9).\n",
+    "\n",
+    "**Important**: We define the variable directly in order to save time and simplify the lab exercises. Do NOT hardcode values of variables storing the dataset in p9. Instead use the appropriate functions to load the data from the corresponding CSV files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db587ca9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "movies_data = [{'title': 'The Trees of the East',\n",
+    "  'year': 2018,\n",
+    "  'duration': 71,\n",
+    "  'genres': ['Thriller'],\n",
+    "  'rating': 9.8,\n",
+    "  'directors': ['Mike Ellwood'],\n",
+    "  'cast': ['Pattyeffinmayo', 'Parlay Pass', 'Sia Poorak', 'Kandisha']},\n",
+    "{'title': 'Avengers: Infinity War',\n",
+    "  'year': 2018,\n",
+    "  'duration': 149,\n",
+    "  'genres': ['Action', 'Adventure', 'Sci-Fi'],\n",
+    "  'rating': 8.4,\n",
+    "  'directors': ['Anthony Russo', 'Joe Russo'],\n",
+    "  'cast': ['Robert Downey Jr.', 'Chris Hemsworth', 'Mark Ruffalo', 'Chris Evans']},\n",
+    "{'title': 'Zodiac',\n",
+    "  'year': 2007,\n",
+    "  'duration': 157,\n",
+    "  'genres': ['Crime', 'Drama', 'Mystery'],\n",
+    "  'rating': 7.7,\n",
+    "  'directors': ['David Fincher'],\n",
+    "  'cast': ['Jake Gyllenhaal', 'Robert Downey Jr.', 'Mark Ruffalo', 'Anthony Edwards']},\n",
+    "{'title': 'Top Gun: Maverick',\n",
+    "  'year': 2022,\n",
+    "  'duration': 130,\n",
+    "  'genres': ['Action', 'Drama'],\n",
+    "  'rating': 8.4,\n",
+    "  'directors': ['Joseph Kosinski'],\n",
+    "  'cast': ['Tom Cruise', 'Jennifer Connelly', 'Miles Teller', 'Val Kilmer']},\n",
+    "{'title': 'Space Jam: A New Legacy',\n",
+    "  'year': 2021,\n",
+    "  'duration': 115,\n",
+    "  'genres': ['Adventure', 'Animation', 'Comedy'],\n",
+    "  'rating': 4.5,\n",
+    "  'directors': ['Malcolm D. Lee'],\n",
+    "  'cast': ['LeBron James', 'Don Cheadle', 'Cedric Joe', 'Khris Davis']},\n",
+    "{'title': 'The Big Short',\n",
+    "  'year': 2015,\n",
+    "  'duration': 130,\n",
+    "  'genres': ['Biography', 'Comedy', 'Drama'],\n",
+    "  'rating': 7.8,\n",
+    "  'directors': ['Adam McKay'],\n",
+    "  'cast': ['Christian Bale', 'Steve Carell', 'Ryan Gosling', 'Brad Pitt']}]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86911132",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Map necessary details into a dictionary\n",
+    "\n",
+    "As you can see, `movies_data` is a **list** of **dictionaries** where each **dictionary** stores information on a particular movie.\n",
+    "\n",
+    "Using the `plot_dict` function, we will now create a **bar plot** that compares the `duration` of each movie in `movies_data`. The *x-axis* will contain the `title` of each movie and your *y-axis* will be the `duration`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2d515ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the dictionary first\n",
+    "plot_movies_data = {}\n",
+    "for movie in movies_data:\n",
+    "    plot_movies_data[movie['title']] = movie['duration']\n",
+    "    \n",
+    "plot_movies_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f25c9a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then plot the dictionary\n",
+    "plot_dict(plot_movies_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a883633d",
+   "metadata": {},
+   "source": [
+    "Notice that in this plot, the *y-axis* is missing a label; that can be specified with the **optional** second argument to `plot_dict`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf7aa633",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# provide the y-axis label as the second argument\n",
+    "plot_dict(plot_movies_data, 'Duration')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdb7f12a",
+   "metadata": {},
+   "source": [
+    "You are now ready to create your own bar plots in p9!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ed0bd84",
+   "metadata": {},
+   "source": [
+    "## Segment 3: Creating a Dictionary with Custom Keys\n",
+    "\n",
+    "### Task 3.1: Convert years to their decades\n",
+    "\n",
+    "### Function 1: `year_to_decade(year)`\n",
+    "\n",
+    "First, we are going to write the `year_to_decade` function. Given a year, this function returns a string that represents the decade that year belongs to. Here are some sample calls and the expected output:\n",
+    "\n",
+    "- `year_to_decade(2003)` **must** return `'2001 to 2010'`\n",
+    "- `year_to_decade(2000)` **must** return `'1991 to 2000'`\n",
+    "\n",
+    "Complete the code below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca83be17",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "def year_to_decade(year):\n",
+    "    if year % 10 == 0:\n",
+    "        decade = str(...) + ' to ' + str(...)\n",
+    "    else:\n",
+    "        # TODO: first find the year in which the decade starts\n",
+    "        #       when year % 10 == 0\n",
+    "        # TODO: define the variable 'decade'\n",
+    "    return decade"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb0e93e9",
+   "metadata": {},
+   "source": [
+    "**Question 1**: Which **decade** does the `year` *2000* belong to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89d3542b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'decade_2000', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "039e3af4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3dd3fbb0",
+   "metadata": {},
+   "source": [
+    "**Question 2**: Which **decade** does the `year` *1843* belong to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e2fe222",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'decade_1843', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53d90b79",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4129af8e",
+   "metadata": {},
+   "source": [
+    "### Task 3.2: Create new dictionaries with custom keys and values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ab0400c",
+   "metadata": {},
+   "source": [
+    "**Question 3**: Create a new **dictionary** that has the `title` of each movie in `movies_data` as a **key** and the **decade** it was released in as the corresponding **value**.\n",
+    "\n",
+    "You **must** use the `year_to_decade` function to create this **dictionary**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7253c88d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'movies_with_decade', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec6bbbd4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28348b71",
+   "metadata": {},
+   "source": [
+    "**Question 4**: Calculate the **average** `rating` of movies in the **decade** *2011 to 2020*.\n",
+    "\n",
+    "**Hint:** You can loop through `movies_data` for each movie and use `movies_with_decade` to check which **decade** each movie is from."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b16f9f05",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'avg_rating_2011_2020', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e71aaf77",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5719558b",
+   "metadata": {},
+   "source": [
+    "## Segment 4: Sorting Dictionaries\n",
+    "\n",
+    "It would be useful if we could somehow **sort** **dictionaries**. Recall however, that Python does not distinguish between two dictionaries which have keys stored in different orders. To test this, run the following cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e1527bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# two dictionaries with the same key/value pairs in different orders\n",
+    "dict_a = {'C': 'S', 2: 20}\n",
+    "dict_b = {2: 20, 'C': 'S'}\n",
+    "\n",
+    "# test if dict_a and dict_b store the same value\n",
+    "dict_a == dict_b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2560d22",
+   "metadata": {},
+   "source": [
+    "Therefore, if we want to **sort** the data stored in a **dictionary**, you must *first* typecast it into some other data type, which has an order. One way to do this is to **convert** it into a **list** of **tuples**, and then **sort** the **list**. The **dict.items()** method will be useful for us here.\n",
+    "\n",
+    "In Task 2.2, we created the variable `plot_movies_data`. It looks like this:\n",
+    "\n",
+    "```python\n",
+    "{'The Trees of the East': 71,\n",
+    " 'Avengers: Infinity War': 149,\n",
+    " 'Zodiac': 157,\n",
+    " 'Top Gun: Maverick': 130,\n",
+    " 'Space Jam: A New Legacy': 115,\n",
+    " 'The Big Short': 130}\n",
+    "```\n",
+    "\n",
+    "Here's what `list(plot_movies_data.items())` looks like:\n",
+    "```python\n",
+    "[('The Trees of the East', 71),\n",
+    " ('Avengers: Infinity War', 149),\n",
+    " ('Zodiac', 157),\n",
+    " ('Top Gun: Maverick', 130),\n",
+    " ('Space Jam: A New Legacy', 115),\n",
+    " ('The Big Short', 130)]\n",
+    "```\n",
+    "This **list** of **tuples** can now be sorted."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "322c8479",
+   "metadata": {},
+   "source": [
+    "### Task 4.1: Sort `plot_movies_data` by title\n",
+    "\n",
+    "We will start simple by **sorting** the **dictionary** by the `title`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b1b32e2",
+   "metadata": {},
+   "source": [
+    "**Question 5**: Sort the `plot_movies_data` **dictionary** in alphabetical order of the `title`.\n",
+    "\n",
+    "**Hint:** Note that in each **tuple**, the `title` appears at index *0* followed by the `duration` at index *1*. By default, when you **sort** a **list** of **tuples**, you will **sort** the **tuples** by the values at index *0*. So, you can answer this question by just **sorting** the **list** of **items** in `plot_movies_data`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c6c4ad1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "sorted_plot_movies_data = sorted(...)\n",
+    "\n",
+    "sorted_plot_movies_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8b30059",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04ca8ef1",
+   "metadata": {},
+   "source": [
+    "### Task 4.2: Sort `plot_movies_data` by `duration`\n",
+    "\n",
+    "Note that by default, the **tuples** were sorted by the value at index *0*, which was also the **key** of the **dictionary**. This time, let us try to **sort** the **dictionary** by its **values**. i.e. we want to sort the movies in **ascending order** of their `durations`.\n",
+    "\n",
+    "To **sort** by **values**, we need to use a **keyword argument** to identify a **function reference**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "242d284a",
+   "metadata": {},
+   "source": [
+    "**Question 6**: Sort `plot_movies_data` by duration (from shortest to longest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de582042",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this for you\n",
+    "\n",
+    "def get_value(item):\n",
+    "    '''get_value(item) takes in a tuple item = (key, value)\n",
+    "    and returns the value which is at index 1 of the tuple'''\n",
+    "    return item[1]\n",
+    "\n",
+    "# the elemtents of the list list(plot_movies_data.items()) are\n",
+    "# sorted by the value returned by the function get_value\n",
+    "sorted_plot_movies_data_duration = sorted(list(plot_movies_data.items()), key=get_value)\n",
+    "\n",
+    "sorted_plot_movies_data_duration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6831043",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ebd5d00",
+   "metadata": {},
+   "source": [
+    "**Question 7**: What is the `title` of the **shortest** movie in the dataset?\n",
+    "\n",
+    "You **must** use `sorted_plot_movies_data_duration` to answer this question.\n",
+    "\n",
+    "You are **allowed** to use the fact that `sorted_plot_movies_data_duration` is sorted in **increasing** order of the `duration`, and the fact that each entry of the **list** is a **tuple** of the format `(<title>, <duration>)` to **index** from `sorted_plot_movies_data_duration`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "617e360e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'shortest_movie', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e648527",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1929505",
+   "metadata": {},
+   "source": [
+    "## Segement 5: Sorting a List of Dictionaries by any Key\n",
+    "\n",
+    "In the past, you have already sorted **lists** made up of **integers**, **floats**, and **strings**. You were able to sort those **lists** because these data types (i.e. **integers**, **floats**, and **strings**) can be **compared** with each other. For example, the expressions: `3 > 1`, `2.5 < 6.1`, and `'a' > 'A'` are valid (and evaluate to `True`). However, an expression such as `dict_a > dict_b` would result in a **TypeError** since the `>` operation is **not** supported between two **dictionaries**.\n",
+    "\n",
+    "Therefore, if we want to **sort** a **list** of **dictionaries**, we will have to tell Python what to sort the **dictionaries** by. Given our `movies_data` **list** of **dictionaries**, over the next few questions, you will **sort** by the **values** attached to different **keys**.\n",
+    "\n",
+    "For example, you could sort `movies_data` by the `year` of release of the movies. In such an instance, you would want to define a function that takes in each element of the list `movies_data` (in this case, movie **dictionaries**) and returns the **value** of the `year` key. Then, if you **sort** `movies_data` by passing this function as a keyword argument to the `key` parameter, then you would **sort** the **list** of **dictionaries** `movies_data` by their `year` of release."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cec0a462",
+   "metadata": {},
+   "source": [
+    "**Question 8**: Create a **list** of **dictionaries** that contains the movies in `movies_data` **sorted** based on the `year` of release of each movie, in **descending order**.\n",
+    "\n",
+    "You **must not** modify the original value of `movies_data` in any way.\n",
+    "\n",
+    "**Hint:** To sort the **list** in **descending order**, you can use the **keyword argument** `reverse=True`.\n",
+    "\n",
+    "You may also use a **lambda** function here as the argument to the `key` parameter, but this is **optional**. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8d6f94b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'sorted_movies_data', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c27ad902",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "641f3d90",
+   "metadata": {},
+   "source": [
+    "**Question 9**: What is the `title` of the **second most recent** movie in the dataset?\n",
+    "\n",
+    "You **must** use `sorted_movies_data` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46fe6bad",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'second_most_recent_movie', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "517114da",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c434188",
+   "metadata": {},
+   "source": [
+    "**Question 10**: How many `years` passed between the release of the **most recent** and the **oldest** movies in the dataset?\n",
+    "\n",
+    "You **must** use `sorted_movies_data` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6dfdbf0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'years_between', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "373e4152",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5d52147",
+   "metadata": {},
+   "source": [
+    "## Segment 6: Bucketizing\n",
+    "\n",
+    "### Task 6.1: Bucketizing by `cast`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a701678",
+   "metadata": {},
+   "source": [
+    "Let's make a bucketized dictionary `movies_by_cast` that has the name of each `cast` member mentioned in `movies_data` as a **key** and a **list** of movies they featured in as **values**. By list of movies, we mean a **list** of **dictionaries** - each **dictionary** represents a movie.\n",
+    "\n",
+    "Suggested Approach:\n",
+    "1. Iterate over each **dictionary** in `movies_data`.\n",
+    "2. Iterate over the **list** of `cast` members in each **dictionary**.\n",
+    "3. For each `cast` member, check if they are already present in `movies_by_cast`. If they are **not** present, **initialize** their **value** as an **empty list**. The **key** will be the name of the `cast` member.\n",
+    "4. For each `cast` member, **append** the current movie (as a **dictionary** with all the associated values) in `movies_data` to the corresponding **list** in `movies_by_cast`.\n",
+    "\n",
+    "Your output **must** look something like this:\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    " 'Pattyeffinmayo': [{'title': 'The Trees of the East',\n",
+    "   'year': 2018,\n",
+    "   'duration': 71,\n",
+    "   'genres': ['Thriller'],\n",
+    "   'rating': 9.8,\n",
+    "   'directors': ['Mike Ellwood'],\n",
+    "   'cast': ['Pattyeffinmayo', 'Parlay Pass', 'Sia Poorak', 'Kandisha']}],\n",
+    " 'Parlay Pass': [{'title': 'The Trees of the East',\n",
+    "   'year': 2018,\n",
+    "   'duration': 71,\n",
+    "   'genres': ['Thriller'],\n",
+    "   'rating': 9.8,\n",
+    "   'directors': ['Mike Ellwood'],\n",
+    "   'cast': ['Pattyeffinmayo', 'Parlay Pass', 'Sia Poorak', 'Kandisha']}],\n",
+    " ...\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1539df87",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "movies_by_cast = {}\n",
+    "for movie in movies_data:\n",
+    "    for cast_member in ...:\n",
+    "        if cast_member not in ...:\n",
+    "            ...\n",
+    "        movies_by_cast[...].append(...)\n",
+    "\n",
+    "movies_by_cast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a3a9aef",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"movies-by-cast\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77b8af3b",
+   "metadata": {},
+   "source": [
+    "**Question 11**: How **many** movies from the dataset has *Mark Ruffalo* acted in?\n",
+    "\n",
+    "You **must** use `movies_by_cast` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15e15292",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'mark_ruffalo_movies', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30051026",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97324766",
+   "metadata": {},
+   "source": [
+    "**Question 12**: How **many** movies from the dataset has *LeBron James* acted in?\n",
+    "\n",
+    "You **must** use `movies_by_cast` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db805078",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'lebron_james_movies', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16bd6f90",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6d94e3c",
+   "metadata": {},
+   "source": [
+    "**Question 13**: Which `cast` member has featured in the **most** number of movies?\n",
+    "\n",
+    "You **must** use `movies_by_cast` to answer this question. You **must** find the `cast` member whose **list** of movies has the **most** movies in it. If there is a **tie**, please choose the `cast` member whom you see **first** in the `movies_by_cast`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dacdcb98",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'most_movies_cast', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "753a99ab",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72264674",
+   "metadata": {},
+   "source": [
+    "## Segment 7: Comprehensions\n",
+    "\n",
+    "We can write comprehensions on dictionaries to form lists or to form new dictionaries. For example, let's compute the median ratings of the movies in `movies_data`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83e2b46e",
+   "metadata": {},
+   "source": [
+    "### Task 7.1: Write the `median` function\n",
+    "\n",
+    "Recall that you wrote a `median` function in lab-p6. You can copy/paste that function to your notebook here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bd1b1ac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the median function you had defined in lab-p6\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f21cf97",
+   "metadata": {},
+   "source": [
+    "### Task 7.2: Create a list of ratings using list comprehension"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae39d59a",
+   "metadata": {},
+   "source": [
+    "**Question 14**: Find the **median** `rating` of all the movies in `movies_data`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "684ee05c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "# note the use of list comprehension here to create the list movie_ratings\n",
+    "movie_ratings = [movie['rating'] for movie in movies_data]\n",
+    "median_rating = median(movie_ratings)\n",
+    "\n",
+    "median_rating"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "844ea9f2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15f4f8fa",
+   "metadata": {},
+   "source": [
+    "### Task 7.3: More list comprehension"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc913d2c",
+   "metadata": {},
+   "source": [
+    "**Question 15**: Create a **dictionary** with the **names** of the `cast` members as **keys** and a **list** of `ratings` of all their movies as the corresponding **values**. \n",
+    "\n",
+    "You **must** use **list comprehension** to create the **lists**.\n",
+    "\n",
+    "**Hint:** Loop through the **dictionary** `movies_by_cast`, and for each `cast` member, use **list comprehension** to create the **list** of `ratings` from the value in `movies_by_cast`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed1a80be",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'ratings_by_cast', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7b9238d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb5760fd",
+   "metadata": {},
+   "source": [
+    "**Question 16**: What is the **median** `rating` of movies starring *Robert Downey Jr.*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08d22a5a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'robert_median_rating', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b71db195",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a4dabd4",
+   "metadata": {},
+   "source": [
+    "### Task 7.4: Create a dictionary of medians using dictionary comprehension"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5d47b1f",
+   "metadata": {},
+   "source": [
+    "**Question 17.1**: Create a new **dictionary** where the **key** is a `cast` member and the **value** is the **median** `rating` of their movies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17953980",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "median_ratings_by_cast = {cast_member:median(ratings_by_cast[cast_member]) for cast_member in ratings_by_cast}\n",
+    "\n",
+    "median_ratings_by_cast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72c56834",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17-1\")"
+   ]
+  },
+  {
+   "attachments": {
+    "median_rating.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "0744797b",
+   "metadata": {},
+   "source": [
+    "**Question 17.2**: **Plot** a bar graph of the **median** `rating` of each `cast` member.\n",
+    "\n",
+    "You **must** plot the **dictionary** `median_ratings_by_cast` to answer this question.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` and `p9_test.py` can check that the **dictionary** has the correct key/value pairs, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots in p9 will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "<div><img src=\"attachment:median_rating.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8bb9258",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# plot 'median_ratings_by_cast' with the y-axis labelled 'median rating'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "621245d9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96ecab61",
+   "metadata": {},
+   "source": [
+    "**Question 18.1**: Create a new **dictionary** where the **key** is a `cast` member and the **value** is the **maximum** `rating` among all of their movies.\n",
+    "\n",
+    "You **must** use **dictionary comprehension** to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc847a9b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'max_ratings_by_cast', then display it\n",
+    "\n",
+    "..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ceea2b51",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38800a79",
+   "metadata": {},
+   "source": [
+    "**Question 18.2**: What is the **maximum** `rating` of any movie starring *Mark Ruffalo*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5431e25",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'mark_ruffalo_max_rating', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53e2dbfd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12332a06",
+   "metadata": {},
+   "source": [
+    "**Question 19**: **Plot** a bar graph of the **maximum** `rating` of each `cast` member.\n",
+    "\n",
+    "You **must** plot the **dictionary** `max_ratings_by_cast` to answer this question.\n",
+    "\n",
+    "**Important Warning:** `practice_test.py` and `p9_test.py` can check that the **dictionary** has the correct key/value pairs, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots in p9 will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "<div><img src=\"attachment:max_rating.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5901644",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# plot 'max_ratings_by_cast' with the y-axis labelled 'maximum rating'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78886dad",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2978449a",
+   "metadata": {},
+   "source": [
+    "**Question 20**: Which `cast` member's **maximum** `rating` is the **lowest**?\n",
+    "\n",
+    "There will be **multiple** `cast` members having the **same** **lowest** `rating` so you will have to make a **list** containing **all** these names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e5efdcf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'lowest_max_rated_cast', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "daa1617f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3388f531",
+   "metadata": {},
+   "source": [
+    "## Great work! You are now ready to start p9."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "movies-by-cast": {
+     "name": "movies-by-cast",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"qmovies-by-cast\", movies_by_cast)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q1\", decade_2000)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q10\", years_between)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q11\", mark_ruffalo_movies)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q12\", lebron_james_movies)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q13\", most_movies_cast)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q14\", median_rating)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q15\", ratings_by_cast)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q16\", robert_median_rating)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17-1": {
+     "name": "q17-1",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17-1\", median_ratings_by_cast)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17-2": {
+     "name": "q17-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q17-2\", median_ratings_by_cast)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18-1": {
+     "name": "q18-1",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q18-1\", max_ratings_by_cast)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18-2": {
+     "name": "q18-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q18-2\", mark_ruffalo_max_rating)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q19\", max_ratings_by_cast)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q2\", decade_1843)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q20\", lowest_max_rated_cast)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q3\", movies_with_decade)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q4\", avg_rating_2011_2020)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q5\", sorted_plot_movies_data)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q6\", sorted_plot_movies_data_duration)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q7\", shortest_movie)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q8\", sorted_movies_data)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> practice_test.check(\"q9\", second_most_recent_movie)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p9/practice.ipynb b/lab-p9/practice.ipynb
index 285c26d427d885f09d1fff49b962a7d4843c3e30..c32fa70f29ba02912f14cea015f3102838410bd0 100644
--- a/lab-p9/practice.ipynb
+++ b/lab-p9/practice.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a63c3861",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1248,7 +1256,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "38800a79",
    "metadata": {},
diff --git a/p1/.ipynb_checkpoints/p1-checkpoint.ipynb b/p1/.ipynb_checkpoints/p1-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c023a55c076ff0784675cb74e87bb52be271de06
--- /dev/null
+++ b/p1/.ipynb_checkpoints/p1-checkpoint.ipynb
@@ -0,0 +1,213 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c96635eb",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c4ab777",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"p1.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e74d2417",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PLEASE FILL IN THE DETAILS\n",
+    "# Enter None if you don't have a project partner\n",
+    "\n",
+    "# project: p1\n",
+    "# submitter: NETID1\n",
+    "# partner: NETID2\n",
+    "# hours: ????"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e26ac162",
+   "metadata": {},
+   "source": [
+    "**Question 1:** \"Hello, World!\" program."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6338f568",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "name = \"World\"\n",
+    "greeting = \"Hello, \" + name + \"!\"\n",
+    "greeting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f91c60dc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a7395bc",
+   "metadata": {},
+   "source": [
+    "**Question 2:** Compute current year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ad71933",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "curr_year = 45 * 45 - 3\n",
+    "curr_year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ddd91fef",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e011d98",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "\n",
+    "Make sure you have run all cells in your notebook in order before running the cell below, so that all images/graphs appear in the output. The cell below will generate a zip file for you to submit. **Please save before exporting!**\n",
+    "\n",
+    "SUBMISSION INSTRUCTIONS: The zipfile automatically gets downloaded. You should have received GradeScope invite - please accept it. Login to GradeScope and upload the zipfile. Check otter results as soon as auto-grader gets completed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df396ce3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Save your notebook first, then run this cell to export your submission.\n",
+    "grader.export(pdf=False, run_tests=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6aa13ea6",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 12.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> greeting == \"Hello, World!\"\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 12.5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> curr_year == 2022\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p1/p1.ipynb b/p1/p1.ipynb
index 9c062a4498e5a815406c5e124f3ef905a0f84476..c023a55c076ff0784675cb74e87bb52be271de06 100644
--- a/p1/p1.ipynb
+++ b/p1/p1.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c96635eb",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p10/.ipynb_checkpoints/p10-checkpoint.ipynb b/p10/.ipynb_checkpoints/p10-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..abe009db6c495b673b610e473362b902e4b608ea
--- /dev/null
+++ b/p10/.ipynb_checkpoints/p10-checkpoint.ipynb
@@ -0,0 +1,2384 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1e9cc6f8",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "823d2b54",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import otter\n",
+    "# nb_name should be the name of your notebook without the .ipynb extension\n",
+    "nb_name = \"p10\"\n",
+    "py_filename = nb_name + \".py\"\n",
+    "grader = otter.Notebook(nb_name + \".ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff087ed4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import p10_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30d663bd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "# Project 10: Stars and Planets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ba07d7c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project, you will demonstrate how to:\n",
+    "\n",
+    "* use `os` module to get information of files in a directory,\n",
+    "* use `os` module to get paths of files,\n",
+    "* look up data between JSON and CSV files using unique keys,\n",
+    "* read JSON and CSV files to store data to `namedTuple` objects,\n",
+    "* clean up missing values and handle cases when the file is too corrupt to parse,\n",
+    "\n",
+    "**Please go through [lab-p10](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p10) before working on this project.** The lab introduces some important techniques related to this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2afc750e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: p10 and p11 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partner up with someone for p10, you have to sustain that partnership until end of p11. Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f22/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63ead371",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `p10_test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a1a6c51",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Setup:\n",
+    "\n",
+    "Before proceeding much further, download `data.zip` and extract it to a directory on your\n",
+    "computer (using [Mac directions](http://osxdaily.com/2017/11/05/how-open-zip-file-mac/) or\n",
+    "[Windows directions](https://support.microsoft.com/en-us/help/4028088/windows-zip-and-unzip-files)).\n",
+    "\n",
+    "You need to make sure that the project files are stored in the following structure:\n",
+    "\n",
+    "```\n",
+    "+-- p10.ipynb\n",
+    "+-- p10_test.py\n",
+    "+-- data\n",
+    "|   +-- .DS_Store\n",
+    "|   +-- .ipynb_checkpoints\n",
+    "|   +-- mapping_1.json\n",
+    "|   +-- mapping_2.json\n",
+    "|   +-- mapping_3.json\n",
+    "|   +-- mapping_4.json\n",
+    "|   +-- mapping_5.json\n",
+    "|   +-- planets_1.csv\n",
+    "|   +-- planets_2.csv\n",
+    "|   +-- planets_3.csv\n",
+    "|   +-- planets_4.csv\n",
+    "|   +-- planets_5.csv\n",
+    "|   +-- stars_1.csv\n",
+    "|   +-- stars_2.csv\n",
+    "|   +-- stars_3.csv\n",
+    "|   +-- stars_4.csv\n",
+    "|   +-- stars_5.csv\n",
+    "```\n",
+    "\n",
+    "Make sure that the files inside `data.zip` are inside the `data` directory. If you place your files inside some other directory, then there is a possibility that your code will **fail on Gradescope** even after passing local tests."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f677746a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Description:\n",
+    "\n",
+    "Cleaning data is an important part of a data scientist's work cycle. As you have already seen, the data we will be analyzing in p10 and p11 has been split up into 15 different files of different formats. Even worse, as you shall see later in this project, some of these files have been corrupted, and lots of data is missing. Unfortunately, in the real world, a lot of data that you will come across will be in rough shape, and it is your job to clean it up before you can analyze it. In p10, you will combine the data in these different files to create a few manageable data structures, which can be easily analyzed. In the process, you will also have to deal with broken CSV files (by skipping rows with broken data), and broken JSON files (by skipping the files entirely).\n",
+    "\n",
+    "After you create these data structures, in p11, you will dive deeper by analyzing this data and arrive at some exciting conclusions about various planets and stars outside our Solar System."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bfc44be",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## The Data:\n",
+    "\n",
+    "In p10 and p11, you will be studying stars and planets outside our Solar System using this dataset from the [NASA Exoplanet Archive](https://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/nph-tblView?app=ExoTbls&config=PSCompPars). You will use Python to ask some interesting questions about the laws of the universe and explore the habitability of other planets in our universe. The raw data from the [NASA Exoplanet Archive](https://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/nph-tblView?app=ExoTbls&config=PSCompPars) has been parsed and stored in multiple different files of different formats. You can find these files inside `data.zip`.\n",
+    "\n",
+    "You can open each of these files using Microsoft Excel or some other Spreadsheet viewing software to see how the data is stored. For example, these are the first three rows of the file `stars_1.csv`:\n",
+    "\n",
+    "|Name|Spectral Type|Stellar Effective Temperature [K]|Stellar Radius [Solar Radius]|Stellar Mass [Solar mass]|Stellar Luminosity [log(Solar)]|Stellar Surface Gravity [log10(cm/s**2)]|Stellar Age [Gyr]|\n",
+    "|----|-------------|---------------------------------|-----------------------------|-------------------------|-------------------------------|----------------------------------------|-----------------|\n",
+    "|11 Com|G8 III|4742.00|19.00|2.70|2.243|2.31||\n",
+    "|11 UMi|K4 III|4213.00|29.79|2.78|2.430|1.93|1.560|\n",
+    "|14 And|K0 III|4813.00|11.00|2.20|1.763|2.63|4.500|\n",
+    "\n",
+    "As you might have already guessed, this file contains data on a number of *stars* outside our solar system along with some important statistics about these stars. The columns here are as follows:\n",
+    "\n",
+    "- `Name`: The name given to the star by the International Astronomical Union,\n",
+    "- `Spectral Type`: The Spectral Classification of the star as per the Morgan–Keenan (MK) system,\n",
+    "- `Stellar Effective Temperature [K]`: The temperature of a black body (in units of Kelvin) that would emit the observed radiation of the star,\n",
+    "- `Stellar Radius [Solar Radius]`: The radius of the star (in units of the radius of the Sun),\n",
+    "- `Stellar Mass [Solar mass]`: The mass of the star (in units of the mass of the Sun),\n",
+    "- `Stellar Luminosity [log(Solar)]`: The total amount of energy radiated by the star each second (represented by the logarithm of the energy radiated by the Sun in each second),\n",
+    "- `Stellar Surface Gravity [log10(cm/s**2)]`: The acceleration due to the gravity of the Star at its surface (represented by the logarithm of the acceleration measured in centimeters per second squared),\n",
+    "- `Stellar Age [Gyr]`: The total age of the star (in units of Giga years, i.e., billions of years).\n",
+    "\n",
+    "The four other files `stars_2.csv`, `stars_3.csv`, `stars_4.csv`, and `stars_5.csv` also store similar data in the same format. At this stage of the project, it is alright if you do not understand what these columns mean - they will be explained to you when they become necessary (in p11).\n",
+    "\n",
+    "On the other hand, here are the first three rows of the file `planets_1.csv`:\n",
+    "\n",
+    "|Planet Name|Discovery Method|Discovery Year|Controversial Flag|Orbital Period [days]|Planet Radius [Earth Radius]|Planet Mass [Earth Mass]|Orbit Semi-Major Axis [au]|Eccentricity|Equilibrium Temperature [K]|Insolation Flux [Earth Flux]|\n",
+    "|-----------|----------------|--------------|------------------|---------------------|----------------------------|------------------------|---------------------------|------------|---------------------------|----------------------------|\n",
+    "|11 Com b|Radial Velocity|2007|0|326.03|12.1|6165.6|1.29|0.231|||\n",
+    "|11 UMi b|Radial Velocity|2009|0|516.21997|12.3|4684.8142|1.53|0.08|||\n",
+    "|14 And b|Radial Velocity|2008|0|185.84|12.9|1525.5|0.83|0|||\n",
+    "\n",
+    "This file contains data on a number of *planets* outside our solar system along with some important statistics about these planets. The columns here are as follows:\n",
+    "\n",
+    "- `Planet Name`: The name given to the planet by the International Astronomical Union,\n",
+    "- `Discovery Method`: The method by which the planet was discovered,\n",
+    "- `Discovery Year`: The year in which the planet was discovered,\n",
+    "- `Controversial Flag`: Indicates whether the status of the discovered object as a planet was disputed at the time of discovery, \n",
+    "- `Orbital Period [days]`: The amount of time (in units of days) it takes for the planet to complete one orbit around its star,\n",
+    "- `Planet Radius [Earth Radius]`: The radius of the planet (in units of the radius of the Earth),\n",
+    "- `Planet Mass [Earth Mass]`: The mass of the planet (in units of the mass of the Earth),\n",
+    "- `Orbit Semi-Major Axis [au]`: The semi-major axis of the planet's elliptical orbit around its host star (in units of Astronomical Units),\n",
+    "- `Eccentricity`: The eccentricity of the planet's orbit around its host star,\n",
+    "- `Equilibrium Temperature [K]`: The temperature of the planet (in units of Kelvin) if it were a black body heated only by its host star,\n",
+    "- `Insolation Flux [Earth Flux]`:  The amount of radiation the planet receives from its host star per unit of area (in units of the Insolation Flux of the Earth from the Sun).\n",
+    "\n",
+    "The four other files `planets_2.csv`, `planets_3.csv`, `planets_4.csv`, and `planets_5.csv` also store similar data in the same format. At this stage of the project, it is alright if you do not understand what these columns mean - they will be explained to you when they become necessary (in p11).\n",
+    "\n",
+    "\n",
+    "Finally, if you take a look at `mapping_1.json` (you can open json files using any Text Editor), you will see that the first three entries look like this:\n",
+    "\n",
+    "```\n",
+    "{\"11 Com b\": \"11 Com\", \"11 UMi b\": \"11 UMi\", \"14 And b\": \"14 And\", ...}\n",
+    "```\n",
+    "\n",
+    "This file contains a *mapping* from each *planet* in `planets_1.csv` to the *star* in `stars_1.csv` that the planet orbits. Similarly, `mapping_2.json` contains a *mapping* from each *planet* in `planets_2.csv` to the *star* in `stars_2.csv` that the planet orbits. The pattern also holds true for `mapping_3.json`, `mapping_4.json`, and `mapping_5.json`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "472dfac2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices in your code, unless the question explicitly. If you open your `.csv` files with Excel, manually count through the rows and use this number to loop through the dataset, this is also considered as hardcoding. We'll **manually deduct** points from your autograder score on Gradescope during code review.\n",
+    "\n",
+    "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer. If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Functions:\n",
+    "- `star_cell`\n",
+    "- `get_stars`\n",
+    "- `planet_cell`\n",
+    "- `get_planets`\n",
+    "\n",
+    "In this project, you will also be required to define certain **data structures**. If you do not create these data structures exactly as specified, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Data Structures:\n",
+    "- `Star` (**namedtuple**)\n",
+    "- `stars_dict` (**dictionary** mapping **strings** to `Star` objects)\n",
+    "- `Planet` (**namedtuple**)\n",
+    "- `planets_list` (**list** of `Planet` objects)\n",
+    "\n",
+    "In addition, you are also **required** to follow the requirements below:\n",
+    "\n",
+    "* You **must** never use the output of the `os.listdir` function directly. You **must** always first remove all files and directories that start with `\".\"`, and sort the list before doing anything with it.\n",
+    "* You are **not** allowed to use *bare* `try/except` blocks. In other words, you can **not** use `try/except` without explicitly specifying the type of exceptions that you want to catch.\n",
+    "* You are **only** allowed to use Python commands and concepts that have been taught in the course prior to the release of p10. In particular, this means that you are **not** allowed to use **modules** like `pandas` to answer the questions in this project.\n",
+    "\n",
+    "You are also **required** to follow good coding practices, such as the following:\n",
+    "\n",
+    "* Do **not** use meaningless names for variables or functions (e.g. uuu = \"my name\").\n",
+    "* Do **not** write the exact same code in multiple places. Instead, wrap this code into a function and call that function whenever the code should be used.\n",
+    "* Avoid **inappropriate** use of data structures. For example, using a for loop to search for a corresponding value in a dictionary with a given key instead of using dictname[key] directly.\n",
+    "* Do **not** name variables or functions as python keywords or built-in functions. Bad example: str = \"23\".\n",
+    "* Do **not** define multiple functions with the same name or define multiple versions of one function with different names. Just keep the best version.\n",
+    "* Do **not** leave in irrelevant output or test code that we didn't ask for.\n",
+    "\n",
+    "We will **manually deduct** points if you do **not** follow these guidelines.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p10/rubric.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "381d4939",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "## Questions and Functions:\n",
+    "\n",
+    "Let us start by importing all the modules we will need for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc356f2a",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8ad69e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### File handling:\n",
+    "\n",
+    "In the next questions, you will be using functions in the `os` module to make **lists** of files and paths in the `data` directory. All your **lists** **must** satisfy the following conditions:\n",
+    "\n",
+    "* Any files with names beginning with `\".\"` **must** be **excluded**.\n",
+    "* The list **must** be **explicitly** sorted in **alphabetical** order.\n",
+    "\n",
+    "You may consider writing a single function to answer several questions, but this is **not required**. If you want to define such a function, you can insert a new cell in your notebook under this cell, and define the function there.\n",
+    "\n",
+    "**Hint**: Things that change for different questions can often be represented with parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "450180ff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 1:** What are the **names** of the files present in the `data` directory\n",
+    "\n",
+    "Your output **must** be a **list** of **strings** representing the **names** of the files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "828825d6",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'files_in_data', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5263007c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdc3629d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 2:** What are the **relative paths** of all the files in the `data` directory?\n",
+    "\n",
+    "Your output **must** be a **list** of **strings** representing the **paths** of the files.\n",
+    "\n",
+    "**Warning:** Please **do not hardcode** `\"/\"` or `\"\\\"` in your path because doing so will cause your function to **fail** on a computer that's not using the same operating system as yours. This may result in your code failing on Gradescope."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "344bfe95",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'file_paths', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fca30cc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e1079d8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 3:** What are the **relative paths** of all the **JSON files** present in `data` directory?\n",
+    "\n",
+    "Your output **must** be filtered to **only** include files ending in `.json`.\n",
+    "\n",
+    "**Warning:** Please **do not hardcode** `\"/\"` or `\"\\\"` in your path because doing so will cause your function to **fail** on a computer that's not using the same operating system as yours. This may result in your code failing on Gradescope."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bc1d9dc",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'json_file_paths', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1772cb9c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c943ff1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 4:** What are the **relative paths** of all the files present in `data` directory, that **begin** with the phrase `'stars'`?\n",
+    "\n",
+    "Your output **must** be filtered to **only** include files start with `stars`.\n",
+    "\n",
+    "**Warning:** Please **do not hardcode** `\"/\"` or `\"\\\"` in your path because doing so will cause your function to **fail** on a computer that's not using the same operating system as yours. This may result in your code failing on Gradescope."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1473255",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'stars_paths', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d972bda",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efefa619",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Structure 1: namedtuple `Star`\n",
+    "\n",
+    "You will be using namedtuples to store the data in the `stars_1.csv`, ..., `stars_5.csv` files. Before you start reading these files however, you **must** create a new `Star` type (using namedtuple). It **must** have the following attributes:\n",
+    "\n",
+    "* `spectral_type`\n",
+    "* `stellar_effective_temperature`\n",
+    "* `stellar_radius`\n",
+    "* `stellar_mass`\n",
+    "* `stellar_luminosity`\n",
+    "* `stellar_surface_gravity`\n",
+    "* `stellar_age`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b72fded",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the namedtuple 'Star' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08d17370",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# run this following cell to initialize and test an example Star object\n",
+    "# if this cell fails to execute, you have likely not defined the namedtuple 'Star' correctly\n",
+    "\n",
+    "sun = Star('G2 V', 5780.0, 1.0, 1.0, 0.0, 4.44, 4.6)\n",
+    "\n",
+    "sun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38e3af94",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"star_object\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2974353",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Creating `Star` objects\n",
+    "\n",
+    "Now that we have created the `Star` namedtuple, our next objective will be to read the files `stars_1.csv`, ..., `stars_5.csv` and create `Star` objects out of all the stars in there. In order to process the CSV files, you will first need to copy/paste the `process_csv` function you have been using since p6."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4863d7b5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy & paste the process_csv file from previous projects here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "129c08de",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "You are now ready to read the data in `stars_1.csv` using `process_csv` and convert the data into `Star` objects. In the cell below, you **must** read the data in `stars_1.csv` and extract the **header** and the non-header **rows** of the file.\n",
+    "\n",
+    "**Warning:** You **must** use the **relative path** of the file to read the file. If you **hardcode** the **absolute path**, your code will pass on your computer, but **fail** on the testing computer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2aed6340",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "stars_1_csv = ... # read the data in 'stars_1.csv'\n",
+    "stars_header = ...\n",
+    "stars_rows = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d29b354",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "If you wish to **verify** that you have read the file and defined the variables correctly, you can check that `stars_header` has the value:\n",
+    "\n",
+    "```python\n",
+    "['Name', 'Spectral Type', 'Stellar Effective Temperature [K]', 'Stellar Radius [Solar Radius]',\n",
+    " 'Stellar Mass [Solar mass]', 'Stellar Luminosity [log(Solar)]', \n",
+    " 'Stellar Surface Gravity [log10(cm/s**2)]', 'Stellar Age [Gyr]']\n",
+    "```\n",
+    "\n",
+    "and that `stars_rows` has **1508** rows of which the **first three** are:\n",
+    "\n",
+    "```python\n",
+    "[['11 Com', 'G8 III', '4742.00', '19.00', '2.70', '2.243', '2.31', ''],\n",
+    " ['11 UMi', 'K4 III', '4213.00', '29.79', '2.78', '2.430', '1.93', '1.560'],\n",
+    " ['14 And', 'K0 III', '4813.00', '11.00', '2.20', '1.763', '2.63', '4.500']]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "208dd21b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 1: `star_cell(row_idx, col_name, stars_rows, header=stars_header)`\n",
+    "\n",
+    "This function **must** read the **list** of **lists** `stars_rows`, and extract the value at **row** index `row_idx` and **column** name `col_name`. The function **must** typecast the value based on `col_name`. If the value in `stars_rows` is **missing** (i.e., it is `''`), then the value returned **must** be `None`.\n",
+    "\n",
+    "The **column** of `stars_rows` where the value should be obtained from, and the correct **data type** for the value are listed in the table below:\n",
+    "\n",
+    "|Column of `stars_rows`|Data Type|\n",
+    "|------|---------|\n",
+    "|Spectral Type|**string**|\n",
+    "|Stellar Effective Temperature [K]|**float**|\n",
+    "|Stellar Radius [Solar Radius]|**float**|\n",
+    "|Stellar Mass [Solar mass]|**float**|\n",
+    "|Stellar Luminosity [log(Solar)]|**float**|\n",
+    "|Stellar Surface Gravity [log10(cm/s**2)]|**float**|\n",
+    "|Stellar Age [Gyr]|**float**|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32eb9c3e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the 'star_cell' function from lab-p10 here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8c8ac4d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 5:** Create a `Star` object for the **third** star in `\"stars_1.csv\"`.\n",
+    "\n",
+    "You **must** access the values in `stars_1.csv` using the `star_cell` function. Note that the third star would be at **index** 2.\n",
+    "\n",
+    "The **attribute** of the `Star` namedtuple object, the corresponding **column** of the `stars_1.csv` file where the value should be obtained from, and the correct **data type** for the value are listed in the table below:\n",
+    "\n",
+    "|Attribute of `Star` object|Column of `stars_1.csv`|Data Type|\n",
+    "|---------|------|---------|\n",
+    "|`spectral_type`|Spectral Type|**string**|\n",
+    "|`stellar_effective_temperature`|Stellar Effective Temperature [K]|**float**|\n",
+    "|`stellar_radius`|Stellar Radius [Solar Radius]|**float**|\n",
+    "|`stellar_mass`|Stellar Mass [Solar mass]|**float**|\n",
+    "|`stellar_luminosity`|Stellar Luminosity [log(Solar)]|**float**|\n",
+    "|`stellar_surface_gravity`|Stellar Surface Gravity [log10(cm/s**2)]|**float**|\n",
+    "|`stellar_age`|Stellar Age [Gyr]|**float**|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "264c79f9",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    " # compute and store the answer in the variable 'third_star', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8de7fd74",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "426e2330",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 2:  `get_stars(star_file)`\n",
+    "\n",
+    "This function **must** take in as its input, the **relative path** of a CSV file `star_file` which contains data on stars in the same format as `stars_1.csv`. It **must** return a **dictionary** mapping the `Name` of each star in `star_file` to a `Star` object containing all the other details of the star.\n",
+    "\n",
+    "You **must** access the values in `stars_1.csv` using the `star_cell` function.\n",
+    "\n",
+    "Once again, as a reminder, the attributes of the `Star` objects should be obtained from the **rows** of `star_file` and stored as follows:\n",
+    "\n",
+    "|Attribute of `Star` object|Column of `star_file`|Data Type|\n",
+    "|---------|------|---------|\n",
+    "|`spectral_type`|Spectral Type|**string**|\n",
+    "|`stellar_effective_temperature`|Stellar Effective Temperature [K]|**float**|\n",
+    "|`stellar_radius`|Stellar Radius [Solar Radius]|**float**|\n",
+    "|`stellar_mass`|Stellar Mass [Solar mass]|**float**|\n",
+    "|`stellar_luminosity`|Stellar Luminosity [log(Solar)]|**float**|\n",
+    "|`stellar_surface_gravity`|Stellar Surface Gravity [log10(cm/s**2)]|**float**|\n",
+    "|`stellar_age`|Stellar Age [Gyr]|**float**|\n",
+    "\n",
+    "In case any data in `star_file` is **missing**, the corresponding value should be `None`.\n",
+    "\n",
+    "For example, when this function is called with the file `stars_1.csv` as the input, the **dictionary** returned should look like:\n",
+    "\n",
+    "```python\n",
+    "{'11 Com': Star(spectral_type='G8 III', stellar_effective_temperature=4742.0, \n",
+    "                stellar_radius=19.0, stellar_mass=2.7, stellar_luminosity=2.243, \n",
+    "                stellar_surface_gravity=2.31, stellar_age=None),\n",
