From 849bdcc30e50ad8464ea021720344de0d23c1aef Mon Sep 17 00:00:00 2001
From: gsingh58 <gurmail.singh@wisc.edu>
Date: Fri, 17 Feb 2023 10:30:58 -0500
Subject: [PATCH] Out Date announcement

---
 .../practice-checkpoint.ipynb                 | 2206 +++++++++++++++++
 lab-p4/practice.ipynb                         |   10 +-
 p4/.ipynb_checkpoints/p4-checkpoint.ipynb     | 1719 +++++++++++++
 p4/p4.ipynb                                   |    8 +
 4 files changed, 3942 insertions(+), 1 deletion(-)
 create mode 100644 lab-p4/.ipynb_checkpoints/practice-checkpoint.ipynb
 create mode 100644 p4/.ipynb_checkpoints/p4-checkpoint.ipynb

diff --git a/lab-p4/.ipynb_checkpoints/practice-checkpoint.ipynb b/lab-p4/.ipynb_checkpoints/practice-checkpoint.ipynb
new file mode 100644
index 0000000..eb0b47f
--- /dev/null
+++ b/lab-p4/.ipynb_checkpoints/practice-checkpoint.ipynb
@@ -0,0 +1,2206 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4d5a95a0",
+   "metadata": {},
+   "source": [
+    "# <font color = red>\"This is an out of date page, don't use it for the current semester\" <font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0569973a",
+   "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": "b1fda094",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c5147b9",
+   "metadata": {},
+   "source": [
+    "# Lab-P4: Conditional statements and Pokémon API\n",
+    "\n",
+    "**WARNING:** Please go through Segment 1 of [lab-p4](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p4) **before** you start to solve this notebook."
+   ]
+  },
+  {
+   "attachments": {
+    "pokemon_stats.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "977e3ce2",
+   "metadata": {},
+   "source": [
+    "## Segment 2: Learning the API\n",
+    "### Task 2.1: Examine the `pokemon_stats` CSV file\n",
+    "Open `pokemon_stats.csv` with Microsoft Excel or any other spreadsheet software first, and take a look at it. It should look something like the image below:\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:pokemon_stats.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5df6316f",
+   "metadata": {},
+   "source": [
+    "Each Pokémon comes from a certain `Region` and has one or two Type(s). A Pokémon with only one Type has `'None'` as its value for `Type 2`. Moreover, each Pokémon has six other statistics, namely:\n",
+    "\n",
+    "1. `Attack` (short for Physical Attack)\n",
+    "2. `Defense` (short for Physical Defense)\n",
+    "3. `HP` (short for Hit Points)\n",
+    "4. `Sp. Atk` (short for Special Attack)\n",
+    "5. `Sp. Def` (short for Special Defense)\n",
+    "6. `Speed` (which is self-explanatory)"
+   ]
+  },
+  {
+   "attachments": {
+    "type_effectiveness_stats.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "121644c8",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Examine the `type_effectiveness_stats` CSV file\n",
+    "Next, open `type_effectiveness_stats.csv` with Microsoft Excel or any other Spreadsheet software, and take a look at it. You will see a table of Pokémon types representing the effectiveness of one type against another. The rows represent the type of the defender, and the columns represent the type of attacker. Read the description below the image (of csv file), to understand these statistics better.\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:type_effectiveness_stats.png\" width=\"900\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e7bf0aa",
+   "metadata": {},
+   "source": [
+    "All Pokémon creatures and their moves are assigned certain types. Each type has several strengths and weaknesses against other Pokémon. In battle, you should use Pokémon and moves that have a type advantage over their opponent; doing so will cause much more damage than otherwise. There are 18 types in this table. Taking the second row (Fire) and the third column (Water) as an example, we see that Water type attack has an effectiveness of `2.0` against Fire type defense."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad20490f",
+   "metadata": {},
+   "source": [
+    "### Task 2.3: Explore the API\n",
+    "\n",
+    "`project.py` is designed to give you access to the data in `pokemon_stats.csv` and `type_effectiveness_stats.csv`. \n",
+    "\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#task-22-inspecting-projectpy) to learn more details of the 'project' API. In lab-p3, we saw how to use `dir`, and `help` to learn the API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa218fc5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# include the relevant import statements in this cell\n",
+    "\n",
+    "import project # we have imported the project module for you here; you will have to add the import statement in p4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5dd9384a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Use the 'dir' function to learn more about the project API."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dca452d",
+   "metadata": {},
+   "source": [
+    "If you were to use the `help` function on the project module, you should find eleven functions here that \n",
+    "do not begin and end with two underscores (`__`). Read the documentation to figure\n",
+    "out what these eleven functions do."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85b0c5cf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# you can find the documentation for any function by calling `help` on that function\n",
+    "# use the help function to read the documentation for the project.print_stats function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a3a0233",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# now try out the print_stats function on the Pokémon \"Pikachu\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfbda4a2",
+   "metadata": {},
+   "source": [
+    "Similarly, try to figure out what each of the ten other functions in `project.py` do, by inspecting them or reading their documentation. When you feel comfortable, proceed with the rest of the lab. If any of these instructions are unclear, or if you are unsure about what these functions do, feel  free to reach out to your TA/PM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75eb1e07",
+   "metadata": {},
+   "source": [
+    "### Task 2.3.1: Getting familiar with `project.py`\n",
+    "\n",
+    "You will now demonstrate your familiarity with the functions inside `project.py` by answering a few simple questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5855c01",
+   "metadata": {},
+   "source": [
+    "**Question 1:** What `region` is `Squirtle` from?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6e8fe63",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "squirtle_region = ...\n",
+    "squirtle_region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8ce1c46",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ff4c363",
+   "metadata": {},
+   "source": [
+    "**Question 2:** What is the first type (i.e., `type1`) of `Vulpix`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a81d369",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "vulpix_type1 = ...\n",
+    "vulpix_type1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1492d699",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09933c43",
+   "metadata": {},
+   "source": [
+    "**Question 3:** What is the second type (i.e., `type2`) of `Heatran`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b4d8f63",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "heatran_type2 = ...\n",
+    "heatran_type2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf71595f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "600797e5",
+   "metadata": {},
+   "source": [
+    "**Question 4:** What is `Mewtwo`'s `HP` stat?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7c633b1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "mewtwo_hp = ...\n",
+    "mewtwo_hp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3549464b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce634ee2",
+   "metadata": {},
+   "source": [
+    "**Question 5:** What is `Rayquaza`'s `Attack` stat?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4e72c13",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "rayquaza_attack = ...\n",
+    "rayquaza_attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fc553b0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "320afd32",
+   "metadata": {},
+   "source": [
+    "**Question 6:** What is `Registeel`'s `Defense` stat?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bab1d7d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "registeel_defense = ...\n",
