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+{
+ "cells": [
+  {
+   "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
+}