+    " '11 UMi': Star(spectral_type='K4 III', stellar_effective_temperature=4213.0, \n",
+    "                stellar_radius=29.79, stellar_mass=2.78, stellar_luminosity=2.43, \n",
+    "                stellar_surface_gravity=1.93, stellar_age=1.56),\n",
+    " '14 And': Star(spectral_type='K0 III', stellar_effective_temperature=4813.0, \n",
+    "                stellar_radius=11.0, stellar_mass=2.2, stellar_luminosity=1.763, \n",
+    "                stellar_surface_gravity=2.63, stellar_age=4.5),\n",
+    " ...\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb7cbd91",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function 'get_stars' here\n",
+    "\n",
+    "def get_stars(star_file):\n",
+    "    # TODO: read star_file to a list of lists\n",
+    "    # TODO: extract the header and rows from star_file\n",
+    "    # TODO: loop through each row in star_file\n",
+    "    # TODO: create a Star object (namedTuple) for each row\n",
+    "    # TODO: add each Star objet to a dictionary\n",
+    "    # TODO: return the dictionary at the end of the loop\n",
+    "    pass # replace with your code\n",
+    "''';# END PROPMT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f920138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# you can now use 'get_stars' to read the data in 'stars_1.csv' but DO NOT display\n",
+    "\n",
+    "stars_1_dict = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd1f23df",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 6:** What is the `Star` object of the star (in `stars_1.csv`) named *DP Leo*?\n",
+    "\n",
+    "You **must** access the `Star` object in `stars_1_dict` **dictionary** defined above to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5b0ce59",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'dp_leo', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d397687d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eee50d46",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 7:** What's the **average** `stellar_luminosity` of **all** the stars in the `star_1.csv` file?\n",
+    "\n",
+    "You **must** use the `stars_1_dict` **dictionary** defined above to answer this question.\n",
+    "\n",
+    "To find the average, you **must** first **add** up the `stellar_luminosity` value of all the stars and **divide** by the total **number** of stars. You **must skip** stars which don't have the `stellar_luminosity` data. Such stars should not contribute to either the sum of `stellar_luminosity` or to the number of stars."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19b731ac",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'avg_lum_stars_1', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f6d3f17",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65596001",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 8:** What is the **average** `stellar_age` of **all** the stars in the `stars_2.csv` file?\n",
+    "\n",
+    "You **must** use the function `get_stars(csv_file)` to read the data in `stars_2.csv`. Your output **must** be a **float** representing the `stellar_age` in units of *gigayears*. You **must** skip stars which have missing `stellar_age` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c2e298d",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'avg_age_stars_2', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b59d6856",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af1b9ba5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Structure 2: `stars_dict`\n",
+    "\n",
+    "You are now ready to read all the data about the stars stored in the `data` directory. You **must** now create a **dictionary** mapping the `Name` of each star in the `data` directory (inside the files `stars_1.csv`, ..., `stars_5.csv`) to the `Star` object containing all the other details about the star.\n",
+    "\n",
+    "You **must not** hardcode the files/paths of the files `stars_1.csv`, ..., `stars_5.csv` to answer this question. Instead, you **must** use the `os` module to find all the files in the `data` directory that begin with `'stars'`, and use the `get_stars` function on each of those files to create `stars_dict`.\n",
+    "\n",
+    "**Hints:** You may use the `stars_paths` variable defined earlier in q4 to get the list of paths needed for this question. You can use the `update` dictionary **method** to combine two **dictionaries**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad991901",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the variable 'stars_dict' here,\n",
+    "# but do NOT display the variable at the end\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0004a50a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "If you wish to **verify** that you have read the files and defined `stars_dict` correctly, you can check that `stars_dict` has **3879** key/value pairs in it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50f7d2e9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 9:** What is the `stellar_effective_temperature` of the star *Kepler-220*?\n",
+    "\n",
+    "You **must** access the correct `Star` object in the `stars_dict` **dictionary** defined above to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd77d1a7",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'kepler_220_temp', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4667f51a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da1992b0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 10:** Find the **name** of the **largest** star (in terms of `stellar_radius`) in the `stars_dict` **dictionary**.\n",
+    "\n",
+    "Your output **must** be a **string**. You do **not** need to worry about any ties. You **must** skip any stars with **missing** `stellar_radius` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d492df4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'biggest_star', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36f1eb95",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "962a19b7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 11:** What is the **average** `stellar_age` (in *gigayears*) of **all** the stars in the `stars_dict` **dictionary** whose names **start with** `\"Kepler\"`?\n",
+    "\n",
+    "Your output **must** be a **float**. You **must** skip all stars with **missing** `stellar_age` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "642cc569",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'avg_age_kepler', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86c6f990",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7485a097",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Structure 3: namedtuple `Planet`\n",
+    "\n",
+    "Just as you did with the stars, you will be using namedtuples to store the data about the planets in the `planets_1.csv`, ..., `planets_5.csv` files. Before you start reading these files however, you **must** create a new `Planet` type (using namedtuple). It **must** have the following attributes:\n",
+    "\n",
+    "* `planet_name`\n",
+    "* `host_name`\n",
+    "* `discovery_method`\n",
+    "* `discovery_year`\n",
+    "* `controversial_flag`\n",
+    "* `orbital_period`\n",
+    "* `planet_radius`\n",
+    "* `planet_mass`\n",
+    "* `semi_major_radius`\n",
+    "* `eccentricity`\n",
+    "* `equilibrium_temperature`\n",
+    "* `insolation_flux`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33a70ada",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the namedtuple 'Planet' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfcaac26",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# run this following cell to initialize and test an example Planet object\n",
+    "# if this cell fails to execute, you have likely not defined the namedtuple 'Star' correctly\n",
+    "jupiter = Planet('Jupiter', 'Sun', 'Imaging', 1610, False, 4333.0, 11.209, 317.828, 5.2038, 0.0489, 110, 0.0345)\n",
+    "\n",
+    "jupiter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a49e74",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"planet_object\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db876580",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Creating `Planet` objects\n",
+    "\n",
+    "We are now ready to read the files in the `data` directory and create `Planet` objects. Creating `Planet` objects however, is going to be more difficult than creating `Star` objects, because the data required to create a single `Planet` object is split up into different files.\n",
+    "\n",
+    "The `planets_1.csv`, ..., `planets_5.csv` files contain all the data required to create `Planet` objects **except** for the `host_name`. The `host_name` for each planet is to be found in the `mapping_1.json`, ..., `mapping_5.json` files."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ac20400",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "First, let us read the data in `planets_1.csv`. Since this is a CSV file, you can use the `process_csv` function from above to read this file. In the cell below, you **must** read the data in `planets_1.csv` and extract the **header** and the non-header **rows** of the file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f52e9eac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "planets_1_csv = process_csv(...) # read the data in 'planets_1.csv'\n",
+    "planets_header = ...\n",
+    "planets_rows = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f5b8055",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "If you wish to **verify** that you have read the file and defined the variables correctly, you can check that `planets_header` has the value:\n",
+    "\n",
+    "```python\n",
+    "['Planet Name', 'Discovery Method', 'Discovery Year', 'Controversial Flag', 'Orbital Period [days]',\n",
+    " 'Planet Radius [Earth Radius]', 'Planet Mass [Earth Mass]', 'Orbit Semi-Major Axis [au]',\n",
+    " 'Eccentricity', 'Equilibrium Temperature [K]', 'Insolation Flux [Earth Flux]']\n",
+    "```\n",
+    "\n",
+    "and that `planets_rows` has **1508** rows of which the **first three** are:\n",
+    "\n",
+    "```python\n",
+    "[['11 Com b', 'Radial Velocity', '2007', '0', '326.03', '12.1', '6165.6', '1.29', '0.231', '', ''],\n",
+    " ['11 UMi b', 'Radial Velocity', '2009', '0', '516.21997', '12.3', '4684.8142', '1.53', '0.08', '', ''],\n",
+    " ['14 And b', 'Radial Velocity', '2008', '0', '185.84', '12.9', '1525.5', '0.83', '0', '', '']]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02946505",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, you are ready to read the data in `mapping_1.json`. Since this is a JSON file, you will need to copy/paste the `read_json` function lab-p10, and use it to read the file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6badd534",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy & paste the read_json file from lab-p10\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5dad7de",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# now use the read_json function to read 'mapping_1.json'\n",
+    "\n",
+    "mapping_1_json = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1fb3b33",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "If you wish to **verify** that you have read the file correctly, you can check that `mapping_1_json` has the value:\n",
+    "\n",
+    "```python\n",
+    "{'11 Com b': '11 Com',\n",
+    " '11 UMi b': '11 UMi',\n",
+    " '14 And b': '14 And',\n",
+    " ...\n",
+    " }\n",
+    "```\n",
+    "\n",
+    "Now that we have read `planets_1.csv` and `mapping_1.json`, we are now ready to combine these two files to create `Planet` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2559eabd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 3: `planet_cell(row_idx, col_name, planets_rows, header=planets_header)`\n",
+    "\n",
+    "This function **must** read the **list** of **lists** `planets_rows`, and extract the value at **row** index `row_idx` and **column** index `col_idx`. The function **must** typecast the value based on `col_name`. If the value in `stars_rows` is **missing** (i.e., it is `''`), then the value returned **must** be `None`.\n",
+    "\n",
+    "The **column** of `planets_rows` where the value should be obtained from, and the correct **data type** for the value are listed in the table below:\n",
+    "\n",
+    "|Column of `planets_rows`|Data Type|\n",
+    "|------|---------|\n",
+    "|Planet Name|**string**|\n",
+    "|Discovery Year|**int**|\n",
+    "|Discovery Method|**string**|\n",
+    "|Controversial Flag|**bool**|\n",
+    "|Orbital Period [days]|**float**|\n",
+    "|Planet Radius [Earth Radius]|**float**|\n",
+    "|Planet Mass [Earth Mass]|**float**|\n",
+    "|Orbit Semi-Major Axis [au]|**float**|\n",
+    "|Eccentricity|**float**|\n",
+    "|Equilibrium Temperature [K]|**float**|\n",
+    "|Insolation Flux [Earth Flux]|**float**|\n",
+    "\n",
+    "**Important Hint:** While computing the value of the attribute `controversial_flag`, note that the `Controversial Flag` column of `planets_1.csv` represents `True` with `'1'` and `False` with `'0'`. You **must** be careful with typecasting **strings** to **booleans**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfef28f6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the function 'planet_cell' from lab-p10 here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "017f11c9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 12:** Create a `Planet` object for the **fifth** planet in the `planets_1.csv` file.\n",
+    "\n",
+    "You **must** access the values in `planets_1.csv` using the `planet_cell` function. Note that the ninth star would be at **index** 4.\n",
+    "\n",
+    "The **attribute** of the `Planet` namedtuple object, the corresponding **column** of the `planets_1.csv` file where the value should be obtained from, and the correct **data type** for the value are listed in the table below:\n",
+    "\n",
+    "|Attribute of `Planet` object|Column of `planets_1.csv`|Data Type|\n",
+    "|---------|------|---------|\n",
+    "|`planet_name`|Planet Name|**string**|\n",
+    "|`host_name`| - |**string**|\n",
+    "|`discovery_method`|Discovery Method|**string**|\n",
+    "|`discovery_year`|Discovery Year|**int**|\n",
+    "|`controversial_flag`|Controversial Flag|**bool**|\n",
+    "|`orbital_period`|Orbital Period [days]|**float**|\n",
+    "|`planet_radius`|Planet Radius [Earth Radius]|**float**|\n",
+    "|`planet_mass`|Planet Mass [Earth Mass]|**float**|\n",
+    "|`semi_major_radius`|Orbit Semi-Major Axis [au]|**float**|\n",
+    "|`eccentricity`|Eccentricity|**float**|\n",
+    "|`equilibrium_temperature`|Equilibrium Temperature [K]|**float**|\n",
+    "|`insolation_flux`|Insolation Flux [Earth Flux]|**float**|\n",
+    "\n",
+    "\n",
+    "The value of the `host_name` attribute is found in `mapping_1.json`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d8625e1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'fifth_planet', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91817571",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "435565ea",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 4: `get_planets(planet_file, mapping_file)`: \n",
+    "\n",
+    "This function **must** take in as its input, a CSV file `planet_file` which contains data on planets in the same format as `planets_1.csv`, as well as a JSON file `mapping_file` which maps planets in `planet_file` to their host star in the same format as `mapping_1.json`. This function **must** return a **list** of `Planet` objects by combining the data in these two files. The `Planet` objects **must** appear in the same order as they do in `planet_file`.\n",
+    "\n",
+    "You **must** access the values in `planets_1.csv` using the `planet_cell` function.\n",
+    "\n",
+    "Once again, as a reminder, the attributes of the `Planet` objects should be obtained from the **rows** of `planet_file` and from `mapping_file` and stored as follows:\n",
+    "\n",
+    "|Attribute of `Planet` object|Column of `planets_1.csv`|Data Type|\n",
+    "|---------|------|---------|\n",
+    "|`planet_name`|Planet Name|**string**|\n",
+    "|`host_name`| - |**string**|\n",
+    "|`discovery_method`|Discovery Method|**string**|\n",
+    "|`discovery_year`|Discovery Year|**int**|\n",
+    "|`controversial_flag`|Controversial Flag|**bool**|\n",
+    "|`orbital_period`|Orbital Period [days]|**float**|\n",
+    "|`planet_radius`|Planet Radius [Earth Radius]|**float**|\n",
+    "|`planet_mass`|Planet Mass [Earth Mass]|**float**|\n",
+    "|`semi_major_radius`|Orbit Semi-Major Axis [au]|**float**|\n",
+    "|`eccentricity`|Eccentricity|**float**|\n",
+    "|`equilibrium_temperature`|Equilibrium Temperature [K]|**float**|\n",
+    "|`insolation_flux`|Insolation Flux [Earth Flux]|**float**|\n",
+    "\n",
+    "The value of the `host_name` attribute is found in `mapping_file`.\n",
+    "\n",
+    "In case any data in `planet_file` is **missing**, the corresponding value should be `None`.\n",
+    "\n",
+    "For example, when this function is called with the file `planets_1.csv` and `mapping_1.json` as the input, the **list** returned should look like:\n",
+    "\n",
+    "```python\n",
+    "[\n",
+    "    Planet(planet_name='11 Com b', host_name='11 Com', discovery_method='Radial Velocity', \n",
+    "           discovery_year=2007, controversial_flag=False, orbital_period=326.03, \n",
+    "           planet_radius=12.1, planet_mass=6165.6, semi_major_radius=1.29, \n",
+    "           eccentricity=0.231, equilibrium_temperature=None, insolation_flux=None),\n",
+    "    Planet(planet_name='11 UMi b', host_name='11 UMi', discovery_method='Radial Velocity', \n",
+    "           discovery_year=2009, controversial_flag=False, orbital_period=516.21997, \n",
+    "           planet_radius=12.3, planet_mass=4684.8142, semi_major_radius=1.53, \n",
+    "           eccentricity=0.08, equilibrium_temperature=None, insolation_flux=None),\n",
+    "    Planet(planet_name='14 And b', host_name='14 And', discovery_method='Radial Velocity', \n",
+    "           discovery_year=2008, controversial_flag=False, orbital_period=185.84, \n",
+    "           planet_radius=12.9, planet_mass=1525.5, semi_major_radius=0.83, \n",
+    "           eccentricity=0.0, equilibrium_temperature=None, insolation_flux=None),\n",
+    "    ...\n",
+    "]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "122cecf4",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_planets(planet_file, mapping_file):\n",
+    "    # TODO: read planet_file to a list of lists\n",
+    "    # TODO: extract the header and rows from planet_file\n",
+    "    # TODO: read mapping_file to a dictionary\n",
+    "    # TODO: loop through each row in planet_file\n",
+    "    # TODO: create a Planet object (namedTuple) for each row\n",
+    "    # TODO: add each Planet objet to a list\n",
+    "    # TODO: return the list at the end of the loop\n",
+    "    pass # replace with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4fbcc97",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 13:** What are the **last five** `Planet` objects in the **list** returned by `get_planets` when `planet_file` is `planets_1.csv` and `mapping_file` is `mapping_1.json`?\n",
+    "\n",
+    "Your output **must** be a **list** of `Planet` objects.\n",
+    "\n",
+    "**Hint:** First, you **must** use the `get_planets` function to parse the data in `planets_1.csv` and `mapping_1.json` and create a **list** of `Planet` objects. Then, you may slice this **list** to get the last five `Planet` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15b69054",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'last_five_planets_1', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d99d276",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccba9efc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 14:** What are the `Planet` objects whose `controversial_flag` attribute is `True` in the **list** returned by `get_planets` when `planet_file` is `planets_2.csv` and `mapping_file` is `mapping_2.json`?\n",
+    "\n",
+    "Your output **must** be a **list** of `Planet` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d988296d",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'controversial_planets', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "987bf9d7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3faaf6aa",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Cleaning 1: broken CSV rows\n",
+    "\n",
+    "Our function `get_planets` works very well so far. However, it is likely that it will not work on all the files in the `data` directory. For example, if you use the function `get_planets` to read the data in `planets_4.csv` and `mapping_4.json`, you will most likely run into an error. **Try it yourself to verify!**\n",
+    "\n",
+    "The reason your code likely crashed is because there the file `planets_4.csv` is **broken**. For some reason, several rows in `planets_4.csv` have their data jumbled up. For example, in the **566**th row of `planets_4.csv`, we come across this row:\n",
+    "\n",
+    "|Planet Name|Discovery Method|Discovery Year|Controversial Flag|Orbital Period [days]|Planet Radius [Earth Radius]|Planet Mass [Earth Mass]|Orbit Semi-Major Axis [au]|Eccentricity|Equilibrium Temperature [K]|Insolation Flux [Earth Flux]|\n",
+    "|-----------|----------------|--------------|------------------|---------------------|----------------------------|------------------------|---------------------------|------------|---------------------------|----------------------------|\n",
+    "|pi Men c|pi Men|Transit|2018|0|6.26790772|2.060|3.63000|0.068647|0.076939|1170|\n",
+    "\n",
+    "We can see that for some reason, the value under the column `Discovery Method` is the name of the planet's host star. This causes the value under the column `Discovery Year` to be a **string** instead of a number.\n",
+    "\n",
+    "We will call such a **row** in a CSV file where the values under a column do not match the expected format to be a **broken row**. While it is possible to sometimes extract useful data from broken rows, in this project, we will simply **skip** broken rows.\n",
+    "\n",
+    "You **must** now go back to your definition of `get_planets` and edit it, so that any **broken rows get skipped**.\n",
+    "\n",
+    "**Hints:**\n",
+    "\n",
+    "1. The simplest way to recognize if a row is broken is if you run into any **RunTime Errors** when you call the `get_planets` function. So, one simple way to skip bad rows would be to use `try/except` blocks to avoid processing any rows that cause the code to crash; remember **not** to use *bare* `try/except` blocks.\n",
+    "2. There are **several different kinds** of errors that you can expect to find when you try to parse `planets_4.csv`. You should **explicitly** handle each of these errors using `try/except` blocks.\n",
+    "3. There are only **5** broken rows in `planets_4.csv`, and they are all bunched up at the very end. You can manually **inspect** these rows, and figure out why these rows are broken.\n",
+    "\n",
+    "**Important Warning:** You are **not** allowed to **hardcode** the indices of the broken rows. You may inspect `planets_4.csv` to identify how to tell a **broken row** apart. Therefore, to use the example of the **broken row** above, you **may not** hardcode to skip the **566**th row of `planets_4.csv`. However, it is **acceptable** to make your function **skip** any row for which the value under the `Discovery Year` is not numeric, by observing that this is the reason why the row is broken."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ebd0c80",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 15:** What are the **last five** `Planet` objects produced by `get_planets` when `planet_file` is `planets_4.csv` and `mapping_file` is `mapping_4.json`?\n",
+    "\n",
+    "Your output **must** be a **list** of `Planet` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0be6239b",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'last_five_planets_4', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bb8823b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa7b37d4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Cleaning 2: broken JSON files\n",
+    "\n",
+    "We are now ready to read **all** the files in the `data` directory and create a **list** of `Planet` objects for all the planets in the directory. However, if you try to use `get_planets` on all the planet CSV files and mapping JSON files, you will likely run into another error. **Try it for yourself by calling `get_planets` on all the files in `data`!**\n",
+    "\n",
+    "It is likely that your code crashed when you tried to read the data in `planets_5.csv` and `mapping_5.json`. This is because the file `mapping_5.json` is **broken**. Unlike **broken** CSV files, where we only had to skip the **broken rows**, it is much harder to parse **broken JSON files**. When a JSON file is **broken**, we often have no choice but to **skip the file entirely**.\n",
+    "\n",
+    "You **must** now go back to your definition of `get_planets` and edit it, so that if the JSON file is **broken**, then the file is completely skipped, and only an **empty list** is returned.\n",
+    "\n",
+    "**Important Warning:** You are **not** allowed to **hardcode** the name of the files to be skipped. You **must** use `try/except` blocks to determine whether the JSON file is **broken** and skip the file if it is. Remmeber **not** to use *bare* `try/except` blocks.\n",
+    "\n",
+    "**Hint:** Your resulting function will need to have **two** separate `try/except blocks` - one for handling broken CSV rows and one for handling the broken JSON files."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70413fa1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Structure 4: `planets_list`\n",
+    "\n",
+    "You are now ready to read all the data about the planets stored in the `data` directory. You **must** now create a **list** containing `Planet` objects by parsing the data inside the files `planets_1.csv`, ..., `planets_5.csv` and `mapping_1.json`, ..., `mapping_5.json`.\n",
+    "\n",
+    "You **must** skip any **broken rows** in the CSV file, and also completely skip any **broken JSON files**. However, you are **not** allowed to **hardcode** the file you need to skip. You **must** call `get_planets` on **all** 5 pairs of files to answer this question.\n",
+    "\n",
+    "You **must** use the `get_planets` function on each of the five pairs of files in the `data` directory to create `planets_list`.\n",
+    "\n",
+    "**Hint:** Recall that you have already created the variable `json_file_paths` in q3. You can similarly create a list of paths of the files that start with `\"planets\"` (see q4 for a similar question). After sorting the paths in these **lists**,  you just need to loop through the indices of these two lists, and use that to extract the pairs of paths from these two lists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99bfdf1b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the variable 'planets_list' here,\n",
+    "# but do NOT display the variable at the end\n",
+    "\n",
+    "# TODO: create empty list planets_list\n",
+    "# TODO: get an alphabetical list of planet files and mapping files\n",
+    "# TODO: iterate through the indices of one of the lists\n",
+    "# TODO: call 'get_planets' and extend to 'planets_list'\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1265fad1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "If you wish to **verify** that you have read the files and defined `planets_list` correctly, you can check that `planets_list` has **4724** `Planet` objects in it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a49a9206",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 16:** What is the output of `planets_list[4520:4525]`?\n",
+    "\n",
+    "Your output **must** be a **list** of `Planet` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecf9f329",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'planets_4520_4525', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbf63953",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf453b2d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 17:** How many planets in `planets_list` were discovered in the year *2022*?\n",
+    "\n",
+    "Your output **must** be an **integer**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72bf6343",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'planets_disc_2022', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ecf7524",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "279cd43c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 18**: Find the `Star` object around which the `Planet` named *TOI-2202 c* orbits.\n",
+    "\n",
+    "Your output **must** be a a `Star` object.\n",
+    "\n",
+    "**Hint:** You **must** first find the `Planet` object with the `planet_name` *TOI-2202 c* and then use the `host_name` attribute to identify the name of the star aroud which the planet orbits. Then, you can get the `Star` object using the `stars_dict` **dictionary** defined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "875e2030",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'toi_2022_c_star', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be624e6a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60edb1d0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 19:** Find the **average** `planet_radius` (in units of the radius of the Earth) of the planets that orbit stars with `stellar_radius` more than *10* (i.e. more than *10* times the radius of the Sun).\n",
+    "\n",
+    "Your output **must** be a **float**. You **must** skip any `Planet` objects with **missing** `planet_radius` data and any `Star` objects with **missing** `stellar_radius` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8537ddc6",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'avg_planet_radius_big_stars', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74dc49fe",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "529b9294",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 20**: Find all the `Planet` objects that orbit the **youngest** `Star` object.\n",
+    "\n",
+    "Your output **must** be a **list** of `Planet` objects (even if there is **only one** `Planet` in the list). The age of a `Star` can be found from its `stellar_age` column. You **must** skip any stars with **missing** `stellar_age` data. You do **not** have to worry about any ties. There is a **unique** `Star` in the dataset which is the youngest star."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2374fc95",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'youngest_star_planets', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30a6f5d4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfe3d9fd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "Make sure you have run all cells in your notebook in order before running the following cells, so that all images/graphs appear in the output.\n",
+    "It is recommended that at this stage, you Restart and Run all Cells in your notebook.\n",
+    "That will automatically save your work and generate a zip file for you to submit.\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**:\n",
+    "1. **Upload** the zipfile to Gradescope.\n",
+    "2. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69ec12b2",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# running this cell will create a new save checkpoint for your notebook\n",
+    "from IPython.display import display, Javascript\n",
+    "display(Javascript('IPython.notebook.save_checkpoint();'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d2372c2",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "!jupytext --to py p10.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "048cc634",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "p10_test.check_file_size(\"p10.ipynb\")\n",
+    "grader.export(pdf=False, run_tests=True, files=[py_filename])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1dafe2e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "encoding": "# coding: utf-8",
+   "executable": "/usr/bin/env python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "planet_object": {
+     "name": "planet_object",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"qplanet_object\", jupiter)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q1\", files_in_data)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q10\", biggest_star)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q11\", avg_age_kepler)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q12\", fifth_planet)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q13\", last_five_planets_1)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q14\", controversial_planets)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q15\", last_five_planets_4)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q16\", planets_4520_4525)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q17\", planets_disc_2022)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q18\", toi_2022_c_star)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q19\", avg_planet_radius_big_stars)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q2\", file_paths)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q20\", youngest_star_planets)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q3\", json_file_paths)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q4\", stars_paths)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q5\", third_star)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q6\", dp_leo)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q7\", avg_lum_stars_1)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q8\", avg_age_stars_2)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"q9\", kepler_220_temp)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "star_object": {
+     "name": "star_object",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p10_test.check(\"qstar_object\", sun)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p10/p10.ipynb b/p10/p10.ipynb
index 6d5fea3826c72419248af568fa966b599963666a..abe009db6c495b673b610e473362b902e4b608ea 100644
--- a/p10/p10.ipynb
+++ b/p10/p10.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1e9cc6f8",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p11/.ipynb_checkpoints/p11-checkpoint.ipynb b/p11/.ipynb_checkpoints/p11-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..258e22b38a116df4af20b8eb5a9523a98bbdc35b
--- /dev/null
+++ b/p11/.ipynb_checkpoints/p11-checkpoint.ipynb
@@ -0,0 +1,2405 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1d13ef78",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74b9ac58",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import otter\n",
+    "# nb_name should be the name of your notebook without the .ipynb extension\n",
+    "nb_name = \"p11\"\n",
+    "py_filename = nb_name + \".py\"\n",
+    "grader = otter.Notebook(nb_name + \".ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc269163",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import p11_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d908b42",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "# Project 11: Analyzing Stars and Planets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bd1b2f9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "# Learning Objectives:\n",
+    "\n",
+    "In this project, you will demonstrate how to:\n",
+    "    \n",
+    "* analyze the data from p10,\n",
+    "* make scatter plots using `matplotlib`,\n",
+    "* remove outliers to make the plots more useful,\n",
+    "* use recursion to gather new data.\n",
+    "\n",
+    "**Please go through [lab-p11](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p11) before working on this project.** The lab introduces some important techniques related to this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a9be33b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: p10 and p11 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partnered up with someone for p10, you have to sustain that partnership until end of p11. Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f22/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e324d36",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the files `p11_test.py` and `p11_plots.json`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f836b43",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Setup:\n",
+    "\n",
+    "In p11, you will be analyzing the same dataset that you parsed in p10. You can either copy/paste the `data` directory to your p11 directory, or download the `data.zip` file provided with this project, and extract it. In addition to the `data` directory, you will also need to download additional datasets for p11. You must download `broken_data.zip` and extract it. You must extract the contents of the directory `broken_data` into the same directory which contains the `data` directory, `p11.ipynb`, `p11_test.py`, and `p11_plots.json`.\n",
+    "\n",
+    "You need to make sure that the project files are stored in the following structure:\n",
+    "\n",
+    "```\n",
+    "+-- p11.ipynb\n",
+    "+-- p11_test.py\n",
+    "+-- p11_plots.json\n",
+    "+-- data\n",
+    "|   +-- .DS_Store\n",
+    "|   +-- .ipynb_checkpoints\n",
+    "|   +-- mapping_1.json\n",
+    "|   +-- mapping_2.json\n",
+    "|   +-- mapping_3.json\n",
+    "|   +-- mapping_4.json\n",
+    "|   +-- mapping_5.json\n",
+    "|   +-- planets_1.csv\n",
+    "|   +-- planets_2.csv\n",
+    "|   +-- planets_3.csv\n",
+    "|   +-- planets_4.csv\n",
+    "|   +-- planets_5.csv\n",
+    "|   +-- stars_1.csv\n",
+    "|   +-- stars_2.csv\n",
+    "|   +-- stars_3.csv\n",
+    "|   +-- stars_4.csv\n",
+    "|   +-- stars_5.csv\n",
+    "+-- broken_data\n",
+    "|   +-- .DS_Store\n",
+    "|   +-- .ipynb_checkpoints\n",
+    "|   +-- hds\n",
+    "|   |   +-- .ipynb_checkpoints\n",
+    "|   |   +-- hd_1000s\n",
+    "|   |   |   +-- hd_10000s.json\n",
+    "|   |   +-- others.json\n",
+    "|   +-- k2s.json\n",
+    "|   +-- keplers\n",
+    "|   |   +-- kepler_100s\n",
+    "|   |   |   +-- kepler_100s\n",
+    "|   |   |   |   +-- kepler_100s\n",
+    "|   |   |   |   |   +-- kepler_100s.json\n",
+    "|   |   |   |   +-- others.json\n",
+    "|   |   |   +-- kepler_200s\n",
+    "|   |   |   |   +-- .ipynb_checkpoints\n",
+    "|   |   |   |   +-- kepler_220s.json\n",
+    "|   |   |   |   +-- kepler_290s.json\n",
+    "|   |   |   |   +-- others\n",
+    "|   |   |   |   |   +-- others.json\n",
+    "|   |   |   +-- others.json\n",
+    "|   |   +-- kepler_10s\n",
+    "|   |   |   +-- kepler_80s\n",
+    "|   |   |   |   +-- kepler_80s.json\n",
+    "|   |   |   +-- others\n",
+    "|   |   |   |   +-- kepler_20s.json\n",
+    "|   |   |   |   +-- kepler_30s.json\n",
+    "|   |   |   |   +-- others.json\n",
+    "|   |   +-- others\n",
+    "|   |   |   +-- .DS_Store\n",
+    "|   |   |   +-- others.json\n",
+    "|   +-- others\n",
+    "|   |   +-- .DS_Store\n",
+    "|   |   +-- gjs.json\n",
+    "|   |   +-- others.json\n",
+    "|   |   +-- tois\n",
+    "|   |   |   +-- tois.json\n",
+    "```\n",
+    "\n",
+    "Make sure that **all** files are stored in this **exact** file structure. Otherwise, then there is a possibility that your code will **fail on Gradescope** even after passing local tests."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "530f3090",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Description:\n",
+    "\n",
+    "You have already parsed the data in the `data` directory in p10. You will now dive deeper by analyzing this data and arrive at some exciting conclusions about various planets and stars outside our Solar System. You will also use recursion to retrieve data from the broken JSON file in the `data` directory, and ask some interesting questions about the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44fc8160",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices in your code, unless the question explicitly. If you open your `.csv` files with Excel, manually count through the rows and use this number to loop through the dataset, this is also considered as hardcoding. We'll **manually deduct** points from your autograder score on Gradescope during code review.\n",
+    "\n",
+    "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer. If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Functions:\n",
+    "- `get_all_paths_in`\n",
+    "- `get_surface_gravity`\n",
+    "- `get_distances_to_star`\n",
+    "- `get_liquid_water_distances`\n",
+    "- `get_surface_temperatures`\n",
+    "\n",
+    "In this project, you will also be required to define certain **data structures**. If you do not create these data structures exactly as specified, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Data Structures:\n",
+    "- `Star` (**namedtuple**)\n",
+    "- `stars_dict` (**dictionary** mapping **strings** to `Star` objects)\n",
+    "- `Planet` (**namedtuple**)\n",
+    "- `planets_list` (**list** of `Planet` objects)\n",
+    "- `star_classes` (**dictionary**)\n",
+    "- `all_planets_list` (**list** of `Planet` objects)\n",
+    "\n",
+    "In addition, you are also **required** to follow the requirements below:\n",
+    "\n",
+    "* You are **not** allowed to use **modules** like `pandas` to answer the questions in this project.\n",
+    "* You **must** properly **label** the axes of all your **plots**.\n",
+    "* Do **not** use meaningless names for variables or functions (example of what **not** to do: `uuu = \"my name\"`).\n",
+    "* Do **not** write the exact same code in multiple places. Instead, wrap this code into a function and call that function whenever the code should be used.\n",
+    "* Avoid **inappropriate** use of data structures. For example, using a for loop to search for a corresponding value in a dictionary with a given key instead of using `dictname[key]` directly.\n",
+    "* Do **not** use python keywords or built-in functions as variable names (example of what **not** to do: `str = \"23\"`).\n",
+    "* Do **not** define multiple functions with the same name or define multiple versions of one function with different names. Just keep the best version.\n",
+    "* Do **not** leave in irrelevant output or test code that we didn't ask for.\n",
+    "\n",
+    "We will **manually deduct** points if you do **not** follow these guidelines.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p11rubric.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bacfee51",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Questions and Functions:\n",
+    "\n",
+    "Let us start by importing all the modules we will need for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9313da0a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48ef8de2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Loading in the Stars and Planets:\n",
+    "\n",
+    "Before we can analyze the data in the `data` directory, you must first copy/paste all the functions and data strucutres you created in p10 to parse the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0d23c35",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the namedtuple 'Star' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d59989a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'process_csv' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3ea707b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'star_cell' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbe40271",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'get_stars' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5cab9b0c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the dictionary 'stars_dict' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "497e348f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the namedtuple 'Planet' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c21b3c3c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'read_json' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee7b3fc5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'planet_cell' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4abf9cdf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'get_planets' here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "191b997e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the list 'planets_list' here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80c26145",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "You used two functions `plot_scatter` and `plot_scatter_multiple` in lab-p11 to create your **scatter plots**. These functions are again provided for you here to use in p11."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c816d5da",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# remember to import matplotlib.pyplot as plt at the top of the notebook to make these functions work\n",
+    "\n",
+    "def plot_scatter(x_data, y_data, x_label='x axis', y_label='y axis', c=None, s=7):\n",
+    "    plt.scatter(x_data, y_data, c=c, s=s)\n",
+    "    plt.xlabel(x_label)\n",
+    "    plt.ylabel(y_label)\n",
+    "    \n",
+    "def plot_scatter_multiple(x_data_dict, y_data_dict, x_label='x axis', y_label='y axis'):\n",
+    "    legend_values = list(x_data_dict.keys())\n",
+    "    for key in x_data_dict:\n",
+    "        plt.scatter(x_data_dict[key], y_data_dict[key], s=7)\n",
+    "    plt.xlabel(x_label)\n",
+    "    plt.ylabel(y_label)\n",
+    "    plt.legend(legend_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48e7f827",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Verifying Laws of Nature:\n",
+    "\n",
+    "We will now use our dataset to verify some well-known laws of nature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf3bb533",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "#### Kepler's Third Law:\n",
+    "\n",
+    "We will first verify [Kepler's Third Law](https://en.wikipedia.org/wiki/Kepler%27s_laws_of_planetary_motion#Third_law). This law states that the **square** of the `orbital_period` of each planet in a solar system is directly proportional to the **cube** of the `semi_major_radius` of its orbit around its host star.\n",
+    "\n",
+    "Since this law relates only to planets that orbit the same host star, we will verify this law using the several planets orbiting around a star named *GJ 9827*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e006d189",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 1:** Compute the **ratio** of the **square** of the `orbital_period` to the **cube** of the `semi_major_radius` of each planet orbiting the star *GJ 9827*.\n",
+    "\n",
+    "Your output **must** be a **list** of **floats**. You may **assume** that the planets orbiting this star do not have any missing `orbital_period` or `semi_major_radius` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2cba30f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'ratios_gj9827', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4da96851",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daf04638",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "The **ratios** of the three stars in this system appear to be very close to each other. It will be useful if we could quantify exactly how close these ratios are to each other. One way to do that would to be compute the [coefficient of variance](https://en.wikipedia.org/wiki/Coefficient_of_variation), which is defined as the **standard deviation** divided by the **mean** of a sequence of numbers. A low value would imply that the numbers are very **close** to each other."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afbe0e71",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 2:** Compute the **coefficient of variance** of the **list** `ratios_gj9827`.\n",
+    "\n",
+    "**Hint:** You can compute the **standard deviation** and the **mean** of a **list** of numbers using the `statistics.stdev` and `statistics.mean` functions inside the `statistics` module. To do this, you must first **import** the `statistics` module. You can read the documentation for the `statistics.stdev` function [here](https://docs.python.org/3.9/library/statistics.html#statistics.stdev), and the documentation for `statistics.mean` [here](https://docs.python.org/3.9/library/statistics.html#statistics.mean)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91d667d5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'coeff_gj9827', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f051105b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eb53310",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "As we can see, the **coefficient of variance** is indeed very low. This lends credibility to Kepler's Third Law. However, there is yet more we can do with this data. After we adjust for the units used in the project, we find that Kepler's Third Law predicts the following:\n",
+    "\n",
+    "$$\\texttt{stellar mass} = \\frac{133408}{\\texttt{ratio}}$$\n",
+    "\n",
+    "where $\\texttt{ratio}$ is the **mean** of the ratios of the **square** of the `orbital_period` to the **cube** of the `semi_major_radius` computed above, and $\\texttt{stellar mass}$ is the mass of the planets' host star.\n",
+    "\n",
+    "We can therefore check how close this **predicted** `stellar_mass` is to the **actual** `stellar_mass` of the star."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75f6137a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 3:** Compute the percentage change of the **predicted** `stellar_mass` from the **actual** `stellar_mass` of the star *GJ 9827*.\n",
+    "\n",
+    "You **must** compute the **predicted** `stellar_mass` as the number *133408* divided by the **mean** of the ratios of the three planets computed in q1. You **must** find the **actual** `stellar_mass` by accessing the correct attribute of the `Star` object of *GJ 9827*. The percentage change can be computed as:\n",
+    "\n",
+    "$$\\texttt{percent change} = \\frac{\\texttt{predicted stellar mass} - \\texttt{actual stellar mass}}{\\texttt{actual stellar mass}} \\times 100$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d65de0f4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'percentage_change', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e22e0a5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77eb3750",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "#### Stefan-Boltzmann Law:\n",
+    "\n",
+    "We will now verify the [Stefan-Boltzmann Law](https://en.wikipedia.org/wiki/Stefan%E2%80%93Boltzmann_law). This law states that the `insolation_flux` of a *black body* is directly proportional to the **fourth** power of the `equilibrium_temperature`. In our dataset, we have the `insolation_flux` and `equilibrium_temperature` data of the `Planet` objects. So, we can verify how well this law is obeyed by our dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2504bc0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 4:** Create a **scatter plot** representing the `insolation_flux` (on the **x-axis**) against the **fourth power** of the `equilibrium_temperature` (on the **y-axis**) of each `Planet` object in `planets_list`.\n",
+    "\n",
+    "You **must** ignore all `Planet` objects with **missing** `insolation_flux`, or `equilibrium_temperature` data.\n",
+    "\n",
+    "You **must** first compute two **lists** containing the **insolation_flux**, and the **equilibrium_temperature** of each `Planet` object (which has all the data available). Then, you **must** use `plot_scatter` to plot the **insolation_flux** against the fourth power of the **equilibrium_temperature**.\n",
+    "\n",
+    "**Important Warning:** `p11_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "insolation_temp.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "5abfa633",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:insolation_temp.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53631371",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the lists 'flux_list', and 'temp_4th_power_list'\n",
+    "# then create the scatter plot using the lists\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6d2ec6e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08ef6ddf",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Why does this graph look so strange with all the points bunched up near the bottom-left corner?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2381e72e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 5:** Excluding planets with `insolation_flux` **greater than** *7000*, create a **scatter plot** representing the `insolation_flux` (on the **x-axis**) against the **fourth power** of the `equilibrium_temperature` (on the **y-axis**) of each `Planet` object in `planets_list`.\n",
+    "\n",
+    "You **must** ignore all `Planet` objects with **missing** `insolation_flux`, or `equilibrium_temperature` data.\n",
+    "\n",
+    "You **must** first compute two **lists** containing the **insolation_flux**, and the **star equilibrium_temperature** of each `Planet` object (which has all the data available). Then, you **must** use `plot_scatter` to plot the **insolation_flux** against the fourth power of the **equilibrium_temperature**.\n",
+    "\n",
+    "**Important Warning:** `p11_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "insolation_temp_wo_outliers.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "b51e505f",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:insolation_temp_wo_outliers.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfc52de0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the lists 'flux_list_no_outliers', and 'temp_4th_power_list_no_outliers'\n",
+    "# then create the scatter plot using the lists\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2151a139",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7a17cde",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Does the relationship between **insolation flux** and the **fourth power of the equilibrium temperature** appear to be **linear** as predicted by the Stefan-Boltzmann Law? Can you explain why the graph isn't perfectly linear?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b60dace",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Stellar Evolution:\n",
+    "\n",
+    "[Stellar Evolution](https://en.wikipedia.org/wiki/Stellar_evolution) is a description of the way that stars change with time. The primary factor determining how a star evolves is its `stellar_mass`. Depending on the `stellar_mass` of each `Star`, astronomers can predict how the `Star` will end up. A `Star` whose `stellar_mass` is $\\geq 0.3$ and $< 8$ times the mass of the Sun will become a [Red Giant](https://en.wikipedia.org/wiki/Red_giant), while a `Star` whose `stellar_mass` is $\\geq 8$ and $< 10.5$ times the mass of the Sun will become a [White Dwarf](https://en.wikipedia.org/wiki/White_dwarf). A `Star` that is even bigger will end up as a [Neutron Star](https://en.wikipedia.org/wiki/Neutron_star)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f427a12",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Structure 1: `star_classes`\n",
+    "\n",
+    "You **must** now classify the `Star` objects in `stars_dict` using their `stellar_mass`. You **must** create a **dictionary** `star_classes` with the **keys**: `Red Giant`, `White Dwarf`, and `Neutron Star`. The **value** of each **key** must be a **list** of **strings** containing the **names** of the `Star` objects.\n",
+    "\n",
+    "You **must** **ignore** `Star` objects for which we do not have `stellar_mass` data or have `stellar_mass` **less** than *0.3* Solar masses.\n",
+    "\n",
+    "**Hint:** Recall that the `stellar_mass` data already uses units of *Solar masses*. So, a `stellar_mass` of *1* means that the `Star` object has the same mass as the Sun, and a `stellar_mass` of 2 means the `Star` object has twice the mass of the Sun, and so on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55b94da4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the variable 'star_classes' here\n",
+    "# but do NOT display\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58c44389",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "You can **verify** that you have defined `star_classes` correctly by checking that there are *3756* Red Giants, *3* White Dwarfs, and *1* Neutron Star in `star_classes`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5f7ebf0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 6:** What is the **average** `stellar_luminosity` of each class of `Star` objects in `star_classes`?\n",
+    "\n",
+    "Your output **must** be a **dictionary** mapping the class of the star to the **average** `stellar_luminosity` value of all `Star` objects of that class. You **must** ignore the `Star` objects with **missing** `stellar_luminosity` data.\n",
+    "\n",
+    "The expected output of this question is:\n",
+    "\n",
+    "```python\n",
+    "{'Red Giant': -0.01901339529797699,\n",
+    " 'White Dwarf': 2.787333333333333,\n",
+    " 'Neutron Star': 2.86}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f719323",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'star_classes_avg_lum', then display it\n",
+    "\n",
+    "# TODO: initialize 'star_classes_avg_lum'\n",
+    "# TODO: loop through each 'star_class' in 'star_classes'\n",
+    "    # TODO: loop through each 'star' in the 'star_class'\n",
+    "        # TODO: skip 'star' if 'stellar_luminosity' data is missing\n",
+    "        # TODO: for the remaining stars, compute the mean of the 'stellar_luminosity'\n",
+    "    # TODO: add the mean luminosity to 'star_classes_avg_lum'\n",
+    "    \n",
+    "# TODO: display 'star_classes_avg_lum'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f359315",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d28754f1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Recall that the `stellar_luminosity` values of the `Star` objects are represented in units of the logarithm of the Sun's luminosity. What does this difference in `stellar_luminosity` signify?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da654437",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Just as the different classes of `Star` objects have different **average luminosities**, they also have different **average densities**. This difference will be easier to visualize as a **scatter plot**.\n",