+    "registeel_defense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d401c067",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27173841",
+   "metadata": {},
+   "source": [
+    "**Question 7:** What is `Mudkip`'s `Special Attack` stat?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afeb5c5f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "mudkip_sp_atk = ...\n",
+    "mudkip_sp_atk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80796611",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b73da356",
+   "metadata": {},
+   "source": [
+    "**Question 8:** What is `Kyogre`'s `Special Defense` stat?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40c11eb3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "kyogre_sp_def = ...\n",
+    "kyogre_sp_def"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1393e83",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88980265",
+   "metadata": {},
+   "source": [
+    "**Question 9:** What is `Slowpoke`'s `Speed` stat?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "223fbd99",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "slowpoke_speed = ...\n",
+    "slowpoke_speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e766d67a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c531c3f3",
+   "metadata": {},
+   "source": [
+    "**Question 10:** What is the `type effectiveness` of a `Water` type attack **against** a `Rock` type opponent?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "517da914",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "# do not hardcode, instead make sure to use the relevant function from the project module\n",
+    "# inspect or read the documentation of all the functions in the project if you are not sure which one to call\n",
+    "water_rock_effectiveness = ...\n",
+    "water_rock_effectiveness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e93d7c8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca23c455",
+   "metadata": {},
+   "source": [
+    "## Segment 3: Conditional Statements\n",
+    "\n",
+    "You will now use conditional statements to answer a few interesting questions about some Pokémon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55ae16d7",
+   "metadata": {},
+   "source": [
+    "### Task 3.1: Exploring the `project.get_region` function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb3b2629",
+   "metadata": {},
+   "source": [
+    "**Question 11:** Is `Pikachu` from `Kanto`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55f4fe32",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# complete the following code that checks if the Pokémon 'Pikachu' comes from the 'Kanto' region.\n",
+    "\n",
+    "pokemon = \"Pikachu\"\n",
+    "if ...: # replace the ... with your code\n",
+    "    is_from_kanto_msg = pokemon + ' is from the Kanto region'\n",
+    "else:\n",
+    "    is_from_kanto_msg = pokemon + ' is not from the Kanto region'\n",
+    "is_from_kanto_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad2fd566",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246d919a",
+   "metadata": {},
+   "source": [
+    "What is the output of the above cell? If you change the name of the Pokémon from `Pikachu` to `Greninja`, does the output change? What happens if you use a bad name (such as `cs220`, which is not the name of any Pokémon)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8f1ea64",
+   "metadata": {},
+   "source": [
+    "### Task 3.2: Helper functions - `compare_hp`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ed891f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run the following code and observe the output\n",
+    "if project.get_hp('Snorlax') >= project.get_hp('Heracross'):\n",
+    "    print('Snorlax')\n",
+    "else:\n",
+    "    print('Heracross')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72db15f0",
+   "metadata": {},
+   "source": [
+    "In p4, you will regularly have to compare the stats of different Pokémon. \n",
+    "So, let's create a **helper function** here. Helper functions are simple functions that are used by other functions to perform more complicated tasks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65122da0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# the function should return the name of the Pokémon with the higher HP\n",
+    "# if both Pokémon have the same HP, your function should return the name of the first Pokémon\n",
+    "# finish coding this function by removing the '...' and replacing them with valid variable names\n",
+    "def compare_hp(pkmn1, pkmn2): # DO NOT EDIT THIS LINE\n",
+    "    if project.get_hp(...) >= project.get_hp(...):\n",
+    "        return ...\n",
+    "    else:\n",
+    "        return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1e01971",
+   "metadata": {},
+   "source": [
+    "**Question 12:** Use the `compare_hp` function to determine whether `Snorlax` or `Heracross` has the higher HP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3cdd91c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# call your function for the Pokémon Snorlax and Heracross to see which Pokémon has the higher HP stat\n",
+    "# replace the ... below with your code\n",
+    "higher_hp_pkmn = ...\n",
+    "higher_hp_pkmn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79a04f94",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d38d707c",
+   "metadata": {},
+   "source": [
+    "### Task 3.3: Helper functions - `compare_speed`\n",
+    "\n",
+    "Now, you will create another similar helper function.\n",
+    "- this function will return the name of the Pokémon with the higher `Speed` stat.\n",
+    "- if both Pokémon have the *same* `Speed` stat, this function will return `'Draw'`.\n",
+    "\n",
+    "The idea behind creating such helper functions is that if in p4 you need to quickly check which Pokémon has a higher `Speed` stat, you can simply call this function, without writing all this code again.\n",
+    "\n",
+    "**Note:** You will have to copy/paste this definition in your p4 notebook to be able to call it there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eecc1a85",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# the function should return the name of the Pokémon with the higher Speed\n",
+    "# if both Pokémon have the same Speed stat, this function should return 'Draw'\n",
+    "def compare_speed(pkmn1, pkmn2): # DO NOT EDIT THIS LINE\n",
+    "    pass # replace this with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61f703e2",
+   "metadata": {},
+   "source": [
+    "**Question 13.1:** What is the output of `compare_speed(\"Bulbasaur\", \"Charmander\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41272e55",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# execute the next cell without changing anything\n",
+    "test_1_higher_speed = compare_speed(\"Bulbasaur\", \"Charmander\")\n",
+    "test_1_higher_speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a08edaa2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e34b3700",
+   "metadata": {},
+   "source": [
+    "**Question 13.2:** What is the output of `compare_speed(\"Beedrill\", \"Pidgey\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d00d18d2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# execute the next cell without changing anything\n",
+    "test_2_higher_speed = compare_speed(\"Beedrill\", \"Pidgey\")\n",
+    "test_2_higher_speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ea75beb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b79af3a2",
+   "metadata": {},
+   "source": [
+    "**Question 13.3:** What is the output of `compare_speed(\"Fennekin\", \"Sylveon\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4aff656",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# execute the next cell without changing anything\n",
+    "test_3_higher_speed = compare_speed(\"Fennekin\", \"Sylveon\")\n",
+    "test_3_higher_speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a066da4f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9500d94",
+   "metadata": {},
+   "source": [
+    "### Task 3.4 More helper functions\n",
+    "\n",
+    "You will now create the following function - `compare_total_atk_def(pkmn1, pkmn2)`\n",
+    "- this function will return the name of the Pokémon which has the higher total of `Attack` + `Defense` + `Sp. Atk.` + `Sp. Def.`\n",
+    "- if both Pokémon have the *same* total, this function will return `'Draw'`.\n",
+    "\n",
