FinalProject_group156.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scientifically Differentiating Legendary Pokémon from the Rest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Group Number:** 156\n",
    "\n",
    "**Team Name:** Gotta Catch 'Em All\n",
    "\n",
    "**Team Members:**\n",
    "\n",
    "* Ryan Keng (XXXXXXXXX)\n",
    "\n",
    "* Steven Khai (XXXXXXXXX)\n",
    "\n",
    "* Bradley Tran (XXXXXXXXX) \n",
    "\n",
    "* Elliot Yoon (XXXXXXXXX)\n",
    "\n",
    "* Adam Yu (XXXXXXXXX)\n",
    "\n",
    "* Ricky Zhao (XXXXXXXXX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "Our goal was to summarize the aspects that differentiate legendary Pokemon from the standard fare. We compared physical attributes such as size, height, and typing, as well as base combat stats and usage in the competitive scene. We compared the distribution for these aspects in legendary Pokemon versus all Pokemon and ascertained whether or not legendary Pokemon followed the same trends as the rest of the Pokemon. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question\n",
    "\n",
    "Legendary Pokémon are some of the most revered Pokémon in the Pokémon universe. But what sets them apart from regular Pokémon? Is it their stats? Their types? Do players use legendary Pokémon more over other Pokémon due to these attributes? According to Bulbapedia, one of the most extensive Pokémon encyclopedias, the only thing that distinguishes legendaries from non-legendaries is a naming convention. If Nintendo decides a certain Pokémon should be legendary, then it is. However we believe there has to be more to it than that. For our project, we will examine various statistics regarding Pokémon and see if we can scientifically deduce what sets legendary Pokémon apart from the rest. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background and Prior Work\n",
    "\n",
    "Over two decades old and a household icon worldwide, the world of Pokémon surpassed 800 unique characters over seven generations with another already announced for later this year. Each Pokémon has unique stats, types, moveset, and design - making every new Pokémon you encounter a brand new sight to behold. But not all Pokémon are created equal - within each generation, there are several designated “legendary Pokémon” that are set a tier above the average Pokémon you might encounter while playing the game. These special Pokémon are set apart through varying combinations of the aforementioned features, and are often the headliners for each generation of Pokémon released. \n",
    "\n",
    "But what defines a legendary is not always clear nor consistent. For example some legendaries do not show up on the cover of the game box, such as in the first generation. Starter Pokémon, which is the first Pokémon the player ever sees, are sometimes highlighted instead of Pokémon in the pool of legendaries. For statistics, certain Pokémon see incredibly high usage in the competitive scene while some legendaries are not even worth considering. \n",
    "\n",
    "Most of the research regarding legendary Pokémon focuses solely on the lore and the constraints of the legendary Pokémon themselves. However, not much research has been done analyzing the numbers that surround legendary Pokémon. There has been, however, many attempts of looking at Pokémon data as a whole, and analyzing the data that way. For example, the following two scatter plots closely mirror the type of analysis we will be performing in our project. \n",
    "\n",
    "https://www.reddit.com/r/dataisbeautiful/comments/780wfi/\n",
    "\n",
    "The first scatter plot, created by Reddit user jmerlinb, compares Pokémon in terms of their height and weight. This is a visualization we drew a lot of inspiration from, but we will make it easier to read, as well as distinguish legendary Pokémon from non-legendary Pokémon in order to prove our hypothesis. \n",
    "\n",
    "https://www.reddit.com/r/dataisbeautiful/comments/91u20t/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis \n",
    "\n",