+    "\n",
+    "However, before you can do that, there is a minor hurdle you need to overcome - we do **not** have the *stellar density* data available for the `Star` objects in our dataset. However, we do have `stellar_mass` and `stellar_radius` data, which allows us to **compute** the *stellar density*. Since the `stellar_mass` and `stellar_radius` data is stored in units of the Sun's mass and radius respectively, we can compute the *stellar density* (i.e., density of the `Star` in units of the Sun's density) as follows:\n",
+    "\n",
+    "$$\\texttt{stellar density} = \\frac{\\texttt{stellar mass}}{(\\texttt{stellar radius})^{3}}.$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e55b1554",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 7:** Create a **scatter plot** representing the *stellar density* (on the **x-axis**) against the `stellar_luminosity` (on the **y-axis**) of each `Star` object of **each class** in `star_classes`.\n",
+    "\n",
+    "You **must** ignore all `Star` objects with **missing** `stellar_mass`, `stellar_radius`, or `stellar_luminosity` data.\n",
+    "\n",
+    "You **must** first compute two **dictionaries**. The **keys** of both dictionaries must be the different **star classes**, and the corresponding values must be the **list** of **densities** and **list** of **luminosities** of `Star` objects of that **star class**. Then, you **must** use `plot_scatter_multiple` to plot the **density** against the **luminosity** of each **star class**.\n",
+    "\n",
+    "**Important Warning:** `p11_test.py` can check that the **dictionaries** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "density_luminosity.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "8c04194f",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:density_luminosity.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da936d95",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the dictionaries 'density_dict', and 'lum_dict'\n",
+    "# then create the scatter plot using the dictionaries\n",
+    "\n",
+    "# TODO: initialize the two dictionaries 'density_dict', and 'lum_dict'\n",
+    "# TODO: loop through each 'star_class' in 'star_classes'\n",
+    "    # TODO: add the 'star_class' to 'density_dict' and 'lum_dict'\n",
+    "    # TODO: loop through each 'star' in the 'star_class'\n",
+    "        # TODO: skip 'star' if mass, radius, or luminosity data is missing\n",
+    "        # TODO: otherwise add the luminosity to the correct key of 'lum_dict'\n",
+    "        # TODO: compute the density and add to the correct key of 'density_dict'\n",
+    "    \n",
+    "# TODO: use the 'plot_scatter_multiple' function to create the plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ea7e85f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f4962ee",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** As you can see, there are **two** extreme outliers with a very high density. If you are interested, you can try to find out the names of these stars, and why they have such extremely high densities (and low luminosities). What (incorrect) assumption did we make when we classified the `Star` objects in `star_classes`? Can you suggest a more accurate way of classifying the stars now?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37fd9bab",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "As you can see, almost all the `Star` objects have low *stellar density*, and the presence of a few extreme outliers is obscuring our view of the other `Star` objects. In fact, it turns out that there are only *10* `Star` objects in the dataset with a *stellar density* **greater than** *25*. We could get a much clearer view of the relationship between *stellar density* and `stellar_luminosity` if we did **not** plot these outliers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd109af4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 8:** **Excluding** stars with *stellar density* **greater than** *25*, create a **scatter plot** representing the *stellar density* (on the **x-axis**) against the `stellar_luminosity` (on the **y-axis**) of each `Star` object of **each class** in `star_classes`.\n",
+    "\n",
+    "You **must** ignore all `Star` objects with **missing** `stellar_mass`, `stellar_radius`, or `stellar_luminosity` data.\n",
+    "\n",
+    "You **must** first compute two **dictionaries**. The **keys** of both dictionaries must be the different **star classes**, and the corresponding values must be the **list** of **densities** and **list** of **luminosities** of `Star` objects of that **star class**. Then, you **must** use `plot_scatter_multiple` to plot the **density** against the **luminosity** of each **star class**.\n",
+    "\n",
+    "**Important Warning:** `p11_test.py` can check that the **dictionaries** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "density_luminosity_wo_outliers.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "87e62b2e",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:density_luminosity_wo_outliers.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d5fd652",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the dictionaries 'density_dict_no_outliers', and 'lum_dict_no_outliers'\n",
+    "# then create the scatter plot using the dictionaries\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f962c158",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59dd755e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Can you guess the relationship between **density** and **luminosity**? Can you spot the `Star` objects in this graph which will end up as White Dwarfs and Neutron Stars? Do they appear to follow the same relationship as the Red Giants? How do they compare to the outliers you found in q7?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab798cec",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Hertzsprung–Russell Diagram:\n",
+    "\n",
+    "The [Hertzsprung–Russell diagram](https://en.wikipedia.org/wiki/Hertzsprung%E2%80%93Russell_diagram) is a scatter plot of stars showing the relationship between the stars' `stellar_luminosity` versus their `stellar_effective_temperature`. The diagram is exceedingly useful for understanding the stellar evolution of stars. We will now use the data we have available to plot this diagram ourselves, so we can better understand stellar evolution.\n",
+    "\n",
+    "We want to plot the `stellar_effective_temperature` against the `stellar_luminosity`, but more importantly, we will use the **color** and **size** parameters to represent the `stellar_age` and `stellar_mass` of the `Star` objects as well. This will allow us to see the effects of `stellar_age` and `stellar_mass` on `stellar_effective_temperature` and `stellar_luminosity`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d740c535",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 9**: Create a **scatter plot** representing the `stellar_effective_temperature` (on the **x-axis**) against the `stellar_luminosity` (on the **y-axis**) of each `Star` object in `stars_dict`. Moreover, represent the `stellar_age` of each `Star` object using the **color** and represent the `stellar_mass` of each `Star` object using the **size** of the star.\n",
+    "\n",
+    "You **must** first compute four **lists** containing the `stellar_effective_temperature`, `stellar_luminosity`, `stellar_age` and the `stellar_mass` of each `Star` object (which has **all** the data available). You **must** ignore any `Star` object which has any of these four attributes **missing**. Then, you **must** use `plot_scatter` to plot the `stellar_effective_temperature` against the `stellar_luminosity` with the `stellar_age` as the **color** and the `stellar_mass` as the **size** of the points.\n",
+    "\n",
+    "**Important Warning:** `p11_test.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "hr_diagram.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "659a9710",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:hr_diagram.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b666700f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the lists 'temp_list', 'lum_list', 'age_list', and 'mass_list'\n",
+    "# then create the scatter plot using the lists\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9211f63",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bde8d153",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Can you tell if there is any relationship between the **temperature**, **luminosity**, **age**, and **mass** of the stars? You might want to remove the outliers with the extremely high `stellar_effective_temperature` to get a better view of the diagram. What effect does the **age** seem to have on the **temperature**? Recall that a **lighter** color implies that the value is higher, while a **darker** color implies that the value is lower. What effect does the **mass** have on **luminosity** and **temperature**? Are stars of all masses distributed evenly across the plot? Where are the heavier stars concentrated?\n",
+    "\n",
+    "**Food for thought:** Notice that there are **two distinct** *clusters* of points in this diagram. If you are interested, look up more information on the Hertzsprung–Russell Diagram to understand what these clusters are. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71143e9a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Recursion:\n",
+    "\n",
+    "You are not done exploring the dataset, and you have more questions left to answer. However, something more important has happened! We have managed to find the data from the corrupted json file (`mapping_5.json`)!\n",
+    "\n",
+    "If you will recall, when we were parsing the files in p10, we found that `mapping_5.json` was **broken**, and we couldn't read it. Therefore, we had no choice but to leave all the planets in `planets_5.csv` out of our analysis. Luckily for you now, the data has shown up intact in the directory `broken_data`. Unfortunately, the data is now no longer stored in a single file, but has been **split up** into **multiple files** and stored in **different subdirectories**.\n",
+    "\n",
+    "You will now create a function to help parse all the data stored within this directory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58cdf18b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 1:  `get_all_paths_in(directory)`\n",
+    "\n",
+    "You **must** write this function that takes in the **relative path** of a `directory` as its input, and returns a **list** of **relative paths** of all the **files** inside `directory` and its subdirectories.\n",
+    "\n",
+    "In other words, if a directory `small_data` looks like this:\n",
+    "```\n",
+    "+-- sample_data\n",
+    "|   +-- .DS_Store\n",
+    "|   +-- file_1.json\n",
+    "|   +-- sample_1\n",
+    "|   |   +-- .ipynb_checkpoints\n",
+    "|   |   +-- file_2.json\n",
+    "|   |   +-- file_3.json\n",
+    "|   +-- sample_2\n",
+    "|   |   +-- file_4.json\n",
+    "|   |   +-- sample_3\n",
+    "|   |   |   +-- .DS_Store\n",
+    "|   |   |   +-- file_5.json\n",
+    "```\n",
+    "\n",
+    "then the output of the function call `get_all_paths_in(\"sample_data\")` **must** be a **list** containing the **relative paths** of the files `file_1.json`, `files_2.json`, `file_3.json`, `file_4.json`, and `file_5.json`.\n",
+    "\n",
+    "You **must** **ignore** all files that start with `\".\"`, and your output **must** be **explicitly** sorted in **alphabetical** order.\n",
+    "\n",
+    "**Important Warning:** You **must** write a **recursive** function here. You are **only allowed** to use the functions from the `os` module, which have been covered in lecture. Here is a list of these functions (you will only need a few of these functions to define `get_all_paths_in`):\n",
+    "* `os.mkdir`\n",
+    "* `os.path.join`\n",
+    "* `os.listdir`\n",
+    "* `os.path.exists`\n",
+    "* `os.path.isfile`\n",
+    "* `os.path.isdir`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0d8b603",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function 'get_all_paths_in' here\n",
+    "\n",
+    "def get_all_paths_in(directory):\n",
+    "    '''get_all_paths_in(directory) recursively \n",
+    "    explores that directory for any files, \n",
+    "    and returns a list of paths to \n",
+    "    files inside that directory'''\n",
+    "    pass # replace with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2254fd9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 10:** What are the **paths** of the files in the `others` directory of the `broken_data` directory?\n",
+    "\n",
+    "Your output **must** be a **list** of **relative paths**. You **must** answer this question by calling the `get_paths_in` function.\n",
+    "\n",
+    "**Warning:** Remember that you **must** only use `os.path.join` to create paths."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "120958a2",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'broken_data_others', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9a83adf",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c357739",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 11:** What are the **paths** of the files in the `kepler_100s` directory of the `keplers` directory of the `broken_data` directory?\n",
+    "\n",
+    "Your output **must** be a **list** of **relative paths**. You **must** answer this question by calling the `get_all_paths_in` function.\n",
+    "\n",
+    "**Hint:** You can pass multiple **strings** as arguments to `os.path.join` to join them together at the same time. For example, to get the path of the required directory here, you could say\n",
+    "```python\n",
+    "os.path.join(\"broken_data\", \"keplers\", \"kepler_100s\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eabfca6b",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'broken_data_keplers_kepler_100s', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e186ec3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e94fe77",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 12:** What are the **paths** of the files in the `others` directory of the `kepler_10s` directory of the `keplers` directory of the `broken_data` directory?\n",
+    "\n",
+    "Your output **must** be a **list** of **relative paths**. You **must** answer this question by calling the `get_all_paths_in` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3ae1714",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'broken_data_keplers_kepler_10s_others', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b53ed2fe",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3cbf6d8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 13:** What are the **paths** of the files in the `broken_data` directory?\n",
+    "\n",
+    "Your output **must** be a **list** of **relative paths**. You **must** answer this question by calling the `get_paths_in` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "345fbf22",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'broken_data', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf3b041b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad157bfb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Structure 2: `all_planets_list`\n",
+    "\n",
+    "We want to read the data that is stored inside the directory `broken_data`. We already have a function `get_planets` (from p10) which can read a CSV file and a JSON file and combine them to create a **list** of `Planet` objects. So, we can repeatedly call `get_planets` on the CSV file `planets_5.csv` and each of the JSON files inside `broken_data` to get a **list** of `Planet` objects of **all** the planets in `planets_5.csv`.\n",
+    "\n",
+    "You **must** **create** the **list** `all_planets_list` by adding in all `Planet` objects from `planets_list`, and then also adding in the `Planet` objects in `planets_5.csv` and the directory `broken_data`.\n",
+    "\n",
+    "**Hint:** You **must** loop through every file in the list `broken_data`, and use `get_planets` on `planets_5.csv` (inside the `data` directory), and this file (from the loop) to create a list of `Planet` objects, and then **extend** `all_planets_list` by the list of new `Planet` objects.\n",
+    "\n",
+    "**Warning:** Do **not** update the value of the **list** `planets_list` when you do this. Otherwise, your answers to some of the previous questions will become incorrect. Instead, make sure that the new `Planet` objects are only added to `all_planets_list` and **not** to `planets_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f470ef3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the variable 'all_planets_list' here,\n",
+    "# but do NOT display the variable at the end\n",
+    "\n",
+    "# TODO: initialize 'all_planets_list'\n",
+    "# TODO: add the planets in 'planets_list' to 'all_planets_list'\n",
+    "# TODO: loop through all paths in 'broken_data'\n",
+    "    # TODO: use 'get_planets' to get the planets in this file and add them to 'all_planets_list'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b023db",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "You can verify that you have not made any mistakes by confirming that `all_planets_list` now has *5174* `Planet` objects in it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e94b7c9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Exploring habitability of exoplanets:\n",
+    "\n",
+    "Now that we have gathered the data on all the `Planet` objects, we are ready to have some fun with this dataset. Over the course of the rest of this project, we will try to find out if there are any planets in our dataset which could potentially support human habitation. Naturally, we cannot say with any certainty that any particular planet is habitable, but we can say with some confidence when a planet is **not** habitable (notwithstanding major technological gains). That is exactly what we will do now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad603705",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "#### Surface Gravitational Force:\n",
+    "\n",
+    "It seems reasonable to expect that for humans to be able to survive on a planet, the gravitational force of the planet on its surface is not too different from that of the Earth.\n",
+    "\n",
+    "We note that this is because the `planet_mass` and `planet_radius` attributes of the `Planet` objects already stores these values in units of the mass of the Earth, and the radius of the Earth respectively. So, the **ratio** of the gravitational force experienced on the surface of a given planet to the force experienced on the surface of the Earth can be computed as:\n",
+    "\n",
+    "$$\\frac{g_{\\texttt{planet}}}{g_{\\texttt{earth}}} = \\frac{\\texttt{planet mass}}{\\texttt{planet radius}^{2}}$$\n",
+    "\n",
+    "So, a **ratio** greater than 1 would imply that a person on the planet's surface would experience a greater force due to gravity than on Earth, while a value lower than 1 would imply that a person on the planet's surface would experience a lesser force due to gravity than on Earth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93266f13",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 2: `get_surface_gravity(planet)`\n",
+    "\n",
+    "You **must** define this function which takes in a `Planet` object as its input, and then **returns** the **ratio** of the gravitational force experienced on the surface of a given planet to the force experienced on the surface of the Earth. If either the `planet_mass` or `planet_radius` data is **missing**, then your function **must** return `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ea5178d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function 'get_surface_gravity' here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aecb82f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 14:** What is the **ratio** of gravitational force experienced on the surface of the planet *GJ 674 b* to the gravitational force experienced on the surface of the Earth?\n",
+    "\n",
+    "**Hint:** You will have to first loop through `all_planets_list` to identify the correct `Planet` object. Remember to `break` out of your loop after you identify the correct `Planet` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e9d5ad6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'gj_674_b_gravity', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5dcb4898",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d50dc41f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "#### Distance to the star:\n",
+    "\n",
+    "Planets follow **elliptical** orbits around their host star. The `eccentricity` of a planet's orbit is a number that measures *how* elliptical the orbit is. An eccentricity of *0* would imply that the orbit is in fact perfectly circular, while an eccentricity close to *1* would imply that the orbit is very skewed and elliptical. As you may expect, if a planet has a highly eccentric orbit, its distance to its host star would vary wildly, leading to a highly variable climate. To determine if a planet could support human habitation, it is therefore important to know the closest and shortest distances between the planet and its host star.\n",
+    "\n",
+    "We can compute these quantities using the attributes `eccentricity` and `semi_major_radius` of each `Planet` object. These distances can be computed as:\n",
+    "\n",
+    "$$\\texttt{shortest distance} = \\texttt{semi major radius} \\times (1 - \\texttt{abs}(\\texttt{eccentricity}))$$\n",
+    "\n",
+    "$$\\texttt{longest distance} = \\texttt{semi major radius} \\times (1 + \\texttt{abs}(\\texttt{eccentricity}))$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20766746",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 3: `get_distances_to_star(planet)`\n",
+    "\n",
+    "You **must** define this function which takes in a `Planet` object as its input, and then **returns** a **list** of two **floats**. The first float should be the **shortest distance** of the `Planet` object to its host star, and the second float should be the **longest distance** to its host star. If either the `eccentricity` or `semi_major_radius` data of the `Planet` is missing, then the function **must** return `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8c12ed8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function 'get_distances_to_star' here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "089e51ff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 15:** Find the **shortest** and **longest** distances for the planet *b Cen AB b* to its host star.\n",
+    "\n",
+    "Your output **must** be a **list** of two **floats** representing the **shortest** and **longest** distances to its host star."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42de2cc5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'distances_to_star_b_cen_ab_b', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84df9fad",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d20ef431",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "#### Presence of Liquid Water :\n",
+    "\n",
+    "It is safe to say that planets which cannot sustain liquid are inhabitable. While we do not have any data on whether the `Planet` objects in our dataset have naturally occurring water, we are able to determine whether the planet can *support* liquid water based on its distance to its host star, and the luminosity of this star. \n",
+    "\n",
+    "Astronomers have [computed](https://pubmed.ncbi.nlm.nih.gov/11536936/) that for Earth-like planets, there is a certain range of distances that a planet can have to its host star, which depends on the `luminosity` of the star, within which, water on the planet's surface can stay in liquid form. These distances are as follows:\n",
+    "\n",
+    "$$\\texttt{liquid water shortest dist} = \\sqrt{\\frac{\\texttt{absolute luminosity}}{1.15}}$$\n",
+    "\n",
+    "$$\\texttt{liquid water longest dist} = \\sqrt{\\frac{\\texttt{absolute luminosity}}{0.53}}$$\n",
+    "\n",
+    "In our dataset, the `stellar_luminosity` is stored in units of the logarithm of the absolute luminosity. So, the distances can be computed from our dataset as follows:\n",
+    "\n",
+    "$$\\texttt{liquid water shortest dist} = \\sqrt{\\frac{10^{\\texttt{stellar luminosity}}}{1.15}}$$\n",
+    "\n",
+    "$$\\texttt{liquid water longest dist} = \\sqrt{\\frac{10^{\\texttt{stellar luminosity}}}{0.53}}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8957b82c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 4: `get_liquid_water_distances(planet)`\n",
+    "\n",
+    "You **must** define this function which takes in a `Planet` object as its input, and then **returns** a **list** of two **floats**. The first float should be the **shortest distance** the `Planet` object can be to its host star while being able to support liquid water, and the second float should be the **longest distance** it can be to its host star while being able to support liquid water. If the `stellar_luminosity` data of the host `Star` object is missing, then the function **must** return `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f1a6ff1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function 'get_liquid_water_distances' here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a278b784",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 16:** Find the **shortest** and **longest** distances for the planet *Kepler-197 e* from its host star, at which it can support liquid water.\n",
+    "\n",
+    "Your output **must** be a **list** of two **floats** representing the **shortest** and **longest** distances that the planet can be from its host star and still support liquid water."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9226415f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'liquid_water_distances_kepler_197_e', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bf476c9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c507c8b6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 17:** **List** the `planet_name` of all the `Planet` objects which can support liquid water when they are at **both** their **shortest** and **longest** distances to their host star.\n",
+    "\n",
+    "Your output **must** be a **list**. You **must** ignore `Planet` objects with missing `eccentricity`, or `semi_major_radius` data and planets whose host `Star` has missing `stellar_luminosity` data.\n",
+    "\n",
+    "**Hint:** You can find the actual shortest and longest distances of the planet with the `get_distances_to_star` function, and the shortest and longest distances at which liquid water can be supported with the `get_liquid_water_distances` function. You must consider `Planet` objects for which the actual distances to their host star lie **within** the distances at which liquid water can be supported."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd75c6b6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'planets_with_liquid_water', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "814f9467",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a023eeff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "#### Surface temperature:\n",
+    "\n",
+    "The temperature on the surface of the planet is another important criteria for deciding whether a planet is habitable. The `equilibrium_temperature` of a `Planet` is the temperature that the planet if it were a [black body](https://en.wikipedia.org/wiki/Black_body), i.e., if it were able to absorb all the radiation it receives from its host star. However, most planets are not perfect black bodies and reflect some of the radiation that they receive from their host star. Astronomers use the quantity [albedo](https://en.wikipedia.org/wiki/Albedo) to measure how much radiation is reflected by the planet. An albedo of *0* implies that the planet is a perfect black body which absorbs all its radiation, while an albedo of *1* implies that the planet is perfectly reflective, and does not retain any radiation. In the real world, most planets have an albedo value between *0* and *0.5*.\n",
+    "\n",
+    "Using the albedo of a planet, we can compute the temperature on the surface of a planet as follows\n",
+    "\n",
+    "$$ \\texttt{surface temperature} = \\left(1- \\texttt{albedo}\\right) ^{1/4} \\times \\texttt{equilibrium temperature}$$\n",
+    "\n",
+    "Unfortunately, we do **not** have the albedo values of the `Planet` objects in our dataset. So, we will instead make some educated guesses and find the **maximum** and **minimum** surface temperatures, assuming that the albedo is within the range of *0* to *0.5* (which is known to be the case for most planets)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cc932b8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 5: `get_surface_temperatures(planet)`\n",
+    "\n",
+    "You **must** define this function which takes in a `Planet` object as its input, and then **returns** a **list** of two **floats**. The first float should be the **minimum surface temperature** of the `Planet` object (which can be computed by assuming a **albedo** value of *0.5*), and the second float should be the **maximum surface temperature** (which can be computed by assuming a **albedo** value of *0.0*). If the `equilibrium_temperature` data of the `Planet` is missing, then the function **must** return `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9ebd0f3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function 'get_surface_temperatures' here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8cbb68e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 18:** Find the **minimum** and **maximum** surface temperatures for the planet *HD 20794 d*.\n",
+    "\n",
+    "Your output **must** be a **list** of two **floats** representing the **minimum** and **maximum** surface temperatures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bf690a3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'surface_temp_hd_20794_d', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bb3adcc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c4dcfcf",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 19:** **List** the `planet_name` of all the `Planet` objects whose **minimum surface temperature** is **greater** than *263* (Kelvin) and **maximum surface temperature** is **less** than *323* (Kelvin).\n",
+    "\n",
+    "Your output **must** be a **list**. You **must** ignore `Planet` objects with missing `equilibrium_temperature` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4565a90",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'pleasant_planets', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7eb04ded",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9fea139",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "#### Putting it all together:\n",
+    "\n",
+    "We are finally ready to combine all our various criteria of habitability to make a list of planets which satisfy all the criteria above, and could potentially be habitable. Unsurprisingly, if we are too strict with our expectations, no planets in the dataset will meet them. So, allowing for some technological improvements in the future, we will make more modest requests of the planets in our dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14fa1a59",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 20:** List the `planet_name` of all the `Planet` objects which satisfy the criteria below:\n",
+    "\n",
+    "1. The gravitational force experienced on the surface of the `Planet` must be **greater** than *0.75* and **less** than *1.25* times that of the Earth.\n",
+    "2. The planet must always **lie within** the range at which it is able to support liquid water.\n",
+    "3. The **minimum** surface temperature must be **greater** than *200* and the **maximum** surface temperature must be **less** than *350*.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**. You **must** ignore any `Planet` objects for which you cannot determine if any of these criteria are met."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc77a0f0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'habitable_planets', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f41dfc1f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c06ab4a6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** If you are interested, you can play around these values more, and introduce more stringent requirements to try and find the single **most** habitable planet."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f196b52",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "Make sure you have run all cells in your notebook in order before running the following cells, so that all images/graphs appear in the output.\n",
+    "It is recommended that at this stage, you Restart and Run all Cells in your notebook.\n",
+    "That will automatically save your work and generate a zip file for you to submit.\n",
+    "\n",
+    "If the last cell fails to run because of the file size, delete the images that we have provided in this notebook as examples, and run the last cell again.\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**:\n",
+    "1. **Upload** the zipfile to Gradescope.\n",
+    "2. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4eb77e04",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# running this cell will create a new save checkpoint for your notebook\n",
+    "from IPython.display import display, Javascript\n",
+    "display(Javascript('IPython.notebook.save_checkpoint();'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff9c4186",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "!jupytext --to py p11.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "462d836f",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "p11_test.check_file_size(\"p11.ipynb\")\n",
+    "grader.export(pdf=False, run_tests=True, files=[py_filename])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a8be918",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "encoding": "# coding: utf-8",
+   "executable": "/usr/bin/env python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q1\", ratios_gj9827)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q10\", broken_data_others)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q11\", broken_data_keplers_kepler_100s)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q12\", broken_data_keplers_kepler_10s_others)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q13\", broken_data)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q14\", gj_674_b_gravity)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q15\", distances_to_star_b_cen_ab_b)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q16\", liquid_water_distances_kepler_197_e)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q17\", planets_with_liquid_water)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q18\", surface_temp_hd_20794_d)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q19\", pleasant_planets)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q2\", coeff_gj9827)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q20\", habitable_planets)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q3\", percentage_change)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q4\", {'flux_list': flux_list, 'temp_4th_power_list': temp_4th_power_list})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q5\", {'flux_list_no_outliers': flux_list_no_outliers, 'temp_4th_power_list_no_outliers': temp_4th_power_list_no_outliers})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q6\", star_classes_avg_lum)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q7\", {'density_dict': density_dict, 'lum_dict': lum_dict})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q8\", {'density_dict_no_outliers': density_dict_no_outliers, 'lum_dict_no_outliers': lum_dict_no_outliers})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p11_test.check(\"q9\", {'temp_list': temp_list, 'lum_list': lum_list, 'age_list': age_list, 'mass_list': mass_list})\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p11/p11.ipynb b/p11/p11.ipynb
index 9f785c547fe110308078181d7c728e8960d5ce38..258e22b38a116df4af20b8eb5a9523a98bbdc35b 100644
--- a/p11/p11.ipynb
+++ b/p11/p11.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1d13ef78",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p12/.ipynb_checkpoints/p12-checkpoint.ipynb b/p12/.ipynb_checkpoints/p12-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..04f530a55dcbe6f30e280cf8905b363fbc73f24a
--- /dev/null
+++ b/p12/.ipynb_checkpoints/p12-checkpoint.ipynb
@@ -0,0 +1,1891 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "42c95cb7",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31b738ff",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import otter\n",
+    "# nb_name should be the name of your notebook without the .ipynb extension\n",
+    "nb_name = \"p12\"\n",
+    "py_filename = nb_name + \".py\"\n",
+    "grader = otter.Notebook(nb_name + \".ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89c21d41",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import p12_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28de6324",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PLEASE FILL IN THE DETAILS\n",
+    "# Enter none if you don't have a project partner\n",
+    "# You will have to add your partner as a group member on Gradescope even after you fill this\n",
+    "\n",
+    "# project: p12\n",
+    "# submitter: NETID1\n",
+    "# partner: NETID2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b58d82b9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "# Project 12: World University Rankings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6963f103",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project, you will demonstrate your ability to\n",
+    "\n",
+    "* read and write files,\n",
+    "* create and use `Pandas DataFrames`,\n",
+    "* use `BeautifulSoup` to parse web pages.\n",
+    "\n",
+    "Please go through [lab-p12](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p12) before working on this project. The lab introduces some useful techniques related to this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ba93fdb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: p12 and p13 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partner up with someone for p12, you have to sustain that partnership until the end of p13. Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f22/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7203bda8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `p12_test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions.\n",
+    "\n",
+    "For answers involving DataFrames, `p12_test.py` compares your tables to those in `p12_expected.html`, so take a moment to open that file on a web browser (from Finder/Explorer).\n",
+    "\n",
+    "`p12_test.py` doesn't care if you have extra rows or columns, and it doesn't care about the order of the rows or columns. However, you must have the correct values at each index/column location shown in `p12_expected.html`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a4464ca",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Introduction:\n",
+    "\n",
+    "For this project, you're going to analyze World University Rankings!\n",
+    "\n",
+    "Specifically, you're going to use Pandas to analyze various statistics of the top ranked universities across the world, over the last three years.\n",
+    "\n",
+    "Start by downloading the files `p12_test.py`, and `p12_expected.html`.\n",
+    "\n",
+    "**Important Warning:** Do **not** download any of the other files manually (you **must** write Python code to download these automatically, as in lab-p12). When we run the autograder, the other files such as `rankings.json`, `2019-2020.html`, `2020-2021.html`, `2021-2022.html` will **not** be in the directory. So, unless your `p12.ipynb` downloads these files, you will get a **zero score** on the project. More details can be found in the **Setup** section of the project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4560d57",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Data:\n",
+    "\n",
+    "For this project, we will be analyzing statistics about world university rankings adapted from [here](https://cwur.org/). These are the specific webpages that we extracted the data from:\n",
+    "\n",
+    "* https://cwur.org/2019-20.php\n",
+    "* https://cwur.org/2020-21.php\n",
+    "* https://cwur.org/2021-22.php\n",
+    "\n",
+    "Later in the project, you will be scraping these webpages and extracting the data yourself. Since we don't want all of you bombarding these webpages with requests, we have made snapshots of these webpages, and hosted them on GitHub. You can find the snapshots here:\n",
+    "\n",
+    "* https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2019-2020.html\n",
+    "* https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2020-2021.html\n",
+    "* https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2021-2022.html\n",
+    "\n",
+    "You will be extracting the data from these three html pages and analyzing them. However, to make the start of the project a little easier, we have already parsed the files for you! We have gathered the data from these html files, and collected them in a single json file, which can be found here:\n",
+    "\n",
+    "* https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/rankings.json\n",
+    "\n",
+    "You will work with this json file for most of this project. At the end of this project, you will generate an identical json file by parsing the html files yourself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23c3c15c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices in your code. You **may not** manually download **any** files for this project, unless you are **explicitly** told to do so. For all other files, you **must** use the `download` function to download the files.\n",
+    "\n",
+    "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer. If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Functions:\n",
+    "- `download`\n",
+    "- `parse_html`\n",
+    "\n",
+    "In this project, you will also be required to define certain **data structures**. If you do not create these data structures exactly as specified, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Data Structures:\n",
+    "- `institutions_df`\n",
+    "\n",
+    "In addition, you are also **required** to follow the requirements below:\n",
+    "* **Avoid using loops to iterate over pandas dataframes and instead use boolean indexing.**\n",
+    "* Do **not** use `loc` to look up data in **DataFrames** or **Series**. You are **allowed** to use `iloc`.\n",
+    "* Do **not** use **absolute** paths such as `C://ms//cs220//p12`. You may **only** use **relative paths**.\n",
+    "* Do **not** use meaningless names for variables or functions (e.g. `uuu = \"my name\"`).\n",
+    "* Do **not** leave irrelevant output or test code that we didn't ask for.\n",
+    "* Do **not** write the exact same code in multiple places. Instead, wrap this code into a function and call that function whenever the code should be used.\n",
+    "* Do **not** call unnecessary functions.\n",
+    "* **Avoid** calling **slow** functions multiple times within a loop.\n",
+    "* **Avoid** inappropriate use of data structures. For instance: do **not** use a `for` loop to search for a corresponding value in a dictionary with a given key; instead use `dictname[key]` directly.\n",
+    "* Do **not** define multiple functions with the same name or define multiple versions of one function with different names. Just keep the best version.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p12/rubric.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3b2a190",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "# Questions and Functions:\n",
+    "\n",
+    "Let us start by importing all the modules we will need for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76f9a202",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7715840a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 1: `download(page, filename)`\n",
+    "\n",
+    "You **must** now copy/paste the `download` function from lab-p12. This function **must** extract the data in the webpage `page` and store it in `filename`. If the `filename` already exists, it **must not** download the file again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f0b294b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the 'download' function from lab-p12\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82d7cc41",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, use `download` to pull the data from here (**do not manually download**): https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/rankings.json and store it in the file `rankings.json`. Once you have created the file, create a Dataframe `rankings` from this file.\n",
+    "\n",
+    "**Warning:** Make sure your `download` function meets the specifications mentioned in lab-p12 and does **not** download the file if it already exists. The TAs will **manually deduct** points otherwise. Make sure you use the `download` function to pull the data instead of manually downloading the files. Otherwise you will get a zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "890682ef",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/rankings.json'\n",
+    "# to the file 'rankings.json'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bd2a73a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# open 'rankings.json' with pd.read_json('rankings.json') and store in the variable 'rankings'\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d45203e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 1:** How **many** countries do we have in our dataset?\n",
+    "\n",
+    "Your output **must** be an **int** representing the number of *unique* countries in the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "289c0d7f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'num_countries', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52da9eeb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c11fe89",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 2:** Generate a `pandas` **DataFrame** containing **all** the statistics of the **highest-ranked** institution based on `World Rank` across all the years.\n",
+    "\n",
+    "Your output **must** be a pandas **DataFrame** with 3 rows and 10 columns. It **must** contain all the data for the institutions with `World Rank` of *1*. It **must** look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "highest_ranked.PNG": {
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+    }
+   },
+   "cell_type": "markdown",
+   "id": "001df536",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:highest_ranked.PNG\" width=\"1000\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba5641c2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'highest_ranked', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc93e4dd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4912b1fd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 3:** Generate a `pandas` **DataFrame** containing **all** the statistics of *University of Wisconsin–Madison*.\n",
+    "\n",
+    "Your output **must** be a pandas **DataFrame** with 3 rows and 10 columns. It **must** look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "uw_madison.PNG": {
+     "image/png": 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+    }
+   },
+   "cell_type": "markdown",
+   "id": "e110fbd1",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:uw_madison.PNG\" width=\"1000\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74d6d54f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'uw_madison', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "564db002",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "418a43c6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 4:** What is the `National Rank` of the *University of Wisconsin–Madison* in the `Year` *2021-2022*?\n",
+    "\n",
+    "Your output **must** be an **int**. You **must** use **Boolean indexing** on the variable `uw_madison` to answer this question.\n",
+    "\n",
+    "**Hint:** Use Boolean indexing on the DataFrame `uw_madison` to find the data for the year `2021-2022`. You may then extract the `National Rank` column from the subset DataFrame. Finally, use `iloc` to lookup the value in the DataFrame which contains only one row and one column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afa9a09e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'uw_madison_nat_rank', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a385d86",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba14d14a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 5:** What is the **average** `Score` of the *University of Wisconsin–Madison*?\n",
+    "\n",
+    "Your output **must** be a **float**. You **must** use the variable `uw_madison` to answer this question.\n",
+    "\n",
+    "**Hint:** You **must** extract the `Score` column of the **DataFrame** `uw_madison` as a **Series**. You can find the **average** of  all the scores in a **Series** with the `Series.mean` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b16696cd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'uw_madison_avg_score', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "687804db",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ae45019",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 6:** Generate a `pandas` **DataFrame** containing **all** the statistics of universities from the `Country` *Singapore* in the `Year` *2020-2021*.\n",
+    "\n",
+    "Your output **must** be a pandas **DataFrame** with 4 rows and 10 columns. It **must** look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "singapore_inst.PNG": {
+     "image/png": 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+    }
+   },
+   "cell_type": "markdown",
+   "id": "aff15cca",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:singapore_inst.PNG\" width=\"1000\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdbee39d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Hint:** When there are **multiple** conditions to filter a **DataFrame**, you can combine all the conditions with `&` as a logical operator between them. For example, you can extract the data for all the institutions with `Quality of Education Rank <= 10` and `Quality of Faculty Rank <= 10` with:\n",
+    "\n",
+    "```python\n",
+    "rankings[(rankings[\"Quality of Education Rank\"] <= 10) & (rankings[\"Quality of Faculty Rank\"] <= 10)]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74d630ad",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'singapore_inst', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bfd58a1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b27b873",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 7:** In the `Year` *2019-2020*, what was the **highest-ranked** institution in the `Country` *Germany*?\n",
+    "\n",
+    "Your output **must** be a **string** representing the **name** of this institution.\n",
+    "\n",
+    "**Hint:** The highest-ranked institution in *Germany* is the institution from Germany with a `National Rank` of *1*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08b64943",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'german_best_name', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0b39d64",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "958f7fd1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 8:** In the `Year` *2019-2020*, list **all** the institutions in the *USA* that were ranked **better** than the highest-ranked institution in *Germany*.\n",
+    "\n",
+    "Your output **must** be a **list** containing the **names** of all universities from *USA* with a **better** `World Rank` than the institution `german_best_name` in the year 2019-2020. By **better** ranked, we refer to institutions with a **lower** value under the `World Rank` column.\n",
+    "\n",
+    "**Hint:** You could store the entire row of the highest ranked institution from Germany in a different variable in q6, and use it to extract its `World Rank`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36d58a49",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'us_better_than_german_best', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3ff4f26",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85bd8e09",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 9:** What is the **highest-ranked** institution based on `Quality of Education Rank` in *China* for the `Year` *2021-2022*?\n",
+    "\n",
+    "Your output **must** be a **string** representing the **name** of this institution. You may **assume** there is only one institution satisfying these requirements. By the **highest-ranked** institution, we refer to the institution with the **least** value under the `Quality of Education Rank` column.\n",
+    "\n",
+    "**Hint:** You can find the **minimum** value in a **Series** with the `Series.min` method. You can find the documentation [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.min.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d4a2c8c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'china_highest_qoe', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "067cb43a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac0ac81c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 10:** What are the **top** *five* **highest-ranked** institutions based on `Research Performance Rank` in *India* for the `Year` *2020-2021*?\n",
+    "\n",
+    "Your output **must** be a **list** of institutions **sorted** in *increasing* order of their `Research Performance Rank`.\n",
+    "\n",
+    "**Hint:** For sorting a DataFrame based on the values of a particular column, you can use the `DataFrame.sort_values(by=\"column_name\")` method (where `column_name` is the column on which you want to sort). You can find the documentation [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80b791f8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'india_highest_research', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06f7c69f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6e59a9a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "For the next few questions, we will be analyzing how the rankings of the institutions change across the three years in the dataset. As you might have already noticed, the list of institutions in each year's rankings are different. As a result, for several institutions in the dataset, we do not have the rankings for all three years. Since it will be more challenging to analyze such institutions, we will simply skip them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52ca1481",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 11:** How **many** institutions have rankings for **all** three years?\n",
+    "\n",
+    "Your output **must** be an **integer**. To get started, you have been provided with a code snippet below.\n",
+    "\n",
+    "**Hint:** You could make **sets** of the institutions that appear in each **DataFrame**, and find their **intersection**. Look up how to find the intersection of two or more sets in Python, on the internet!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9981a2ad",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "\n",
+    "year_2019_ranking_df = rankings[rankings[\"Year\"] == \"2019-2020\"]\n",
+    "year_2020_ranking_df = ...\n",
+    "year_2021_ranking_df = ...\n",
+    "\n",
+    "# TODO: make sets of the institutions in each of the three years\n",
+    "institutions_2019 = set(year_2019_ranking_df[\"Institution\"])\n",
+    "institutions_2020 = ...\n",
+    "institutions_2021 = ...\n",
+    "# TODO: find the intersection of the three sets\n",
+    "institutions_2019_2020_2021 = ...\n",
+    "# TODO: find the length of the intersection\n",
+    "num_institutions_2019_2020_2021 = len(institutions_2019_2020_2021)\n",
+    "\n",
+    "num_institutions_2019_2020_2021\n",
+    "# TODO: make sets of the institutions in each of the three years\n",
+    "# TODO: find the length of the intersection of the three sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9ed5f9a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dbc7bbb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Structure 1: `institutions_df`\n",
+    "\n",
+    "You are now going to create a new **DataFrame** with a **unique** list of institutions which have featured in the rankings for **all** three years, along with their `World Ranking` across the three years. Specifically, the **DataFrame** would have the following four columns - `Institution`, `2019_ranking`, `2020_ranking`, and `2021_ranking`. To get started, you can use the following code snippet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09a74e61",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the variable 'institutions_df'\n",
+    "\n",
+    "# TODO: initalize an empty list to store the list of institutions\n",
+    "# TODO: loop through the variable 'institutions_2019_2020_2021' defined above\n",
+    "    # TODO: create a new dictionary with the necessary key/value pairs\n",
+    "    # TODO: append the dictionary to the list\n",
+    "# TODO: create the DataFrame from the list of dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ada67ebd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"institutions_df\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97e1bfa0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 12:** Between the years *2019-2020* and *2021-2022*, **list** the institutions which have seen an **improvement** in their `World Rank` by **more than** *500* ranks.\n",
+    "\n",
+    "Your output **must** be a **list** of institution names. The **order** does **not** matter. You **must** use the DataFrame `institutions_df` to answer this question.\n",
+    "\n",
+    "**Hints:**\n",
+    "\n",
+    "1. In pandas, subtraction of two columns can be simply done using subtraction(`-`) operator. For example,\n",
+    "``` python\n",
+    "df[\"difference\"] = df[\"column1\"] - df[\"column2\"]\n",
+    "```\n",
+    "will create a *new column* `difference` with the difference of the values from the columns `column1` and `column2`.\n",