+    "Before you start defining this function, it would be a good idea to create another **helper** function. It will be inefficient to write the same code to find the total attack and defense for both `pkmn1` and `pkmn2`. So, you might find it useful to create another function `get_total_atk_def(pkmn)` to compute this total, and then use this function inside `compare_total_atk_def`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "278afa98",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# this function should return the total attack + defense + sp. atk. + sp. def. stats of the given pkmn\n",
+    "def get_total_atk_def(pkmn):\n",
+    "    pass # replace this with your code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "636b2172",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# the function should return the name of the Pokémon with the higher total attack and defense\n",
+    "# if both Pokémon have the same total, this function should return 'Draw'\n",
+    "# you MUST call the get_total_atk_def function here\n",
+    "def compare_total_atk_def(pkmn1, pkmn2): # DO NOT EDIT THIS LINE\n",
+    "    pass # replace this with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd99d887",
+   "metadata": {},
+   "source": [
+    "If your `get_total_atk_def` function works properly, you should get the following outputs:\n",
+    "1. `get_total_atk_def('Piplup')`: 221\n",
+    "2. `get_total_atk_def('Torchic')`: 220\n",
+    "3. `get_total_atk_def('Rowlet')`: 210"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6163e43e",
+   "metadata": {},
+   "source": [
+    "**Question 14.1:** What is the output of `compare_total_atk_def(\"Arcanine\", \"Carnivine\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0778f9ac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# execute the next cell without changing anything\n",
+    "test_1_total_atk_def = compare_total_atk_def(\"Arcanine\", \"Carnivine\")\n",
+    "test_1_total_atk_def"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03d7d3b1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2e10e57",
+   "metadata": {},
+   "source": [
+    "**Question 14.2:** What is the output of `compare_total_atk_def(\"Lucario\", \"Scizor\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c28b976",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# execute the next cell without changing anything\n",
+    "test_2_total_atk_def = compare_total_atk_def(\"Lucario\", \"Scizor\")\n",
+    "test_2_total_atk_def"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e1f1d42",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4dbc865",
+   "metadata": {},
+   "source": [
+    "**Question 14.3:** What is the output of `compare_total_atk_def(\"Gyarados\", \"Lugia\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecac8455",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# execute the next cell without changing anything\n",
+    "test_3_total_atk_def = compare_total_atk_def(\"Gyarados\", \"Lugia\")\n",
+    "test_3_total_atk_def"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab3f95f5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8a419a2",
+   "metadata": {},
+   "source": [
+    "## Segment 4: Advanced Conditional Statements\n",
+    "\n",
+    "So far, we have only used if/else statements to compare numbers. Let us do something fancier now. As you might have seen in the `pokemon_stats.csv` file, a Pokémon might have one or more types. For instance, `Pikachu` has just one type: `Electric`, whereas `Charizard` has two: `Fire` and `Flying`.\n",
+    "\n",
+    "### Task 4.1: Count how many types a Pokémon has\n",
+    "\n",
+    "Pokémon may have up to two different types associated with them. Let's write a function that returns the number of types a Pokémon has."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "078ed1a8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# this function should return 0 if type1 is 'None', 1 if type1 is not 'None' but type2 is, and 2 if neither type is 'None'\n",
+    "# replace the '...' from the code below to complete the get_num_types function\n",
+    "def get_num_types(pkmn):\n",
+    "    if project.get_type1(pkmn) == 'None':\n",
+    "        return 0\n",
+    "    elif project.get_type2(pkmn) == 'None':\n",
+    "        return ...\n",
+    "    else:\n",
+    "        return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4794cd2a",
+   "metadata": {},
+   "source": [
+    "**Question 15.1:** What is the output of `get_num_types(\"Kubfu\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbc0b43e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# execute the next cell without changing anything\n",
+    "test_1_num_types = get_num_types(\"Kubfu\")\n",
+    "test_1_num_types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48baec5b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4fe3396",
+   "metadata": {},
+   "source": [
+    "**Question 15.2:** What is the output of `get_num_types(\"Geodude\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea69ae04",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# execute the next cell without changing anything\n",
+    "test_2_num_types = get_num_types(\"Geodude\")\n",
+    "test_2_num_types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28033cec",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e0eea78",
+   "metadata": {},
+   "source": [
+    "### Task 4.2: Determine if two Pokémon have a matching type.\n",
+    "Let us create a function that checks if two Pokémon have the same types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ef32dd8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# we have written this function for you - can you figure out what is going on?\n",
+    "# if you want to improve this function, make edits right here; do not redefine this function elsewhere\n",
+    "def same_types(pkmn1, pkmn2):    \n",
+    "    if project.get_type1(pkmn1) == project.get_type1(pkmn2):\n",
+    "        if project.get_type2(pkmn1) == project.get_type2(pkmn2):\n",
+    "            return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a77d2b19",
+   "metadata": {},
+   "source": [
+    "### Task 4.3: Debug your function\n",
+    "\n",
+    "There is a semantic error in the function definition above! Can you spot it? If not, see the output of the cell below to understand what the error is.\n",
+    "\n",
+    "Once identified, you **must** modify the function definition above to fix the bug.\n",
+    "\n",
+    "**Do not create a new copy of / duplicate the `same_types` function definition.** You should go back to the cell where the function is already defined, make edits by adding several lines or possibly rewriting the code already there, and then re-run the function definition cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "677a9866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# look at the stats below - what does `same_types` do when these Pokémon are your inputs?\n",
+    "# what should it do instead?\n",
+    "project.print_stats(\"Dragonite\")\n",
+    "print()\n",
+    "project.print_stats(\"Noivern\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1224adf9",
+   "metadata": {},
+   "source": [
+    "**Question 16:** Do `Dragonite` and `Noivern` have the same types?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9f48a96",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# go back and edit the definition of the `same_types` function so that the output for this cell is correct\n",
+    "are_same_types = same_types(\"Dragonite\", \"Noivern\")\n",
+    "are_same_types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01337282",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5fff68b",
+   "metadata": {},
+   "source": [
+    "## Segment 5: Modify Previous functions\n",
+    "\n",
+    "### Task 5.1: Use Boolean operators to refactor `same_types`\n",
+    "\n",
+    "The code you wrote above may be somewhat messy and hard to read. We will now **refactor** your function definition above - which is just a fancy way of saying that we will make the code a little easier to read, by making efficient use of Boolean and logical operators.\n",
+    "\n",
+    "We will provide you with a code snippet, but you must fill in the rest yourself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "407c3253",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# this function does the same as `same_types` but will hopefully be a little easier to read\n",
+    "# replace the '...' from the below code with appropriate operators/Boolean expressions:\n",
+    "def same_types_refactored(pkmn1, pkmn2):\n",
+    "    pkmn1_type1 = project.get_type1(pkmn1)\n",
+    "    pkmn1_type2 = project.get_type2(pkmn1)\n",