    "From our knowledge of the games and franchise, we can deduce possible aspects that categorizes these Pokémon apart. We know that Legendary Pokémon are considered special, and as such they probably have special features that set them apart from normal Pokémon. We decided to examine this question by looking at two main factors: their capabilities and physical composition. Their capabilities can be defined in two ways. One of these ways is their stats. Every Pokémon have base stats that are universal across all Pokémon of a particular species, we believe that Legendary Pokémon will have higher base stats than all of the other non-Legendary Pokemon. For their physical composition, we believe that Legendary Pokémon are predominantly of the Dragon-type. Also regarding their actual size, we believe that Legendary Pokémon exist in the extremes of the Pokémon world - meaning that they are either extremely large, or extremely small. We believe that these attributes will also mean that their competitive usage is notably higher than normal Pokémon. We came to this hypothesis because we believed that as the headliner Pokemon for every generation, Nintendo would want these Pokemon to stand out amongst the other 100+ Pokemon introduced with every new iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets\n",
    "\n",
    "**PokéAPI https://pokeapi.co/**\n",
    "\n",
    "**Pokebase https://pypi.org/project/pokebase/**\n",
    "\n",
    "In this dataset, we plan to collect all legendary and non-legendary Pokémon stats, types, and physical attributes and analyze trends we observed that set the two categories apart. There are 6 stats (Hit Points, Attack, Defense, Special Attack, Special Defense, and Speed) and 18 types (Fire, Water, Grass, Electric, Psychic, Steel, Normal, Fairy, Dark, Flying, Ghost, Poison, Ice, Ground, Rock, Dragon, Fighting, and Bug). Physical attributes, i.e. height and weight, are measured in decimeters and hectograms respectively. This data is publicly available for everyone to access. We are using a package called Pokebase that is provided by the same creators to easily access the API. pokebase is licensed under the 3-Clause BSD License which is a fairly permissive license. We determined whether a Pokemon is legendary or not based on its classification on Bulbapedia, and only used the default forms for Pokemon with multiple in our analysis for consistency. \n",
    "\n",
    "\n",
    "**Bulbapedia https://bulbapedia.bulbagarden.net/wiki/Legendary_Pok%C3%A9mon**\n",
    "\n",
    "Bulbapedia is a community-driven resource about Pokémon and is one of the biggest Pokémon resources in the community. We use their page on legendary Pokémon to for a list of legendary Pokémon to classify our data.\n",
    "\n",
    "\n",
    "**Smogon University https://www.smogon.com/stats**\n",
    "\n",
    "Smogon University is a competitive Pokémon community. They are particularly known for running an online Pokémon battle simulator called Pokémon Showdown, where players can build teams and battle in various competitive rulesets. During peak times, the player count can go up to the twenty thousands (https://pokemonshowdown.com/userstats). Every month, Smogon publicly provides plaintext statistics about how Pokémon are used per competitive ruleset. This data is from March 2019, as that is when we began our analysis. We are specifically interested in the Uber ruleset, which allows the use of practically every Pokémon, including potentially powerful legendaries (\"Ubers is the most inclusive of Smogon's tiers, allowing the use of any Pokémon species\" per https://www.smogon.com/dex/sm/formats/uber/). Smogon's data also is separated by various skill level tiers. Since we want to look at usage data across all skill levels, we use the dataset that includes all results regardless of skill level. There are three usage statistics: usage, raw, and real. \"Usage\" is the amount a Pokemon is seen on a team, weighted by the player's skill level. \"Raw\" is the amount a Pokemon is seen on a team, unweighted by skill level. \"Real\" is the amount a Pokemon is actually \"sent out\" and battles. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Privacy and Ethics Considerations \n",
    "\n",