+    "2. Note that an *improved* ranking means that the `World Rank` has *decreased*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cda0a5ae",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'improved_institutions', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8d2ca77",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bb526df",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 13:** Between the years 2019-2020 and 2021-2022, which institution had the **largest** change in its `World Rank`?\n",
+    "\n",
+    "Your output **must** be a **string** representing the name of the institution with the **greatest absolute difference** between its `World Rank` in 2019-2020 and 2021-2022. You **must** use the DataFrame `institutions_df` to answer this question.\n",
+    "\n",
+    "**Hint:** You can find maximum value in a Series with the `Series.max` method. You can find the documentation [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.max.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3188fca1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'most_change_inst', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae48cab2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a218a92f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 14:** For all the three years, find the **number** of institutions that **improved** their `World Rank` **each year**.\n",
+    "\n",
+    "Your output **must** be an **integer** representing the number of institutions whose `World Rank` **strictly** increased each year. You **must** use the DataFrame `institutions_df` to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c16ac155",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'strictly_improved', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24b1de42",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c340354f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 15:** In the `Year` *2020-2021*, **list** the institutions which are within the **top** 10 in the world based on `Alumni Employment Rank` but do **not** feature in the top 10 of the `World Ranking`.\n",
+    "\n",
+    "\n",
+    "Your output **must** be a **list** of institutions. The **order** does **not** matter. You **must** use the `year_2020_ranking_df` DataFrame that you created in q11 to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5549e0a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_only_aer', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8d3133b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d625cca0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 16:** **List** the universities which ranked in the **top** 100 of world rankings (`World Rank`) in the `Year` *2019-2020* but **failed** to do so in the `Year` *2021-2022*.\n",
+    "\n",
+    "Your output **must** be a **list** of institutions. The **order** does **not** matter. You **must** use the `year_2019_ranking_df` and `year_2021_ranking_df` DataFrames that you created in q11 to answer this question.\n",
+    "\n",
+    "**Hints:**\n",
+    "1. There could be institutions that are ranked in the **top** 100 in *2019-2020* but do not feature in *2021-2022*; you still want to include them in your list.\n",
+    "2. You can use `sort_values` to identify the **top** 100 institutions.\n",
+    "3. Given two *sets* `A` and `B`, you can find the elements which are in `A` but not in `B` using `A - B`. For example,\n",
+    "```python\n",
+    "set_A = {10, 20, 30, 40, 50}\n",
+    "set_B = {20, 40, 70}\n",
+    "set_A - set_B == {10, 30, 50} # elements which are in set_A but not in set_B\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d23a40d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_only_2019', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9534d3ff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f2f2ab8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 17:** **List** the countries which have **at least** *10* institutions featuring in the **top** *100* of world rankings (`World Rank`) in the `Year` *2020-2021*.\n",
+    "\n",
+    "Your output **must** be a **list**.\n",
+    "\n",
+    "**Hints:**\n",
+    "\n",
+    "1. In a **DataFrame**, to find the **number** of times each unique value in a column repeats, you can use the `DataFrame.value_counts` method. For example,\n",
+    "``` python\n",
+    "rankings[\"Country\"].value_counts()\n",
+    "```\n",
+    "would output a `pandas` **Series** with the **indices** being the unique values of `Country` and the **values** being the **number** of times each country has featured in the `rankings` **DataFrame**. You can find the documentation [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.value_counts.html). You can adapt this code to find the number of institutions from each country that features in the `Year` *2020-2021*.\n",
+    "2. Just like with **DataFrames**, you can use Boolean indexing on **Series**. For example, try something like this in a separate cell below:\n",
+    "```python\n",
+    "a = pd.Series([100, 200, 300])\n",
+    "a[a > 100]\n",
+    "```\n",
+    "3. You can extract the **indices** of a **Series**, `s` with `s.index`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23cc4e9f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_countries', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58ce02c7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba13a862",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Beautiful Soup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bb1add3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Setup\n",
+    "\n",
+    "In real life, you don't often have data in nice JSON format like `rankings.json`. Instead, data needs to be *scraped* from multiple webpages and requires some cleanup before it can be used.\n",
+    "\n",
+    "Most of the projects in CS220 have used data obtained via web scraping, including this one. For p12, as explained above, we obtained the data by scraping the following websites:\n",
+    "\n",
+    "* https://cwur.org/2021-22.php\n",
+    "* https://cwur.org/2020-21.php\n",
+    "* https://cwur.org/2019-20.php\n",
+    "\n",
+    "Our `rankings.json` file was created using data from these webpages. For the rest of this project, you will write the code to **recreate** `rankings.json` file from the tables in these html pages yourself! We also do **not** want all students in this class to be making multiple requests to the webpages above, as that could be very costly for the people managing the webpages. Instead, we have made **copies** of the webpages above, which can be found here:\n",
+    "\n",
+    "* https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2019-2020.html\n",
+    "* https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2020-2021.html\n",
+    "* https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2021-2022.html\n",
+    "\n",
+    "Before you can parse these html files, you must first *download* them. You **must** use your `download` function to download these files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd8de9a9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2019-2020.html'\n",
+    "# to the file '2019-2020.html'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3d7eb35",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2020-2021.html'\n",
+    "# to the file '2020-2021.html'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "582656d3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p12/2021-2022.html'\n",
+    "# to the file '2021-2022.html'\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad8cae8c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 18:** Use `BeautifulSoup` to **parse** `2019-2020.html`, and find the **table** containing the ranking data. What are the **column names** of this table?\n",
+    "\n",
+    "Your output **must** be a **list** of **column names** from this table. There are no restrictions on 'hardcoding' **indices** or **html tags**.\n",
+    "\n",
+    "**Hint:** You **must** use the `find` or `find_all` **methods** to identify the table and its header."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62ae5cc7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'header', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d170cf19",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b9a569e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 2: `parse_html(filename)`\n",
+    "\n",
+    "You **must** write this function which takes in a HTML file `filename` as its input, parses it, and returns a **list** of **dictionaries** containing all the data in the **table** stored in `filename`.\n",
+    "\n",
+    "There are **no** restrictions on 'hardcoding' html tags.\n",
+    "\n",
+    "For example, the output of the function call `parse_html(\"2019-2020.html\")` **must** look like this:\n",
+    "\n",
+    "```python\n",
+    "[{'Year': '2019-2020',\n",
+    "  'World Rank': 1,\n",
+    "  'Institution': 'Harvard University',\n",
+    "  'Country': 'USA',\n",
+    "  'National Rank': 1,\n",
+    "  'Quality of Education Rank': 2,\n",
+    "  'Alumni Employment Rank': 1,\n",
+    "  'Quality of Faculty Rank': 1,\n",
+    "  'Research Performance Rank': 1,\n",
+    "  'Score': 100},\n",
+    " {'Year': '2019-2020',\n",
+    "  'World Rank': 2,\n",
+    "  'Institution': 'Massachusetts Institute of Technology',\n",
+    "  'Country': 'USA',\n",
+    "  'National Rank': 2,\n",
+    "  'Quality of Education Rank': 1,\n",
+    "  'Alumni Employment Rank': 10,\n",
+    "  'Quality of Faculty Rank': 2,\n",
+    "  'Research Performance Rank': 5,\n",
+    "  'Score': 96.7},\n",
+    "...]\n",
+    "```\n",
+    "\n",
+    "You can copy/paste this function from lab-p12 if you have already defined it there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bdb914c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function 'parse_html' here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeb9f4a3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 19:** List the **statistics** of the **first** 5 dictionaries institutions in the file `2019-2020.html`.\n",
+    "\n",
+    "Your output **must** be a **list** of **dictionaries**. You **must** use the `parse_html` function to parse the file, and **slice** the first five **lists** to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "842b5089",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rankings_2019_top_5', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2065883f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58bdf997",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 20:** Parse the contents of the **three** files `2019-2020.html`, `2020-2021.html`, and `2021-2022.html` and combine them to create a **single** file named `my_rankings.json`.\n",
+    "\n",
+    "You **must** create a **file** named `my_rankings.json` in your current directory. The contents of this file **must** be **identical** to `rankings.json`.\n",
+    "\n",
+    "**Hints:**\n",
+    "1. Using the logic from the question above, combine the data from these three files into a single list of dicts, and write it into the file `\"my_rankings.json\"`.\n",
+    "2. You can use the `write_json` function that was introduced in lecture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c82d4c9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# the 'write_json' function from lecture has been provided for you here\n",
+    "\n",
+    "def write_json(path, data):\n",
+    "    with open(path, 'w', encoding = \"utf-8\") as f:\n",
+    "        json.dump(data, f, indent = 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cd7c751",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# parse the three files and write the contents into 'my_rankings.json'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44cc1acc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa9d9c14",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "Make sure you have run all cells in your notebook in order before running the following cells, so that all images/graphs appear in the output.\n",
+    "It is recommended that at this stage, you Restart and Run all Cells in your notebook.\n",
+    "That will automatically save your work and generate a zip file for you to submit.\n",
+    "\n",
+    "If the last cell fails to run because of the file size, delete the images that we have provided in this notebook as examples, and run the last cell again.\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**:\n",
+    "1. **Upload** the zipfile to Gradescope.\n",
+    "2. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c93ac6a",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# running this cell will create a new save checkpoint for your notebook\n",
+    "from IPython.display import display, Javascript\n",
+    "display(Javascript('IPython.notebook.save_checkpoint();'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c3136f8",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "!jupytext --to py p12.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2160b0c8",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "p12_test.check_file_size(\"p12.ipynb\")\n",
+    "grader.export(pdf=False, run_tests=True, files=[py_filename])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73d64995",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "institutions_df": {
+     "name": "institutions_df",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"qinstitutions_df\", institutions_df.set_index('Institution').to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q1\", num_countries)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q10\", india_highest_research)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q11\", num_institutions_2019_2020_2021)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q12\", improved_institutions)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q13\", most_change_inst)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q14\", strictly_improved)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q15\", top_only_aer)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q16\", top_only_2019)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q17\", top_countries)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q18\", header)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q19\", rankings_2019_top_5)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q2\", highest_ranked.to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q20\", \"my_rankings.json\")\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q3\", uw_madison.to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q4\", uw_madison_nat_rank)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q5\", uw_madison_avg_score)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q6\", singapore_inst.to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q7\", german_best_name)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q8\", us_better_than_german_best)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p12_test.check(\"q9\", china_highest_qoe)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p12/p12.ipynb b/p12/p12.ipynb
index b9a1f1fdde45ff2bc9182ce67cfda424ef57278b..04f530a55dcbe6f30e280cf8905b363fbc73f24a 100644
--- a/p12/p12.ipynb
+++ b/p12/p12.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "42c95cb7",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p13/.ipynb_checkpoints/p13-checkpoint.ipynb b/p13/.ipynb_checkpoints/p13-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..48e28aeaacf20e534e2033ebcbf42205b97347c4
--- /dev/null
+++ b/p13/.ipynb_checkpoints/p13-checkpoint.ipynb
@@ -0,0 +1,2084 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "42eda1ed",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0f9b47e",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import otter\n",
+    "# nb_name should be the name of your notebook without the .ipynb extension\n",
+    "nb_name = \"p13\"\n",
+    "py_filename = nb_name + \".py\"\n",
+    "grader = otter.Notebook(nb_name + \".ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89d613de",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import p13_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fd0001b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PLEASE FILL IN THE DETAILS\n",
+    "# enter none if you don't have a project partner\n",
+    "# you will have to add your partner as a group member on Gradescope even after you fill this\n",
+    "\n",
+    "# project: p13\n",
+    "# submitter: NETID1\n",
+    "# partner: NETID2  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3d090d7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " # Project 13: World University Rankings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01736d04",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project, you will demonstrate how to:\n",
+    "\n",
+    "* query a database using SQL,\n",
+    "* process data using `pandas` **DataFrames**,\n",
+    "* create different types of plots.\n",
+    "\n",
+    "Please go through [lab-p13](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p13) before working on this project. The lab introduces some useful techniques related to this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c59ea910",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: p12 and p13 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partnered up with someone for p12, you have to sustain that partnership until end of p13. Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f22/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64f1d95e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `p13_test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions.\n",
+    "\n",
+    "For answers involving DataFrames, `p13_test.py` compares your tables to those in `p13_expected.html`, so take a moment to open that file on a web browser (from Finder/Explorer).\n",
+    "\n",
+    "For answers involving plots, `p13_test.py` can **only** check that the **DataFrames** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "**IMPORTANT Warning:** Do **not** download the dataset `QSranking.json` **manually**. Use the `download` function from p12 to download it. When we run the autograder, this file `QSranking.json` will **not** be in the directory. So, unless your `p13.ipynb` downloads this file, you will get a **zero score** on the project. Also, make sure your `download` function includes code to check if the file already exists. Otherwise, you will **lose** points for **hardcoding**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ef54b6d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Description:\n",
+    "\n",
+    "For your final cs220 project, you're going to continue analyzing world university rankings. However, we will be using a different dataset this time. The data for this project has been extracted from [here](https://www.topuniversities.com/university-rankings/world-university-rankings/2022). Unlike the CWUR rankings we used in p12, the QS rankings dataset has various scores for the universities, and not just the rankings. This makes the QS rankings dataset more suitable for plotting (which you will be doing a lot of!).\n",
+    "\n",
+    "In this project, you'll have to dump your DataFrame to a SQLite database. You'll answer questions by doing queries on that database. Often, your answers will be in the form of a plot. Check these carefully, as the tests only verify that a plot has been created, not that it looks correct (TAs will manually deduct points for plotting mistakes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "733697d6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices in your code. You **may not** manually download **any** files for this project, unless you are **explicitly** told to do so. For all other files, you **must** use the `download` function to download the files.\n",
+    "\n",
+    "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer. If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Functions:\n",
+    "- `bar_plot`\n",
+    "- `scatter_plot`\n",
+    "- `horizontal_bar_plot`\n",
+    "- `pie_plot`\n",
+    "- `get_regression_coeff`\n",
+    "- `regression_line_plot`\n",
+    "- `download`\n",
+    "\n",
+    "In this project, you will also be required to define certain **data structures**. If you do not create these data structures exactly as specified, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Data Structures:\n",
+    "- `conn`\n",
+    "\n",
+    "You **must** write SQL queries to solve the questions in this project, unless you are **explicitly** told otherwise. You will **not get any credit** if you use `pandas` operations to extract data. We will give you **specific** instructions for any questions where `pandas` operations are allowed. In addition, you are also **required** to follow the requirements below:\n",
+    "\n",
+    "* You **must** close the connection to `conn` at the end of your notebook.\n",
+    "* Do **not** use **absolute** paths such as `C://ms//cs220//p12`. You may **only** use **relative paths**.\n",
+    "* Do **not** hardcode `//` or `\\` in any of your paths. You **must** use `os.path.join` to create paths.\n",
+    "* Do **not** use meaningless names for variables or functions (e.g. `uuu = \"my name\"`).\n",
+    "* Do **not** leave irrelevant output or test code that we didn't ask for.\n",
+    "* Do **not** write the exact same code in multiple places. Instead, wrap this code into a function and call that function whenever the code should be used.\n",
+    "* Do **not** call unnecessary functions.\n",
+    "* **Avoid** calling **slow** functions multiple times within a loop.\n",
+    "* **Avoid** inappropriate use of data structures. For instance: do **not** use a `for` loop to search for a corresponding value in a dictionary with a given key; instead use `dictname[key]` directly.\n",
+    "* Do **not** define multiple functions with the same name or define multiple versions of one function with different names. Just keep the best version.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p13/rubric.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f6cca34",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Questions and Functions:\n",
+    "\n",
+    "Let us start by importing all the modules we will need for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebc66f1d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1ddf124",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, you may copy/paste some of the functions and data structures you defined in lab-p13 and p12, which will be useful for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "353af03e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# this ensures that font.size setting remains uniform\n",
+    "%matplotlib inline \n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "matplotlib.rcParams[\"font.size\"] = 13 # don't use value > 13! Otherwise your y-axis tick labels will be different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02fdf0b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'bar_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe781869",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'scatter_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3c8327b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'horizontal_bar_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ace4146",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'pie_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8510c983",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'get_regression_coeff' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a04154d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'regression_line_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "767349f7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'download' from p12 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a3cd154",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/raw/main/p13/QSranking.json'\n",
+    "# to the file 'QSranking.json'\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a16c10d0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Structure 1: `conn`\n",
+    "\n",
+    "You **must** now create a **database** called `rankings.db` out of `QSranking.json`, connect to it, and save it in a variable called `conn`. You **must** use this connection to the database `rankings.db` to answer the questions that follow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2de3b81",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create a database called 'rankings.db' out of 'QSranking.json'\n",
+    "\n",
+    "# TODO: load the data from 'QSranking.json' into a variable called 'qs_ranking' using pandas' 'read_json' function\n",
+    "# TODO: connect to 'rankings.db' and save it to a variable called 'conn'\n",
+    "# TODO: write the contents of the DataFrame 'qs_ranking' to the sqlite database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ef2bb33",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# run this cell and confirm that you have defined the variables correctly\n",
+    "\n",
+    "pd.read_sql(\"SELECT * FROM rankings LIMIT 5\", conn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "090d4dce",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 1:** What are the **top** *10* institutions in *Canada* which had the **highest** number of `international_students` in the `year` *2020*?\n",
+    "\n",
+    "You **must** display the columns `institution_name` and `international_students`. The rows **must** be in *descending* order of `international_students`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**institution_name**|**international_students**|\n",
+    "|---------|------|---------|\n",
+    "|**0**|Mcgill University|96.2|\n",
+    "|**1**|University of Toronto|93.0|\n",
+    "|**2**|Simon Fraser University|91.5|\n",
+    "|**3**|University of Waterloo|84.1|\n",
+    "|**4**|University of Alberta|80.8|\n",
+    "|**5**|University of British Columbia|79.3|\n",
+    "|**6**|Dalhouise University|73.4|\n",
+    "|**7**|Université De Montréal|61.8|\n",
+    "|**8**|University of Calgary|61.2|\n",
+    "|**9**|University of Ottawa|56.7|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70618ad4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'canada_top_10_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca580bdd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86bc4054",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 2:** What are the **top** *10* institutions in the *United States* which had the **highest** *reputation* in the `year` *2019*?\n",
+    "\n",
+    "The *reputation* of an institution is defined as the sum of `academic_reputation` and `employer_reputation`. You **must** display the columns `institution_name` and `reputation`. The rows **must** be in *descending* order of `reputation`. In case the `reputation` is tied, the rows must be in *alphabetical* order of `institution_name`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**institution_name**|**reputation**|\n",
+    "|---------|------|---------|\n",
+    "|**0**|Harvard University|200.0|\n",
+    "|**1**|Massachusetts Institute Of Technology|200.0|\n",
+    "|**2**|Stanford University|200.0|\n",
+    "|**3**|University Of California, Berkeley|199.8|\n",
+    "|**4**|Yale University|199.6|\n",
+    "|**5**|University Of California, Los Angeles|199.1|\n",
+    "|**6**|Columbia University|197.1|\n",
+    "|**7**|Princeton University|196.6|\n",
+    "|**8**|University Of Chicago|190.3|\n",
+    "|**9**|Cornell University|189.2|\n",
+    "\n",
+    "**Hint:** You can use mathematical expressions in your **SELECT** clause. For example, if you wish to add the `academic_reputation` and `employer_reputation` for each institution, you could use the following query:\n",
+    "\n",
+    "```sql\n",
+    "SELECT (`academic_reputation` + `employer_reputation`) FROM rankings\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d0aac41",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'us_top_10_rep', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "741beecf",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a50e1501",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 3:** What are the **top** *10* countries which had the **most** *institutions* listed in the `year` *2020*?\n",
+    "\n",
+    "You **must** display the columns `country` and `num_of_institutions`. The rows **must** be in *descending* order of `num_of_institutions`. In case the `num_of_institutions` is tied, the rows must be in *alphabetical* order of `country`.\n",
+    "\n",
+    "**Hint:** You **must** use the `COUNT` SQL function to answer this question.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**country**|**num_of_institutions**|\n",
+    "|---------|------|---------|\n",
+    "|**0**|United States|74|\n",
+    "|**1**|United Kingdom|45|\n",
+    "|**2**|Germany|23|\n",
+    "|**3**|Australia|21|\n",
+    "|**4**|Canada|14|\n",
+    "|**5**|China|14|\n",
+    "|**6**|France|14|\n",
+    "|**7**|Japan|14|\n",
+    "|**8**|Netherlands|13|\n",
+    "|**9**|Russia|13|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da768ae6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_10_countries', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2f48453",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fd65519",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 4:** Using the data from q3, create a **bar plot** with the `country` on the **x-axis** and the `num_of_institutions` on the **y-axis**.\n",
+    "\n",
+    "You **must** plot the data for the **top** *10* countries in the database. You **must** also aggregate the data for **all** the other countries, and represent that number in the column `Other`. You are **allowed** do this using any combination of  SQL queries and pandas operations.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `num_institutions` containing the **country**, and the **num_of_institutions** data. Then, you **must** use `bar_plot` function to plot this data.\n",
+    "\n",
+    "**Hint**: You can use the `append` function of a DataFrame to add a single row to the end of your **DataFrame** from q3. You'll also need the keyword argument `ignore_index=True`. For example:\n",
+    "\n",
+    "```python\n",
+    "my_new_dataframe = my_dataframe.append({\"country\": \"CS220\", \"num_of_institutions\": 22}, ignore_index=True)\n",
+    "```\n",
+    "will create a *new* **DataFrame** `my_new_dataframe` which contains all the rows from `my_dataframe`, along with the **additional row** which has been appended. You can **ignore** any warnings about `append` being deprecated.\n",
+    "\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "num_institutions_bar.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "565b25fe",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:num_institutions_bar.PNG\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fdfc2cb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the bar plot using the DataFrame 'num_institutions'\n",
+    "\n",
+    "# TODO: use a SQL query similar to q3 to get the number of institutions of all countries\n",
+    "#       (not just the top 10), ordered by the number of institutions, and store in a DataFrame\n",
+    "# TODO: Use pandas to find the sum of the institutions in all countries except the top 10\n",
+    "# TODO: create a new dictionary with the data about the new row that needs to be added\n",
+    "# TODO: properly append this new dictionary to 'num_institutions' and update 'num_institutions'\n",
+    "# TODO: create a bar plot using 'num_institutions'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aac6091e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7b51776",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 5:** What are the **top** *10* countries which had the **highest** *total* `overall_score` listed in the `year` *2019*?\n",
+    "\n",
+    "The *total* `overall_score` of a `country` is defined as the **sum** of `overall_score` of **all** institutions in that `country`. You **must** display the columns `country` and `total_score`. The rows **must** be in *descending* order of `total_score`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**country**|**total_score**|\n",
+    "|---------|------|---------|\n",
+    "|**0**|United States|4298.4|\n",
+    "|**1**|United Kingdom|2539.2|\n",
+    "|**2**|Germany|1098.2|\n",
+    "|**3**|Australia|1093.8|\n",
+    "|**4**|Japan|752.9|\n",
+    "|**5**|China|743.4|\n",
+    "|**6**|Canada|705.3|\n",
+    "|**7**|Netherlands|674.9|\n",
+    "|**8**|South Korea|612.8|\n",
+    "|**9**|France|595.2|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2685ceea",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_10_total_score', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8978c544",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9defacf",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 6:** Using the data from q5, create a **bar plot** with the `country` on the **x-axis** and the `total_score` on the **y-axis**.\n",
+    "\n",
+    "You **must** plot the data for the **top** *10* countries in the database. Unlike q4, you do **not** have to compute the `total_score` of the other countries.\n",
+    "\n",
+    "You have **already** computed the **DataFrame** `top_10_total_score` containing the **country**, and the **total_score** data. Now, you **must** use `bar_plot` function to plot this data.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "top_10_total_score.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "542c5eb2",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:top_10_total_score.PNG\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5447f43f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the bar plot using the DataFrame 'top_10_total_score'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b84c0ed",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c13c4dcc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 7:** What are the **top** *10* institutions in the *United States* which had the **highest** *international_score* in the `year` *2020*?\n",
+    "\n",
+    "The *international_score* of an institution is defined as the **sum** of `international_faculty` and `international_students` scores of that institution. You **must** display the columns `institution_name` and `international_score`. The rows **must** be in *descending* order of `international_score`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**institution_name**|**international_score**|\n",
+    "|---------|------|---------|\n",
+    "|**0**|Massachusetts Institute Of Technology|194.1|\n",
+    "|**1**|California Institute Of Technology|186.7|\n",
+    "|**2**|Carnegie Mellon University|183.5|\n",
+    "|**3**|Rice University|180.4|\n",
+    "|**4**|Northeastern University|179.1|\n",
+    "|**5**|Stanford University|167.5|\n",
+    "|**6**|Cornell University|166.1|\n",
+    "|**7**|Purdue University|158.2|\n",
+    "|**8**|University Of Rochester|157.9|\n",
+    "|**9**|University Of Chicago|151.2|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "030ebf1e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_10_inter_score', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d0ae56d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "149dc084",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 8:** Create a **scatter plot** representing the `citations_per_faculty` (on the **x-axis**) against the `overall_score` (on the **y-axis**) of each institution in the `year` *2018*.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `citations_overall` containing the **citations_per_faculty**, and the **overall_score** data from the `year` *2018*, of each **institution**. Then, you **must** use `scatter_plot` function to plot this data.\n",
+    "\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "citations_overall.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "686ceacb",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:citations_overall.PNG\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9924232c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'citations_overall'\n",
+    "# then create the scatter plot using the DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf813d37",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f590b2e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 9:** Create a **scatter plot** representing the `academic_reputation` (on the **x-axis**) against the `employer_reputation` (on the **y-axis**) of each institution from the *United States* in the `year` *2019*.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `reputations_usa` containing the **academic_reputation**, and the **employer_reputation** data from the `year` *2019*, of each **institution** in the `country` *United States*. Then, you **must** use `scatter_plot` function to plot this data.\n",
+    "\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "reputations_usa.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "5b29f5bf",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:reputations_usa.PNG\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12fb84c0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'reputations_usa'\n",
+    "# then create the scatter plot using the DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e19cbd6e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "391181e2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 10:** Create a **scatter plot** representing the `international_students` (on the **x-axis**) against the `faculty_student_score` (on the **y-axis**) for the **top ranked** institution of **each** `country` in the `year` *2020*.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `top_ranked_inter_faculty` containing the **international_students**, and the **faculty_student_score** data from the `year` *2020*, of the **top** ranked **institution** (i.e., the institution with the **least** `rank`) of each **country**. Then, you **must** use `scatter_plot` function to plot this data.\n",
+    "\n",
+    "**Hint:** You can use the `MIN` SQL function to return the least value of a selected column. However, there are a few things to keep in mind while using this function.\n",
+    "* The function must be in **uppercase** (i.e., you must use `MIN`, and **not** `min`).\n",
+    "* The column you are finding the minimum of must be inside backticks (``` ` ```). For example, if you want to find the minimum `rank`, you need to say ```MIN(`rank`)```.\n",
+    "\n",
+    "If you do not follow the syntax above, your code will likely fail.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "top_ranked_inter_faculty.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "8d2faf07",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:top_ranked_inter_faculty.PNG\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29450209",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'top_ranked_inter_faculty'\n",
+    "# then create the scatter plot using the DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e302d4ae",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "144427bb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Correlations:\n",
+    "\n",
+    "You can use the `.corr()` method on a **DataFrame** that has **two** columns to get the *correlation* between those two columns.\n",
+    "\n",
+    "For example, if we have a **DataFrame** `df` with the two columns `citations_per_faculty` and `overall_score`, `df.corr()` would return\n",
+    "\n",
+    "||**citations_per_faculty**|**overall_score**|\n",
+    "|---------|------|---------|\n",
+    "|citations_per_faculty|1.000000|0.574472|\n",
+    "|overall_score|0.574472|1.000000|\n",
+    "\n",
+    "You can use `.loc` here to **extract** the *correlation* between the two columns (`0.574472` in this case)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d8048b9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 11:** Find the **correlation** between `international_students` and `overall_score` for institutions from the `country` *United Kingdom* that were ranked in the **top** *100* in the `year` *2020*.\n",
+    "\n",
+    "Your output **must** be a **float** representing the absolute correlations. The **only** `pandas` operations you are **allowed** to use are: `.corr`, `.loc` and `.iloc`. You **must** use SQL to gather all other data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7be5fc8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'uk_inter_score_corr', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c0477e2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f0e911b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Let us now define a new score called `citations_per_international` as follows:\n",
+    "\n",
+    "$$\\texttt{citations}\\_\\texttt{per}\\_\\texttt{international} = \\frac{\\texttt{citations}\\_\\texttt{per}\\_\\texttt{faculty} \\times \\texttt{international}\\_\\texttt{faculty}}{100}.$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db0f284f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 12:** Find the **correlation** between `citations_per_international` and `overall_score` for **all** institutions in the `year` *2019*.\n",
+    "\n",
+    "Your output **must** be a **float** representing the absolute correlations. The **only** `pandas` operations you are **allowed** to use are: `.corr`, `.loc` and `.iloc`. You **must** use SQL to gather all other data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5e70180",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'cit_per_inter_score_corr', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92736e16",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5ed6baa",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 13:** What are the **top** *15* countries with the **highest** *total* of `citations_per_international` in the `year` *2019*.\n",
+    "\n",
+    "\n",
+    "The *total* `citations_per_international` of a `country` is defined as the **sum** of `citations_per_international` scores of **all** institutions in that `country`. You **must** display the columns `country` and `sum_inter_citations`. The rows **must** be in *descending* order of `sum_inter_citations`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**country**|**sum_inter_citations**|\n",
+    "|----|-----------|-----------------------|\n",
+    "|**0**|United States|2623.8207|\n",
+    "|**1**|United Kingdom|2347.1602|\n",
+    "|**2**|Australia|1255.5530|\n",
+    "|**3**|Netherlands|748.4268|\n",
+    "|**4**|Canada|724.5029|\n",
+    "|**5**|Switzerland|561.8790|\n",
+    "|**6**|China|482.2577|\n",
+    "|**7**|Germany|455.5466|\n",
+    "|**8**|Hong Kong|375.3032|\n",
+    "|**9**|New Zealand|327.3357|\n",
+    "|**10**|Sweden|305.3745|\n",
+    "|**11**|Belgium|255.0750|\n",
+    "|**12**|France|198.0860|\n",
+    "|**13**|Denmark|186.4904|\n",
+    "|**14**|Singapore|160.3000|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af375ba0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_cit_per_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "595d9a34",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b57f98e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 14:** Among the institutions ranked within the **top** *300*, find the **average** `citations_per_international` for **each** `country` in the `year` *2019.\n",
+    "\n",
+    "You **must** display the columns `country` and `avg_inter_citations` representing the **average** of `citations_per_international` for **each** country. The rows **must** be in *descending* order of `avg_inter_citations`.\n",
+    "\n",
+    "**Hint:** To find the **average**, you can use `SUM()` and `COUNT()` or you can simply use `AVG()`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** whose **first ten rows** look like this:\n",
+    "\n",
+    "||**country**|**avg_inter_citations**|\n",
+    "|----|-----------|----------------------|\n",
+    "|**0**|Singapore|80.150000|\n",
+    "|**1**|Switzerland|75.497000|\n",
+    "|**2**|Hong Kong|62.550533|\n",
+    "|**3**|Australia|61.362388|\n",
+    "|**4**|Netherlands|56.166733|\n",
+    "|**5**|New Zealand|53.226220|\n",
+    "|**6**|United Kingdom|52.889084|\n",
+    "|**7**|Canada|50.779723|\n",
+    "|**8**|Denmark|46.196200|\n",
+    "|**9**|Norway|46.083300|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f57a383",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'avg_cit_per_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81fa9b61",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32c56bbe",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 15** Find the **institution** with the **highest** value of `citations_per_international` for **each** `country` in the `year` *2020*.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** with the columns `country`, `institution_name`, and a new column `max_inter_citations` representing the **maximum** value of `citations_per_international` for that country. The rows **must** be in *descending* order of `max_inter_citations`. You **must** **omit** rows where `max_inter_citations` is **missing** by using the clause:\n",
+    "\n",
+    "```sql\n",
+    "HAVING `max_inter_citations` IS NOT NULL\n",
+    "```\n",
+    "\n",
+    "**Hint:** You can use the `MAX()` function to return the largest value within a group.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** whose **first ten rows** look like this:\n",
+    "\n",
+    "||**country**|**institution_name**|**max_inter_citations**|\n",
+    "|----|-----------|--------------------|----------------------|\n",
+    "|**0**|United States|Massachusetts Institute Of Technology|99.8000|\n",
+    "|**1**|Switzerland|Ecole Polytechnique Fédérale De Lausanne|98.9000|\n",
+    "|**2**|Netherlands|Eindhoven University Of Technology|95.4493|\n",
+    "|**3**|United Kingdom|London School Of Economics And Political Science|91.1000|\n",
+    "|**4**|Hong Kong|The Hong Kong University Of Science And Technology|89.5000|\n",
+    "|**5**|Singapore|Nanyang Technological University|88.8000|\n",
+    "|**6**|Australia|The University Of Western Australia|88.3000|\n",
+    "|**7**|Belgium|Katholieke Universiteit Leuven|76.7700|\n",
+    "|**8**|New Zealand|University Of Waikato|73.6434|\n",
+    "|**9**|Canada|Western University|72.3240|\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75421dbe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'max_cit_per_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b910c24",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c031039",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 16**: Among the institutions ranked within the **top** *50*, create a **horizontal bar plot** representing the **average** of both the`citations_per_faculty` and `international_faculty` scores for **all** institutions in **each** `country` in the `year` *2018*.\n",
+    "\n",
+    "You **must** first create a **DataFrame** `country_citations_inter` with **three** columns: `country`, `avg_citations` and `avg_inter_faculty` representing the name, the average value of `citations_per_faculty` and the average value of `international_faculty` for each country respectively.\n",
+    "\n",
+    "Then, you **must** use the `horizontal_bar_plot` function to plot this data. You **must** ensure that the countries in the **horizontal bar plot** are **ordered** in **increasing** order of the **difference** between `avg_citations` and `avg_inter_faculty` by **appropriately ordering** the **DataFrame**.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "country_citations_inter.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "ecbf0a8c",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:country_citations_inter.PNG\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a566b3a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'country_citations_inter'\n",
+    "# then create the horizontal bar plot using the DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e04b679",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05bbe2ce",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 17:** Create a **scatter plot** representing the `overall_score` (on the **x-axis**) against the `rank` (on the **y-axis**) for **all** institutions in the `year` *2020*. Additionally, **plot** a **regression line**.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `overall_rank` containing the **overall_score**, and the **rank** data from the `year` *2020*. Then, you **must** use the `regression_line_plot` function to plot this data along with the regression line.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "overall_rank.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "1bc21fc4",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:overall_rank.PNG\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6dd231e7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'overall_rank'\n",
+    "# then create the scatter plot and the regression line using the DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "155f310d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aac492c7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Does our linear regression model fit the points well? It looks like the relationship between the `overall_score` and `rank` is **not quite linear**. In fact, a cursory look at the data suggests that the relationship is in fact, inverse."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cd43343",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 18:** Create a **scatter plot** representing the **inverse** of the `overall_score` (on the **x-axis**) against the `rank` (on the **y-axis**) for **all** institutions in the `year` *2020*. Additionally, **plot** a **regression line**.\n",
+    "\n",
+    "The `inverse_overall_score` for each institution is simply defined as `1/overall_score` for that institution. You **must** first compute a **DataFrame** `inverse_overall_rank` containing the **inverse_overall_score**, and the **rank** data from the `year` *2020*. You are **allowed** to use `pandas` operations to calculate the `inverse_overall_score`.\n",
+    "\n",
+    "Then, you **must** use the `regression_line_plot` function to plot this data along with the regression line.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "inverse_overall_rank.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "76ba7bef",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:inverse_overall_rank.PNG\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43c88c7f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'inverse_overall_rank'\n",
+    "# then create the scatter plot and the regression line using the DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1df27935",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6321e051",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "This seems to be much better! Let us now use this **regression line** to **estimate** the `rank` of an institution given its `overall_score`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e350bf3e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 19:** Use the regression line to **estimate** the `rank` of an institution with an `overall_score` of *72*.\n",
+    "\n",
+    "Your output **must** be an **int**. If your **estimate** is a **float**, *round it up* using `math.ceil`.\n",
+    "\n",
+    "\n",
+    "**Hints:**\n",
+    "1. Use the coefficients `m, b` that `get_regression_coeff` returns in q18. You can make another call to `get_regression_coeff` if needed.\n",
+    "2. Recall that the equation of a line is `y = m * x + b`. What are `x` and `y` here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a0de61c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rank_score_72', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a98d388e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c177205b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Can you find out the `overall_score` of the university with this `rank` in the `year` *2020*? Does it match your prediction?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8adee240",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 20:** Using the data from q4, create a **pie plot** representing the number of institutions from each country.\n",
+    "\n",
+    "\n",
+    "You **have** already computed a **DataFrame** `num_institutions` (in q4) containing the **country**, and the **num_of_institutions** data. Now, you **must** create a **pie plot** with this data. The **colors** do **not** matter, but the plot **must** be titled `Number of institutions`, and **must** be labelled as in the sample output below.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "num_institutions_pie.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "ae31de6d",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:num_institutions_pie.PNG\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32addf78",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the pie plot using the DataFrame 'num_institutions'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "339f18f7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ed68bc2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** It seems that we'll run out of colors! How can we make it so that **no two neighbors share a color**? You'll probably have to look online."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22e0bc8c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Closing the database connection:\n",
+    "\n",
+    "Now, before you **submit** your notebook, you **must** **close** your connection `conn`. Not doing this might make **Gradescope fail**. Please do this right away and save your precious time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fce682d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# close your connection here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df30edca",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "Make sure you have run all cells in your notebook in order before running the following cells, so that all images/graphs appear in the output.\n",
+    "It is recommended that at this stage, you Restart and Run all Cells in your notebook.\n",
+    "That will automatically save your work and generate a zip file for you to submit.\n",
+    "\n",
+    "You will have to delete the images that we have provided in this notebook as examples, to execute the last cell.\n",
+    "Make sure you do not delete your own output.\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**:\n",
+    "1. **Upload** the zipfile to Gradescope.\n",
+    "2. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b95997c",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# running this cell will create a new save checkpoint for your notebook\n",
+    "from IPython.display import display, Javascript\n",
+    "display(Javascript('IPython.notebook.save_checkpoint();'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97cbfc3f",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "!jupytext --to py p13.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82b3c051",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "p13_test.check_file_size(\"p13.ipynb\")\n",
+    "grader.export(pdf=False, run_tests=True, files=[py_filename])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcbfdd29",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q1\", canada_top_10_inter.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q10\", top_ranked_inter_faculty[[\"international_students\", \"faculty_student_score\"]].reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q11\", uk_inter_score_corr)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q12\", cit_per_inter_score_corr)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q13\", top_cit_per_inter.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q14\", avg_cit_per_inter.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q15\", max_cit_per_inter.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q16\", country_citations_inter.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q17\", overall_rank[[\"overall_score\", \"rank\", \"fit\"]].reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q18\", inverse_overall_rank[[\"inverse_overall_score\", \"rank\", \"fit\"]].reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q19\", rank_score_72)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q2\", us_top_10_rep.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q4\", num_institutions.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q3\", top_10_countries.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q4\", num_institutions.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q5\", top_10_total_score.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q5\", top_10_total_score.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q7\", top_10_inter_score.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q8\", citations_overall.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q9\", reputations_usa.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p13/p13.ipynb b/p13/p13.ipynb
index 86b913cfca6d1ab80ec5b2b8622044a0f690a1bd..48e28aeaacf20e534e2033ebcbf42205b97347c4 100644
--- a/p13/p13.ipynb
+++ b/p13/p13.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "42eda1ed",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p2/.ipynb_checkpoints/p2-checkpoint.ipynb b/p2/.ipynb_checkpoints/p2-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a8c7286fcd3fe6d6c12f9827dbdc318a62eabf43
--- /dev/null
+++ b/p2/.ipynb_checkpoints/p2-checkpoint.ipynb
@@ -0,0 +1,1397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "455a0934",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7de7b5cb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"p2.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08ea2118",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dcd8a05",
+   "metadata": {},
+   "source": [
+    "# Project 2: Operators, expressions, and variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "196d64e8",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives:\n",
+    "In this project you will demonstrate your ability to:\n",
+    "\n",
+    "- Use arithmetic operators, including the floor division operator.\n",
+    "- Call the type function on an expression\n",
+    "- Use logical operators such as `and`, `or`, and `not`.\n",
+    "- Use comparison operators.\n",
+    "- Store values and results of expressions into variables."