+    "    pkmn2_type1 = project.get_type1(pkmn2)\n",
+    "    pkmn2_type2 = project.get_type2(pkmn2)\n",
+    "    \n",
+    "    if pkmn1_type1 == pkmn2_type1 ... pkmn1_type2 == pkmn2_type2: # replace ... with appropriate logical operator\n",
+    "        return True\n",
+    "    elif ...: # replace ... with an appropriate Boolean expression\n",
+    "        return True        \n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f61f470a",
+   "metadata": {},
+   "source": [
+    "**Question 17:** What is the output of `same_types_refactored(\"Dewgong\", \"Spheal\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52df8611",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# execute the next cell without changing anything (for now)\n",
+    "are_same_types_refactored = same_types_refactored(\"Dewgong\", \"Spheal\")\n",
+    "are_same_types_refactored"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cd247c9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "761b79fa",
+   "metadata": {},
+   "source": [
+    "### Task 5.1.1: Good coding practices\n",
+    "\n",
+    "It is generally considered a bad coding practice to define two different functions that do the same or very similar things. We have currently defined two functions, `same_types` and `same_types_refactored`, which do the same thing. In such circumstances, we should delete one of these functions, and replace all calls to the deleted function with calls to the remaining function.\n",
+    "\n",
+    "Here, you **must** delete your definition of `same_types`. Follow the steps below:\n",
+    "1. Delete the definition of `same_types` from above.\n",
+    "2. Cut and paste your definition of `same_types_refactored` in place of the (now deleted) definition of `same_types`.\n",
+    "3. The definition of `same_types_refactored` **must** appear *before* any calls to either `same_types_refactored` or `same_types`.\n",
+    "4. Replace the name `same_types_refactored` with `same_types`.\n",
+    "5. Replace all calls to the function `same_types_refactored` with `same_types`.\n",
+    "\n",
+    "The result should be that there is **one** definition of `same_types` and **no** definitions of `same_types_refactored`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d899f75e",
+   "metadata": {},
+   "source": [
+    "### Task 5.2: Write the function `same_region`\n",
+    "Write a new function that checks if two Pokémon come from the **same region**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4993fa9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# this function should return True if pkmn1 and pkmn2 both come from the same region, and False otherwise\n",
+    "def same_region(pkmn1, pkmn2): # DO NOT EDIT THIS LINE\n",
+    "    pass # replace with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "827eacf1",
+   "metadata": {},
+   "source": [
+    "**Question 18:** What is the output of `same_region(\"Dialga\", \"Palkia\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be902be2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "from_same_region = ...\n",
+    "from_same_region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af63fa5d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58fe34f7",
+   "metadata": {},
+   "source": [
+    "### Task 5.3: Write the function `same_types_and_region`\n",
+    "Write a new function that checks if two Pokémon are of the same type **and** come from the same region. Make sure to use the functions we created in Task 5.1 and Task 5.2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80cd4b6d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define the function same_types_and_region(pkmn1, pkmn2) here\n",
+    "# this function should return True if pkmn1 and pkmn2 are from the same region and have the same type\n",
+    "# this function should return False otherwise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9c906ec",
+   "metadata": {},
+   "source": [
+    "You will have to answer the next few questions by calling the `same_types_and_region` function defined above. If you do not pass any of the tests below, debug your code as you did in Task 4.3 above - use `print_stats` to print the stats of the Pokémon which cause your code to fail, and go through your code line by line to verify that your code is doing what it is supposed to be doing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f661bcbb",
+   "metadata": {},
+   "source": [
+    "**Question 19.1:** What is the output of `same_types_and_region(\"Bulbasaur\", \"Ivysaur\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbe3ba8b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "same_region_and_type_1 = ...\n",
+    "same_region_and_type_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e497037",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6a17aa8",
+   "metadata": {},
+   "source": [
+    "**Question 19.2:** What is the output of `same_types_and_region(\"Zangoose\", \"Rattata\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c4d4f49",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "same_region_and_type_2 = ...\n",
+    "same_region_and_type_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57718912",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce3a9375",
+   "metadata": {},
+   "source": [
+    "**Question 19.3:** What is the output of `same_types_and_region(\"Espeon\", \"Umbreon\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7380587",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "same_region_and_type_3 = ...\n",
+    "same_region_and_type_3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac1df31c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "020ff981",
+   "metadata": {},
+   "source": [
+    "**Question 19.4:** What is the output of `same_types_and_region(\"Gible\", \"Golbat\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1728ea89",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "same_region_and_type_4 = ...\n",
+    "same_region_and_type_4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5656ba26",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19-4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d271f865",
+   "metadata": {},
+   "source": [
+    "**Question 19.5:** What is the output of `same_types_and_region(\"Rhydon\", \"Golem\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76bfc16d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "same_region_and_type_5 = ...\n",
+    "same_region_and_type_5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eaa96924",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19-5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba0eee1c",
+   "metadata": {},
+   "source": [
+    "### Task 5.4: Write a function that determines the stronger type\n",
+    "\n",
+    "In p4, you will have to compare the strengths of different types against each other. The `get_type_effectiveness` function in `project.py` will be useful for us here. Given two types `type1` and `type2`, we can use that function to find the effectiveness of `type1` against `type2`, and also, the effectiveness of `type2` against `type1`. So, we can compare the effectiveness of the two types against each other, to determine which is stronger. \n",
+    "\n",
+    "- Write a function that determines the stronger type out of two."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "007e4f0b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... with your code to finish this function definition\n",
+    "def stronger_type(type1, type2):\n",
+    "    type_1_effectiveness = project.get_type_effectiveness(..., ...)\n",
+    "    type_2_effectiveness = ...\n",
+    "    if ...:\n",
+    "        return type1 + \" is stronger than \" + type2\n",
+    "    elif ...:\n",
+    "        return type2 + \" is stronger than \" + type1\n",
+    "    else:\n",
+    "        return type1 + \" and \" + type2 + \" are equally strong\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e40ed1f",
+   "metadata": {},
+   "source": [
+    "**Question 20:** What is the output of `stronger_type(\"Fire\", \"Grass\")`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "532816c3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# replace the ... below with your code\n",
+    "stronger_type = ...\n",
+    "stronger_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c939d78",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56304cea",
+   "metadata": {},
+   "source": [
+    "## Great work! You are now ready to start [p4](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/p4) and become a master Pokémon trainer."