    "For the data from PokéAPI, there are no privacy concerns nor terms of use concerns that we will need to comply with. The data on PokéAPI is not personally identifiable whatsoever, as they are based on created data within the games. There are no other issues related to our data and analyses that could be problematic in terms of privacy. Since Pokémon data concerns objective data, there should not be any biases. The data the API provides has been collected from another open source project and a fan-run wiki; however, these have been vetted as they were scraped and should be free of errors. \n",
    "\n",
    "For the legendary list taken from Bulbapedia, there is a possibility that people may maliciously edit the page which would lead to incorrect data being used for analysis. However, Bulbapedia has many guidelines on editing and user interaction (https://bulbapedia.bulbagarden.net/wiki/Category:Bulbapedia_policies), and is one of the largest and longest standing community resources (https://bulbapedia.bulbagarden.net/wiki/Bulbapedia:About). This combined with the fact that the list of legendary Pokémon is a fairly static list gives us the confidence that this data is safe to use and free of bias.\n",
    "\n",
    "Competitive usage data was taken from Smogon’s Pokémon Showdown statistics. These statistics are collected by Smogon from their Pokémon Showdown server. There are no privacy concerns as the data available here is not personally identifiable. The only data that is recorded is how often a Pokémon shows up on a player’s team, and what moves they use. The data can be grouped by player ranking; however, these groups each contain many players and have no personally identifiable information.\n",
    "\n",
    "Since this usage data is sorted by month/year and optionally by player ranking, there is a potential for bias in both the date and rank. Pokémon usage may rise and/or fall over time, especially if a new game has been released or other rumors. However, since the data we analyzed was collected long after the current generation has been released, and long before a new generation will be released, this should not be too much of an issue. In addition, we used statistics for all players and not just the top players so that the popularity can be seen amongst all skill levels.\n",
    "\n",
    "We believe that there are no more ethical concerns to address. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F9bp1r-UK7gk"
   },
   "source": [
    "# Part 0: Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "n25zgJ5dJfe6",
    "outputId": "a0e4b113-0e3d-4986-b46c-590985033751",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pokebase in c:\\users\\ellio\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (1.2.0)\n",
      "Requirement already satisfied: requests in c:\\users\\ellio\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pokebase) (2.22.0)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\ellio\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from requests->pokebase) (1.25.3)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\users\\ellio\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from requests->pokebase) (3.0.4)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in c:\\users\\ellio\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from requests->pokebase) (2.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\ellio\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from requests->pokebase) (2019.3.9)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from urllib.request import urlopen\n",
    "import json\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "!{sys.executable} -m pip install pokebase\n",
    "import pokebase as pb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: Data Collection and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limit dataframe views to reduce page count\n",
    "pd.options.display.max_rows = 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gWOOeW9qQ-aJ"
   },
   "source": [
    "## Fetch and Clean Smogon Usage Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UWfTpo7RNpcX"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Pokemon</th>\n",
       "      <th>Raw</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Groudon-Primal</td>\n",
       "      <td>274400</td>\n",