+   ]
+  },
+  {
+   "attachments": {
+    "pass_msg.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "cf38b4d0",
+   "metadata": {},
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions. It is okay if you do not understand how this file works for now. We promise that you will be able to understand everything going on in that file by the end of this semester.\n",
+    "\n",
+    "In the meantime, after answering each question (say Question 1), you can test your answer directly on the notebook by running the cell below that question which says (in the case of Question 1) `grader.check(\"q1\")`. If you have answered the question correctly, you will see the following:\n",
+    "\n",
+    "\n",
+    "<div><img src=\"attachment:pass_msg.PNG\" width=\"800\"/></div>"
+   ]
+  },
+  {
+   "attachments": {
+    "error_msg.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "6e178a7c",
+   "metadata": {},
+   "source": [
+    "Instead, if you make a semantic error, you might see an error message similar to the one below:\n",
+    "\n",
+    "<div><img src=\"attachment:error_msg.PNG\" width=\"800\"/></div>\n",
+    "\n",
+    "You can ignore the first few lines of this message. You need to focus on the very last line here. That is the line that begins with <b style=\"color:red\">ERROR:</b>. This message will tell you what is wrong with your code so that you can hopefully fix it."
+   ]
+  },
+  {
+   "attachments": {
+    "traceback_msg.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "72583978",
+   "metadata": {},
+   "source": [
+    "On the other hand, if you make any syntax or runtime errors, you might see an error message similar to the one below:\n",
+    "\n",
+    "<div><img src=\"attachment:traceback_msg.PNG\" width=\"800\"/></div>\n",
+    "\n",
+    "Try figuring out by yourself, what this error message is telling you. As the course progresses, you will learn how to read the Traceback from your error messages. For now, try to avoid making syntax errors, and if you are unable to fix your code, attend office hours and have a TA or Peer Mentor look at your code."
+   ]
+  },
+  {
+   "attachments": {
+    "add_group_member.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "e9eaa206",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading:\n",
+    "\n",
+    "After you finish this project, you will have to submit it via [Gradescope](https://www.gradescope.com/), just as you did for [P1](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p1). Remember that your final score for the project is **not** the score you see on Gradescope after the autograder runs. TAs and Graders will **manually review** your code, and deduct points if you do not satisfy the requirements of the Grading rubric. Grading rubric is available on the [Gradescope](https://www.gradescope.com/) project page. \n",
+    "\n",
+    "Before you run the cell containing `export`, you **MUST SAVE** the notebook file. If you don't do this, you will lose points (see grading rubric).\n",
+    "\n",
+    "If you completed the project with a **partner**, make sure to add their name by clicking \"Add Group Member\" in Gradescope when uploading the P2 zip file.\n",
+    "\n",
+    "<div><img src=\"attachment:add_group_member.png\" width=\"800\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "363628e7",
+   "metadata": {},
+   "source": [
+    "## Project questions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e35471c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This line is a comment because it starts with a pound sign (#). That \n",
+    "# means Python ignores it. A comment is just for a human reading the\n",
+    "# code. This project involves 20 small problems to give you practice\n",
+    "# with operators, types, boolean logic, and variables assignment. \n",
+    "# We'll give you directions on what to do for each problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1066e404",
+   "metadata": {},
+   "source": [
+    "**Question 1:** What does the expression `44 * 5` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9908ced3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "course_num = 44 * 5 # We did this one for you\n",
+    "course_num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65c51d0d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60c0fc4c",
+   "metadata": {},
+   "source": [
+    "**Question 2:** What does the expression `350 - 31` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ff620da",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Replace the ... with the correct expression, similar to the answer for Question 1.\n",
+    "# INCORRECT ANSWER: grad_course_num = 319 => this is considered HARDCODING.\n",
+    "grad_course_num = ...\n",
+    "grad_course_num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98703fc4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5217a4a",
+   "metadata": {},
+   "source": [
+    "**Question 3:** If you have 145 eggs, and can put 12 eggs in one carton, how many cartons can you fill completely? Write the appropriate expression to answer this question.\n",
+    "**Hint**: Use the floor division (`//`) operator to answer this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eeca70a4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Replace the ... with the correct expression, similar to the answer for Question 1.\n",
+    "# INCORRECT ANSWER: num_cartons = 12 => this is considered HARDCODING.\n",
+    "num_cartons = ...\n",
+    "num_cartons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7a63ac7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "568428db",
+   "metadata": {},
+   "source": [
+    "**Question 4:** What does `type` of `22 * 10` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3a0515c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data_type = type(22 * 10) # We did this one for you\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3bdd86d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93a60c17",
+   "metadata": {},
+   "source": [
+    "**Question 5:** What does `type` of `220 // 9` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d07fe33",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# INCORRECT ANSWER: data_type = int => this is considered HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da95c2e2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a786548",
+   "metadata": {},
+   "source": [
+    "**Question 6:** What does `type` of `2200 / 10` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9f2cf8c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# INCORRECT ANSWER: data_type = float => this is considered HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "411da734",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4edcd38",
+   "metadata": {},
+   "source": [
+    "**Question 7:** What does `type` of `\"220\"` evaluate to? Note the **quotes**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "290f664b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# INCORRECT ANSWER: data_type = str => this is considered HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e35026c6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ae4fa33",
+   "metadata": {},
+   "source": [
+    "**Question 8:** What does `type` of `True` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ceb43977",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# INCORRECT ANSWER: data_type = bool => this is considered HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "501c31f2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afce5783",
+   "metadata": {},
+   "source": [
+    "**Question 9:** What does `type` of `\"True\"` evaluate to? Note the **quotes**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e80ae3e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# DO NOT HARCODE the final type value.\n",
+    "# See questions 4 through 8 for examples of HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14b840ce",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecafa82e",
+   "metadata": {},
+   "source": [
+    "**Question 10:** What does `type` of `319 > 220` evaluate to?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51bab290",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Replace the ... with the correct answer, similar to the answer for Question 4.\n",
+    "# DO NOT HARDCODE the final type value.\n",
+    "# See questions 4 through 8 for examples of HARDCODING.\n",
+    "data_type = ...\n",
+    "data_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f3145c1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aee1d915",
+   "metadata": {},
+   "source": [
+    "**Question 11:** Fix the expression `\":-(\" * 3 + \":-)\" * 5`, to display *2 sad smileys* \":-(\" and *20 happy smileys* \":-)\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d67d49a9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Replace the ... with the correct expression\n",
+    "# INCORRECT ANSWER (see below): \n",
+    "# smileys = ':-(:-(:-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-)' => this is considered HARDCODING.\n",
+    "smileys = \":-(\" * 3 + \":-)\" * 5 # Fix this expression\n",
+    "smileys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "846ad636",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23db8f78",
+   "metadata": {},
+   "source": [
+    "**Question 12:** Fix the expression `20 + 22` to display `\"2022\"`. Note the **quotes**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d24cc600",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Replace the ... with the correct expression\n",
+    "# INCORRECT ANSWER: curr_year = \"2022\" => this is considered HARDCODING.\n",
+    "curr_year = 20 + 22 # Fix this expression\n",
+    "curr_year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcc71810",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9d38987",
+   "metadata": {},
+   "source": [
+    "**Question 13:** What is the *volume* of a cube with a side length of 5? *You **must** use the variable **cube_side** in your solution.*\n",
+    "\n",
+    "**Hint**: Use the exponent (\\*\\*) operator to answer this. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6cee281",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cube_side = 5\n",
+    "# Replace the ... with the correct expression. We expect you to use the above variable.\n",
+    "# INCORRECT ANSWER: cube_volume = 125 => this is considered HARDCODING.\n",
+    "cube_volume = ...\n",
+    "cube_volume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d63beff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3aeb038c",
+   "metadata": {},
+   "source": [
+    "**Question 14:** What is the *volume* of a cylinder with a radius of 2 and height of 3? *You **must** define, initialize, and use the variables **cylinder_height** and **cylinder_radius** in your solution. You **must** use the variable **pi** in your solution.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53f9c7e7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pi = 3.14\n",
+    "# Replace the ... with the correct expression. We expect you to use the above variable.\n",
+    "# We expect you to define, initalize, and use the variables cylinder_height and cylinder_radius.\n",
+    "# INCORRECT ANSWER: cylinder_volume = 37.68 => this is considered HARDCODING.\n",
+    "\n",
+    "# Create the required variables here\n",
+    "\n",
+    "cylinder_volume = ...\n",
+    "cylinder_volume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f641a0a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "677f6071",
+   "metadata": {},
+   "source": [
+    "### Boolean Word Problems\n",
+    "\n",
+    "We're now going to do a few word problems. The most important skill you're going to learn in this class is translating English sentences to code. This will be good practice!\n",
+    "\n",
+    "Here are simple example translations between English phrases and comparison operators:\n",
+    "\n",
+    "\"x is at most y\" or \"x is no more than y\" => `x <= y`  \n",
+    "\"x is less than y\" or \"x is below y\" or \"x is under y\" => `x < y`  \n",
+    "\"x is at least y\" => `x >= y`  \n",
+    "\"x is more than y\"or \"x is above y\" => `x > y`  \n",
+    "\"x is equal to y\" => `x == y`  \n",
+    "\"y is within the range of x and z\" or \"y is in between x and z\" => `x <= y <= z`\n",
+    "\n",
+    "You can use the above translations as verification for your q15 and q16 solutions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9df70d4e",
+   "metadata": {},
+   "source": [
+    "**Question 15:** Suppose, the *safe operation weight limit* for a trailer is *3000 lbs*. Grace's trailer weighs *2000 lbs*. To safely operate the trailer, Grace needs to ensure that her trailer weight is *at most* the operation weight limit. How can Grace figure out if she can safely operate her truck? You **must not** change the variables' values.\n",
+    "\n",
+    "**Hint**: Use the appropriate comparison operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef336bb3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# DO NOT CHANGE the values of the below variables\n",
+    "TRAILER_LIMIT = 3000 # constants are typically stored in variable names with all capital case letters\n",
+    "trailer_weight = 2000\n",
+    "\n",
+    "# Replace the ... with the correct expression. We expect you to use the above variables.\n",
+    "# INCORRECT ANSWER: safe_operation = True => this is considered HARDCODING.\n",
+    "safe_operation = ...\n",
+    "safe_operation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b963754",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ddba620",
+   "metadata": {},
+   "source": [
+    "**Question 16:** To safely pull a trailer of weight 2000 lbs, Rahul's truck should weigh between 1000 and 3000 lbs. How can Rahul figure out if his truck is heavy enough to operate the trailer? You **must not** change the variables' values.\n",
+    "\n",
+    "**Hint**: Use the appropriate comparison operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "842c2078",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# DO NOT CHANGE the values of the below variables\n",
+    "LOWER_LIMIT = 1000 # constants are typically stored in variable names with all capital case letters\n",
+    "UPPER_LIMIT = 3000 # constants are typically stored in variable names with all capital case letters\n",
+    "truck_weight = 1500\n",
+    "\n",
+    "# Replace the ... with the correct expression. We expect you to use the above variables.\n",
+    "# INCORRECT ANSWER: safe_operation = True => this is considered HARDCODING.\n",
+    "safe_operation = ...\n",
+    "safe_operation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e94be80",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "307d4e39",
+   "metadata": {},
+   "source": [
+    "**Question 17:** Carlos wants to go trick-or-treating. To do so he must either make a costume *or* buy a costume. Also, he must walk around *and* have chocolates at home. Given the below variable initializations, Carlos currently isn't successful with trick-or-treating. Change exactly *one variable's initial value* to help Carlos go trick-or-treating. You **must not** change the expression.\n",
+    "\n",
+    "```\n",
+    "make_costume = False\n",
+    "buy_costume = True\n",
+    "\n",
+    "walk_around = False\n",
+    "have_chocolates = True\n",
+    "\n",
+    "success = (make_costume or buy_costume) and (walk_around and have_chocolates)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47353290",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# change exactly one variable's initial value to help Carlos go trick-or-treating\n",
+    "make_costume = False\n",
+    "buy_costume = True\n",
+    "\n",
+    "walk_around = False\n",
+    "have_chocolates = True\n",
+    "\n",
+    "\n",
+    "\n",
+    "# DO NOT CHANGE the expression\n",
+    "success = (make_costume or buy_costume) and (walk_around and have_chocolates)\n",
+    "success"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90faac05",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a986b41c",
+   "metadata": {},
+   "source": [
+    "**Question 18:** Angel wants to buy either a bright and long shirt or a short and dark shirt. Currently, they are getting `True` for *success*, even though they have only found a long and dark shirt. Fix the Boolean expression to help them make a correct shirt selection. You **must not** change the values of the variables.\n",
+    "\n",
+    "```\n",
+    "short = False\n",
+    "dark = True\n",
+    "\n",
+    "success = (dark and not short) or (short and dark)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8dff1665",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# DO NOT CHANGE the values of the variables\n",
+    "short = False\n",
+    "dark = True\n",
+    "\n",
+    "# Fix the below Boolean expression to help Angel make a correct shirt selection\n",
+    "success = (dark and not short) or (short and dark) \n",
+    "success"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aeb009c4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "928045c2",
+   "metadata": {},
+   "source": [
+    "**Question 19:** *red*, *green*, and *blue* are the primary colors. How can we correct the expression `color == \"red\" or \"green\" or \"blue\"` to correctly verify whether `color` is a primary color? You **must not** change the color variable's value.\n",
+    "\n",
+    "**Hint**: In lab-p2, there was a section on \"Correct way to write boolean expressions\". Now would be a good time to go back and refresh that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a9df412",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# DO NOT CHANGE the value of the below variable\n",
+    "color = \"blue\"\n",
+    "# INCORRECT ANSWER: primary_color = True => this is considered HARDCODING.\n",
+    "primary_color = color == \"red\" or \"green\" or \"blue\" # Fix this expression\n",
+    "primary_color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8429fb12",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bfc6bd9",
+   "metadata": {},
+   "source": [
+    "**Question 20:** Students *Alice*, *Bob*, *Chang*, and *Divya* have exam scores of 31, 35, 34, and 35. The expression `alice_score + bob_score + chang_score + divya_score / 4` produces incorrect student average. How can we fix this expression to compute the correct average score? You **must** define, initialize, and use the score variables mentioned in the incorrect expression.\n",
+    "\n",
+    "**Hint**: To override default operator order precedence, parentheses can be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2195430f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Create the required variables here\n",
+    "\n",
+    "# We expect you to define, initialize, and use the score variables mentioned in the original expression\n",
+    "# INCORRECT ANSWER: average_score = 33.75 => this is considered HARDCODING.\n",
+    "average_score = alice_score + bob_score + chang_score + divya_score / 4 # Fix this expression\n",
+    "average_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d6f016a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78a64b66",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "\n",
+    "Make sure you have run all cells in your notebook in order before running the cell below, so that all images/graphs appear in the output. The cell below will generate a zip file for you to submit. **Please save before exporting!**\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**: 1. **Save** the notebook file **now (before you run the next cell of code)**. 2. **Upload** the zipfile to Gradescope. 3. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e86e845",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Save your notebook first, then run this cell to export your submission.\n",
+    "grader.export(pdf=False, run_tests=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "913df734",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q1\", course_num)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q10\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q11\", smileys)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q12\", curr_year)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q13\", cube_volume)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q14\", cylinder_volume)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q15\", safe_operation)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q16\", safe_operation)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q17\", success)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q18\", success)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q19\", primary_color)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q2\", grad_course_num)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q20\", average_score)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q3\", num_cartons)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q4\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q5\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q6\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q7\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q8\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q9\", data_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p2/p2.ipynb b/p2/p2.ipynb
index 3b249b5e11995e5498786eeb9bcd0441e871067e..a8c7286fcd3fe6d6c12f9827dbdc318a62eabf43 100644
--- a/p2/p2.ipynb
+++ b/p2/p2.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "455a0934",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p3/.ipynb_checkpoints/p3-checkpoint.ipynb b/p3/.ipynb_checkpoints/p3-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b9dcbc2e03c2b9481895950bd9e25d9b074e5c68
--- /dev/null
+++ b/p3/.ipynb_checkpoints/p3-checkpoint.ipynb
@@ -0,0 +1,1615 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9590d418",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5c9d46c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"p3.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "807ba163",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DO NOT MODIFY the code in this cell\n",
+    "# You must run this cell before you start working on the project\n",
+    "import test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b7bd284",
+   "metadata": {},
+   "source": [
+    "# Project 3: Electric Vehicle Sales"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a65bc9e",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project you will demonstrate your ability to:\n",
+    "- import a module and use its functions,\n",
+    "- write functions,\n",
+    "- use default arguments when calling functions,\n",
+    "- use positional and keyword arguments when calling functions,\n",
+    "- avoid hardcoding, and\n",
+    "- work with the index of a row of data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7156aa8",
+   "metadata": {},
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions. You can have a look at [p2](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p2) if you have forgotten how to read the outputs of the `grader.check(...)` function calls."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "860ad0d7",
+   "metadata": {},
+   "source": [
+    "**Please go through [lab-p3](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p3) before starting this project.** The lab introduces some useful techniques necessary for this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed309451",
+   "metadata": {},
+   "source": [
+    "## Project Description:\n",
+    "\n",
+    "In this project, you'll analyze data on six different electric vehicle models sold in the United States between 2015 and 2019. The dataset we will analyze is truncated and modified from [the Alternative Fuels Data Center](https://afdc.energy.gov/data/) published by the U.S. Department of Energy.\n",
+    "\n",
+    "You'll get practice calling functions from the `project` module, which we've provided, and practice writing your own functions.\n",
+    "\n",
+    "If you haven't already downloaded `project.py`, `test.py`, and  `car_sales_data.csv` (you can verify by running `ls` in a new terminal tab from your `p3` project directory). , please terminate the current `jupyter notebook` session, download all the required files, launch a `jupyter notebook` session again and click on *Kernel* > *Restart and Clear Output*. Start by executing all the cells (including the ones containing `import` statements).\n",
+    "\n",
+    "\n",
+    "We won't explain how to use the `project` module here (i.e., the code in the `project.py` file).  Refer to [lab-p3](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p3) to understand how the inspection process works and use the `help(...)` function to learn about the various functions inside `project.py`. Feel free to take a look at the `project.py` code, if you are curious about how it works.\n",
+    "\n",
+    "This project consists of writing code to answer 20 questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3ac111b",
+   "metadata": {},
+   "source": [
+    "## Dataset:\n",
+    "\n",
+    "The dataset you will be working with for this project is reproduced here:\n",
+    "\n",
+    "id|vehicle|2015|2016|2017|2018|2019\n",
+    "------|------|------|------|------|------|------|\n",
+    "958|Tesla Model S|26200|30200|26500|25745|15090\n",
+    "10|Chevy Volt|15393|24739|20349|18306|4915\n",
+    "64|Nissan Leaf|17269|14006|11230|14715|12365\n",
+    "977|Toyota Prius PHEV|4191|2474|20936|27595|23630\n",
+    "332|Ford Fusion Energi|9750|15938|9632|8074|7476\n",
+    "951|Tesla Model X|208|19600|21700|26100|19425\n",
+    "\n",
+    "\n",
+    "This table lists 6 different electric vehicle models, and how many cars of each model were sold each year between 2015 and 2019 (inclusive of both years).\n",
+    "\n",
+    "The dataset is in the `car_sales_data.csv` file which you downloaded. Alternatively, you can open the `car_sales_data.csv` file, to look at the same data and verify answers to simple questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "183a81a3",
+   "metadata": {},
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices in your code. For example, if we ask how many Nissan Leaf cars were sold in 2016, you could obtain the answer with this code: `get_sales(get_id(\"Nissan Leaf\"), 2016)`.  If you don't use `get_id` and instead use `get_sales(64, 2016)`, we'll **manually deduct** points from your autograder score on Gradescope during code review.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer.  If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Students are only allowed to use Python commands and concepts that have been taught in the course before the release of p3. In particular, you are **NOT** allowed to use Conditionals or Iteration on this project. We will **manually deduct** points from your autograder score on Gradescope otherwise.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p3/rubric.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a43cc524",
+   "metadata": {},
+   "source": [
+    "## Incremental Coding and Testing:\n",
+    "\n",
+    "You should always strive to do incremental coding. Incremental coding enables you to avoid challenging bugs. Always write a few lines of code and then test those lines of code, before proceeding to write further code. You can call the `print` function to test intermediate step outputs. Store your final answer for each question in the variable recommended for each question. This step is important because Otter grades your work by comparing the value of this variable against the correct answer. So, if you store your answer in a different variable, you will not get points for it.\n",
+    "\n",
+    "We also recommend you do incremental testing: make sure to run the local tests as soon as you are done with a question. This will ensure that you haven't made a big mistake that might potentially impact the rest of your project solution. Please refrain from making multiple submissions on Gradescope for testing individual questions' answers. Instead use the local tests, to test your solution on your laptop. \n",
+    "\n",
+    "That said, it is very **important** that you check the *Gradescope* test results as soon as you submit your project on Gradescope. Test results on *Gradescope* are typically available somewhere between 2 to 10 minutes after the submission."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90a9199a",
+   "metadata": {},
+   "source": [
+    "## Project Questions and Functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27e49de7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Include the relevant import statements in this cell\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7d6af3f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Call the init function to load the dataset\n",
+    "\n",
+    "# You may call the dump function here to test if you have loaded the dataset correctly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d81882b5",
+   "metadata": {},
+   "source": [
+    "**Question 1:** What is the `id` of the *Toyota Prius PHEV*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef8226c8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# INCORRECT ANSWER prius_id = 977 => this is considered hardcoding\n",
+    "prius_id = ...\n",
+    "prius_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9007c6a9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01f6a286",
+   "metadata": {},
+   "source": [
+    "Instead of repeatedly calling `project.get_id` function for each question, make these calls once at the beginning of your notebook and save the results in variables. Recall that calling the same function multiple times with the same argument(s) is a waste of computation. Complete the code in the below cell and make sure to use the relevant ID variables for the rest of the project questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4bada37",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model_s_id = project.get_id('Tesla Model S') # we have done this for you\n",
+    "# replace the ... in the line below with code to get the id of 'Chevy Volt'\n",
+    "volt_id = ...\n",
+    "# invoke get_id for the other car models and store the result into similar variable names\n",
+    "\n",
+    "# considering that you already invokved get_id for Toyota Prius PHEV, you need to \n",
+    "# make 3 more function calls to store the ID for the rest of the vehicles\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00ec7bdd",
+   "metadata": {},
+   "source": [
+    "**Question 2:** How many *Nissan Leaf* cars were sold in *2017*?\n",
+    "\n",
+    "Your answer should just be a number (without any units at the end). You **should not** hardcode the ID of the car. You **must** use the variable that you used to store the ID of Nissan Leaf (assuming you already invoked `get_id` for all the car models in the cell right below Question 1)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e94197e7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "num_leaf = ...\n",
+    "num_leaf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb8ad42a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0be93e7",
+   "metadata": {},
+   "source": [
+    "### Function 1: `year_max(year)`\n",
+    "\n",
+    "This function will compute the highest number of sales for any model in the given `year`.\n",
+    "\n",
+    "It has already been written for you, so you do not have to modify it. You can directly call this function to answer the following questions. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45fcf54b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def year_max(year):\n",
+    "    \"\"\"\n",
+    "    computes the highest number of sales for any model in the given year\n",
+    "    \"\"\"\n",
+    "    # get the sales of each model in the given year\n",
+    "    model_s_sales = project.get_sales(project.get_id('Tesla Model S'), year)\n",
+    "    volt_sales = project.get_sales(project.get_id('Chevy Volt'), year)\n",
+    "    leaf_sales = project.get_sales(project.get_id('Nissan Leaf'), year)\n",
+    "    prius_sales = project.get_sales(project.get_id('Toyota Prius PHEV'), year)\n",
+    "    fusion_sales = project.get_sales(project.get_id('Ford Fusion Energi'), year)\n",
+    "    model_x_sales = project.get_sales(project.get_id('Tesla Model X'), year)\n",
+    "\n",
+    "    # use the built-in max function to get the maximum of the six values\n",
+    "    return max(model_s_sales, volt_sales, leaf_sales, prius_sales, fusion_sales, model_x_sales)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e8f0f4e",
+   "metadata": {},
+   "source": [
+    "**Question 3:** What was the highest number of sales for *any* model in the year *2017*?\n",
+    "\n",
+    "You **must** call the `year_max` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d27d5af4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "max_sales_2017 = ...\n",
+    "max_sales_2017"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fa4cd59",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2771e9a",
+   "metadata": {},
+   "source": [
+    "**Question 4:** What was the highest number of sales for *any* model in a single year in the period *2016-2018*?\n",
+    "\n",
+    "Recall that we can use the `max` function to compute the maximum of some values. Look at the lab examples where you used the `max` function or the `year_max` function definition. To be clear, the answer to this question is a single integer whose value is the highest sales number achieved by any model in a single year during these three years. You **must** invoke the `year_max` function in your answer to this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbf1273e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "max_sales_2016_to_2018 = ...\n",
+    "max_sales_2016_to_2018"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fece83de",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8551c39",
+   "metadata": {},
+   "source": [
+    "### Function 2: `sales_min(model)`\n",
+    "\n",
+    "This function should compute the lowest number of sales in a year for the given `model` considering every year in the dataset.\n",
+    "\n",
+    "We'll help you get started with this function, but you need to fill in the rest of the function yourself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cceaa0a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def sales_min(model):\n",
+    "    \"\"\"\n",
+    "    computes the lowest number of sales in a year for the given model\n",
+    "    \"\"\"\n",
+    "    model_id = project.get_id(model)    \n",
+    "    sales_2015 = project.get_sales(model_id, 2015)\n",
+    "    sales_2016 = project.get_sales(model_id, 2016)\n",
+    "    # get the sales from other years\n",
+    "    \n",
+    "    # use the built-in min function (similar to the max function) to get the minimum across the \n",
+    "    # five years and return that value\n",
+    "    \n",
+    "    min_sales_2015_to_2019 = ...\n",
+    "    return min_sales_2015_to_2019"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daee0146",
+   "metadata": {},
+   "source": [
+    "**Question 5:** What was the lowest number of sales for the *Tesla Model S* in a *single* year?\n",
+    "\n",
+    "You **must** call the `sales_min` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7a9c9dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "min_sales_model_S = ...\n",
+    "min_sales_model_S"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d7dea80",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e56e8c40",
+   "metadata": {},
+   "source": [
+    "**Question 6:** What was the lowest sales number in a *single* year between the *Chevy Volt*, *Ford Fusion Energi*, and the *Nissan Leaf*?\n",
+    "\n",
+    "Recall that we can use the `min` function to compute the minimum of some values. To be clear, the answer to this question is a single integer whose value is the lowest sales number achieved in a single year during this entire period between 2015-2019 by any of the 3 models mentioned. You **must** invoke the `sales_min` function in your answer to this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed169781",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'min_sales_CV_FFE_NL'\n",
+    "\n",
+    "# display the variable 'min_sales_CV_FFE_NL' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1756b19",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90e72f55",
+   "metadata": {},
+   "source": [
+    "### Function 3: `sales_avg(model) `\n",
+    "\n",
+    "This function should compute the average yearly sales number for the given `model` across the five years in the dataset (i.e. *2015 - 2019*).\n",
+    "\n",
+    "**Hint:** start by copy/pasting the `sales_min` function definition, and renaming your copy to `sales_avg` (this is not necessary, but it will save you time).  \n",
+    "Instead of returning the minimum of `sales_2015`, `sales_2016`, etc., return the average of these by adding them together, then dividing by five. \n",
+    "**You may hardcode the number 5 for this computation**.\n",
+    "\n",
+    "The type of the *return value* should be `float`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b34026b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function sales_avg(model) here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73c1c0aa",
+   "metadata": {},
+   "source": [
+    "**Question 7:** What was the average number of *Toyota Prius PHEV* cars sold per year between *2015* and *2019*?\n",
+    "\n",
+    "You **must** call the `sales_avg` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4441e7b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'sales_avg_prius_2015_to_2019'\n",
+    "\n",
+    "# display the variable 'sales_avg_prius_2015_to_2019' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eedd985b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81ea558c",
+   "metadata": {},
+   "source": [
+    "**Question 8:** What was the average number of *Chevy Volt* cars sold per year between *2015* and *2019*?\n",
+    "\n",
+    "You **must** call the `sales_avg` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25fc97fd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'sales_avg_volt_2015_to_2019'\n",
+    "\n",
+    "# display the variable 'sales_avg_volt_2015_to_2019' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47e895d5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ece2df7d",
+   "metadata": {},
+   "source": [
+    "**Question 9:** Relative to its 5-year average, how many more or fewer *Nissan Leaf* cars were sold in *2018*?\n",
+    "\n",
+    "**Hint:** Call the `sales_avg` function, to compare the *Nissan Leaf* average sales to the *Nissan Leaf* sales in *2018*. \n",
+    "Your answer will be a positive number if more Nissan Leafs were sold in 2018 than on average. Your answer will be a negative number if fewer Nissan Leafs were sold in 2018 than on average."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "719989d1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'diff_leaf_2018_to_average'.\n",
+    "# it is recommended that you create more intermediary variables to make your code easier to write and read.\n",
+    "# some useful intermediary variables you could create are: 'leaf_id', 'num_sales_leaf_2018'.\n",
+    "\n",
+    "\n",
+    "# display the variable 'diff_leaf_2018_to_average' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "185807f7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e0fc1ec",
+   "metadata": {},
+   "source": [
+    "### Function 4: `year_sum(year)`\n",
+    "\n",
+    "This function should compute the total number of sales across every model for the given `year`.\n",
+    "\n",
+    "You can start from the following code snippet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4c3511b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def year_sum(year=2019): # DO NOT EDIT THIS LINE\n",
+    "    \"\"\"\n",
+    "    computes the total number of sales across every model for the given year\n",
+    "    \"\"\"\n",
+    "    pass # this statement tells Python to do nothing.\n",
+    "    # since this function has no code inside, we have added the pass statement \n",
+    "    # inside so the code does not crash.\n",
+    "    # once you have added code to this function, you can (and should) \n",
+    "    # remove the pass statement as it does nothing.\n",
+    "    \n",
+    "    # finish this function definition and return the total number of sales \n",
+    "    # across every model for the given 'year'\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "134a04c1",
+   "metadata": {},
+   "source": [
+    "**Question 10:** What was the *total* number of vehicles sold in *2019*?\n",
+    "\n",
+    "You **must** call the `year_sum` function to answer this question. Use the default argument (your call to `year_sum` function **should not** pass any arguments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70c663d9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'sales_sum_2019'\n",
+    "\n",
+    "# display the variable 'sales_sum_2019' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c26eb96e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b82bdc41",
+   "metadata": {},
+   "source": [
+    "**Question 11:** What was the *total* number of vehicles sold between *2017* and *2019*?\n",
+    "\n",
+    "You **must** invoke the `year_sum` function in your answer to this question. To be clear, the answer to this question is a single integer whose value is the total sales number achieved by all six models during these three years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4b07d54",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'sales_sum_2017_to_2019'\n",
+    "\n",
+    "# display the variable 'sales_sum_2017_to_2019' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5e3d6ae",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c78acee",
+   "metadata": {},
+   "source": [
+    "### Function 5: `change_per_year(model, start_year, end_year)`\n",
+    "\n",
+    "This function should return the average increase/decrease in sales (could be positive if there's an increase, negative if there’s a decrease) over the period from `start_year` to `end_year` for the given `model`.\n",
+    "\n",
+    "The type of the *return value* should be `float`.\n",
+    "\n",
+    "We're not asking you to do anything complicated here; you just need to compute the difference in sales between the last year and the first year, then divide by the number of elapsed years. Recall that you created a similar function in the lab. You can start with the following code snippet (with the default arguments):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71d79355",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def change_per_year(model, start_year=2015, end_year=2019): # DO NOT EDIT THIS LINE\n",
+    "    \"\"\"\n",
+    "    computes the average increase/decrease in sales (could be positive if there's an increase, \n",
+    "    negative if there’s a decrease) over the period from start_year to end_year for the given model\n",
+    "    \"\"\"\n",
+    "    pass # as before, you should delete this statement after finishing your function.\n",
+    "    \n",
+    "    # compute and return the change per year in sales of the model between start_year and end_year\n",
+    "    # it is recommended that you create intermediary variables to make your code easier to write and read.\n",
+    "    # some useful intermediary variables you could create are: \n",
+    "    # 'sales_start_year', 'sales_end_year', 'sales_difference'.\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7cd1df4",
+   "metadata": {},
+   "source": [
+    "**Question 12:** How much have the sales of the *Ford Fusion Energi* changed per year (on average) from *2015* to *2019*?\n",
+    "\n",
+    "You **must** call the `change_per_year` function to answer this question. Use the default arguments (your call to `change_per_year` function **should not** pass any more arguments than is absolutely necessary)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae6d3bb6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'fusion_average_change'\n",
+    "\n",
+    "# display the variable 'fusion_average_change' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93fe2fac",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90323750",
+   "metadata": {},
+   "source": [
+    "**Question 13:** How much have the sales of the *Chevy Volt* changed per year (on average) from *2016* to *2019*?\n",
+    "\n",
+    "You **must** call the `change_per_year` function to answer this question. Use the default arguments (your call to `change_per_year` function **should not** pass any more arguments than is absolutely necessary)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50f1be71",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'volt_average_change'\n",
+    "\n",
+    "# display the variable 'volt_average_change' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90937e00",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3c29ca7",
+   "metadata": {},
+   "source": [
+    "**Question 14:** How much have the sales of the *Tesla Model X* changed per year (on average) from *2015* to *2018*?\n",
+    "\n",
+    "You **must** call the `change_per_year` function to answer this question. Use the default arguments (your call to `change_per_year` function **should not** pass any more arguments than is absolutely necessary)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c85fe535",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'model_x_average_change'\n",
+    "\n",
+    "# display the variable 'model_x_average_change' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e721e07",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd483b11",
+   "metadata": {},
+   "source": [
+    "### Function 6: `estimate_sales(model, target_year, start_year, end_year)`\n",
+    "\n",
+    "This function should estimate what the sales would be for the given `model` in the given `target_year` assuming that there is a constant rate of change in the sales after `end_year` that is equal to the average change per year in the period between `start_year` and `end_year`.\n",
+    "\n",
+    "The type of the *return value* should be `float`.\n",
+    "\n",
+    "You **must** define `estimate_sales` so that the parameter `start_year` has the default argument `2015` and `end_year` has the default argument `2019`.\n",
+    "\n",
+    "You **must** call the `change_per_year` function in the definition of `estimate_sales`. **Do not** manually compute the average change in sales."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7ce9565",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function estimate_sales(model, target_year, start_year, end_year) here.\n",
+    "# it should return the estimated sales of the model in target_year based on the change in \n",
+    "# sales between start_year and end_year.\n",
+    "# it is recommended that you create intermediary variables to make your code easier to write and read.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d296f6c1",
+   "metadata": {},
+   "source": [
+    "**Question 15:** What are the estimated sales for the *Nissan Leaf* in *2021* based on the average change in sales per year for it between *2015* and *2019*?\n",
+    "\n",
+    "You **must** call the `estimate_sales` function to answer this question. Use the default arguments if possible (your call to `estimate_sales` function **should not** pass any more arguments than is absolutely necessary)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8184db1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'leaf_sales_in_2021'\n",
+    "\n",
+    "# display the variable 'leaf_sales_in_2021' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "361d8591",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8705d87f",
+   "metadata": {},
+   "source": [
+    "**Question 16:** What are the estimated sales for the *Toyota Prius PHEV* in *2022* based on the average change in sales per year for it between *2016* and *2018*?\n",
+    "\n",
+    "You **must** call the `estimate_sales` function to answer this question. Use the default arguments if possible (your call to `estimate_sales` function **should not** pass any more arguments than is absolutely necessary)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ccbe75d",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'prius_sales_in_2022'\n",
+    "\n",
+    "# display the variable 'prius_sales_in_2022' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d353943",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fbc8b3e",
+   "metadata": {},
+   "source": [
+    "**Question 17:** What is the difference between estimated sales for the *Tesla Model X* in *2030* based on the average change per year between *2015* and *2018* and between *2015* and *2019*?\n",
+    "\n",
+    "You **must** invoke the `estimate_sales` function in your answer to this question. Use the default arguments if possible (your call to `estimate_sales` function **should not** pass any more arguments than is absolutely necessary). A positive answer implies that the estimate based on the sales between *2015* and *2018* is higher, while a negative answer implies that it is lower."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "355e84c4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'diff_sales_model_x_2030'\n",
+    "\n",
+    "# display the variable 'diff_sales_model_x_2030' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45f378b1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e741ad5",
+   "metadata": {},
+   "source": [
+    "**Question 18:** What is the difference between estimated sales for the *Nissan Leaf* in *2030* based on the average change per year between *2015* and *2017* and between *2016* and *2017*?\n",
+    "\n",
+    "You **must** invoke the `estimate_sales` function in your answer to this question. Use the default arguments if possible (your call to `estimate_sales` function **should not** pass any more arguments than is absolutely necessary). A positive answer implies that the estimate based on the sales between *2015* and *2017* is higher, while a negative answer implies that it is lower."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8227071f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'diff_sales_leaf_2030'\n",
+    "\n",
+    "# display the variable 'diff_sales_leaf_2030' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04e40066",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14de7b6a",
+   "metadata": {},
+   "source": [
+    "The wild answers we get to **Question 17** and **Question 18** suggest that our function `estimated_sales` is not very good at estimating the sales of any model in the future. This is not surprising since it is clearly not reasonable to assume that the rate of change in sales will remain constant over a period of time. We will now try to see how much the change per year in the sales of the models varies over time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75d264ae",
+   "metadata": {},
+   "source": [
+    "**Question 19:** What is the difference in change per year of *Chevy Volt* sales between the time periods of *2017* to *2019* and *2015* to *2016*?\n",
+    "\n",
+    "You **must** invoke the `change_per_year` function in your answer to this question. Use the default arguments if possible (your call to `change_per_year` function **should not** pass any more arguments than is absolutely necessary). A positive answer would imply that more cars were sold each year on average during the period *2017-2019* than during the period *2015-2016*, while a negative answer would imply that fewer cars were sold during the period *2017-2019*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07c3ab5c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'volt_diff_change_per_year'\n",
+    "\n",
+    "# display the variable 'volt_diff_change_per_year' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac11f6d0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9ba490c",
+   "metadata": {},
+   "source": [
+    "**Question 20:** What is, for the *Toyota Prius PHEV* sales, the ratio of the change per year between *2017* and *2018* to the change per year between *2015* and *2019*?\n",
+    "\n",
+    "You **must** invoke the `change_per_year` function in your answer to this question. Use the default arguments if possible (your call to `change_per_year` function **should not** pass any more arguments than is absolutely necessary). A value *greater than 1* here would imply that on average, more cars were sold each year during the period *2017-2018* than normal, while a value *less than 1* would imply that on average, fewer cars were sold each year during the period *2017-2018* than normal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adf4b064",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'prius_change_per_year_ratio'\n",
+    "\n",
+    "# display the variable 'prius_change_per_year_ratio' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15557c8f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "674ea8ba",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "\n",
+    "Make sure you have run all cells in your notebook in order before running the cell below, so that all images/graphs appear in the output. The cell below will generate a zip file for you to submit. **Please save before exporting!**\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**: 1. **Save** the notebook file **now (before you run the next cell of code)**. 2. **Upload** the zipfile to Gradescope. 3. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "517f8222",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Save your notebook first, then run this cell to export your submission.\n",
+    "grader.export(pdf=False, run_tests=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3c33ea6",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q1\", prius_id)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q10\", sales_sum_2019)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q11\", sales_sum_2017_to_2019)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q12\", fusion_average_change)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q13\", volt_average_change)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q14\", model_x_average_change)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q15\", leaf_sales_in_2021)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q16\", prius_sales_in_2022)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q17\", diff_sales_model_x_2030)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q18\", diff_sales_leaf_2030)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q19\", volt_diff_change_per_year)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q2\", num_leaf)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q20\", prius_change_per_year_ratio)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q3\", max_sales_2017)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q4\", max_sales_2016_to_2018)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q5\", min_sales_model_S)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q6\", min_sales_CV_FFE_NL)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q7\", sales_avg_prius_2015_to_2019)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q8\", sales_avg_volt_2015_to_2019)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q9\", diff_leaf_2018_to_average)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f08154012ddadd8e950e6e9e035c7a7b32c136e7647e9b7c77e02eb723a8bedb"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p3/p3.ipynb b/p3/p3.ipynb