+   ]
+  }
+ ],
+ "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\", squirtle_region)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q10\", water_rock_effectiveness)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q11\", is_from_kanto_msg)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q12\", higher_hp_pkmn)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13-1": {
+     "name": "q13-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q13-1\", test_1_higher_speed)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13-2": {
+     "name": "q13-2",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q13-2\", test_2_higher_speed)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13-3": {
+     "name": "q13-3",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q13-3\", test_3_higher_speed)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14-1": {
+     "name": "q14-1",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q14-1\", test_1_total_atk_def)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14-2": {
+     "name": "q14-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q14-2\", test_2_total_atk_def)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14-3": {
+     "name": "q14-3",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q14-3\", test_3_total_atk_def)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15-1": {
+     "name": "q15-1",
+     "points": 3,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q15-1\", test_1_num_types)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15-2": {
+     "name": "q15-2",
+     "points": 2,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q15-2\", test_2_num_types)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q16\", are_same_types)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q17\", are_same_types_refactored)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q18\", from_same_region)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19-1": {
+     "name": "q19-1",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q19-1\", same_region_and_type_1)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19-2": {
+     "name": "q19-2",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q19-2\", same_region_and_type_2)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19-3": {
+     "name": "q19-3",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q19-3\", same_region_and_type_3)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19-4": {
+     "name": "q19-4",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q19-4\", same_region_and_type_4)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19-5": {
+     "name": "q19-5",
+     "points": 1,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q19-5\", same_region_and_type_5)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q2\", vulpix_type1)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q20\", stronger_type)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q3\", heatran_type2)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q4\", mewtwo_hp)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q5\", rayquaza_attack)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q6\", registeel_defense)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q7\", mudkip_sp_atk)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q8\", kyogre_sp_def)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check(\"q9\", slowpoke_speed)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f08154012ddadd8e950e6e9e035c7a7b32c136e7647e9b7c77e02eb723a8bedb"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p4/practice.ipynb b/lab-p4/practice.ipynb
index 832b5b7..eb0b47f 100644
--- a/lab-p4/practice.ipynb
+++ b/lab-p4/practice.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4d5a95a0",
+   "metadata": {},
+   "source": [
+    "# <font color = red>\"This is an out of date page, don't use it for the current semester\" <font>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1615,7 +1623,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.12 ('base')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/p4/.ipynb_checkpoints/p4-checkpoint.ipynb b/p4/.ipynb_checkpoints/p4-checkpoint.ipynb
new file mode 100644
index 0000000..25eb7d1
--- /dev/null
+++ b/p4/.ipynb_checkpoints/p4-checkpoint.ipynb
@@ -0,0 +1,1719 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "567b8fba",
+   "metadata": {},
+   "source": [
+    "# <font color = red>\"This is an out of date page, don't use it for the current semester\" <font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f4971a4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"p4.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73f67018",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74449ba4",
+   "metadata": {},
+   "source": [
+    "## Project 4: Pokémon Battle Simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "685b1664",
+   "metadata": {},
+   "source": [
+    "### Learning Objectives:\n",
+    "\n",
+    "In this project, you will demonstrate how to\n",
+    "\n",
+    "* Use conditional statements to implement decisions,\n",
+    "* Write functions using parameters, return values, and conditional logic,\n",
+    "* Use good coding practices as outlined in lab-p4.\n",
+    "\n",
+    "**Please go through [lab-p4](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p4) before working on this project.** The lab introduces some useful techniques related to this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9048e74",
+   "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."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97e2319d",
+   "metadata": {},
+   "source": [
+    "## Project Description:\n",
+    "\n",
+    "For this project, you'll be using the data from `pokemon_stats.csv` and `type_effectiveness_stats.csv` to simulate Pokémon battles and to check the compatibility for friendships between different Pokémon. This data was gathered by the Python program `gen_csv.ipynb` from the website https://www.pokemondb.net/.\n",
+    "\n",
+    "* To start, download `project.py`, `test.py`, `type_effectiveness_stats.csv`, and `pokemon_stats.csv`.\n",
+    "* You'll do all your work on this notebook, and turn it into Gradescope just as you did for the previous projects.\n",
+    "\n",
+    "We won't explain how to use the project module here (the code in the `project.py` file), or the dataset that you will be working with. The lab this week is designed to teach you how it works. So, before starting p4, take a look at lab-p4.\n",
+    "\n",
+    "In this project, you will have to write several functions and keep adding more details to them according to the instructions. When you are adding more things to your functions, we want you to follow the **Good Coding Style for Functions** described in [lab-p4](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f22-projects/-/tree/main/lab-p4). Therefore, you should only keep the latest version of your functions in your notebook file. For example, in this project, you will keep improving the functions you write, and add more functionality to them as the project progresses. However, you should not define a new function each time you do so. Instead, you should keep only one copy of each of the six functions that you will need to define in this project (`damage`, `type_bonus`, `effective_damage`, `num_hits`, `battle`, and `friendship_score`). You can do this by replacing your old function definition with the new one after you have confirmed that the new one works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87f85ab5",
+   "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": "c66e0e14",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "\n",
+    "# please place all your import statements in this cell if you need to import \n",
+    "# any more modules for this project\n"
+   ]
+  },
+  {
+   "attachments": {
+    "pokemon.jpg": {
+     "image/jpeg": 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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAf/9k="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "72d653db",
+   "metadata": {},
+   "source": [
+    "In the first stage of this project, we will be simulating Pokémon battles. Before we proceed any further, let us take a look at the Pokémon we will be dealing with in this project (let us know what your favorite Pokémon is in a comment):\n",
+    "\n",
+    "![pokemon.jpg](attachment:pokemon.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3373235f",
+   "metadata": {},
+   "source": [
+    "## Rules for Pokémon battles:\n",
+    "\n",
+    "Now, here are the *rules* governing Pokémon battles:\n",
+    "\n",
+    "1. A Pokémon battle takes place between two Pokémon\n",
+    "2. The two Pokémon take turns attacking each other.\n",
+    "3. The Pokémon with the higher **Speed** stat attacks first.\n",
+    "4. On each turn, the attacking Pokémon can choose between two modes of attack - **Physical** or **Special**.\n",
+    "5. In addition to the attack mode, each Pokémon can choose the **type** of its attack.\n",
+    "6. Based on the move chosen by the attacking Pokémon, the defending Pokémon receives damage to its **HP**.\n",
+    "7. If a Pokémon's **HP** drops to (or below) 0, it faints and therefore loses the battle.\n",
+    "8. However, if one Pokémon is much stronger than the other, then the weaker Pokémon will **run away** instead of fighting."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfb15f57",
+   "metadata": {},
+   "source": [
+    "Throughout this project, we will break this down into smaller parts and slowly build up to the `battle()` function.\n",
+    "\n",