       "      <td>39.068%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Xerneas</td>\n",
       "      <td>198831</td>\n",
       "      <td>28.309%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Necrozma-Dusk-Mane</td>\n",
       "      <td>149044</td>\n",
       "      <td>21.220%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Yveltal</td>\n",
       "      <td>147205</td>\n",
       "      <td>20.959%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>954</td>\n",
       "      <td>Karrablast</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>955</td>\n",
       "      <td>Whismur</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>956</td>\n",
       "      <td>Pumpkaboo-Large</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>957</td>\n",
       "      <td>Lurantis-Totem</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>957 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank             Pokemon     Raw        %\n",
       "0      1      Groudon-Primal  274400  39.068%\n",
       "1      2             Xerneas  198831  28.309%\n",
       "2      3  Necrozma-Dusk-Mane  149044  21.220%\n",
       "3      4             Yveltal  147205  20.959%\n",
       "..   ...                 ...     ...      ...\n",
       "953  954          Karrablast       4   0.001%\n",
       "954  955             Whismur       4   0.001%\n",
       "955  956     Pumpkaboo-Large       2   0.000%\n",
       "956  957      Lurantis-Totem       1   0.000%\n",
       "\n",
       "[957 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = urlopen(\"https://www.smogon.com/stats/2019-03/gen7ubers-0.txt\")\n",
    "usage = []\n",
    "for line in data:\n",
    "    decoded = line.decode('utf-8')\n",
    "    decoded = decoded.replace(\" \", \"\")\n",
    "    if decoded.startswith('|'):\n",
    "        # usage - weighted based on matchmaking rating\n",
    "        # raw - unweighted usage (on team)\n",
    "        # real - actually used in battle\n",
    "        decodedList = decoded.split('|')[1:-1]\n",
    "        decodedList.pop(2) # remove usage %\n",
    "        decodedList.pop() # remove real\n",
    "        decodedList.pop() # remove real %\n",
    "        usage.append(decodedList)\n",
    "smogon_df = pd.DataFrame(usage[1:], columns=usage[0])\n",
    "smogon_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The specific usage statistic we are interested is called \"raw [usage]\", as we do not want to weigh by a player's skill. In addition, a Pokémon merely appearing on a team means a player has made the decision to reserve a valuable team spot for a Pokémon. The \"usage\" and \"real\" columns have been dropped to make data manipulation easier.\n",
    "\n",
    "From now on, \"raw\" and \"usage\" both refer to the this raw statistic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MHxfVJQfJIkf"
   },
   "source": [
    "## Obtain List of Legendary and Mystical Pokémon from Bulbapedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1207
    },
    "colab_type": "code",
    "id": "ZBpves8WAUAh",
    "outputId": "7500dcc4-5053-43e3-be23-1e6fad9d2283"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['articuno', 'zapdos', 'moltres', 'mewtwo', 'mew', 'raikou', 'entei', 'suicune', 'lugia', 'ho-oh', 'celebi', 'regirock', 'regice', 'registeel', 'latias', 'latios', 'kyogre', 'groudon', 'rayquaza', 'jirachi', 'deoxys', 'uxie', 'mesprit', 'azelf', 'dialga', 'palkia', 'heatran', 'regigigas', 'giratina', 'cresselia', 'phione', 'manaphy', 'darkrai', 'shaymin', 'arceus', 'victini', 'cobalion', 'terrakion', 'virizion', 'tornadus', 'thundurus', 'reshiram', 'zekrom', 'landorus', 'kyurem', 'keldeo', 'meloetta', 'genesect', 'xerneas', 'yveltal', 'zygarde', 'diancie', 'hoopa', 'volcanion', 'type-null', 'silvally', 'tapu-koko', 'tapu-lele', 'tapu-bulu', 'tapu-fini', 'cosmog', 'cosmoem', 'solgaleo', 'lunala', 'necrozma', 'magearna', 'marshadow', 'zeraora', 'meltan', 'melmetal', 'zacian', 'zamazenta']\n"
     ]
    }
   ],
   "source": [
    "# scrape list of legendary and mystical pokemon from bulbapedia\n",
    "page = requests.get(\"https://bulbapedia.bulbagarden.net/wiki/Legendary_Pok%C3%A9mon\")\n",
    "soup = BeautifulSoup(page.content, 'html.parser')\n",