index 1030201d3f77317ba24773e26dc1d34a67e235a1..b9dcbc2e03c2b9481895950bd9e25d9b074e5c68 100644
--- a/p3/p3.ipynb
+++ b/p3/p3.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9590d418",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p5/.ipynb_checkpoints/p5-checkpoint.ipynb b/p5/.ipynb_checkpoints/p5-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fb2c5985a1be842a1c32c6690b9697b2b862d6fe
--- /dev/null
+++ b/p5/.ipynb_checkpoints/p5-checkpoint.ipynb
@@ -0,0 +1,1493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2c18ea39",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f6c8d37",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"p5.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e01d7d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import p5_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c555bb0",
+   "metadata": {},
+   "source": [
+    "# Project 5: Investigating Hurricane Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "882eebe1",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project you will demonstrate how to:\n",
+    "- Write fundamental loop structures,\n",
+    "- Perform basic string manipulations,\n",
+    "- Create your own helper functions as outlined in lab-p5."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c39ead2c",
+   "metadata": {},
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `p5_test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions. You can have a look at [p2](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p2/p2.ipynb) if you have forgotten how to read the outputs of the `grader.check()` function calls."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6345a4fd",
+   "metadata": {},
+   "source": [
+    "**Please go through [lab-p5](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p5) before starting this project.** The lab introduces some useful techniques necessary for this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8fdac7e",
+   "metadata": {},
+   "source": [
+    "## Project Description:\n",
+    "\n",
+    "Hurricanes often count among the worst natural disasters, both in terms of monetary costs, and more importantly, human life. Data Science can help us better understand these storms. For example, take a quick look at this FiveThirtyEight analysis by Maggie Koerth-Baker: [Why We're Stuck With An Inadequate Hurricane Rating System](https://fivethirtyeight.com/features/why-were-stuck-with-an-inadequate-hurricane-rating-system/)\n",
+    "\n",
+    "For this project, you'll be analyzing data in the `hurricanes.csv` file. We generated this data file by writing a Python program to extract data from several lists of hurricanes over the Atlantic Ocean on Wikipedia (here is an [example](https://en.wikipedia.org/wiki/2022_Atlantic_hurricane_season)). You can take a look at the script `gen_csv.ipynb` yourself. At the end of the semester, you will be able to write it yourself. \n",
+    "\n",
+    "Start by downloading `project.py`, `p5_test.py`, and `hurricanes.csv` if you haven't already downloaded them. Double check that these files don't get renamed by your browser (by running `ls` in the terminal from your `p5` project directory). \n",
+    "\n",
+    "\n",
+    "We won't explain how to use the `project` module here (the code in the `project.py` file). Refer to [lab-p5](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p5) to understand how the module works. If necessary, use the `help` function to learn about the various functions inside `project.py`. Feel free to take a look at the `project.py` code, if you are curious about how it works.\n",
+    "\n",
+    "This project consists of writing code to answer 20 questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d3e93cc",
+   "metadata": {},
+   "source": [
+    "## Dataset:\n",
+    "\n",
+    "The dataset you will be working with in this project is linked here [hurricanes](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p5/hurricanes.csv). Be sure to look at this csv to see what it contains, and specifically what the names of the columns are.\n",
+    "\n",
+    "If needed, you can open the `hurricanes.csv` file, to verify answers to simple questions, but you must still have the correct code in your submission!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c45dc69a",
+   "metadata": {},
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices in your code. If you hardcode the value of `project.count()`, we'll **manually deduct** points from your autograder score on Gradescope during code review. If you are not sure what hardcoding is, here is a simple test you can use to determine whether you have hardcoded:\n",
+    "\n",
+    "*If we were to change the data (e.g. add more hurricanes, or switch the columns or rows) would your code still find the correct answer to the question as it is asked?*\n",
+    "\n",
+    "If your answer to that question is *No*, then you have likely hardcoded something. Please reach out to TAs/PMs during office hours to find out how you can **avoid hardcoding**.\n",
+    "\n",
+    "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer.  If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer. \n",
+    "\n",
+    "Required Functions:\n",
+    "- `get_month(date)`\n",
+    "- `get_day(date)`\n",
+    "- `get_year(date)`\n",
+    "- `format_damage(damage)`\n",
+    "- `deadliest_in_range(year1, year2)`\n",
+    "- `get_year_total(year)`\n",
+    "    \n",
+    "Students are only allowed to use Python commands and concepts that have been taught in the course prior to the release of p5. Therefore, **you should not use concepts/modules such as lists, dictionaries, or the pandas module, to name a few examples**.  We will **manually deduct** points from your autograder score on Gradescope otherwise.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p5/rubric.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b702c61",
+   "metadata": {},
+   "source": [
+    "## Incremental Coding and Testing:\n",
+    "\n",
+    "You should always strive to do incremental coding. Incremental coding enables you to avoid challenging bugs. Always write a few lines of code and then test those lines of code, before proceeding to write further code. You can call the `print` function to test intermediate step outputs.\n",
+    "\n",
+    "We also recommend you do incremental testing: make sure to run the local tests as soon as you are done with a question. This will ensure that you haven't made a big mistake that might potentially impact the rest of your project solution. Please refrain from making multiple submissions on Gradescope for testing individual questions' answers. Instead use the local tests, to test your solution on your laptop.\n",
+    "\n",
+    "That said, it is **important** that you check the Gradescope test results as soon as you submit your project on Gradescope. Test results on Gradescope are typically available somewhere between 2 to 10 minutes after the submission.\n",
+    "\n",
+    "Also, remember to check with the [p5 rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p5/rubric.md) to verify that you will not be losing any points during manual review."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f03e868f",
+   "metadata": {},
+   "source": [
+    "## Project Questions and Functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b4a55fe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "attachments": {
+    "table.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c56d7e7e",
+   "metadata": {},
+   "source": [
+    "For the first three questions, you do not have to define any of your own functions. Use the `project` module by calling the specific function needed to solve a certain question.\n",
+    "\n",
+    "*Please Note*, indexing in python starts from **0**. Therefore, if a question asks you to use a certain value's **index**, do not be confused that with the **location** of the value in the dataset. In our dataset here,\n",
+    "\n",
+    "![table.PNG](attachment:table.PNG)\n",
+    "\n",
+    "the **index** for `1804 New England Hurricane` is 0, but the **location** is 1, and the **row number** is 2. Be sure to keep this concept in mind for *all* questions asking for the value at a particular **index**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "642cda16",
+   "metadata": {},
+   "source": [
+    "**Question 1:** How many hurricanes does the dataset have?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffcbcaad",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'num_hurricanes'\n",
+    "\n",
+    "# display the variable 'num_hurricanes' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c10fe9a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c43b3449",
+   "metadata": {},
+   "source": [
+    "**Question 2:** How many `deaths` were caused by the hurricane at index `220`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d7afa09",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'deaths_220'\n",
+    "\n",
+    "# display the variable 'deaths_220' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19d067d2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f301eea",
+   "metadata": {},
+   "source": [
+    "**Question 3:** What is the `name` of the hurricane at the `last` index?\n",
+    "\n",
+    "**Hint**: Your code should work even if the number of hurricanes in the dataset were to change. You **must not hardcode** the index of the last hurricane."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c602b566",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'name_last_index'\n",
+    "\n",
+    "# display the variable 'name_last_index' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcc23702",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6706bfc4",
+   "metadata": {},
+   "source": [
+    "**Question 4:** How many hurricanes `named` *Ida* are in the dataset?\n",
+    "\n",
+    "**Hint:** Loop through *all* hurricanes and count the hurricanes whose `name` is *Ida*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aafa3946",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'ida_count'\n",
+    "\n",
+    "# display the variable 'ida_count' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89661779",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c4d57a6",
+   "metadata": {},
+   "source": [
+    "**Question 5:** What is the **fastest** speed (in `mph`) of a hurricane in the dataset?\n",
+    "\n",
+    "**Hint**: Look at the section in lab-p5 on finding the maximum/minimum. Here you will have to find the function value of the function `project.get_mph`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c29b26b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'max_speed'\n",
+    "\n",
+    "# display the variable 'max_speed' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d4069fa",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e531358",
+   "metadata": {},
+   "source": [
+    "### Function 1: `format_damage(damage)`\n",
+    "\n",
+    "You will notice if you look at the dataset that the damages caused by the hurricanes are not stored directly as numbers. Instead the damages have a suffix (`\"K\"`, `\"M\"`, or `\"B\"`) attached at the very end. You will have to convert these 'numbers' into integers before you can perform any mathematical operations on them. \n",
+    "\n",
+    "Since you will need to format damages for multiple hurricanes, you **must** create a general helper function that handles the `\"K\"`, `\"M\"`, and `\"B\"` suffixes. Remember that `\"K\"` stands for thousand, `\"M\"` stands for million, and `\"B\"` stands for billion. For example, your function should convert the string `\"13.5M\"` to `13500000`, `\"6.9K\"` to `6900` and so on. Note that for **some** hurricanes, the `damage` does **not** have **any** suffixes. For instance, the hurricane `Florence` at index `308` did damage `'0'`. Your function **must** also deal with such inputs, by directly typecasting them to ints.\n",
+    "\n",
+    "This function should take in the strings from the `damage` column as input, and return an **int**.\n",
+    "\n",
+    "**Warning:** Your function `format_damage` must take in the damage as a **string**, and **not** an index. If you code your function to take in the index of a hurricane, and return the damage caused as an int, it will be useful only for this project. To make your function more useful, you must make it accept the damage itself (i.e., a string like `\"13.5M\"` or `\"6.9K\"`) as input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5f948b6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def format_damage(damage):\n",
+    "    pass # TODO: replace this with your code\n",
+    "    #TODO: use relevant intermediary variables to simplify your code\n",
+    "    #TODO: check the last character of the string `damage`\n",
+    "    #TODO: type cast the string (except for last character - use appropriate slicing) into a float\n",
+    "    #TODO: use the last character of string to determine what factor to multiply the float with\n",
+    "    #TODO: type cast the final computation to int\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "887d1890",
+   "metadata": {},
+   "source": [
+    "**Question 6:** What is the `damage` (in dollars) caused by the hurricane named *Gilbert*?\n",
+    "\n",
+    "There is **exactly one** hurricane in this dataset named *Gilbert*. You **must** exit the loop, and **stop** iterating as soon as you find the hurricane named *Gilbert*.\n",
+    "\n",
+    "You **must** use the `format_damage` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "885a7748",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'damage_gilbert'\n",
+    "\n",
+    "# display the variable 'damage_gilbert' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5149122",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7d581e",
+   "metadata": {},
+   "source": [
+    "**Question 7:** What is the **total** `damage` (in dollars) caused by all hurricanes named *Karen* in the dataset? \n",
+    "\n",
+    "There are **multiple** hurricanes in this dataset named *Karen*. You must add up the damages caused by all of them. You **must** use the `format_damage` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "372c1c00",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'total_damage_karen'\n",
+    "\n",
+    "# display the variable 'total_damage_karen' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0496807",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d77b71ee",
+   "metadata": {},
+   "source": [
+    "**Question 8:** What is the **average** `damage` caused by hurricanes with names starting with the letter *G*?\n",
+    "\n",
+    "You should only consider hurricanes whose **first character** is `\"G\"`. Remember to search for `\"G\"` and not `\"g\"`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f9ee223",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'average_damage_starts_g'\n",
+    "# use relevant intermediary variables to simplify your code\n",
+    "\n",
+    "# display the variable 'average_damage_starts_g' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "530c6e45",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21504d6c",
+   "metadata": {},
+   "source": [
+    "**Question 9:** What is the `name` of the **slowest** hurricane in the dataset?\n",
+    "\n",
+    "To break ties (if there are multiple hurricanes with the same speed), you **must** consider the **last** one you find."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34e3c619",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'slowest_hurricane'\n",
+    "\n",
+    "# display the variable 'slowest_hurricane' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d746b25a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "696ff74d",
+   "metadata": {},
+   "source": [
+    "**Question 10:** What is the `damage` (in dollars) caused by the **slowest** hurricane (found in `Q9`)?\n",
+    "\n",
+    "**Hint:** If you find the **index** of the slowest hurricane in `Q9` instead of just the **name** of the hurricane, you can solve `Q10` very easily using the appropriate function from the project module (i.e., without writing a new loop)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0347a62a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'slowest_hurricane_damage'\n",
+    "\n",
+    "# display the variable 'slowest_hurricane_damage' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fa40e27",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b582fd3d",
+   "metadata": {},
+   "source": [
+    "### Functions 2, 3, 4: `get_year(date)`, `get_month(date)`, and `get_day(date)`\n",
+    "\n",
+    "Now would be a good time to copy the `get_year`, `get_month`, and `get_day` functions you created in lab-p5 to your project notebook. You will need these functions for the upcoming questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1d0ad30",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the get_year, get_month, and get_day functions here from your lab-p5 practice notebook\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65bc253e",
+   "metadata": {},
+   "source": [
+    "**Question 11:** What is the `name` of the **earliest** hurricane which caused over *1 billion* dollars in `damages`?\n",
+    "\n",
+    "You **must** use the `year` of formation of the hurricane to identify the earliest hurricane. There are **no** other hurricanes in that year which caused over 1 billion dollars in damages, so you do not have to worry about breaking ties. You **must not** use the indices of the hurricanes to determine the earliest hurricane.\n",
+    "\n",
+    "You need to find the hurricane with the earliest year of formation among those hurricanes with more than 1 billion dollars in damages. You **must not** initialize your variable to be some hurricane which caused less than 1 billion dollars in damages, such as the hurricane at index `0` for example. If you do so, you will find that you are finding the hurricane with the earliest year of formation among the hurricanes with **either** more than 1 billion dollars in damages **or** have index `0`. This is **not** what you need to do.\n",
+    "\n",
+    "**Hint:** Take a look at the [lecture notes for October 3](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-12) if you do not remember how to find the maximum/minimum with `None` initialization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6401c2e9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'earliest_billion_dollar_hurr'\n",
+    "\n",
+    "# display the variable 'earliest_billion_dollar_hurr' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1dadb685",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a49f4af3",
+   "metadata": {},
+   "source": [
+    "**Question 12:** What is the `name` of the **most recent** hurricane which caused over *100 billion* dollars in `damages`?\n",
+    "\n",
+    "You **must** use the `year` of formation of the hurricane to identify the most recent hurricane. There are **no** other hurricanes in that year which caused over 100 billion dollars in damages, so you do not have to worry about breaking ties. You **must not** use the indices of the hurricanes to determine the most recent hurricane.\n",
+    "\n",
+    "As in `Q11`, you **must** initialize the variable you use to store the index of the most recent hurricane as `None`, and update it for the first time only when you come across the first hurricane in the dataset which caused over 100 billion dollars in damages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19bb1889",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'most_recent_100_billion_hurr'\n",
+    "\n",
+    "# display the variable 'most_recent_100_billion_hurr' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63487578",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eff4c0f",
+   "metadata": {},
+   "source": [
+    "### Function 5: `deadliest_in_range(year1, year2)`\n",
+    "\n",
+    "This function should take in two years, `year1` and `year2` as its inputs and return the **index** of the hurricane which formed **or** dissipated between `year1` and `year2` and caused the **most** `deaths`. In case of any ties, you must return the index of the **first** hurricane in the dataset with the most deaths.\n",
+    "\n",
+    "As in `Q11` and `Q12`, you **must** initialize the variable you use to store the index of the deadliest hurricane as `None`, and update it for the first time only when you come across the first hurricane in the dataset within the year range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3d26550",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def deadliest_in_range(year1, year2):\n",
+    "    \"\"\"\n",
+    "    gets the index of the deadliest (most deaths) hurricane formed or dissipated within the given year range.\n",
+    "    year1 and year2 are inclusive bounds.\n",
+    "\n",
+    "    returns the index of the worst hurricane within the year range.\n",
+    "    \"\"\"\n",
+    "    pass # TODO: replace with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fe5b265",
+   "metadata": {},
+   "source": [
+    "**Question 13:** How much `damage` (in dollars) was done by the **deadliest** hurricane this century thus far (*2001 to 2022*, both inclusive)?\n",
+    "\n",
+    "Your answer **must** be an `int`. For this question, you may hardcode the years in the range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6371f4c6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'damage_by_deadliest_21st_century'\n",
+    "\n",
+    "# display the variable 'damage_by_deadliest_21st_century' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bba0b0a8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "779abbbb",
+   "metadata": {},
+   "source": [
+    "**Question 14:** What was the speed (in `mph`) of the **deadliest** hurricane of the 20th century (*1901 to 2000*, both inclusive)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8455b3f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'speed_of_deadliest_20th_century'\n",
+    "\n",
+    "# display the variable 'speed_of_deadliest_20th_century' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca8d0783",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4accf073",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "**Question 15:** In this century (*2001 to 2021*, both inclusive) how many hurricanes formed on **average**, in the `month` of *October*?\n",
+    "\n",
+    "We will ignore the year *2022*, since *October* has just started. Your answer must be a  **float**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56414513",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'avg_hurricanes_in_oct'\n",
+    "\n",
+    "# display the variable 'avg_hurricanes_in_oct' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23fb4b69",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b359401",
+   "metadata": {},
+   "source": [
+    "### Function 6: `get_year_total(year)`\n",
+    "\n",
+    "This function should take in `year` as its input and return the number of hurricanes that were **formed** in the given `year`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39b289e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function `get_year_total` here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b52c8985",
+   "metadata": {},
+   "source": [
+    "**Question 16:** How many hurricanes were formed in the `year` *2018*?\n",
+    "\n",
+    "You **must** answer this question by calling `get_year_total`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85998b5d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'total_hurricanes_2018'\n",
+    "\n",
+    "# display the variable 'total_hurricanes_2018' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41b77e0f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8258ae0d",
+   "metadata": {},
+   "source": [
+    "**Question 17:** How many hurricanes were formed in the last `decade` (*2011 to 2020*, both inclusive)?\n",
+    "\n",
+    "You **must** answer this question by looping across the years in this decade, and calling the function `get_year_total`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "425f1a9e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'total_hurricanes_in_last_decade'\n",
+    "\n",
+    "# display the variable 'total_hurricanes_in_last_decade' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d52a186",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69c4ed25",
+   "metadata": {},
+   "source": [
+    "**Question 18:** Which `year` in the 20th century (*1901 to 2000*, both inclusive) suffered the **most** number of hurricanes?\n",
+    "\n",
+    "You **must** answer this question by calling the function `get_year_total`. You **must** break ties in favor of the most recent year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c51684f8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'year_with_most_hurricanes'\n",
+    "\n",
+    "# display the variable 'year_with_most_hurricanes' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b61c3df5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1903467b",
+   "metadata": {},
+   "source": [
+    "**Question 19:** How many hurricanes lasted across at least 2 *different* `months`?\n",
+    "\n",
+    "**Hint:** You can determine if a hurricane lasted across two different months by comparing the month of formation and the month of dissipation of the hurricane. Note that there may be hurricanes which formed late in the year, and dissipated early in the next year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73de692e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'multiple_months_hurrs'\n",
+    "\n",
+    "# display the variable 'multiple_months_hurrs' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9de0ae91",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9605846",
+   "metadata": {},
+   "source": [
+    "**Question 20:** What is the **average** `damage` caused by the **deadliest** hurricane of each year from *2001 - 2022*, both inclusive?\n",
+    "\n",
+    "You **must** use the `deadliest_in_range` function to identify the deadliest hurricane of each year, and you **must** use `format_damage` to convert the `damages` into an `int`. You may assume that there is **at least** one hurricane in each of these years, so each year will have a deadliest hurricane. If two hurricanes in a year have the **same** deaths, you must break ties in favor of the hurricane that appears **first** in the dataset.\n",
+    "\n",
+    "Your answer must be a  **float**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a09751d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'average_damage_deadliest'\n",
+    "\n",
+    "# display the variable 'average_damage_deadliest' here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae722427",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28946414",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "\n",
+    "Make sure you have run all cells in your notebook in order before running the cell below, so that all images/graphs appear in the output. The cell below will generate a zip file for you to submit. **Please save before exporting!**\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**: 1. **Save** the notebook file **now (before you run the next cell of code)**. 2. **Upload** the zipfile to Gradescope. 3. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7e23627",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Save your notebook first, then run this cell to export your submission.\n",
+    "grader.export(pdf=False, run_tests=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8387638d",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q1\", num_hurricanes)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q10\", slowest_hurricane_damage)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q11\", earliest_billion_dollar_hurr)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q12\", most_recent_100_billion_hurr)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q13\", damage_by_deadliest_21st_century)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q14\", speed_of_deadliest_20th_century)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q15\", avg_hurricanes_in_oct)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q16\", total_hurricanes_2018)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q17\", total_hurricanes_in_last_decade)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q18\", year_with_most_hurricanes)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q19\", multiple_months_hurrs)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q2\", deaths_220)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q20\", average_damage_deadliest)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q3\", name_last_index)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q4\", ida_count )\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q5\", max_speed)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q6\", damage_gilbert)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q7\", total_damage_karen)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q8\", average_damage_starts_g)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p5_test.check(\"q9\", slowest_hurricane)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p5/p5.ipynb b/p5/p5.ipynb
index 1de2c97b0aeb062d950725bd8d4f8abe65a8c25d..fb2c5985a1be842a1c32c6690b9697b2b862d6fe 100644
--- a/p5/p5.ipynb
+++ b/p5/p5.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2c18ea39",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p6/.ipynb_checkpoints/p6-checkpoint.ipynb b/p6/.ipynb_checkpoints/p6-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c1593107c4cee14e874c59d49f4e3221acbc6226
--- /dev/null
+++ b/p6/.ipynb_checkpoints/p6-checkpoint.ipynb
@@ -0,0 +1,1517 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66622690",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cb8f079",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"p6.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf8f4503",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import p6_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3116a04d",
+   "metadata": {},
+   "source": [
+    "# Project 6: Airbnb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc4a993d",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project, you will demonstrate how to:\n",
+    "\n",
+    "* access and utilize data in CSV files,\n",
+    "* process real world datasets,\n",
+    "* use string methods and sorting function / method to order data.\n",
+    "\n",
+    "Please go through [lab-p6](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p6) before working on this project. The lab introduces some useful techniques related to this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b7d3514",
+   "metadata": {},
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `p6_test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8befadb",
+   "metadata": {},
+   "source": [
+    "## Project Description:\n",
+    "\n",
+    "Data Science can help us understand user behavior on online platform services. This project is about the rooms listed on Airbnb. Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present a more unique, personalized way of experiencing the world. `airbnb.csv` has data of nearly 50,000 listings on Airbnb from New York City, NY from the year 2019. This file includes a lot of information about the hosts, geographical availability of the listings, and other necessary metrics to make predictions and draw conclusions. You will be using various string manipulation methods that come with Python as well as creating some of your own functions to solve the problems posed. Happy coding!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f0aee24",
+   "metadata": {},
+   "source": [
+    "## Dataset:\n",
+    "\n",
+    "A small portion of the dataset `airbnb.csv` you will be working with for this project is reproduced here:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54c0f066",
+   "metadata": {},
+   "source": [
+    "room_id|name|host_id|host_name|neighborhood_group|neighborhood|latitude|longitude|room_type|price|minimum_nights|number_of_reviews|last_review|reviews_per_month|calculated_host_listings_count|availability_365\n",
+    "------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|\n",
+    "2539|Clean & quiet apt home by the park|2787|John|Brooklyn|Kensington|40.64749000000001|-73.97237|Private room|149|1|9|2018-10-19|0.21|6|365\n",
+    "2595|Skylit Midtown Castle|2845|Jennifer|Manhattan|Midtown|40.75362|-73.98376999999998|Entire home/apt|225|1|45|2019-05-21|0.38|2|355\n",
+    "3647|THE VILLAGE OF HARLEM....NEW YORK !|4632|Elisabeth|Manhattan|Harlem|40.80902|-73.9419|Private room|150|3|0|||1|365\n",
+    "3831|Cozy Entire Floor of Brownstone|4869|LisaRoxanne|Brooklyn|Clinton Hill|40.68514|-73.95976|Entire home/apt|89|1|270|2019-07-05|4.64|1|194\n",
+    "5022|Entire Apt: Spacious Studio/Loft by central park|7192|Laura|Manhattan|East Harlem|40.79851|-73.94399|Entire home/apt|80|10|9|2018-11-19|0.1|1|0\n",
+    "5099|Large Cozy 1 BR Apartment In Midtown East|7322|Chris|Manhattan|Murray Hill|40.74767|-73.975|Entire home/apt|200|3|74|2019-06-22|0.59|1|129"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "028bb42a",
+   "metadata": {},
+   "source": [
+    "You can find more details on the dataset in [lab-p6](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p6)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e3421eb",
+   "metadata": {},
+   "source": [
+    "## Questions and Functions:\n",
+    "\n",
+    "Let us start by importing all the modules we will need for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49d20857",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7e60768",
+   "metadata": {},
+   "source": [
+    "#### Now, copy and paste the `process_csv` and `cell` functions from your [lab-p6](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p6) notebook to the cell below.\n",
+    "\n",
+    "You are expected to call the `process_csv` function correctly, and read the data on `airbnb.csv`. After reading the file, define the `csv_header`, and `csv_rows` variables as in [lab-p6](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p6), and define the `cell` function.\n",
+    "\n",
+    "**Important:** You **must** only use the `cell` function to extract data from the dataset. If you extract any data without explicitly using this function, you will **lose points** during manual review. It is recommended but **optional** that you use the `cell_v2` function defined in lab-p6. However, you **must** rename the function to `cell` in your p6 notebook. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c1cdda2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e714104",
+   "metadata": {},
+   "source": [
+    "**Question 1:** What **unique** `neighborhood_groups` are included in the dataset?\n",
+    "\n",
+    "Your output **must** be a *list* which stores all the **unique** neighborhood groups (i.e., without any duplicates). The order **does not** matter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2beafa70",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'neighborhood_groups', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14176f10",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b785fff1",
+   "metadata": {},
+   "source": [
+    "**Question 2:** What is the **average** `price` of rooms in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6528c6a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'avg_price', then display it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "233c68fe",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cbddd95",
+   "metadata": {},
+   "source": [
+    "**Question 3:** How many rooms are in the `neighborhood` of *SoHo*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3276b660",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'count_soho', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1179043",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a655178b",
+   "metadata": {},
+   "source": [
+    "### Function 1: `find_room_names(phrase)`\n",
+    "\n",
+    "We require you to complete the below function to answer the next several questions (this is a **requirement**, and you will **lose points** if you do not implement this function)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7290e8d6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def find_room_names(phrase):\n",
+    "    \"\"\"\n",
+    "    find_room_names(phrase) returns a list of all the room names that contain the \n",
+    "    substring (case insensitive match) `phrase`.\n",
+    "    \"\"\"\n",
+    "    pass # replace with your code    \n",
+    "    # TODO: create a list\n",
+    "    # TODO: ignore rooms that do not have data entry for name, as indicated by a value of None\n",
+    "    # TODO: check if the room name string contains phrase (case insensitive match)\n",
+    "    # TODO: if so, add these room names to the list (the room names should be as in the dataset)\n",
+    "    # TODO: return your list of room names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8d252c1",
+   "metadata": {},
+   "source": [
+    "**Question 4:** Find all room `names` that contain the string `\"CBG\"`.\n",
+    "    \n",
+    "Your output **must** be a *list*. The order **does not** matter. You **must** call the `find_room_names` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a9596cf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rooms_contain_cbg', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7037bdef",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af8bfbfd",
+   "metadata": {},
+   "source": [
+    "**Question 5:** Find all room `names` that contain **either** `\"cinema\"` **or** `\"film\"`.\n",
+    "\n",
+    "Your output **must** be a *list*. The order **does not** matter, but if a room's `name` contains **both** `\"cinema\"` and `\"film\"`, then the room must be included **only once** in your list. You **must** call the `find_room_names` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59ca038d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rooms_contain_cinema_film', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7dc2aa0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39758383",
+   "metadata": {},
+   "source": [
+    "**Question 6:** Find the **longest** room `name` that contains the word `\"fun\"`.\n",
+    "\n",
+    "There is a **unique** such room with the longest `name`, so you **do not** have to worry about breaking ties. You **must** call the `find_room_names` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df10f226",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'funnest_room', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "149fd1f6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b449f7b",
+   "metadata": {},
+   "source": [
+    "**Question 7:** Find all the `names` of the rooms which have `price` *0* and have **more than** *30* `reviews`.\n",
+    "\n",
+    "Your output **must** be a *list*. The names **must** be sorted in **ascending (alphabetical) order**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "560ffc64",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'no_cost_rooms', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab4e1521",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6143b4f2",
+   "metadata": {},
+   "source": [
+    "**Question 8:**  What `neighborhoods` are the rooms that have `prices` greater than *9999* located in?\n",
+    "\n",
+    "Your output **must** be a *list* of **unique** neighborhoods (i.e., without any duplicates). The names **must** be sorted in **descending (reverse-alphabetical) order**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a75c710d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'pricey_neighborhoods', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b971371d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "895f7490",
+   "metadata": {},
+   "source": [
+    "**Question 9:** How many rooms received their `last_review` in or before *2015*?\n",
+    "\n",
+    "You should **ignore** rooms for which the `last_review` data is missing.\n",
+    "\n",
+    "**Hint:** You can find the date of the last review in the `last_review` column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fefd654e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'last_review_before_2015', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11a617ea",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fffea252",
+   "metadata": {},
+   "source": [
+    "### Function 2: `avg_price_per_room_type(room_type, neighborhood)`\n",
+    "\n",
+    "We require you to complete the below function to answer the next several questions (this is a **requirement**, and you will **lose points** if you do not implement this function)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1200df6c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def avg_price_per_room_type(room_type, neighborhood):\n",
+    "    '''\n",
+    "    avg_price_per_room_type(room_type, neighborhood) returns the average price of \n",
+    "    rooms of the type `room_type` in the given `neighborhood`; if there are no\n",
+    "    rooms of the type `room_type` in the given `neighborhood`, it returns `None`\n",
+    "    '''\n",
+    "    pass # replace with your code    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fa0cd52",
+   "metadata": {},
+   "source": [
+    "**Question 10:** What is the  **average** cost of a *Private room* in the`neighborhood` *Little Neck*?\n",
+    "\n",
+    "You **must** call the `avg_price_per_room_type` function to answer this question.\n",
+    "\n",
+    "**Hint:** To help you debug your code in case you run into any bugs, we have reproduced in the cell below, **all** the rows in the dataset from the `neighborhood` *Little Neck*. If you run into bugs with `avg_price_per_room_type`, it is recommended that you go through your code and verify that it does what it is supposed to, for this tiny dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53e5a8f7",
+   "metadata": {},
+   "source": [
+    "room_id|name|host_id|host_name|neighborhood_group|neighborhood|latitude|longitude|room_type|price|minimum_nights|number_of_reviews|last_review|reviews_per_month|calculated_host_listings_count|availability_365\n",
+    "------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|\n",
+    "20227428|Douglaston Apartment Room A|18996093|Leonard|Queens|Little Neck|40.75794000000001|-73.72955999999998|Private room|45|1|12|2019-06-22|0.55|5|133\n",
+    "21025083|Douglaston (apt 2) Room one\\n(Largest room)|18996093|Leonard|Queens|Little Neck|40.75777|-73.72949|Private room|50|1|6|2018-12-16|0.31|5|94\n",
+    "30325639|Cozy shared studio in a safe neighborhood|21495656|Ramy|Queens|Little Neck|40.76212|-73.71928|Shared room|32|3|1|2018-12-04|0.14|1|88\n",
+    "31553066|Near major transportation|41090359|Abi|Queens|Little Neck|40.77122|-73.738|Private room|100|1|0|||1|88\n",
+    "35515780|30-min to Manhattan Quiet Big House in Great Neck|31859704|Vincent|Queens|Little Neck|40.77444000000001|-73.73373000000002|Entire home/apt|149|3|0|||1|3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a0e2bdc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'pvt_room_little_neck', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0667f9cd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61cffaaa",
+   "metadata": {},
+   "source": [
+    "**Question 11:** How much more expensive is a *Entire home/apt* than a *Private room* in the `neighborhood` *Astoria*?\n",
+    "\n",
+    "You **must** call the `avg_price_per_room_type` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87968123",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'home_pvt_room_astoria_diff', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2746b67f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17498e25",
+   "metadata": {},
+   "source": [
+    "### Function 3: `find_prices_within(lat_min, lat_max, long_min, long_max)` \n",
+    "\n",
+    "We require you to complete the below function to answer the next several questions (this is a **requirement**, and you will **lose points** if you do not implement this function)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71f869cd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def find_prices_within(lat_min, lat_max, long_min, long_max):\n",
+    "    \"\"\"\n",
+    "    find_prices_within(lat_min, lat_max, long_min, long_max) returns an unordered \n",
+    "    list of prices of all the rooms within the geographical location between and including\n",
+    "    the latitudes lat_min and lat_max and longitudes long_min and long_max.\n",
+    "    \"\"\"\n",
+    "    pass # replace with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9875b5ad",
+   "metadata": {},
+   "source": [
+    "**Question 12:** What is the **lowest** `price` room near *NYU* (`40.729 <= latitude <= 40.73, -74.01 <= longitude <= -74.00`)?\n",
+    "\n",
+    "You **must** call the `find_prices_within` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d169db65",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'min_price_nyu', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44395227",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a68871e",
+   "metadata": {},
+   "source": [
+    "### Function 4: `median(items)` \n",
+    "\n",
+    "We require you to complete the below function to answer the next several questions (this is a **requirement**, and you will **lose points** if you do not implement this function)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fafd6aef",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def median(items):\n",
+    "    \"\"\"\n",
+    "    median(items) returns the median of the list `items`\n",
+    "    \"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # you may copy/paste this function from your lab-p6 notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1dfbc53",
+   "metadata": {},
+   "source": [
+    "**Question 13:** What is the **median** `price` of the rooms near *Columbia University* (`40.79 <= latitude <= 40.80, -73.96 <= longitude <= -73.95`)?\n",
+    "\n",
+    "You **must** call the `find_prices_within` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "426c2346",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'median_price_columbia', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "368b96eb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdd2571a",
+   "metadata": {},
+   "source": [
+    "**Question 14:** What **percentage** of rooms near *Rockefeller Center* (`40.749 <= latitude <= 40.75, -73.98 <= longitude <= -73.97`) have a `price` **more than** *100*?\n",
+    "\n",
+    "Your answer **must** be a *float* value between *0* and *100*. You **must** call the `find_prices_within` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccdee937",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'pct_price_over_hundred', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8668e977",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59fa206d",
+   "metadata": {},
+   "source": [
+    "### Function 5: `review_avail_ratio(neighborhood)` \n",
+    "\n",
+    "We require you to complete the below function to answer the next several questions (this is a **requirement**, and you will **lose points** if you do not implement this function)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9363a6a",
+   "metadata": {},
+   "source": [
+    "You should ignore rooms that have `availability_365` data of 0. You should also ignore rooms for which the ratio cannot be computed due to missing data.\n",
+    "\n",
+    "1.  The denominator is the availability of a room (`availability_365` column). The numerator is the number of reviews of a room (`number_of_reviews column`). \n",
+    "2.  Be careful! You need to compute the ratio for each room in the given neighborhood, then take the average of those ratios. Simply dividing the sum of reviews by the sum of availability will calculate the wrong answer.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10165bad",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def review_avail_ratio(neighborhood):\n",
+    "    \"\"\"\n",
+    "    review_avail_ratio(neighborhood) returns the average of the ratios of \n",
+    "    number of reviews to availability of all rooms in the `neighborhood`\n",
+    "    \"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: you should **ignore** rooms that have `availability_365` data of 0\n",
+    "    # TODO: you should **ignore** rooms for which the ratio cannot be computed due to missing data\n",
+    "    # Hint: the numerator is the number of reviews of a room (`number_of_reviews column`)\n",
+    "    # Hint: the denominator is the availability of a room (`availability_365` column)\n",
+    "    # Hint: note that you need to compute the average of the ratios, **not** the ratio of the averages.\n",
+    "    #       you must compute the ratio for each room in the `neighborhood`, then take the average of those ratios.\n",
+    "    #       simply dividing the sum of reviews by the sum of availability will calculate the wrong answer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6dca681",
+   "metadata": {},
+   "source": [
+    "**Question 15:** What is the **average of the ratios** of the `number_of_reviews` to `availability_365` in the `neighborhood` *Bushwick*?\n",
+    "\n",
+    "You **must** call the `review_avail_ratio` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4afd95a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'bushwick_avg_ratio', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2aa4c910",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e716f7f4",
+   "metadata": {},
+   "source": [
+    "**Question 16:** What is the **average of the ratios** of the `number_of_reviews` to `availability_365` in the `neighborhood` *Manhattan Beach*?\n",
+    "\n",
+    "You **must** call the `review_avail_ratio` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b168d4de",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'manhattan_beach_avg_ratio', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7d3a2a5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcfb9f21",
+   "metadata": {},
+   "source": [
+    "**Question 17:** Which `neighborhood` in the `neighborhood_group` *Staten Island* has the **highest average of ratios** of the `number_of_reviews` to `availability_365`?\n",
+    "\n",
+    "**Clarification:** Don't worry about it if this cell takes around 10 seconds to run, that is expected. If it takes much longer (i.e., more than 30 seconds), you **must** optimize your code. Attend office hours if you are unable to get your code to run faster.\n",
+    "\n",
+    "**Hint:** You do not need to compute the average of ratios for the **same** `neighborhood` more than once. Make a list of the **unique** neighborhoods in *Staten Island* first, and then find the **highest average of ratios** among those `neighborhoods`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5076061e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'max_nbhd_staten_island', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f142c802",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6085a9f",
+   "metadata": {},
+   "source": [
+    "### Function 6: `secondary_word_in_found_rooms(find_room_word, secondary_word)`\n",
+    "\n",
+    "\n",
+    "We require you to complete the below function to answer the next several questions (this is a **requirement**, and you will **lose points** if you do not implement this function).\n",
+    "\n",
+    "`secondary_word_in_found_rooms` function definition **must** invoke the function `find_room_names`. **We'll manually deduct points** if you don't use `find_room_names`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87e29aaf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def secondary_word_in_found_rooms(find_room_word, secondary_word):\n",
+    "    \"\"\"\n",
+    "    secondary_word_in_found_rooms(find_room_word, secondary_word) returns \n",
+    "    the percentage of names containing the word `find_room_word` (case insensitive match)\n",
+    "    that also contains the word `secondary_word` (case insensitive match).\n",
+    "    \"\"\"    \n",
+    "    pass # replace with your code\n",
+    "    # Hint: the denominator is the number of rooms with `find_room_word` in their name.\n",
+    "    # Hint: the numerator is the number of rooms that have **both** `find_room_word` and `secondary_word` in their name."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7526d52e",
+   "metadata": {},
+   "source": [
+    "**Question 18:** What **percentage** of rooms whose `names` contain the word `\"downtown\"` **also** contain the word `\"spacious\"`?\n",
+    "\n",
+    "Your answer **must** be a *float* value between *0* and *100*. You **must** call the `secondary_word_in_found_rooms` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1345be1a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'downtown_and_spacious', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22281790",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "960e302e",
+   "metadata": {},
+   "source": [
+    "**Question 19:** What **percentage** of rooms whose `names` contain the word `\"university\"` **also** contain the word `\"studio\"`?\n",
+    "\n",
+    "Your answer **must** be a *float* value between *0* and *100*. You **must** call the `secondary_word_in_found_rooms` function to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52be0ff8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'university_and_studio', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cce2b96a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5e9df6d",
+   "metadata": {},
+   "source": [
+    "**Question 20:** On a trip to NYC, you need to stay for *3* days in *Queens*, and then *4* days in *Brooklyn*. What is the **minimum** amount of money you need to spend on this trip?\n",
+    "\n",
+    "Note that:\n",
+    "1. The price of each room is for each day.\n",
+    "2. The total cost = (lowest price in Queens) * 3 + (lowest price in Brooklyn) * 4.\n",
+    "3. You'll need to **skip** those rooms that don't have enough availability.\n",
+    "\n",
+    "\n",
+    "**Hint:** You might want to define a helper function to compute the **minimum** daily `price` of a room in a given `neighborhood_group` among rooms whose `availability_365` is equal to or larger than the number of days one will be staying in that neighborhood group."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70e3cc8e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'min_cost_trip', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ef260be",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d205702b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "\n",