+    "The first thing we need to do is calculate the damage caused by one Pokémon's attack on another Pokémon. To accomplish this, we need to create the function `damage(attacker, defender)`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e372943",
+   "metadata": {},
+   "source": [
+    "### Function 1: `damage(attack, defender)`\n",
+    "\n",
+    "The `attacker` can choose between two attack modes - **Physical** or **Special**. The damage caused by the attacker's **Physical** move is `10 * Attack stat of Attacker / Defense stat of Defender`, and the damage caused by the attacker's **Special** move is `10 * Sp. Atk. stat of Attacker / Sp. Def. stat of Defender`.\n",
+    "\n",
+    "**If the attacker wants to win, it should always choose the move which will do more damage.** So, that is what we want our function `damage` to do. We want this function to find out which mode of attack the attacker would choose, and return the damage that the attacker would do to the defender.\n",
+    "\n",
+    "Use the following code snippet and fill in the details to complete the `damage` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e25aad46",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def damage(attacker, defender):\n",
+    "    # TODO: replace the ... in this function with your code\n",
+    "    physical_damage = 10 * project.get_attack(attacker) / project.get_defense(defender)\n",
+    "    special_damage = ...\n",
+    "    if ...:\n",
+    "        return physical_damage\n",
+    "    else:\n",
+    "        ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a85162d8",
+   "metadata": {},
+   "source": [
+    "Now, let's find out if this function works. Use `damage()` to answer the next two questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e55f571",
+   "metadata": {},
+   "source": [
+    "**Question 1:** How much damage does `Zacian` do to `Mewtwo`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "941c2328",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "damage_zacian_mewtwo = ...\n",
+    "damage_zacian_mewtwo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0015d9c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d82754af",
+   "metadata": {},
+   "source": [
+    "**Question 2:** How much damage does `Slowbro` do to `Slowpoke`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70275003",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "damage_slowbro_slowpoke = ...\n",
+    "damage_slowbro_slowpoke"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f47ea6e7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04931e74",
+   "metadata": {},
+   "source": [
+    "In addition to choosing the attack **mode** (i.e. **Physical** or **Special**), the attacker can also (sometimes) choose the **type** of attack. Before we figure out what type the attacker should choose, we first need to find out the *effect* of the attack on the defender. Each attack type offers a **type bonus** to the attack damage that we calculated with the `damage` function.\n",
+    "\n",
+    "If the attacker chooses an attack of type `attack_type` against a defender with only one type, `type1` (i.e. its `type2` is `None`), then the **type bonus** of this attack is `get_type_effectiveness(attack_type, type1)`. If the defender has two types `type1` and `type2`, then the **type bonus** of this attack is `get_type_effectiveness(attack_type, type1) * get_type_effectiveness(attack_type, type2)`.\n",
+    "\n",
+    "For example, let the `attack_type` be `Fire` and the defender be the Pokémon `Squirtle`. `Squirtle` has only one type, `Water` (with its `type2` being `None`). In this case, we see that"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e0de658",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the effectiveness of Fire against Water is...\n",
+    "project.get_type_effectiveness(\"Fire\", \"Water\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eca3588e",
+   "metadata": {},
+   "source": [
+    "Therefore, the type bonus of a `Fire` type attack on `Squirtle` is `0.5`. On the other hand, consider a `Fire` type attack on the Pokémon `Bulbasaur`. `Bulbasaur` has 2 types, `Grass` and `Poison`. In this case, we see that"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9a665df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the effectiveness of Fire against Grass is...\n",
+    "project.get_type_effectiveness(\"Fire\", \"Grass\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e12ec94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the effectiveness of Fire against Poison is...\n",
+    "project.get_type_effectiveness(\"Fire\", \"Poison\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "257dd3e6",
+   "metadata": {},
+   "source": [
+    "Therefore, the type bonus of a `Fire` type attack on `Bulbasaur` is the product of these two numbers `2.0 * 1.0 = 2.0`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4aa96fa1",
+   "metadata": {},
+   "source": [
+    "### Function 2: `type_bonus(attack_type, defender)`\n",
+    "We are now ready to write the definition of the `type_bonus` function, which will calculate the type bonus of an `attack_type` against a `defender`. We have provided a code snippet for you to work with. You may rewrite the entire function from scratch if you want to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ee20b33",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def type_bonus(attack_type, defender):\n",
+    "    # TODO: store the `type1` and `type2` of the `defender` in variables \n",
+    "    #       `defender_type1` and `defender_type2`\n",
+    "    # TODO: replace the ... in this function with your code\n",
+    "    bonus = project.get_type_effectiveness(attack_type, defender_type1)\n",
+    "    \n",
+    "    if ...:\n",
+    "        return bonus\n",
+    "    else:\n",
+    "        bonus = ...\n",
+    "        return bonus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb759927",
+   "metadata": {},
+   "source": [
+    "Use this function to answer the next two questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca4f4017",
+   "metadata": {},
+   "source": [
+    "**Question 3:** How effective is `Water` type against `Charmander`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f03d61ee",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "bonus_water_charmander = ...\n",
+    "bonus_water_charmander"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cd8d706",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4c9676d",
+   "metadata": {},
+   "source": [
+    "**Question 4:** How effective is `Poison` type against `Roserade`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f1c29d4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "bonus_poison_roserade = ...\n",
+    "bonus_poison_roserade"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee313016",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f18a6b9c",
+   "metadata": {},
+   "source": [
+    "When an `attacker` chooses an attack of type `attack_type` against a `defender`, the damage done is `type_bonus(attack_type, defender) * damage(attacker, defender)`.\n",
+    "\n",
+    "An attacker can choose between any of its types for its attack type. So, if an attacker has two types, it can choose either type 1 or type 2 as its attack type. However, if it has only one type (i.e. its `type2` is `None`), it has no choice but to choose type 1 as its attack type. For example, a Pokémon like `Weedle` which has two types (`Bug` and `Poison`) can choose to make its attack either `Bug` type or `Poison` type. On the other hand, a Pokémon like `Magikarp` which has only one type (`Water`) can only make its attack a `Water` type attack.\n",
+    "\n",
+    "While a Pokémon with only one type doesn't have a choice, **a Pokémon with two types can choose its attack between its two types**. If the attacker wants to win, it should always choose the type which will do more damage.\n",
+    "\n",
+    "Let us consider the case when an **attacker has only one type**. (i.e. `type2` is `None`). To illustrate this, we take `Magikarp` as the attacker and `Charizard` as the defender. Let us first ensure that `Magikarp` has only 1 type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "341840ac",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# type1 of Magikarp is...\n",
+    "project.get_type1(\"Magikarp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a200ae6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# and type2 of Magikarp is...\n",
+    "project.get_type2(\"Magikarp\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "117cc103",
+   "metadata": {},
+   "source": [
+    "In this case, we simply take the `type_bonus` of the first type against `Charizard` (the defender)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d434163",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# so the bonus that Magikarp gets against Charizard is...\n",
+    "bonus = type_bonus(project.get_type1(\"Magikarp\"), \"Charizard\")\n",
+    "bonus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32c4ccef",
+   "metadata": {},
+   "source": [
+    "`bonus` should contain the value `2.0`. To calculate the effective damage that Magikarp does to Charizard, we take `damage(\"Magikarp\", \"Charizard\") * bonus`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3171385",
+   "metadata": {},
+   "source": [
+    "We will now consider the case where an **attacker has two types**.\n",
+    "\n",