    "\n",
    "# list of <a> tags of legendary pokemon\n",
    "a_list = soup.select('td[style*=\"background: #e6e6ff\"] a')\n",
    "\n",
    "# extract text from <a> tags\n",
    "legend_list = [lp.get_text() for lp in a_list]\n",
    "\n",
    "# data cleaning: replace whitespace with hyphen, remove colon\n",
    "legend_list = [p.lower().replace(' ', '-').replace(':', '') for p in legend_list]\n",
    "legend_list_smogon = legend_list.copy()\n",
    "print(legend_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Their page on legendary and mythical Pokémon has a convenient table at the end with a list of legendary Pokémon. We select this table and scrape it for a list of Pokémon names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JdrXsuZlKc26"
   },
   "source": [
    "## Test Legendary Name Validity by Searching Each Name in the API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "mMAUfppCG8v6",
    "outputId": "4e7dac0f-f86c-42e2-fddf-a1cf766653db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot find deoxys\n",
      "Cannot find giratina\n",
      "Cannot find shaymin\n",
      "Cannot find tornadus\n",
      "Cannot find thundurus\n",
      "Cannot find landorus\n",
      "Cannot find keldeo\n",
      "Cannot find meloetta\n",
      "Cannot find meltan\n",
      "Cannot find melmetal\n",
      "Cannot find zacian\n",
      "Cannot find zamazenta\n"
     ]
    }
   ],
   "source": [
    "for p in legend_list:\n",
    "    try:\n",
    "        pb.pokemon(p)\n",
    "    except ValueError:\n",
    "        print(\"Cannot find\", p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wcgNG2SGLcI_"
   },
   "source": [
    "## Handle Alternate Forms Manually\n",
    "\n",
    "In the Pokémon universe, each Pokémon species may have multiple forms. We want to make sure we properly classify each legendary species' form.\n",
    "\n",
    "P.S. Meltan and Melmetal, mythical Pokémon introduced in generation VII, haven't been added to the database at the moment. https://github.com/PokeAPI/pokeapi/issues/414\n",
    "\n",
    "P.S. Zacian and Zamazenta, new legendary Pokémon introduced in generation VIII, haven't been added to the database at the moment.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LQwJAJZ7J2Ah"
   },
   "outputs": [],
   "source": [
    "# deoxys: deoxys-normal, deoxys-attack, deoxys-defense, deoxys-speed\n",
    "try:\n",
    "    legend_list.remove('deoxys')\n",
    "    legend_list.extend(['deoxys-normal', 'deoxys-attack', 'deoxys-defense', 'deoxys-speed'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# giratina: giratina-altered, giratina-origin\n",
    "try:\n",
    "    legend_list.remove('giratina')\n",
    "    legend_list.extend(['giratina-altered', 'giratina-origin'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# shaymin: shaymin-land, shaymin-sky\n",
    "try:\n",
    "    legend_list.remove('shaymin')\n",
    "    legend_list.extend(['shaymin-land', 'shaymin-sky'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# tornadus: tornadus-incarnate, tornadus-therian\n",
    "try:\n",
    "    legend_list.remove('tornadus')\n",
    "    legend_list.extend(['tornadus-incarnate', 'tornadus-therian'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# thundurus: thundurus-incarnate, thundurus-therian\n",
    "try:\n",
    "    legend_list.remove('thundurus')\n",
    "    legend_list.extend(['thundurus-incarnate', 'thundurus-therian'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# landorus: landorus-incarnate, landorus-therian\n",
    "try:\n",
    "    legend_list.remove('landorus')\n",
    "    legend_list.extend(['landorus-incarnate', 'landorus-therian'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# keldeo: keldeo-ordinary, keldeo-resolute\n",
    "try:\n",
    "    legend_list.remove('keldeo')\n",
    "    legend_list.extend(['keldeo-ordinary', 'keldeo-resolute'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# meloetta: meloetta-aria, meloetta-pirouette\n",
    "try:\n",
    "    legend_list.remove('meloetta')\n",
    "    legend_list.extend(['meloetta-aria', 'meloetta-pirouette'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# meltan: remove for now\n",
    "try:\n",