+    "Make sure you have run all cells in your notebook in order before running the cell below, so that all images/graphs appear in the output. The cell below will generate a zip file for you to submit. **Please save before exporting!**\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**: 1. **Save** the notebook file **now (before you run the next cell of code)**. 2. **Upload** the zipfile to Gradescope. 3. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae4e418c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Save your notebook first, then run this cell to export your submission.\n",
+    "grader.export(pdf=False, run_tests=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b3d53d9",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q1\", neighborhood_groups)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q10\", pvt_room_little_neck)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q11\", home_pvt_room_astoria_diff)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q12\", min_price_nyu)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q13\", median_price_columbia)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q14\", pct_price_over_hundred)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q15\", bushwick_avg_ratio)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q16\", manhattan_beach_avg_ratio)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q17\", max_nbhd_staten_island)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q18\", downtown_and_spacious)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q19\", university_and_studio)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q2\", avg_price)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q20\", min_cost_trip)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q3\", count_soho)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q4\", rooms_contain_cbg)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q5\", rooms_contain_cinema_film)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q6\", funnest_room)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q7\", no_cost_rooms)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q8\", pricey_neighborhoods)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p6_test.check(\"q9\", last_review_before_2015)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p6/p6.ipynb b/p6/p6.ipynb
index a9a1bf65153fad23d82f9a62f602f8581a2d7885..c1593107c4cee14e874c59d49f4e3221acbc6226 100644
--- a/p6/p6.ipynb
+++ b/p6/p6.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66622690",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p7/.ipynb_checkpoints/p7-checkpoint.ipynb b/p7/.ipynb_checkpoints/p7-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..049b17b72f270fce09e75abcb0832fcaba06597f
--- /dev/null
+++ b/p7/.ipynb_checkpoints/p7-checkpoint.ipynb
@@ -0,0 +1,1854 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c10c22",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a8e80d7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"p7.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec9598fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import p7_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3f003e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PLEASE FILL IN THE DETAILS\n",
+    "# Enter none if you don't have a project partner\n",
+    "\n",
+    "# project: p7\n",
+    "# submitter: NETID1\n",
+    "# partner: NETID2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e247a2c",
+   "metadata": {},
+   "source": [
+    "# Project 7: Drinking Water Accessibility"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a9dab5",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project you will demonstrate how to:\n",
+    "\n",
+    "- Write programs to interpret data present in csv files,\n",
+    "- Use lists and dictionaries effectively to manage data,\n",
+    "- **Develop good coding styling habits (points may be deducted for bad coding styles)**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d10aa40",
+   "metadata": {},
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `p7_test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions. You can have a look at [p2](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p2/p2.ipynb) if you have forgotten how to read the outputs of the `grader.check` function calls."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f02855e",
+   "metadata": {},
+   "source": [
+    "**Please go through [lab-p7](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p7) before starting this project.** The lab introduces some useful techniques necessary for this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59162236",
+   "metadata": {},
+   "source": [
+    "## Project Description:\n",
+    "\n",
+    "Universal access to safe drinking water is a fundamental need and human right. Securing access for all would go a long way in reducing illness and death, especially among children. \"Safely managed\" drinking water services represent an ambitious new rung on the ladder used to track progress on drinking water. Since 2000, 2 billion people have gained access to safely managed services (i.e., accessible on-premises, available when needed, and free from contamination). In 2020, 5.8 billion people used safely managed services and a further 2 billion people used basic services. However, 771 million people still lacked even a basic level of service, including 282 million who used a “limited” water service (source from which water collection time exceeds 30 minutes), 367 million who used unimproved sources and 122 million who still collected drinking water directly from rivers, lakes, and other surface water sources. The data reveal pronounced disparities, with the poorest and those living in rural areas least likely to use a basic service. In most countries, the burden of water collection continues to fall mainly to women and girls.\n",
+    "\n",
+    "[The Unicef website](https://data.unicef.org/) states that \"consistent, credible data about children’s situations are critical to the improvement of their lives – and indispensable to realizing the rights of every child.\" Data Scientists will play an important role in reaching this goal.\n",
+    "\n",
+    "For this project, you'll be analyzing data drawn from multiple sources. Our data is primarily drawn from the report titled [\"Progress on Household Drinking Water, Sanitation and Hygiene\"](https://washdata.org/sites/default/files/2021-07/jmp-2021-wash-households.pdf) data published by the Unicef/WHO Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2021). The original dataset can be found [here](https://data.unicef.org/topic/water-and-sanitation/drinking-water/) if you are interested in exploring the dataset yourself. Our dataset is further augmented by data from The World Bank on the [income levels of each country](https://datatopics.worldbank.org/world-development-indicators/the-world-by-income-and-region.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef7100c7",
+   "metadata": {},
+   "source": [
+    "## Dataset:\n",
+    "\n",
+    "The JMP report defines *people who have access to an [improved source of water](https://www.cdc.gov/healthywater/global/assessing.html#ImprovedDrinking) within 30 minutes round trip collection time* as having [at least basic access](https://www.cdc.gov/healthywater/global/assessing.html#DrinkingWaterSources) to water. For this project, we will focus on the **percentage of population** of each country who had **at least basic** water supply in the years **2015** and **2020**. Open `water_accessibility.csv` with Microsoft Excel or some other Spreadsheet viewer and look at the list of countries in the dataset. Data for each country appears twice, one row for the year *2015* and the other row for year *2020*. Countries which had incomplete data have been **omitted** from the dataset, and we will **ignore** those countries in this project. You do **not** have to deal with any **missing data** in the dataset.\n",
+    "\n",
+    "The data shows:\n",
+    "- `country_code` : the unique country code that consists of three alphabet letters\n",
+    "- `country_name` : the name of the country\n",
+    "- `region` : the geographical location of the country (does not equal to its corresponding continents, but follows the administrative groupings from [The World Bank](https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups))\n",
+    "- `year` : the year in which it was subject to data collection\n",
+    "- `income_level` : the classification of income level based on GNI per capita in US dollars ([The World Bank Atlas Method](https://datahelpdesk.worldbank.org/knowledgebase/articles/378834-how-does-the-world-bank-classify-countries))\n",
+    "- `pop` : population of the country in a specific year (in thousands)\n",
+    "- `urban_percent` : the percentage of population in a given country that is urban\n",
+    "- `national_alb` : the percentage of a country's population that has access to at least basic water supply\n",
+    "- `urban_alb` : the percentage of a country's urban population that has access to at least basic water supply"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "518b6e01",
+   "metadata": {},
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices in your code, unless the question explicitly . If you open your `.csv` file with Excel, manually count through the rows and use this number to loop through the dataset, this is also considered as hardcoding. We'll **manually deduct** points from your autograder score on Gradescope during code review.\n",
+    "\n",
+    "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer. If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer. \n",
+    "\n",
+    "Required Functions:\n",
+    "- cell\n",
+    "- get_col_dict\n",
+    "\n",
+    "In this project, you will also be required to define certain **data structures**. If you do not create these data structures exactly as specified, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Data Structures:\n",
+    "- dict_2015\n",
+    "- dict_2020\n",
+    "- rural_non_alb_bin_2015_dict\n",
+    "- rural_non_alb_bin_2020_dict\n",
+    "    \n",
+    "Students are only allowed to use Python commands and concepts that have been taught in the course prior to the release of p7. Therefore, **you should not use the pandas module**.  We will **manually deduct** points from your autograder score on Gradescope otherwise.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p7/rubric.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a900d754",
+   "metadata": {},
+   "source": [
+    "## Incremental Coding and Testing:\n",
+    "\n",
+    "You should always strive to do incremental coding. **Incremental coding enables you to avoid challenging bugs.** Always write a few lines of code and then test those lines of code, before proceeding to write further code. You can call the `print` function to test intermediate step outputs.\n",
+    "\n",
+    "We also recommend you do incremental testing: make sure to run the local tests as soon as you are done with a question. This will ensure that you haven't made a big mistake that might potentially impact the rest of your project solution. Please refrain from making multiple submissions on Gradescope for testing individual questions' answers. Instead use the local tests, to test your solution on your laptop.\n",
+    "\n",
+    "That said, it is **important** that you check the Gradescope test results as soon as you submit your project on Gradescope. Test results on Gradescope are typically available somewhere between 2 to 10 minutes after the submission.\n",
+    "\n",
+    "Also, remember to check with the [p7 rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p7/rubric.md) to verify that you will not be losing any points during manual review."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e83e952a",
+   "metadata": {},
+   "source": [
+    "## Project Questions and Functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2398e7fe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f11c98c",
+   "metadata": {},
+   "source": [
+    "First, read the data stored in `water_accessibility.csv` and store it in a suitable variable. You can follow the same procedure you used in p6."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93b28af9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# read the data stored in water_accessibility.csv\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44be4e8f",
+   "metadata": {},
+   "source": [
+    "### Function 1: `cell(row_idx, col_name)` \n",
+    "\n",
+    "This function must take in a row index, `row_idx` and a column name, `col_name` as its inputs, and return the value in `water_accessibility.csv` stored there. There is **no missing data** in this dataset.\n",
+    "\n",
+    "You **must** define the variables `csv_header` and `csv_rows` as in lab-p7, and you **must** copy/paste your `cell` function from lab-p7.\n",
+    "\n",
+    "**Important:** You **must** only use the `cell` function to extract data from the dataset. If you extract any data without explicitly using this function, you will **lose points** during manual review."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9375c56",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the cell function here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31941314",
+   "metadata": {},
+   "source": [
+    "You are all set! You are now ready to start solving the questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48025962",
+   "metadata": {},
+   "source": [
+    "**Question 1:** Which country had the highest population (`pop`) in *2020*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0f6aff9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'highest_pop_country', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e815811",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "014b296b",
+   "metadata": {},
+   "source": [
+    "**Question 2:** Which country had the highest population (`pop`) **increase** between *2015* and *2020*?\n",
+    "\n",
+    "There is a **unique** country in this dataset whose population increased the most. You **do not** have to worry about ties.\n",
+    "\n",
+    "**Hint:** Recall how to loop through the dataset and extract data from each year from [lab-p7](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p7)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50bae682",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'highest_pop_inc_country', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cab54382",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d20308e4",
+   "metadata": {},
+   "source": [
+    "**Question 3:** Which country had the highest **increase** in at least basic (`national_alb`) water supply between the years of *2015* and *2020*? \n",
+    "\n",
+    "There is a **unique** country in this dataset whose `national_alb` value increased the most. You **do not** have to worry about ties.\n",
+    "\n",
+    "**Hint:** Take a look at q7 in lab-p7 to see how to compute the change in `national_alb` between *2015* and *2020* for each country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c74753f3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'highest_nat_alb_inc_country', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e821bca",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c261877a",
+   "metadata": {},
+   "source": [
+    "**Question 4:** What was the `income_level` in *2020* of the country with the highest increase in at least basic (`national_alb`) water supply between *2015* and *2020*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfd79c67",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'highest_alb_inc_income_level', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "801e8266",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2372cfca",
+   "metadata": {},
+   "source": [
+    "**Question 5:** What is the **total** population (`pop`) of **all** the countries (in the dataset) in the `year` *2020*?\n",
+    "\n",
+    "The `pop` column stores the population in thousands. So, you will have to multiply the population of each country by *1000* to get (an approximation of) the actual number.\n",
+    "\n",
+    "**Hint:** You may modify your `cell` function, so that it automatically multiplies the values in the `pop` column by *1000* before returning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d2cb2ae",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'total_pop_2020', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b11c00dd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c7ba440",
+   "metadata": {},
+   "source": [
+    "**Question 6:** What was the global **percentage** of urban population (`urban_percent`) across **all** countries (in the dataset) in the `year` *2020*?\n",
+    "\n",
+    "You need to find the **total** urban population by *multiplying* the `pop` and `urban_percent` columns of each country and *adding* it up. Then you need to *divide* by the **total** population to get the percentage of urban population across all the countries.\n",
+    "\n",
+    "Your output **must** be an **int**. You **must** use the `round` function to round your answer to the nearest integer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b10e0b02",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'urban_pop_percent_2020', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a39adce3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f26afbc0",
+   "metadata": {},
+   "source": [
+    "**Question 7:** What was the **total** population (`pop`) of countries that were in the *High income* group (`income_level`) in the `year` *2015*?\n",
+    "\n",
+    "Your output **must** be an **int**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b77975b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'high_income_pop', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83319447",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e35aba24",
+   "metadata": {},
+   "source": [
+    "**Question 8:** Which *income group* (`income_level`) had the **least** population (`pop`) in the `year` *2015*?\n",
+    "\n",
+    "You must find the **total** population (`pop`) for each `income_level`, and find the `income_level` which has the **least** total population.\n",
+    "\n",
+    "**Hint:** There are several ways to solve this problem efficiently (including using `dicts`). You can try to solve this problem using dicts if you want to. However, another approach that you might already be familiar with from p6 is to first create a *list* of all the **unique** income levels, and then loop through the entire dataset for **each** income level to find the total population of that income level, before comparing these numbers to find the income level with the least population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e06ca080",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'least_pop_income_group', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07f8b1f2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bd9c162",
+   "metadata": {},
+   "source": [
+    "**Question 9:** Create a **list** of the names (`country_name`) of all countries in the *North America* `region` that **tied** for the **maximum** `national_alb` in *2015*.\n",
+    "\n",
+    "You need to first find the **maximum** value of `national_alb` among all countries in the `region` *North America*, and then make a `list` of **all** the countries in this region having this `national_alb` value. **Multiple** countries from *North America* have the same maximum `national_alb` value, so your output **must** be a `list` of **all** those countries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0ea7e4e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'na_max_alb_countries', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8102455a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "397c51c5",
+   "metadata": {},
+   "source": [
+    "### Function 2: `get_col_dict(col_name, year)` \n",
+    "\n",
+    "This function should take in a column `col_name` and a `year` (*2015* or *2020*) as its inputs, and return a `dict` where each key is a `country_code` and the corresponding value is the value under the given `col_name` for the country with the said `country_code` in the given `year`.\n",
+    "\n",
+    "For example, the value returned by `get_col_dict('country_name', 2015)` should be something like the following:\n",
+    "```python\n",
+    "{'AFG': 'Afghanistan',\n",
+    " 'ALB': 'Albania',\n",
+    " 'DZA': 'Algeria',\n",
+    " 'AND': 'Andorra',\n",
+    " 'AGO': 'Angola',\n",
+    " 'ARM': 'Armenia',\n",
+    " 'AUS': 'Australia',\n",
+    " 'AUT': 'Austria',\n",
+    " 'AZE': 'Azerbaijan',\n",
+    " 'BGD': 'Bangladesh',\n",
+    " ...\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "and the value returned by `get_col_dict('pop', 2020)` should be something like the following:\n",
+    "```python\n",
+    "{'AFG': 38928000,\n",
+    " 'ALB': 2878000,\n",
+    " 'DZA': 43851000,\n",
+    " 'AND': 77000,\n",
+    " 'AGO': 32866000,\n",
+    " 'ARM': 2963000,\n",
+    " 'AUS': 25500000,\n",
+    " 'AUT': 9006000,\n",
+    " 'AZE': 10139000,\n",
+    " 'BGD': 164689000,\n",
+    " ...\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "Start with the following code snippet and complete the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb5377bd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code\n",
+    "def get_col_dict(col_name, year):\n",
+    "    col_dict = {}\n",
+    "    if year == 2015:\n",
+    "        for idx in range (0, len(csv_rows), 2):\n",
+    "            col_dict[...] = ... \n",
+    "    elif year == 2020:\n",
+    "        for idx in range (1, len(csv_rows), 2):\n",
+    "            col_dict[...] = ... \n",
+    "    return col_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbd91286",
+   "metadata": {},
+   "source": [
+    "After you define the function `get_col_dict`, run the following two cells to test whether it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7bf550a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "get_col_dict_test1 = get_col_dict('region', 2020)\n",
+    "get_col_dict_test2 = get_col_dict('national_alb', 2015)\n",
+    "get_col_dict_test3 = get_col_dict('pop', 2020)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ec9b77f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"get_col_dict\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "474ffcde",
+   "metadata": {},
+   "source": [
+    "### Data Structures 1: `dict_2015`\n",
+    "\n",
+    "You must now create a data structure named `dict_2015`. This data structure must be a **dict**. Each key must be a `country_code`, and the corresponding value must be another **dict**. As for the inner dictionary, the keys must be the various column names, and the values must be the values under the column name for `country_code` in the `year` *2015*.\n",
+    "\n",
+    "The keys for each of the *inner* dictionary are the column names:\n",
+    "- `'country_name'`\n",
+    "- `'region'`\n",
+    "- `'income_level'`\n",
+    "- `'year'`\n",
+    "- `'pop'`\n",
+    "- `'urban_percent'`\n",
+    "- `'national_alb'`\n",
+    "- `'urban_alb'`\n",
+    "\n",
+    "You are **allowed** to *hardcode* the **names** of all these columns (i.e., the keys of the *inner* dictionaries).\n",
+    "\n",
+    "The data structure `dict_2015` should look something like this:\n",
+    "```python\n",
+    "{'AFG': {'country_name': 'Afghanistan',\n",
+    "  'region': 'South Asia',\n",
+    "  'income_level': 'Low income',\n",
+    "  'year': 2015,\n",
+    "  'pop': 34414000,\n",
+    "  'urban_percent': 25,\n",
+    "  'national_alb': 61,\n",
+    "  'urban_alb': 87},\n",
+    " 'ALB': {'country_name': 'Albania',\n",
+    "  'region': 'Europe & Central Asia',\n",
+    "  'income_level': 'Upper middle income',\n",
+    "  'year': 2015,\n",
+    "  'pop': 2891000,\n",
+    "  'urban_percent': 57,\n",
+    "  'national_alb': 93,\n",
+    "  'urban_alb': 95},\n",
+    "  ...\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40853d94",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the variable 'dict_2015' here as described above\n",
+    "# you may display the variable for testing purposes while you define it,\n",
+    "# BUT you MUST remove the line displaying 'dict_2015' before submission as the output will be too large to display\n",
+    "\n",
+    "# initialize as an empty dictionary\n",
+    "dict_2015 = ...\n",
+    "\n",
+    "country_dict = get_col_dict('country_name', 2015)\n",
+    "region_dict = get_col_dict(..., ...)\n",
+    "# call get_col_dict for other columns\n",
+    "...\n",
+    "\n",
+    "# add data from all these dicts to dict_2015"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26db8e45",
+   "metadata": {},
+   "source": [
+    "After you define the data structure `dict_2015`, run the following cell to test whether you have defined it properly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f4cb77b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"dict_2015\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4db68c64",
+   "metadata": {},
+   "source": [
+    "### Data Structures 2: `dict_2020`\n",
+    "\n",
+    "You must now create a data structure named `dict_2020`. This data structure must be a **dict**. Each key must be a `country_code`, and the corresponding value must be another **dict**. As for the inner dictionary, the keys must be the various column names, and the values must be the values under the column name for `country_code` in the `year` *2020*.\n",
+    "\n",
+    "The keys for each of the *inner* dictionary are the column names:\n",
+    "- `'country_name'`\n",
+    "- `'region'`\n",
+    "- `'income_level'`\n",
+    "- `'year'`\n",
+    "- `'pop'`\n",
+    "- `'urban_percent'`\n",
+    "- `'national_alb'`\n",
+    "- `'urban_alb'`\n",
+    "\n",
+    "You are **allowed** to *hardcode* the **names** of all these columns (i.e., the keys of the *inner* dictionaries)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8d17191",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the variable 'dict_2020' here as described above\n",
+    "# you may display the variable for testing purposes while you define it,\n",
+    "# BUT you MUST remove the line displaying 'dict_2020' before submission as the output will be too large to display\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b701542",
+   "metadata": {},
+   "source": [
+    "After you define the data structure `dict_2020`, run the following cell to test whether you have defined it properly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbe0074a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"dict_2020\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "899845d5",
+   "metadata": {},
+   "source": [
+    "#### From this point onwards, you are only allowed to access data from `water_accessibility.csv` by querying from the **dicts** `dict_2015` and `dict_2020`. You will **lose points** during manual review if you access the data through any other means."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "907b2aa7",
+   "metadata": {},
+   "source": [
+    "**Question 10:** Output the data from *India* (`country_code`: *IND*) for the `year` *2020*.\n",
+    "\n",
+    "Your output **must** be a **dict** mapping each column name to the value for the country *IND* in the year *2020*. You **must** answer this by querying data from `dict_2020`.\n",
+    "\n",
+    "The expected output is:\n",
+    "```python\n",
+    "{'country_name': 'India',\n",
+    " 'region': 'South Asia',\n",
+    " 'income_level': 'Lower middle income',\n",
+    " 'year': 2020,\n",
+    " 'pop': 1380004000,\n",
+    " 'urban_percent': 35,\n",
+    " 'national_alb': 90,\n",
+    " 'urban_alb': 94}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7ef6ab2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'ind_2020_dict', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6cf0a94",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05c378dd",
+   "metadata": {},
+   "source": [
+    "**Question 11:** What is the national at least basic (`national_alb`) water supply for *Great Britain* (`country_code`: *GBR*) in the `year` *2015*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49e78262",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'gbr_national_alb_2015', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fda299f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "460f900f",
+   "metadata": {},
+   "source": [
+    "**Question 12:** How much did the population (`pop`) of *France* **increase** (`country_code`: *FRA*) from the `year` *2015* to *2020*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bd5e06d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'population_change_fra', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e77629eb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a537b9d8",
+   "metadata": {},
+   "source": [
+    "**Question 13:** For each `income_level`, find the **total** population (`pop`) of all countries within that `income_level` in *2020*.\n",
+    "\n",
+    "Your output **must** be a **dict** where each key is a `income_level`, and the corresponding value is the **sum** of populations (`pop`) of all the countries from that `income_level` in the `year` *2020*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a059c7b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'income_level_pops', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1576ca67",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0b03a15",
+   "metadata": {},
+   "source": [
+    "**Question 14:** For each `income_level`, find the **total** population (`pop`) of all countries who have access to at least basic water supply within that `income_level` in *2020*.\n",
+    "\n",
+    "Your output **must** be a **dict** where each key is a `income_level`, and the corresponding value is the **sum** of populations (`pop`) which have access to at least basic water supply of all the countries from that `income_level` in the `year` *2020*.\n",
+    "\n",
+    "You **must** round the population of **each** country with access to at least basic water supply to the **nearest** integer **before** adding them up.\n",
+    "\n",
+    "**Hint:** For each country, the population with at least basic water supply is `pop * national_alb / 100`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bed43973",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'income_level_alb_pops', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40cd048e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a70bdcfd",
+   "metadata": {},
+   "source": [
+    "**Question 15:** For each `income_level`, find the **percentage** of population (`pop`) of all countries within that `income_level` with at least basic water supply in *2020*.\n",
+    "\n",
+    "Your output **must** be a **dict** where each key is a `income_level`, and the corresponding value is the **percentage** of the population (`pop`) which have access to at least basic water supply of all the countries from that `income_level` in the `year` *2020*. The percentages **must** be represented as **int**s between *0* and *100*. You **must** round each of the percentages to the **nearest** integer.\n",
+    "\n",
+    "**Hint:** You need to loop through the dictionaries you found in Q13 (or Q14), and for each key, you need to divide the corresponding value in the Q14 dictionary by the value of the same key in the Q13 dictionary and multiply by 100. Take another look at Task 3.6 from lab-p7, if you are not sure how to proceed here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ed2d02f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'income_level_alb_percent', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcbaa5b1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfec7c03",
+   "metadata": {},
+   "source": [
+    "### Data Structure 3: Adding `rural_alb`  to `dict_2015` and `dict_2020`\n",
+    "\n",
+    "Our dataset has data on the percentage of **national** and **urban** populations with at least basic water supply. However, it is usually **rural** populations which have the greatest difficulty in getting access to water.\n",
+    "\n",
+    "Luckily, we are able to calculate **rural_alb** from the given data using the formula:\n",
+    "\n",
+    "$$\n",
+    "rural_{alb} = \\frac{national_{alb} - \\left(urban_{alb} \\times \\frac{urban\\_percent}{100}\\right)}{\\left(1 - \\frac{urban\\_percent}{100}\\right)}\n",
+    "$$\n",
+    "\n",
+    "*If a country has `urban_percent` equal to `100`, then the country has a negligible rural population, and the formula above is not valid. For such countries, we will assume that `rural_alb` is the **same** as `urban_alb`.*\n",
+    "\n",
+    "You **must** loop through each country in `dict_2015` and `dict_2020`, and add an **additional** key value pair for each country. The new key should be the string: `\"rural_alb\"`, and the corresponding value should be the `rural_alb` value for that country as given by the formula above. You **must** round each number to the **nearest** integer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e989a3e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# add the additional key-value pair to both dicts 'dict_2015' and 'dict_2020' here\n",
+    "# you may display the variable for testing purposes while you define it,\n",
+    "# BUT you MUST remove the line displaying the dicts before submission as the output will be too large to display\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c45dfc67",
+   "metadata": {},
+   "source": [
+    "**Question 16:** What's the percentage of rural population with at least basic (`rural_alb`) water supply in *Australia* (`country_code`: *AUS*) in *2020*? \n",
+    "\n",
+    "You **must** answer this question by querying data from the dict `dict_2020`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7ad07fa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'australia_rural_alb_2020', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64e29eef",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b121e095",
+   "metadata": {},
+   "source": [
+    "### Data Structure 4: `rural_non_alb` bins\n",
+    "\n",
+    "We have now managed to extract the percentage of rural population with access to atleast basic water supply for each of the countries in the dataset. We can now use this information to find out the countries whose rural populations do **not** have access to at least basic water supply.\n",
+    "\n",
+    "You **must** create two **dict**s (one for the `year` *2015* and one for *2020*) where the keys are the integers *0*, *10*, *20*, ..., *100*. The value corresponding to the integer *0* **must** be a **list** containing the names of all the countries for which their rural population **without** access to at least basic (which we can represent as `rural_non_alb`) water supply is `0 <= rural_non_alb < 10`. Similarly, the value corresponding to the key *10* must be a **list** of all countries for which `10 <= rural_non_alb < 20`, and so on.\n",
+    "\n",
+    "**Hints:**\n",
+    "1. You can find `rural_non_alb` as `rural_non_alb = 100 - rural_alb`.\n",
+    "2. You can find the bin which any country falls into by using the formula:\n",
+    "```python\n",
+    "rural_non_alb_bin = ((100 - rural_alb)//10) * 10\n",
+    "```\n",
+    "3. Even if a particular bin has no countries in it, you **must** still create a bin for it in your dict (with the value being an empty list). The starter code below will help you accomplish this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2dd8461",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rural_non_alb_bin_2015_dict'\n",
+    "\n",
+    "# initialize as an empty dictionary\n",
+    "rural_non_alb_bin_2015_dict = ...\n",
+    "\n",
+    "# loop through the keys we want for the dictionary - 0, 10, 20, ..., 100 (inclusive of 100)\n",
+    "# and add them to the dictionary as keys with the value as an empty list\n",
+    "for rural_non_alb_bin in range(...):\n",
+    "    rural_non_alb_bin_2015_dict[rural_non_alb_bin] = []\n",
+    "\n",
+    "# loop through each country and add to the correct bin of rural_non_alb_bin_2015_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a766c7d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rural_non_alb_bin_2020_dict'\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72206139",
+   "metadata": {},
+   "source": [
+    "After you define the data structures `rural_non_alb_bin_2015_dict` and `rural_non_alb_bin_2020_dict`, run the following cell to test whether you have defined them properly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6af9d9fa",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"rural_non_alb_bins\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58827c0d",
+   "metadata": {},
+   "source": [
+    "**Question 17:** List all the countries which had `rural_non_alb` value between *0* and *9* (both inclusive) in the `year` *2020*.\n",
+    "\n",
+    "You **must** answer this question by querying the the **dict** `rural_non_alb_bin_2020_dict`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fba480f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'bin_0_countries', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bcb03a0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "514e78b4",
+   "metadata": {},
+   "source": [
+    "**Question 18:** What are the countries in the **last** non-empty bin in the `year` *2015*?\n",
+    "\n",
+    "Your output **must** be a **list** of the countries in the bin with the **highest** percentage of rural population without at least basic access to water.\n",
+    "\n",
+    "**Hint:** You must first find the largest key with a non-empty bin, and then find the value of that key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17841d16",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'last_non_empty_bin_2015', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cd7688e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17d87381",
+   "metadata": {},
+   "source": [
+    "**Question 19:** What countries have **regressed** by moving to a **higher** bin from *2015* to *2020*?\n",
+    "\n",
+    "Your answer **must** be a **list** of countries which have regressed by having their percentage of rural population without at least basic access to water move to a bin with a **higher** key.\n",
+    "\n",
+    "**Hint:** You may need a *nested* loop to go through all possible combinations of keys in both the dicts `rural_non_alb_bin_2015_dict` and `rural_non_alb_bin_2020_dict`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9510c92",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'countries_regressed', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f36d34b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e39b809d",
+   "metadata": {},
+   "source": [
+    "**Question 20:** What countries have **improved** by moving to a **lower** bin from *2015* to *2020*?\n",
+    "\n",
+    "Your answer **must** be a **list** of countries which have improved by having their percentage of rural population without at least basic access to water move to a bin with a **lower** key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "463e6955",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'countries_improved', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bb5fc84",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4589d90b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "\n",
+    "Make sure you have run all cells in your notebook in order before running the cell below, so that all images/graphs appear in the output. The cell below will generate a zip file for you to submit. **Please save before exporting!**\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**: 1. **Save** the notebook file **now (before you run the next few cells of code)**. 2. **Upload** the zipfile to Gradescope. 3. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15598b58",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Save your notebook first, then run this cell to export your submission.\n",
+    "grader.export(pdf=False, run_tests=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee1b1468",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "dict_2015": {
+     "name": "dict_2015",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"qdict_2015_test\", dict_2015)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "dict_2020": {
+     "name": "dict_2020",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"qdict_2020_test\", dict_2020)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "get_col_dict": {
+     "name": "get_col_dict",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test1_output = p7_test.check(\"qget_col_dict_test1\", get_col_dict_test1)\n>>> test2_output = p7_test.check(\"qget_col_dict_test2\", get_col_dict_test2)\n>>> test3_output = p7_test.check(\"qget_col_dict_test3\", get_col_dict_test3)\n>>> test_output = True\n>>> \n>>> if test1_output != True:\n...     test_output = \"get_col_dict_test1 >> \" + test1_output\n... elif test2_output != True:\n...     test_output = \"get_col_dict_test2 >> \" + test2_output\n... elif test3_output != True:\n...     test_output = \"get_col_dict_test3 >> \" + test3_output\n>>> \n>>> test_output\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q1\", highest_pop_country)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q10\", ind_2020_dict)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q11\", gbr_national_alb_2015)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q12\", population_change_fra)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q13\", income_level_pops)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q14\", income_level_alb_pops)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q15\", income_level_alb_percent)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q16\", australia_rural_alb_2020)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q17\", bin_0_countries)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q18\", last_non_empty_bin_2015)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q19\", countries_regressed)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q2\", highest_pop_inc_country)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q20\", countries_improved)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q3\", highest_nat_alb_inc_country)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q4\", highest_alb_inc_income_level)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q5\", total_pop_2020)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q6\", urban_pop_percent_2020)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q7\", high_income_pop)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q8\", least_pop_income_group)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p7_test.check(\"q9\", na_max_alb_countries)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "rural_non_alb_bins": {
+     "name": "rural_non_alb_bins",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> rural_non_alb_2015_test = p7_test.check(\"qrural_non_alb_bin_2015\", rural_non_alb_bin_2015_dict)\n>>> rural_non_alb_2020_test = p7_test.check(\"qrural_non_alb_bin_2020\", rural_non_alb_bin_2020_dict)\n>>> rural_non_alb_bins_test = True\n>>> \n>>> if rural_non_alb_2015_test != True:\n...     test_output = \"rural_non_alb_bin_2015_dict >> \" + rural_non_alb_2015_test\n... elif rural_non_alb_2020_test != True:\n...     test_output = \"rural_non_alb_bin_2020_dict >> \" + rural_non_alb_2020_test\n...     \n>>> rural_non_alb_bins_test\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p7/p7.ipynb b/p7/p7.ipynb
index 7b006ed5dc86a62cc57aba9a3b1b83e65aadf019..049b17b72f270fce09e75abcb0832fcaba06597f 100644
--- a/p7/p7.ipynb
+++ b/p7/p7.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5c10c22",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p8/.ipynb_checkpoints/p8-checkpoint.ipynb b/p8/.ipynb_checkpoints/p8-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..874d095727d0d62df09cfad38aa86af20e8b2876
--- /dev/null
+++ b/p8/.ipynb_checkpoints/p8-checkpoint.ipynb
@@ -0,0 +1,1852 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b1eb253c",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8645c672",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import otter\n",
+    "# nb_name should be the name of your notebook without the .ipynb extension\n",
+    "nb_name = \"p8\"\n",
+    "py_filename = nb_name + \".py\"\n",
+    "grader = otter.Notebook(nb_name + \".ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "504ce384",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import p8_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa5fbac6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PLEASE FILL IN THE DETAILS\n",
+    "# Enter none if you don't have a project partner\n",
+    "# You will have to add your partner as a group member on Gradescope even after you fill this\n",
+    "\n",
+    "# project: p8\n",
+    "# submitter: NETID1\n",
+    "# partner: NETID2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3dbdc8e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "# Project 8: Going to the Movies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "285cf39b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project, you will demonstrate how to:\n",
+    "\n",
+    "* integrate relevant information from various sources (e.g. multiple csv files),\n",
+    "* build appropriate data structures for organized and informative presentation (e.g. list of dictionaries),\n",
+    "* practice good coding style\n",
+    "\n",
+    "Please go through [lab-p8](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p8) before working on this project. The lab introduces some useful techniques related to this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3533ec2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: p8 and p9 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partner up with someone for p8, you have to sustain that partnership until end of p9. Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f22/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba644259",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `p8_test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "037dbf1b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Introduction:\n",
+    "\n",
+    "In this project and the next, we will be working on the [IMDb Movies Dataset](https://www.imdb.com/interfaces/). We will use Python to discover some cool facts about our favorite movies, cast, and directors.\n",
+    "\n",
+    "In this project, you will combine the data from the movie and mapping files into a more useful format.\n",
+    "Start by downloading the following files: `p8_test.py`, `small_mapping.csv`, `small_movies.csv`, `mapping.csv`, and `movies.csv`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a602bb94",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## The Data:\n",
+    "\n",
+    "Open `movies.csv` and `mapping.csv` in any spreadsheet viewer, and see what the data looks like.\n",
+    "`movies.csv` has ~100,000 rows and `mapping.csv` has ~350,000 rows. These files store information about **every** movie on the IMDb dataset which was released in the US. These datasets are **very** large when compared to `small_movies.csv` and `small_mapping.csv` from [lab-p8](https://github.com/msyamkumar/cs220-f22-projects/tree/main/lab-p8), but the data is stored in the **same format**. For description of the datasets, please refer back to [lab-p8](https://github.com/msyamkumar/cs220-f22-projects/tree/main/lab-p8).\n",
+    "\n",
+    "Before we start working with these very large datasets, let us start with the much smaller datasets, `small_movies.csv` and `small_mapping.csv` from lab-p8. In the latter half of p8 and in p9, you will be working with `movies.csv` and `mapping.csv`. Since the files `movies.csv` and `mapping.csv` are large, some of the functions you write in p8 and p9 **may take a while to execute**. You do not have to panic if a single cell takes between 5 to 10 seconds to run. If any cell takes significantly longer, follow the recommendations below:\n",
+    "\n",
+    "- **Do not** calling **slow functions** multiple times within a loop.\n",
+    "- **Do not** calling functions that **iterate over the entire dataset within a loop**; instead, call the function before the loop and store the result in a variable.\n",
+    "- **Do not** compute quantities **inside a loop** if it can be computed outside the loop; for example, if you want to calculate the average of a list, you should use the loop to find the numerator and denominator but divide **once** after the loop ends instead of inside the loop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a564432",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices in your code, unless the question explicitly . If you open your `.csv` files with Excel, manually count through the rows and use this number to loop through the dataset, this is also considered as hardcoding. We'll **manually deduct** points from your autograder score on Gradescope during code review.\n",
+    "\n",
+    "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer. If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Functions:\n",
+    "- `get_mapping`\n",
+    "- `get_raw_movies`\n",
+    "- `get_movies`\n",
+    "- `find_specific_movies`\n",
+    "- `bucketize_by_genre`\n",
+    "\n",
+    "In this project, you will also be required to define certain **data structures**. If you do not create these data structures exactly as specified, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Data Structures:\n",
+    "- `small_movies`\n",
+    "- `movies`\n",
+    "- `genre_dict`\n",
+    "\n",
+    "You are only allowed to define these data structures **once** and we'll **manually deduct** points from your autograder score on Gradescope if you redefine the values of these variables.\n",
+    "\n",
+    "In this project (and the next), you will be asked to create **lists** of movies. For all such questions, **unless it is explicitly mentioned otherwise**, the movies should be in the **same order** as in the `movies.csv` (or `small_movies.csv`) file. Similarly, for each movie, the **list** of `genres`, `directors`, and `cast` members should always be in the **same order** as in the `movies.csv` (or `small_movies.csv`) file.\n",
+    "\n",
+    "Students are only allowed to use Python commands and concepts that have been taught in the course prior to the release of p8. Therefore, you should not use the pandas module.  We will **manually deduct** points from your autograder score on Gradescope otherwise.\n",
+    "\n",
+    "In addition, you are also **required** to follow the requirements below:\n",
+    "- **Do not use the method `csv.DictReader` for p8**. Although the required output can be obtained using this method, one of the learning outcomes of this project is to demonstrate your ability to build dictionaries with your own code.  \n",
+    "- Additional import statements beyond those that are stated in the directions are not allowed. For this project, we allow you to use `csv` and `copy` packages (that is, you can use the `import csv` and `import copy` statements in your submission). You should not use concepts / modules that are yet to be covered in this course; for example: you should not use modules like `pandas`. **We'll manually deduct points** accordingly, if you don't follow the provided directions.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p8/rubric.md).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e060d3ff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Questions and Functions:\n",
+    "\n",
+    "Let us start by importing all the modules we will need for this project.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b0a0757",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42a92141",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 1: `get_mapping(path)`\n",
+    "\n",
+    "We require you to complete the below function to answer the next several questions (this is a **requirement**, and you will **lose points** if you do not implement this function). You may copy/paste code from your lab-p8 notebook to finish this function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f9974b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_mapping(path):\n",
+    "    \"\"\"\n",
+    "    get_mapping(path) converts a mapping csv in 'path' \n",
+    "    into a dict with keys as IDs and values as names\n",
+    "    \"\"\"\n",
+    "    pass # replace with your code\n",
+    "#     TODO: process path\n",
+    "#     TODO: create a dictionary  \n",
+    "#     TODO: iterate through each row of processed path\n",
+    "#     TODO: map value in first column (ID) to value in second column (name/title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f1d06c8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 1:** What is returned by `get_mapping(\"small_mapping.csv\")`?\n",
+    "\n",
+    "Your output **must** be a **dictionary** which maps the *IDs* in `small_mapping.csv` to *names*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea02bcd6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'small_mapping', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "822054fa",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a666e9f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 2:** What is the **value** associated with the **key** *nm2110418*?\n",
+    "\n",
+    "Your output **must** be a **string**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2606487",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# access and store the answer in the variable 'nm2110418_value', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95e8aa69",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3edb29c2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 3:** What are the **values** associated with **keys** that **begin** with *nm*?\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4289a1d2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'nm_values', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7856aed4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bcb2a89",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 4:** Find the **keys** of the people (keys **beginning** with *nm*) whose **last name** is *Spencer*.\n",
+    "\n",
+    "Your output **must** be a **list** of **string(s)**.\n",
+    "\n",
+    "**Requirements:** Your **code** must be robust and satisfy all the requirements, even if you were to run this on a larger dataset (such as `mapping.csv`). In particular:\n",
+    "1. You will **lose points** if your code would find people whose **first** name or **middle** name is *Spencer* (e.g. *Spencer Garrett* or *Charlie Spencer Clark*).\n",
+    "2. You will **lose points** if your code would find people whose **last** name contains *Spencer* as a **substring** (e.g. *Tara Spencer-Nairn*). The name should be **exactly** *Spencer*. \n",
+    "3. You will **lose points** if your code would find any **movie titles** (e.g. *Meeting Spencer*)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1e79a7b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'nm_spencer', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6e22ec0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9232bdf3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "#### Now, let's move on to reading the movie files!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb96c0dc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 2: `get_raw_movies(path)`\n",