+    "To illustrate this, we take `Weedle` as the `attacker` and `Charizard` as the `defender`. If your `type_bonus` function works correctly the values obtained by measuring the type bonus of `Weedle` against `Charizard` are as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3d87e1a",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# the type bonus of type1(Bug) of Weedle against Charizard is...\n",
+    "type_bonus(project.get_type1(\"Weedle\"), \"Charizard\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acbba866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# and the type bonus of type2(Poison) of Weedle against Charizard is...\n",
+    "type_bonus(project.get_type2(\"Weedle\"), \"Charizard\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67107117",
+   "metadata": {},
+   "source": [
+    "Clearly, `Weedle`'s second type (`Poison`) causes more damage to `Charizard` than its first type (`Bug`). So, **`Weedle` would choose its `Poison` type attack instead of its `Bug` type attack against `Charizard`.**\n",
+    "\n",
+    "Therefore, the effective `bonus` is `max(0.25, 1.0) = 1.0`. So, the effective damage that `Weedle` does to `Charizard` is `damage(\"Weedle\", \"Charizard\") * 1.0`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a292a30",
+   "metadata": {},
+   "source": [
+    "### Function 3: `effective_damage(attacker, defender)`\n",
+    "\n",
+    "We now write a function `effective_damage` to compute the actual damage that an `attacker` would do to the `defender`, taking into account, both the **attack mode** and **attack type**.\n",
+    "\n",
+    "The `effective_damage` function definition **must** invoke the `get_num_types` function you wrote during lab. Create a new cell in your Jupyter notebook above the definition of `effective_damage` and copy/paste the definition of `get_num_types` there. **We'll manually deduct points** if you don't invoke `get_num_types`.\n",
+    "\n",
+    "Start with the code snippet provided below. Use the `effective_damage` function to answer the next three questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed4607cf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def effective_damage(attacker, defender):\n",
+    "    pass # TODO: replace with your code\n",
+    "    #TODO: check if the attacker has two types; you must invoke the relevant \n",
+    "    #      function you defined in lab-p4\n",
+    "    #TODO: compute the bonus of the attacker's type(s) against the defender\n",
+    "    #TODO: find the attack_type with the higher bonus\n",
+    "    #TODO: compute the damage caused by attack, considering the higher bonus, and return it\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c20e2b7",
+   "metadata": {},
+   "source": [
+    "**Question 5:** How much damage does `Garchomp` do to `Gligar`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "175549eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "eff_damage_garchomp_gligar = ...\n",
+    "eff_damage_garchomp_gligar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09ed2d7e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b41f553",
+   "metadata": {},
+   "source": [
+    "**Question 6:** How much damage does `Sceptile` do to `Umbreon`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e2d4b4e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "eff_damage_sceptile_umbreon = ...\n",
+    "eff_damage_sceptile_umbreon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "258f02ce",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "855d1002",
+   "metadata": {},
+   "source": [
+    "**Question 7:** How much damage does `Infernape` do to `Starly`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98a29c7a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "eff_damage_infernape_starly = ...\n",
+    "eff_damage_infernape_starly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "889a5d3d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15dbeb3f",
+   "metadata": {},
+   "source": [
+    "### Function 4: `num_hits(attacker, defender)`\n",
+    "\n",
+    "Now that we have a way of calculating the damage done by the Pokémon during battle, we have to calculate **how many hits** each Pokémon can take before fainting.\n",
+    "\n",
+    "The number of hits a Pokémon can take is calculated by taking its **HP** and dividing it by the attacking Pokémon's **effective damage**.\n",
+    "\n",
+    "If the defending pokemon has `30 HP` and the attacking pokemon does `20` effective damage each turn, it will take `2` turns before the defender faints instead of `30 / 20 = 1.5`. You might want to use the method `math.ceil` here. First import the module `math` (remember to add the `import math` call at the **top of your notebook** in the cell where you have been asked to place all `import` statements) and then look up the documentation of `math.ceil` to see how you could use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0700d60",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def num_hits(attacker, defender):\n",
+    "    pass # TODO: replace with your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63e41c00",
+   "metadata": {},
+   "source": [
+    "Use `num_hits` to answer the next two questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3e89510",
+   "metadata": {},
+   "source": [
+    "**Question 8:** How many hits can the *defending* Pokémon `Mewtwo` take from `Bellsprout`(*attacker*)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b12f76ac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "hits_bellsprout_mewtwo = ...\n",
+    "hits_bellsprout_mewtwo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcdefbda",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8eb0bbd2",
+   "metadata": {},
+   "source": [
+    "**Question 9:** How many hits can the *defending* Pokémon `Torterra` take from `Metapod`(*attacker*)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f92df847",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "hits_metapod_torterra = ...\n",
+    "hits_metapod_torterra"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05739588",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3e082a5",
+   "metadata": {},
+   "source": [
+    "## Function 5: `battle(pkmn1, pkmn2)`\n",
+    "\n",
+    "With the functions we have created so far, we can now finally start creating our battle simulator.\n",
+    "\n",
+    "This function should take in two Pokémon `pkmn1`, and `pkmn2` as its parameters, and it should output the name of the Pokémon which wins the battle.\n",
+    "\n",
+    "However, it might still be a little overwhelming to code all the rules in one go. So, let us break it up into several steps, and implement the function over the next several questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6df0ef2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def battle(pkmn1, pkmn2):\n",
+    "    pass # TODO: replace with your code\n",
+    "    # TODO: let us ignore the rules that have to do with Speed \n",
+    "    #       and Pokémon running away for now\n",
+    "    # TODO: implement code to check whether pkmn1 or pkmn2 can take more \n",
+    "    #       hits from the other before fainting\n",
+    "    # TODO: the Pokémon which can take more hits before fainting should be the winner\n",
+    "    # TODO: if the two Pokémon can take the same number of hits from \n",
+    "    #       the other, your output should be 'Draw'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46e268ca",
+   "metadata": {},
+   "source": [
+    "Use the `battle()` function to answer the next two questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "353e529d",
+   "metadata": {},
+   "source": [
+    "**Question 10**: What is the output of `battle('Bulbasaur', 'Flareon')`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "563a71eb",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "battle_bulbasaur_flareon = ...\n",
+    "battle_bulbasaur_flareon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "deade753",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38afdd35",
+   "metadata": {},
+   "source": [
+    "**Question 11**: What is the output of `battle('Blastoise', 'Charizard')`??"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acb7b968",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "battle_blastoise_charizard = ...\n",
+    "battle_blastoise_charizard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e002f7aa",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b5423b5",
+   "metadata": {},
+   "source": [
+    "The function `battle` seems to be working well so far, but it does not quite follow all the rules that we laid out at the beginning. The function currently returns `\"Draw\"` if both Pokémon can take the same number of hits from each other. However, when we look at the rules from above, we notice that the Pokémon with **higher speed attacks first**. This means that even if both Pokémon go down in the same number of hits, the Pokémon with the higher **speed** stat will attack first, and will therefore land its last hit before the other Pokémon can hit back.\n",
+    "\n",
+    "In other words, if both Pokémon faint within the same number of moves, the Pokémon with the higher **speed** stat should win the battle. Go back and modify `battle` so that if both Pokémon faint in the same number of moves, the Pokémon with the higher **speed** wins. If they both have the same **speed**, then the battle should be a `'Draw'`.\n",
+    "\n",
+    "**Warning:** Do **not** redefine `battle`. You may make a *copy* of the function as it is when you start working on updating its definition, but the notebook you turn in should only have *one* definition of `battle`. So, you should **delete** any older versions of the function after your new code demonstrably works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6766101a",