    "    legend_list.remove('meltan')\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# melmetal: remove for now\n",
    "try:\n",
    "    legend_list.remove('melmetal')\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "    \n",
    "# zacian: remove for now\n",
    "try:\n",
    "    legend_list.remove('zacian')\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# zamazenta: remove for now\n",
    "try:\n",
    "    legend_list.remove('zamazenta')\n",
    "except ValueError:\n",
    "    print('Error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5BZbiJRxTzPm"
   },
   "source": [
    "## Test Again for Validity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U9STW874Q1Hy"
   },
   "outputs": [],
   "source": [
    "for p in legend_list:\n",
    "    try:\n",
    "        pb.pokemon(p)\n",
    "    except ValueError:\n",
    "        print(\"Cannot find\", p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create CSV File for All Pokémon Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Only run if you need the DF in the code itself, the cell above should retrieve the file\n",
    "# import re\n",
    "# pokemonList = []\n",
    "# pokemonList.append(['ID', 'Pokemon', 'Legendary', 'Stat Total', 'ATK Sum', 'DEF Sum', 'Height', 'Weight', 'Type 1', 'Type 2'])\n",
    "\n",
    "# for pokemonID in range(1,808):\n",
    "#   pokemon_stats = []\n",
    "#   pokemon_info = pb.pokemon(pokemonID)\n",
    "  \n",
    "#   pokemon_stats.append(pokemonID) # Add ID\n",
    "#   pokemon_stats.append(pokemon_info.name) # Add Name\n",
    "#   pokemon_stats.append(pokemon_info.name in legend_list) # Add Legendary or Not\n",
    "#   pokemon_stats.extend([0,0,0]) # Add categories for stat totals\n",
    "#   pokemon_stats.append(pokemon_info.height) # Add Height\n",
    "#   pokemon_stats.append(pokemon_info.weight) # Add Weight\n",
    "\n",
    "#   print(pokemonID)\n",
    "#   j = 0 # Counter because I didnt want to change my code anymore\n",
    "#   for i in (pokemon_info.stats):\n",
    "#     stringStats = str(i)\n",
    "#     value = [int(s) for s in re.findall(r'\\b\\d+\\b', stringStats)[:1]]\n",
    "#     pokemon_stats[3] += value[0]\n",
    "#     pokemon_stats[4+(j%2)] += value[0]\n",
    "#     j += 1\n",
    "   \n",
    "#   for i in pokemon_info.types:\n",
    "#     stringType = str(i)\n",
    "#     typing = re.findall('\\'name\\': \\'(.+?)\\'', stringType)\n",
    "#     pokemon_stats.append(typing[0])\n",
    "    \n",
    "    \n",
    "#   if len(pokemon_info.types) == 1:\n",
    "#     pokemon_stats.append(\"N/A\")\n",
    "    \n",
    "#   pokemonList.append(pokemon_stats)\n",
    "\n",
    "# statsDF = pd.DataFrame(pokemonList[1:], columns=pokemonList[0])\n",
    "# statsDF.to_csv ('./statsDF.csv', index = None, header=True)\n",
    "\n",
    "# statsDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Pokemon</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Stat Total</th>\n",
       "      <th>ATK Sum</th>\n",
       "      <th>DEF Sum</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>False</td>\n",
       "      <td>318</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>7</td>\n",
       "      <td>69</td>\n",
       "      <td>poison</td>\n",
       "      <td>grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ivysaur</td>\n",
       "      <td>False</td>\n",
       "      <td>405</td>\n",
       "      <td>202</td>\n",
       "      <td>203</td>\n",
       "      <td>10</td>\n",
       "      <td>130</td>\n",
       "      <td>poison</td>\n",
       "      <td>grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>venusaur</td>\n",
       "      <td>False</td>\n",
       "      <td>525</td>\n",
       "      <td>262</td>\n",
       "      <td>263</td>\n",
       "      <td>20</td>\n",
       "      <td>1000</td>\n",
       "      <td>poison</td>\n",
       "      <td>grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>charmander</td>\n",
       "      <td>False</td>\n",
       "      <td>309</td>\n",
       "      <td>177</td>\n",
       "      <td>132</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>naganadel</td>\n",
       "      <td>False</td>\n",
       "      <td>540</td>\n",
       "      <td>321</td>\n",