+    "\n",
+    "We require you to complete the below function to answer the next several questions (this is a **requirement**, and you will **lose points** if you do not implement this function).\n",
+    "\n",
+    "This function **must** return a **list** of **dictionaries**, where each **dictionary** is of the following format:\n",
+    "\n",
+    "```python\n",
+    "   {\n",
+    "        'title': <title-id>,\n",
+    "        'year': <the year as an integer>,\n",
+    "        'duration': <the duration as an integer>,\n",
+    "        'genres': [<genre1>, <genre2>, ...],\n",
+    "        'rating': <the rating as a float>,\n",
+    "        'directors': [<director-id1>, <director-id2>, ...],\n",
+    "        'cast': [<actor-id1>, <actor-id2>, ....]\n",
+    "    }\n",
+    "```\n",
+    "\n",
+    "Here is an example:\n",
+    "\n",
+    "```python\n",
+    "    {\n",
+    "        'title': 'tt0033313',\n",
+    "        'year': 1941,\n",
+    "        'duration': 59,\n",
+    "        'genres': ['Western'],\n",
+    "        'rating': 5.2,\n",
+    "        'directors': ['nm0496505'],\n",
+    "        'cast': ['nm0193318', 'nm0254381', 'nm0279961', 'nm0910294', 'nm0852305']\n",
+    "    }\n",
+    "```\n",
+    "\n",
+    "You may copy/paste code from your lab-p8 notebook to finish this function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e91c5e9e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_raw_movies(path):\n",
+    "    \"\"\"\n",
+    "    get_raw_movies(path) converts a movies csv in 'path' \n",
+    "    into a list of dicts with column names as keys and\n",
+    "    the corresponding type converted values as the values\n",
+    "    \"\"\"\n",
+    "    pass # replace with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a27fc397",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 5:** What is returned by `get_raw_movies(\"small_movies.csv\")`?\n",
+    "\n",
+    "Your output **must** be a **list** of **dictionaries** where each dictionary contains information about a movie."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "760d6376",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'raw_small_movies', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c5b3731",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41143f42",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "If your answer looks correct, but does not pass `grader.check`, make sure that the **datatypes** are all correct. Also make sure that the **directors** and **cast**  are in the **same order** as in `small_movies.csv`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a634eb65",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 6:** How **many** cast members does the **first** movie have?\n",
+    "\n",
+    "Your output **must** be an **int**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cfed920",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'num_cast_first_movie', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dce007ca",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21365df9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 7:** What is the *ID* of the **first** cast member listed for the **first** movie of the dataset?\n",
+    "\n",
+    "Your output **must** be a **string**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bfa3bbb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'first_actor_id_first_movie', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38711e1e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46268cb1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 3: `get_movies(movies_path, mapping_path)`\n",
+    "\n",
+    "We require you to complete the below function to answer the next several questions (this is a **requirement**, and you will **lose points** if you do not implement this function).\n",
+    "\n",
+    "\n",
+    "This function **must** return a **list** of **dictionaries**, where each **dictionary** is of the following format:\n",
+    "\n",
+    "```python\n",
+    "   {\n",
+    "        'title': \"the movie name\",\n",
+    "        'year': <the year as an integer>,\n",
+    "        'duration': <the duration as an integer>,\n",
+    "        'genres': [<genre1>, <genre2>, ...],\n",
+    "        'rating': <the rating as a float>,\n",
+    "        'directors': [\"director-name1\", \"director-name2\", ...],\n",
+    "        'cast': [\"actor-name1\", \"actor-name2\", ....]\n",
+    "    }\n",
+    "```\n",
+    "\n",
+    "Here is an example:\n",
+    "\n",
+    "```python\n",
+    "    {\n",
+    "        'title': 'Across the Sierras',\n",
+    "        'year': 1941,\n",
+    "        'duration': 59,\n",
+    "        'genres': ['Western'],\n",
+    "        'rating': 5.2,\n",
+    "        'directors': ['D. Ross Lederman'],\n",
+    "        'cast': ['Dick Curtis', 'Bill Elliott', 'Richard Fiske', 'Luana Walters', 'Dub Taylor']\n",
+    "    }\n",
+    "```\n",
+    "\n",
+    "You may copy/paste code from your lab-p8 notebook to finish this function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c794fdb2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_movies(movies_path, mapping_path):\n",
+    "    \"\"\"\n",
+    "    get_movies(movies_path, mapping_path) converts a movies csv in 'movies_path' \n",
+    "    into a list of dicts with column names as keys and the corresponding \n",
+    "    type converted values as the values; then uses the mapping csv in 'mapping_path'\n",
+    "    to replace the IDs of the titles, cast, and directors into actual names\n",
+    "    \"\"\"\n",
+    "    pass # replace this code\n",
+    "    # you are allowed to call get_mapping and get_raw_movies\n",
+    "    # on movies_path and mapping_path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30a62b5a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 8:** What is returned by `get_movies(\"small_movies.csv\", \"small_mapping.csv\")`?\n",
+    "\n",
+    "Your output **must** be a **list** of **dictionaries** where each dictionary contains information about a movie."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a233b116",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'small_movies_data', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a3c4c7d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f4bded0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 9:** What is `title` of the **second** movie in `small_movies_data`?\n",
+    "\n",
+    "Your output **must** be a **string**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "379ccd60",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'second_movie_title_small_movies', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86612bef",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3b64019",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 10:** Who are the `cast` members of the **second** movie in `small_movies_data`?\n",
+    "\n",
+    "Your output **must** be a **list** of **string(s)**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccae5a99",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'second_movie_cast_small_movies', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f44333b3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbe093e2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 11:** Who are the `directors` of the **last** movie in `small_movies_data`?\n",
+    "\n",
+    "Your output **must** be a **list** of **string(s)**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d877d8d2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'last_movie_directors_small_movies', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3521cab",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2034bd93",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "#### Now that you’ve made it this far, your functions must be working pretty well with small datasets. Next, let's try a much bigger dataset!\n",
+    "\n",
+    "Run the following code to open the full dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c483018d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "movies = get_movies(\"movies.csv\", \"mapping.csv\")\n",
+    "len(movies)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3de7014e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "As the files are very large, this cell is expected to take around ten seconds to run. If it takes much longer (say, around a minute), then you will **need** to **optimize** your `get_movies` function so it runs faster.\n",
+    "\n",
+    "**Warning**: You are **not** allowed to call `get_movies` more than once on the full dataset (`movies.csv` and `mapping.csv`) in your notebook. Instead, reuse the `movies` variable, which is more efficient. You will **lose points** during manual review if you call `get_movies` again on these files.\n",
+    "\n",
+    "**Warning:** Do **not** display the value of the variable `movies` **anywhere** in your notebook. It will take up a **lot** of space, and your **Gradescope code will not be displayed** for grading. So, you will receive **zero points** for p8. Instead you should verify `movies` has the correct value by looking at a small *slice* of the **list** as in the question below. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6eb99345",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 12:** What are the movies in `movies[20200:20220]`?\n",
+    "\n",
+    "Your answer should be a *list* of *dictionaries* that follows the format below:\n",
+    "\n",
+    "```python\n",
+    "[{'title': 'Aliens in the Attic',\n",
+    "  'year': 2009,\n",
+    "  'duration': 86,\n",
+    "  'genres': ['Adventure', 'Comedy', 'Family'],\n",
+    "  'rating': 5.4,\n",
+    "  'directors': ['John Schultz'],\n",
+    "  'cast': ['Ashley Tisdale',\n",
+    "   'Robert Hoffman',\n",
+    "   'Carter Jenkins',\n",
+    "   'Austin Butler']},\n",
+    " {'title': 'Dark Buenos Aires',\n",
+    "  'year': 2010,\n",
+    "  'duration': 90,\n",
+    "  'genres': ['Thriller'],\n",
+    "  'rating': 4.8,\n",
+    "  'directors': ['Ramon Térmens'],\n",
+    "  'cast': ['Francesc Garrido',\n",
+    "   'Daniel Faraldo',\n",
+    "   'Natasha Yarovenko',\n",
+    "   'Julieta Díaz']},\n",
+    " ...\n",
+    "]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acbc0896",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'movies_20200_20220', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6568df9e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4829a1a9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 13:** What is the **number** of movies released in the `year` *2018*?\n",
+    "\n",
+    "Your outuput must be an **int**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8362c104",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'num_movies_2018', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9835ddc3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d506a2e8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 4: `find_specific_movies(movies, keyword)`\n",
+    "\n",
+    "Now that we have created this data structure `movies`, we can start doing some fun things with the data!\n",
+    "We will continue working on this data structure for the next project (p9) as well.\n",
+    "\n",
+    "Let us now use this data structure `movies` to create a **search bar** like the one in Netflix!\n",
+    "**Do not change the below function in any way**.\n",
+    "This function takes in a keyword like a substring of a title, a genre, or the name of a person, and returns a list of relevant movies with that title, genre, or cast member/director.\n",
+    "\n",
+    "**Warning:** As `movies` is very large, the function `find_specific_movies` may take five to ten seconds to run. This is normal and you should not panic if it takes a while to run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60bb789b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# DO NOT EDIT OR REDEFINE THIS FUNCTION\n",
+    "def find_specific_movies(movies, keyword):\n",
+    "    \"\"\"\n",
+    "    find_specific_movies(movies, keyword) takes a list of movie dictionaries \n",
+    "    and a keyword; it returns a list of movies that contain the keyword\n",
+    "    in either its title, genre, cast or directors.\n",
+    "    \"\"\"\n",
+    "    idx = 0\n",
+    "    while idx < len(movies):\n",
+    "        movie = movies[idx]\n",
+    "        # note: \\ enables you split a long line of code into two lines\n",
+    "        if (keyword not in movie['title']) and (keyword not in movie[\"genres\"]) \\\n",
+    "        and (keyword not in movie[\"directors\"]) and (keyword not in movie[\"cast\"]):\n",
+    "            movies.pop(idx)\n",
+    "        else:\n",
+    "            idx += 1\n",
+    "    return movies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4916323f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Important:** While it might look as if we are making it easy for you by providing `find_specific_movies`, there is a catch! There is a subtle flaw with the way the function is defined, that will cause you issues in the next two questions. If you can spot this flaw by just observing the definition of `find_specific_movies`, congratulations! Since you are **not** allowed to modify the function definition, you will have to be a little clever with your function arguments to sidestep the flaw with the function definition.\n",
+    "\n",
+    "If you don't see anything wrong with the function just yet, don't worry about it. Solve q14 and q15 as you normally would, and see if you notice anything suspicious about your answers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55c4a4c4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 14:** List all the movies that *Katharine Hepburn* acted in.\n",
+    "\n",
+    "Your answer **must** be a **list** of **dictionaries**.\n",
+    "\n",
+    "You **must** answer this question by calling `find_specific_movies` with the keyword `\"Katharine Hepburn\"`.\n",
+    "\n",
+    "The `find_specific_movies` function is expected to take around 5 seconds to run, so do not panic if it takes so long to run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4efab1a7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'hepburn_films', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebc41df3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25b47805",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 15:** List all the movies that contain the string *Wisconsin* in their `title`.\n",
+    "\n",
+    "Your answer **must** be a **list** of **dictionaries**.\n",
+    "\n",
+    "You **must** answer this question by calling `find_specific_movies` with the keyword `\"Wisconsin\"`.\n",
+    "\n",
+    "**Important Hint:**  If you did not notice the flaw with the definition of `find_specific_movies` before, you are likely to have run into an issue with this quetsion. It is likely that you will see that your output for this question is an empty list. To see why this happened, find the value of `len(movies)` and see if it is equal to the value you found earlier.\n",
+    "\n",
+    "Remember that you are **not** allowed to modify the definition of `find_specific_movies`. You will need to cleverly pass arguments to `find_specific_movies` (in both q14 and q15) to ensure that `movies` does not get modified by the function calls. Take a look at the [lecture slides](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-21) from October 26 for more hints. You will have to Restart and Run all your cells to see the correct output after you fix your answer for q14 (and q15)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff176cc8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'wisconsin_movies', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fea52538",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbab7de5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 5: `bucketize_by_genre(movies)`\n",
+    "\n",
+    "We require you to complete the below function to answer the next several questions (this is a **requirement**, and you will **lose points** if you do not implement this function)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be9e9097",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def bucketize_by_genre(movies):\n",
+    "    \"\"\"bucketize_by_genre(movies) takes a list of movie dictionaries;\n",
+    "    it returns a dict in which each genre is a key and\n",
+    "    the value is a list of all movies that contain that genre\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: initialize a dictionary\n",
+    "    # TODO: loop through all movies\n",
+    "    # TODO: loop through all genres in this movie\n",
+    "    # TODO: if this genre is not a key in our dictionary, set the value associated with this genre to an empty list\n",
+    "    # TODO: if we already have this genre in our dictionary, add the movie to the list associated with this genre\n",
+    "    # TODO: return the dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a7e1d4f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# call the function bucketize_by_genre on 'movies' and store it in the variable 'genre_dict'\n",
+    "# do NOT display the output directly\n",
+    "\n",
+    "genre_dict = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c3019b1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Warning:** You are **not** allowed to call `bucketize_by_genre` more than once on the full list of movies (`movies`) in your notebook. You will **lose points** during manual review if you call `bucketize_by_genre` again on `movies`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf588c26",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 16:** How many **unique** movie `genres` are present in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5de4e9a6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'num_genres', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "327432dd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6913f3f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 17:** How many *Music* movies (i.e. movies with *Music* as one of their `genres`) do we have in the dataset released **after** the `year` *2019*?\n",
+    "\n",
+    "Your output **must** be an **int**. You **must** use the `genre_dict` data structure to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "719d013f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'music_after_2019', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65c19782",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a96adf0a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 18:** List the `title` of all *Horror* movies (i.e. movies with *Horror* as one of their `genres`) with `rating` **larger** than *9.0* in the dataset.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings**. You **must** use the `genre_dict` data structure to answer this question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "475e0d81",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'horror_movies_above_9', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca535410",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5f42d47",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 19:** Which movie `genre` does *Jennifer Aniston* play the most?\n",
+    "\n",
+    "There is a **unique** `genre` that *Jennifer Aniston* has played the most. You do **not** have to worry about breaking ties.\n",
+    "\n",
+    "**Hint:** You can combine the *two* functions above to bucketize the movies that *Jennifer Aniston* has acted in by their `genres`. Then, you can loop through each genre to find the one with the most number of movies in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0eaac53",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'jen_aniston_genre, then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55e4c949",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58f8029b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 20:** Who are the `directors` of the *Documentary* movies with the **highest** `rating` in the movies dataset?\n",
+    "\n",
+    "There are **multiple** *Documentary* movies in the dataset with the joint highest rating. You **must** output a **list** of **strings** containing the **names** of **all** the `directors` of **all** these movies.\n",
+    "\n",
+    "**Hint:** If you are unsure how to efficiently add the elements of one list to another, take a look at the [lecture slides](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f22/meena_lec_notes/lec-14) from October 10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a7f5a12",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'max_docu_rating_directors', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56505bf1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d5d6800",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "Make sure you have run all cells in your notebook in order before running the following cells, so that all images/graphs appear in the output.\n",
+    "It is recommended that at this stage, you Restart and Run all Cells in your notebook.\n",
+    "That will automatically save your work and generate a zip file for you to submit.\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**:\n",
+    "1. **Upload** the zipfile to Gradescope.\n",
+    "2. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82b86e8c",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# running this cell will create a new save checkpoint for your notebook\n",
+    "from IPython.display import display, Javascript\n",
+    "display(Javascript('IPython.notebook.save_checkpoint();'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6c0f1cd",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "!jupytext --to py p8.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35cc4a4c",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "p8_test.check_file_size(\"p8.ipynb\")\n",
+    "grader.export(pdf=False, run_tests=True, files=[py_filename])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6efaf4e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q1\", small_mapping)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q10\", second_movie_cast_small_movies)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q11\", last_movie_directors_small_movies)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q12\", movies_20200_20220)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q13\", num_movies_2018)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q14\", hepburn_films)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q15\", wisconsin_movies)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q16\", num_genres)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q17\", music_after_2019)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q18\", horror_movies_above_9)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q19\", jen_aniston_genre)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q2\", nm2110418_value)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q20\", max_docu_rating_directors)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q3\", nm_values)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q4\", nm_spencer)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q5\", raw_small_movies)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q6\", num_cast_first_movie)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q7\", first_actor_id_first_movie)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q8\", small_movies_data)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p8_test.check(\"q9\", second_movie_title_small_movies)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "ddadfa966245fdd88d86598ab7fd899e4c6b5bf38d3fe509005bdb7f4648c1a5"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p8/p8.ipynb b/p8/p8.ipynb
index c99d93a4fe63ab038c4b6d53a2586b8038409e6c..874d095727d0d62df09cfad38aa86af20e8b2876 100644
--- a/p8/p8.ipynb
+++ b/p8/p8.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b1eb253c",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/p9/.ipynb_checkpoints/p9-checkpoint.ipynb b/p9/.ipynb_checkpoints/p9-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..07200ce4d2e4ea0792758f5b5b87ad2300019055
--- /dev/null
+++ b/p9/.ipynb_checkpoints/p9-checkpoint.ipynb
@@ -0,0 +1,1426 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3137f060",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f5bc8b5",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import otter\n",
+    "# nb_name should be the name of your notebook without the .ipynb extension\n",
+    "nb_name = \"p9\"\n",
+    "py_filename = nb_name + \".py\"\n",
+    "grader = otter.Notebook(nb_name + \".ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e16fd978",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import p9_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d7248b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PLEASE FILL IN THE DETAILS\n",
+    "# Enter none if you don't have a project partner\n",
+    "\n",
+    "# project: p9\n",
+    "# submitter: NETID1\n",
+    "# partner: NETID2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43cef781",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "# Project 9: Analyzing the Movies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43174132",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project, you will demonstrate your ability to:\n",
+    "- Use `matplotlib` to plot bar graphs and visualize statistics\n",
+    "- Process data using dictionaries and lists that you build\n",
+    "- Implement binning by writing algorithms that create dictionaries\n",
+    "- Custom sort a list using the keyword parameter `key`'s argument.\n",
+    "\n",
+    "Please go through [lab-p9](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p9) before starting this project. The lab introduces some useful techniques necessary for this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c99dead",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: p8 and p9 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partnered up with someone for p8, you have to work on p9 with the **same partner**. Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f22/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e697420a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `p9_test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions.\n",
+    "\n",
+    "**Important:** `p9_test.py` **cannot** verify your answers when the output is an image. Your **plots** will be **manually graded** by graders, so you must **manually** confirm that your plots look correct by comparing with the images provided in the notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2df4204",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Introduction:\n",
+    "\n",
+    "In p8, you created very useful helper functions to parse the raw IMDb dataset. You also created useful data structures to store the data. In this project, you will be building on the work you did in p8 to analyze your favorite movies. This is a shorter project than usual, and **p9 will only have 10 questions for you to solve**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bcca677",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Data:\n",
+    "\n",
+    "Between p8 and p9, the movies dataset has been updated to hold many more movies. The `movies.csv` file that you will use in p9 contains ~200,000 movies, and the `mapping.csv` file contains data on ~600,000 movie titles and people. Thankfully, the data in both files are stored in exactly the same format as the files you worked with in p8. So, all your functions should continue to work in p9. Please remember to download the [latest datasets](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p9) before starting this project. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38a8ca20",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices or the lengths of lists in your code. We'll **manually deduct** points from your autograder score on Gradescope during code review.\n",
+    "\n",
+    "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer. If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Functions:\n",
+    "- `get_movies`\n",
+    "- `plot_dict`\n",
+    "- `bucketize`\n",
+    "\n",
+    "In this project, you will also be required to define certain **data structures**. If you do not create these data structures exactly as specified, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Data Structures:\n",
+    "- `movies`\n",
+    "- `cast_buckets`\n",
+    "- `director_buckets`\n",
+    "- `genre_buckets`\n",
+    "- `year_buckets`\n",
+    "\n",
+    "You are only allowed to define these data structures **once** and we'll **manually deduct** points from your autograder score on Gradescope if you redefine the values of these variables.\n",
+    "\n",
+    "In this project, you will be asked to create **lists** of movies. For all such questions, **unless it is explicitly mentioned otherwise**, the movies should be in the **same order** as in the `movies.csv` file. Similarly, for each movie, the **list** of `genres`, `directors`, and `cast` members should always be in the **same order** as in the `movies.csv` file.\n",
+    "\n",
+    "Students are only allowed to use Python commands and concepts that have been taught in the course prior to the release of p9. We will **manually deduct** points from your autograder score on Gradescope otherwise.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/blob/main/p9/rubric.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b65e37ae",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Questions and Functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f93151c9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2d6c098",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Loading the Movies Data\n",
+    "\n",
+    "For all these questions, we will be looking at the movies in `mapping.csv` and `movies.csv`. You can load the list of movies using the functions you wrote in the last project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2403d06d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Copy the functions you wrote in `p8.ipynb` to `p9.ipynb` to read the movies data. The functions you should include are `process_csv`, `get_mapping`, `get_raw_movies`, and `get_movies` along with any helper functions you used to write these. Do **not** copy/paste `find_specific_movies` here. Later in p9, we will provide you with a simpler version of that function, which does not require the use of the `copy` module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39672b67",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definitions of get_mapping, get_raw_movies, get_movies from p8\n",
+    "# as well as any helper functions used by these functions here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ba976bc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, you can use `get_movies` to read the data in `movies.csv` and `mapping.csv` as you did in p8."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f15f6974",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create a list of dictionaries named 'movies' to store the data in 'movies.csv' and 'mapping.csv' as in p8\n",
+    "# do NOT display the value of this variable anywhere in this notebook\n",
+    "\n",
+    "movies = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7299f093",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "There should be *198610* **dictionaries** in the **list** `movies` and the first entry of `movies` should be a **dictionary** that looks as follows:\n",
+    "\n",
+    "```python\n",
+    "{'title': 'Lejos de África',\n",
+    " 'year': 1996,\n",
+    " 'duration': 115,\n",
+    " 'genres': ['Drama'],\n",
+    " 'rating': 6.4,\n",
+    " 'directors': ['Cecilia Bartolomé'],\n",
+    " 'cast': ['Alicia Bogo', 'Xabier Elorriaga', 'Isabel Mestres', 'Carlos Cruz']}\n",
+    "```\n",
+    "\n",
+    "**Warning:** At this stage, it is expected that the function `get_movies` works correctly, and that `movies` is defined as it was in p8. If not, your code will run into issues in p9. So, make sure that this function works properly before you start p9. You can do that by **inserting a new cell** in Jupyter below this cell and verifying that the size of your variable `movies`, and that the first **dictionary** in `movies` are as they should be. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b50bfa3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, copy over the functions `plot_dict`, `median` and `year_to_decade` from lab-p9."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa45134b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definitions of plot_dict, median, year_to_decade from lab-p9\n",
+    "# as well as any helper functions used by these functions here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "080fd02a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "In p8, you were provided with a function `find_specific_movies` which functioned as some sort of a 'search bar' for the movies dataset. However, in order to use that function properly, you had to used the `copy` module to pass a *copy* of your list of movies to `find_specific_movies`. Making copies frequently is **not** a good coding practice. For this project, we will provide **a new version** of `find_specific_movies` that does **not** require `copy`. Please go through the following function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cb60aae",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# modified find_specific_movies (doesn't require using copy module)\n",
+    "def find_specific_movies(movies, keyword):\n",
+    "    \"\"\"\n",
+    "    find_specific_movies(movies, keyword) takes a list of movie dictionaries \n",
+    "    and a keyword; it returns a list of movies that contain the keyword\n",
+    "    in either its title, genre, cast or directors.\n",
+    "    \"\"\"\n",
+    "    movies_with_keyword = []\n",
+    "    for movie in movies:\n",
+    "        if (keyword in movie['title']) or (keyword in movie['genres']) \\\n",
+    "            or (keyword in movie['directors']) or (keyword in movie['cast']):\n",
+    "            movies_with_keyword.append(movie)\n",
+    "    return movies_with_keyword"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8e126f1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Important:** Even when you are not explicitly prompted to do so, using the `find_specific_movies` function cleverly can simplify your code significantly. Keep an eye out for how you can simplify your code by making use of `find_specific_movies`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06c7f534",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Analyzing the Movies data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce67f948",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 1:** What is the **median** `rating` of the *Harry Potter* movies directed by *David Yates*?\n",
+    "\n",
+    "You **must** consider movies which have *Harry Potter* as a substring of the `title` **and** also have *David Yates* as one of the `directors`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "658216e4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'median_hp_rating', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3b42aab",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be310fb6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 2:** Among all the *Avengers* movies, which ones are the **highest** rated?\n",
+    "\n",
+    "Your output **must** be a **list** of **dictionaries**. You **must** consider all movies which have *Avengers* as a substring of their `title`.\n",
+    "\n",
+    "**Hint:** You could first find the **highest** `rating` that any *Avengers* movie received, and then find all the *Avengers* movies that received that `rating`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f299dce",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'highest_rated_avengers_movies', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e8ca02b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5706252b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Function 1: `bucketize(movies_list, category)` \n",
+    "\n",
+    "This function should take in a **list** of movie **dictionaries** as well as a **category** (i.e. `title`, `year`, `duration`, `genres`, `rating`, `directors`, or `cast`), and *bucketize* the **list** of movie **dictionaries** by this **category**.\n",
+    "\n",
+    "For example, the output of `bucketize(movies, 'rating')` should be a **dictionary** so that all the unique values of `rating` of the movies in `movies` are the **keys** and the correspoding **values** would be a **list** of all movie **dictionaries** with that rating (e.g., the value of the key *6.4* should be the **list** of movie dictionaries with `rating` of *6.4*).\n",
+    "\n",
+    "The output of `bucketize(movies, 'rating')` should look like this:\n",
+    "\n",
+    "```python\n",
+    "{6.4: [{'title': 'Lejos de África',\n",
+    "   'year': 1996,\n",
+    "   'duration': 115,\n",
+    "   'genres': ['Drama'],\n",
+    "   'rating': 6.4,\n",
+    "   'directors': ['Cecilia Bartolomé'],\n",
+    "   'cast': ['Alicia Bogo',\n",
+    "    'Xabier Elorriaga',\n",
+    "    'Isabel Mestres',\n",
+    "    'Carlos Cruz']},\n",
+    "  {'title': 'Kobbari Bondam',\n",
+    "   'year': 1991,\n",
+    "   'duration': 149,\n",
+    "   'genres': ['Comedy'],\n",
+    "   'rating': 6.4,\n",
+    "   'directors': ['Raviteja K.'],\n",
+    "   'cast': ['Rajendra Prasad', 'Nirosha', 'Sudhakar', 'Kota Srinivasa Rao']},\n",
+    "       ...\n",
+    "      ],\n",
+    " 2.1: [{'title': 'Amityville Cult',\n",
+    "  'year': 2021,\n",
+    "  'duration': 85,\n",
+    "  'genres': ['Horror'],\n",
+    "  'rating': 2.1,\n",
+    "  'directors': ['Trey Murphy'],\n",
+    "  'cast': ['James Burleson',\n",
+    "   'Chance Gibbs',\n",
+    "   'Micha Marie Stevens',\n",
+    "   'Tom Young',\n",
+    "   'Patrick McAlister',\n",
+    "   'Eric Oberto',\n",
+    "   'Lara Williams']},\n",
+    " {'title': 'Jungle Goddess',\n",
+    "  'year': 1948,\n",
+    "  'duration': 62,\n",
+    "  'genres': ['Action', 'Adventure', 'Crime'],\n",
+    "  'rating': 2.1,\n",
+    "  'directors': ['Lewis D. Collins'],\n",
+    "  'cast': ['George Reeves', 'Wanda McKay', 'Ralph Byrd', 'Armida']},\n",
+    "       ...\n",
+    "      ],\n",
+    " ...\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "Similarly, the output of `bucketize(movies, 'cast')` should be a **dictionary** so that all the unique `cast` members of the movies in `movies` are the **keys** and the correspoding **values** would be a **list** of all movie **dictionaries** with that cast member as one of their `cast` (e.g., the value of the key *Kate Winslet* should be the **list** of movie dictionaries with *Kate Winslet* as one of their `cast` members).\n",
+    "\n",
+    "The output of `bucketize(movies, 'cast')` should look like this:\n",
+    "\n",
+    "```python\n",
+    "{'Alicia Bogo': [{'title': 'Lejos de África',\n",
+    "   'year': 1996,\n",
+    "   'duration': 115,\n",
+    "   'genres': ['Drama'],\n",
+    "   'rating': 6.4,\n",
+    "   'directors': ['Cecilia Bartolomé'],\n",
+    "   'cast': ['Alicia Bogo',\n",
+    "    'Xabier Elorriaga',\n",
+    "    'Isabel Mestres',\n",
+    "    'Carlos Cruz']}],\n",
+    " 'Xabier Elorriaga': [{'title': 'Lejos de África',\n",
+    "   'year': 1996,\n",
+    "   'duration': 115,\n",
+    "   'genres': ['Drama'],\n",
+    "   'rating': 6.4,\n",
+    "   'directors': ['Cecilia Bartolomé'],\n",
+    "   'cast': ['Alicia Bogo',\n",
+    "    'Xabier Elorriaga',\n",
+    "    'Isabel Mestres',\n",
+    "    'Carlos Cruz']},\n",
+    "  {'title': 'Companys, procés a Catalunya',\n",
+    "   'year': 1979,\n",
+    "   'duration': 125,\n",
+    "   'genres': ['Biography', 'Drama', 'History'],\n",
+    "   'rating': 6.2,\n",
+    "   'directors': ['Josep Maria Forn'],\n",
+    "   'cast': ['Luis Iriondo',\n",
+    "    'Marta Angelat',\n",
+    "    'Montserrat Carulla',\n",
+    "    'Xabier Elorriaga']},\n",
+    "     ...\n",
+    "    ],\n",
+    " ...\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "**Hints:** Note that depending on whether or not the `category` represents a **list** or not, your function will have to behave differently. In p8, you created a function `bucketize_by_genre` that *bucketized* the list of movies by their genre. Take a moment to find that function; it will help you here. Also, take a moment to look at the buckets you made in lab-p9."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "232c05e7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code to finish the definition of bucketize\n",
+    "\n",
+    "def bucketize(movie_list, category):\n",
+    "    buckets = {}\n",
+    "    for movie in movie_list:\n",
+    "        category_value = ??? #TODO: Access the category value from a movie\n",
+    "        if type(category_value) == list:\n",
+    "            ...\n",
+    "        else:\n",
+    "            ...\n",
+    "    return buckets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6aef85d3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Important:** Just like `get_movies`, `bucketize` is quite a time-consuming function to run. Hence, you do **not** want to call `bucketize` on the same list of movies and category **more than once**. Throughout the project, we will frequently use bucketized lists of movies organized by their `cast`, `directors`, `genre`, and `year`. Rather than calling `bucketize` several times, we will store the bucketized lists in following variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42ff40a8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define buckets for categories mentioned below, but do NOT display any of them\n",
+    "\n",
+    "# bucketize the full list of movies by their cast.\n",
+    "cast_buckets = bucketize(???)\n",
+    "# bucketize the full list of movies by their directors.\n",
+    "director_buckets = bucketize(???)\n",
+    "# bucketize the full list of movies by their genre.\n",
+    "genre_buckets = bucketize(???)\n",
+    "# bucketize the full list of movies by their year.\n",
+    "year_buckets = bucketize(???)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03aa2905",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Even when you are not explicitly prompted to do so, using these data structures and the `bucketize` function cleverly can simplify your code significantly. Keep an eye out for how you can simplify your code by making use of these data structures and the `bucketize` function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5fd3889",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 3:** List the movies that *Viola Davis* was `cast` in?\n",
+    "\n",
+    "Your output **must** be a **list** of **dictionaries**. You **must** answer this question by accessing the **value** of the correct **key** from the correct **bucket** defined in the previous cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c66ac530",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'viola_movies', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52c45d1a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "attachments": {
+    "num_genres.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "40f47424",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 4:** **Plot** the **number** of movies in each *genre* as a **bar graph**.\n",
+    "\n",
+    "You **must** first compute a **dictionary** which maps each **genre** to the **number** of movies in that **genre**. Then, you may use `plot_dict` to plot the data in that dictionary.\n",
+    "\n",
+    "**Important Warning:** `p9_test.py` can check that the **dictionary** has the correct key/value pairs, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "<div><img src=\"attachment:num_genres.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a70fb5aa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the dictionary in the variable 'genre_num', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16a0c55b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# now plot 'genre_num' with the y-axis labelled 'number of movies'\n",
+    "\n",
+    "plot_dict(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98a7c468",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8013dd2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Can you tell what the most popular **genres** are from the plot? Do you see anything surprising here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad8abe06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is an entirely OPTIONAL exercise\n",
+    "# you may leave your thoughts here as a comment if you wish to\n"
+   ]
+  },
+  {
+   "attachments": {
+    "kurosawa_films.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "eeb5d212",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 5:** **Plot** the **number** of movies **directed** by *Akira Kurosawa* in each *genre* as a **bar graph**.\n",
+    "\n",
+    "You **must** only include those `genres` in which *Akira Kurosawa* has directed **at least** one movie, in your plot.\n",
+    "\n",
+    "You **must** first compute a **dictionary** which maps each **genre** to the **number** of movies in that **genre** directed by *Akira Kurosawa*. Then, you may use `plot_dict` to plot the data in that dictionary.\n",
+    "\n",
+    "**Important Warning:** `p9_test.py` can check that the **dictionary** has the correct key/value pairs, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "<div><img src=\"attachment:kurosawa_films.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5a43380",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the dictionary in the variable 'kurosawa_genres', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4458b02e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# now plot 'kurosawa_genres' with the y-axis labelled 'number of movies'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3d973ec",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72839702",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Can you similarly **plot** the **number** of films directed by your favorite director or starring your favorite cast member in each **genre**?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5d0e10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is an entirely OPTIONAL exercise\n",
+    "# you may leave your thoughts here as a comment if you wish to\n"
+   ]
+  },
+  {
+   "attachments": {
+    "num_decade.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "dee034ef",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 6:** **Plot** the **number** of movies released in each *decade* as a **bar graph**.\n",
+    "\n",
+    "You **must** first compute a **dictionary** which maps each **decade** to the **number** of movies in released in that **decade**. This dictionary should look like this:\n",
+    "\n",
+    "```python\n",
+    "{'1991 to 2000': 18496,\n",
+    " '2021 to 2030': 9173,\n",
+    " '1961 to 1970': 14216,\n",
+    " '1951 to 1960': 10981,\n",
+    " '2011 to 2020': 59249,\n",
+    " '2001 to 2010': 33658,\n",
+    " '1941 to 1950': 7807,\n",
+    " '1971 to 1980': 15556,\n",
+    " '1981 to 1990': 17181,\n",
+    " '1921 to 1930': 3014,\n",
+    " '1931 to 1940': 8201,\n",
+    " '1911 to 1920': 1068,\n",
+    " '1901 to 1910': 9,\n",
+    " '1891 to 1900': 1}\n",
+    "```\n",
+    "\n",
+    "Then, you may use `plot_dict` to plot the data in that dictionary.\n",
+    "\n",
+    "**Important Warning:** `p9_test.py` can check that the **dictionary** has the correct key/value pairs, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "<div><img src=\"attachment:num_decade.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57ed7bb1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the dictionary in the variable 'decade_mapping', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f53eda7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# now plot 'decade_mapping' with the y-axis labelled 'movies released'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4d3766f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b164db7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Can you explain the shape of this plot? Why do you think there was a dip between the decades `1931 to 1940` and `1941 to 1950`? Can you explain why the number of movies appears to plateau around `1991 to 2000` before increasing drastically? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e360292e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is an entirely OPTIONAL exercise\n",
+    "# you may leave your thoughts here as a comment if you wish to\n"
+   ]
+  },
+  {
+   "attachments": {
+    "median_rating.PNG": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "24bd74e0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 7:** **Plot** the **median** `rating` of movies in each *genre* as a **bar graph**.\n",
+    "\n",
+    "You **must** first compute a **dictionary** which maps each **genre** to the **median** `rating` of all movies in that **genre**. Then, you may use `plot_dict` to plot the data in that dictionary.\n",
+    "\n",
+    "**Important Warning:** `p9_test.py` can check that the **dictionary** has the correct key/value pairs, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "<div><img src=\"attachment:median_rating.PNG\" width=\"400\"/></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73025823",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the dictionary in the variable 'median_genres', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c0fb7e2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# now plot 'median_genres' with the y-axis labelled 'median rating'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59c0ae5c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aa0c1d6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Do you spot any outliers in this graph? Can you explain why they are such outliers?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3684ad97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is an entirely OPTIONAL exercise\n",
+    "# you may leave your thoughts here as a comment if you wish to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc38f48d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "The visualization in q7 immediately tells us that the **median** *Documentary* movie is rated higher than the **median** *Crime* movie. However, it is a little hard to tell how the **median** *Action* movie fares against the **median** *Fantasy* movie. In order to compare the `genres`, it would be useful to **sort** the `genres` by their **median** `rating`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3655d14b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 8:** Produce a **list** of `genres` sorted in **decreasing order** of their **median** `rating`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c38cc15",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'genres_desc', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75fb9c23",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11239ffd",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 9:** Produce a **list** of *Animation* movies from the *Shrek* franchise sorted in **increasing** order of their `year` of release.\n",
+    "\n",
+    "Your output **must** be a **list** of **dictionaries** of movies having *Shrek* as a **substring** of the `title` and *Animation* as one of their `genres`, that are **sorted** in **increasing** order of their `year`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d9ac7e9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'shrek_inc', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "655f9f8a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14e1c3d1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 10:** Produce a **list** of `directors` who have directed movies in which *Denzel Washington* was `cast` in, **sorted** in **decreasing** order of the number of times they have worked with *Denzel Washington*.\n",
+    "\n",
+    "Your output **must** be a **list** of **strings** of the names of the `directors` who have directed movies in which *Denzel Washington* was a `cast` member, and the **list** should be sorted in **decreasing** order of the number of times that director has worked with *Denzel Washington*.\n",
+    "\n",
+    "**Hint:** If you use your `bucketize` function cleverly, you can easily organize the movies that *Denzel Washington* was `cast` in, by their director(s)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e10c1c3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'denzel_directors', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63bc039c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06613bf4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "Make sure you have run all cells in your notebook in order before running the following cells, so that all images/graphs appear in the output.\n",
+    "It is recommended that at this stage, you Restart and Run all Cells in your notebook.\n",
+    "That will automatically save your work and generate a zip file for you to submit.\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**:\n",
+    "1. **Upload** the zipfile to Gradescope.\n",
+    "2. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c818f43f",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# running this cell will create a new save checkpoint for your notebook\n",
+    "from IPython.display import display, Javascript\n",
+    "display(Javascript('IPython.notebook.save_checkpoint();'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f532d70",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "!jupytext --to py p9.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f6243c1",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "p9_test.check_file_size(\"p9.ipynb\")\n",
+    "grader.export(pdf=False, run_tests=True, files=[py_filename])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13845e51",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p9_test.check(\"q1\", median_hp_rating)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p9_test.check(\"q10\", denzel_directors)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p9_test.check(\"q2\", highest_rated_avengers_movies)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p9_test.check(\"q3\", viola_movies)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p9_test.check(\"q4\", genre_num)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p9_test.check(\"q5\", kurosawa_genres)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p9_test.check(\"q6\", decade_mapping)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p9_test.check(\"q7\", median_genres)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p9_test.check(\"q8\", genres_desc)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p9_test.check(\"q9\", shrek_inc)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p9/p9.ipynb b/p9/p9.ipynb
index 1c2ae5d732bb6f4aec6b117183f318c400704bf5..07200ce4d2e4ea0792758f5b5b87ad2300019055 100644
--- a/p9/p9.ipynb
+++ b/p9/p9.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3137f060",
+   "metadata": {},
+   "source": [
+    "<h1><font color = \"red\">\"This is an out of date page, don't use it for the current semester\" </font></h1>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,