+   "metadata": {},
+   "source": [
+    "**Question 12**: What is the output of `battle('Pidgey', 'Starly')`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0ac6cb2",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "battle_pidgey_starly = ...\n",
+    "battle_pidgey_starly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adff787d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be90a4db",
+   "metadata": {},
+   "source": [
+    "**Question 13**: What is the output of `battle('Mudkip', 'Gulpin')`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "235500c4",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "battle_mudkip_gulpin = ...\n",
+    "battle_mudkip_gulpin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8787c57",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbdc7400",
+   "metadata": {},
+   "source": [
+    "One last rule we need to implement is the **runaway** feature. For example, consider a battle between `Pikachu` and `Glaceon`. `Glaceon` can take `13` hits from `Pikachu`, but `Pikachu` can only take `2` hits from `Glaceon`. Because of this massive difference, `Pikachu` should choose to **run away** from this battle rather than attempt to fight.\n",
+    "\n",
+    "Modify `battle` so that if one Pokémon can take **at least** 10 more hits than the other, the weaker one runs away. The function should return `\"<pkmn_name> ran away\"`. Make sure the function says the Pokémon that can take fewer hits ran away.\n",
+    "\n",
+    "**Hint:** Even though this is the *last* rule to implement, it is the *first* thing that the battle function should check. Also, here's another reminder to *not* redefine `battle`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "000cba63",
+   "metadata": {},
+   "source": [
+    "**Question 14**: What is the output of `battle('Rayquaza', 'Pikachu')`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51ad95d6",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "battle_rayquaza_pikachu = ...\n",
+    "battle_rayquaza_pikachu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8e754ff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3359a6fc",
+   "metadata": {},
+   "source": [
+    "**Question 15**: What is the output of `battle('Lucario', 'Jigglypuff')`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43d3a142",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "battle_lucario_jigglypuff = ...\n",
+    "battle_lucario_jigglypuff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "049101e3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5bed4af",
+   "metadata": {},
+   "source": [
+    "**Question 16**: What is the output of `battle('Mew', 'Celebi')`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "685fcdc2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "battle_mew_celebi = ...\n",
+    "battle_mew_celebi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "341b38c5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71004a50",
+   "metadata": {},
+   "source": [
+    "## Function 6: `friendship_score(pkmn1, pkmn2)`\n",
+    "\n",
+    "Pokémon aren't always violent. They are at most times quite friendly. However, some Pokémon are more friendly with some than they are with others. Trainers need to know which Pokémon get along well and which do not, to avoid unnecessary conflict. Thankfully for trainers, there is an almost scientific way to determine how well two different Pokémon can get along with each other.\n",
+    "\n",
+    "Given two Pokémon `pkmn1` and `pkmn2`, we can compute the **friendship score** between them. A high friendship score (5) means the two Pokémon will get along really well, while a low friendship score (0) means they won't.\n",
+    "\n",
+    "We can check whether a pair of Pokémon has a high friendship score based on the below rules:\n",
+    "\n",
+    "1. Pokémon from the **same region** gain a friendship point.\n",
+    "\n",
+    "2. Pokémon gain a  friendship point if their **difference** in **stat total** is **at most** 20 points. The **stat total** of a Pokémon is the sum of its Attack, Defense, HP, Sp. Atk., Sp. Def., and Speed stats. \n",
+    "   \n",
+    "3. Pokémon gain a friendship point if they have the **same `type1`**.\n",
+    "    \n",
+    "4. Pokémon gain a friendship point if they have the **same `type2`**, provided that this common `type2` is **not** `None`.\n",
+    "    \n",
+    "5. If a Pokémon's `type1` is the same as another Pokémon's `type2` (or vice versa), they do **not** gain any friendship points for it. They only gain points if the **corresponding** types are the same (and not `None`).\n",
+    "    \n",
+    "6. Additionally, if the two Pokémon share **both** types in common (and their `type2` is **not** `None`), they get **another** point for synergy. For example, if two Pokémon have both their corresponding types in common they will get a total of `3` points (2 for the common types and 1 for synergy).\n",
+    "\n",
+    "\n",
+    "Define the function `friendship_score` that takes in two Pokémon as its arguments and returns their friendship score.\n",
+    "\n",
+    "**Hint:** You might also want to define another *helper* function to compute the **stat total** of a Pokémon. You may also *reuse* other helper functions you wrote in lab-p4 (remember to copy/paste them into this notebook before you try to use them)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d7bb58c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# define friendship_score() here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5beacc6b",
+   "metadata": {},
+   "source": [
+    "**Question 17**: What is the output of `friendship_score('Pikachu', 'Squirtle')`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe9713a6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "friendship_pikachu_squirtle = ...\n",
+    "friendship_pikachu_squirtle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48e0b1fa",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8c37108",
+   "metadata": {},
+   "source": [
+    "**Question 18**: What is the output of `friendship_score('Growlithe', 'Umbreon')`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8119a113",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "friendship_growlithe_umbreon = ...\n",
+    "friendship_growlithe_umbreon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71f789e0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4327bfbf",
+   "metadata": {},
+   "source": [
+    "**Question 19**: What is the output of `friendship_score('Nidoking', 'Nidoqueen')`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb058d56",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "friendship_nidoking_nidoqueen = ...\n",
+    "friendship_nidoking_nidoqueen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df78fa75",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ba77667",
+   "metadata": {},
+   "source": [
+    "**Question 20**: What is the output of `friendship_score('Snivy', 'Snivy')`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "920569cd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "friendship_snivy_snivy = ...\n",
+    "friendship_snivy_snivy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d09f1c82",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f5f6fa1",
+   "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": "49e522d2",
+   "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": "852f8933",
+   "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' , damage_zacian_mewtwo)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q10' , battle_bulbasaur_flareon)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q11' , battle_blastoise_charizard)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q12' , battle_pidgey_starly)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q13' , battle_mudkip_gulpin)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q14' , battle_rayquaza_pikachu)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q15' , battle_lucario_jigglypuff)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q16' , battle_mew_celebi)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q17' , friendship_pikachu_squirtle)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q18' , friendship_growlithe_umbreon)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q19' , friendship_nidoking_nidoqueen)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q2' , damage_slowbro_slowpoke)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q20' , friendship_snivy_snivy)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q3' , bonus_water_charmander)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q4' , bonus_poison_roserade)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q5' , eff_damage_garchomp_gligar)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q6' , eff_damage_sceptile_umbreon)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q7' , eff_damage_infernape_starly)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q8' , hits_bellsprout_mewtwo)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> test.check('q9' , hits_metapod_torterra)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f08154012ddadd8e950e6e9e035c7a7b32c136e7647e9b7c77e02eb723a8bedb"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p4/p4.ipynb b/p4/p4.ipynb
index 4adc24e..25eb7d1 100644
--- a/p4/p4.ipynb
+++ b/p4/p4.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "567b8fba",
+   "metadata": {},
+   "source": [
+    "# <font color = red>\"This is an out of date page, don't use it for the current semester\" <font>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-- 
GitLab