       "      <td>219</td>\n",
       "      <td>36</td>\n",
       "      <td>1500</td>\n",
       "      <td>dragon</td>\n",
       "      <td>poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>805</td>\n",
       "      <td>stakataka</td>\n",
       "      <td>False</td>\n",
       "      <td>570</td>\n",
       "      <td>197</td>\n",
       "      <td>373</td>\n",
       "      <td>55</td>\n",
       "      <td>8200</td>\n",
       "      <td>steel</td>\n",
       "      <td>rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>806</td>\n",
       "      <td>blacephalon</td>\n",
       "      <td>False</td>\n",
       "      <td>570</td>\n",
       "      <td>385</td>\n",
       "      <td>185</td>\n",
       "      <td>18</td>\n",
       "      <td>130</td>\n",
       "      <td>ghost</td>\n",
       "      <td>fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>807</td>\n",
       "      <td>zeraora</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "      <td>357</td>\n",
       "      <td>243</td>\n",
       "      <td>15</td>\n",
       "      <td>445</td>\n",
       "      <td>electric</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>807 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID      Pokemon  Legendary  Stat Total  ATK Sum  DEF Sum  Height  \\\n",
       "0      1    bulbasaur      False         318      159      159       7   \n",
       "1      2      ivysaur      False         405      202      203      10   \n",
       "2      3     venusaur      False         525      262      263      20   \n",
       "3      4   charmander      False         309      177      132       6   \n",
       "..   ...          ...        ...         ...      ...      ...     ...   \n",
       "803  804    naganadel      False         540      321      219      36   \n",
       "804  805    stakataka      False         570      197      373      55   \n",
       "805  806  blacephalon      False         570      385      185      18   \n",
       "806  807      zeraora       True         600      357      243      15   \n",
       "\n",
       "     Weight    Type 1  Type 2  \n",
       "0        69    poison   grass  \n",
       "1       130    poison   grass  \n",
       "2      1000    poison   grass  \n",
       "3        85      fire     NaN  \n",
       "..      ...       ...     ...  \n",
       "803    1500    dragon  poison  \n",
       "804    8200     steel    rock  \n",
       "805     130     ghost    fire  \n",
       "806     445  electric     NaN  \n",
       "\n",
       "[807 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('statsDF.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dTPd1pXcsIFJ"
   },
   "source": [
    "## Obtain List of Pokémon Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GdJIE2NLx5WS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Normal', 'Fire', 'Fighting', 'Water', 'Flying', 'Grass', 'Poison', 'Electric', 'Ground', 'Psychic', 'Rock', 'Ice', 'Bug', 'Dragon', 'Ghost', 'Dark', 'Steel', 'Fairy']\n"
     ]
    }
   ],
   "source": [
    "# scrape list of pokemon types from bulbapedia\n",
    "page = requests.get(\"https://bulbapedia.bulbagarden.net/wiki/Type\")\n",
    "soup = BeautifulSoup(page.content, 'html.parser')\n",
    "\n",
    "# list of <a> tags of types\n",
    "a_list = soup.select('td a[title*=\"(type)\"]')\n",
    "\n",
    "# extract text from <a> tags\n",
    "type_list = [lp.get_text() for lp in a_list]\n",
    "\n",
    "# ignore ??? type\n",
    "type_list = type_list[:-1]\n",
    "print(type_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Match Legendary Names to Smogon Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Articuno', 'Zapdos', 'Moltres', 'Mewtwo', 'Mew', 'Raikou', 'Entei', 'Suicune', 'Lugia', 'Ho-Oh', 'Celebi', 'Regirock', 'Regice', 'Registeel', 'Latias', 'Latios', 'Kyogre', 'Groudon', 'Rayquaza', 'Jirachi', 'Deoxys', 'Uxie', 'Mesprit', 'Azelf', 'Dialga', 'Palkia', 'Heatran', 'Regigigas', 'Giratina', 'Cresselia', 'Phione', 'Manaphy', 'Darkrai', 'Shaymin', 'Arceus', 'Victini', 'Cobalion', 'Terrakion', 'Virizion', 'Tornadus', 'Thundurus', 'Reshiram', 'Zekrom', 'Landorus', 'Kyurem', 'Keldeo', 'Meloetta', 'Genesect', 'Xerneas', 'Yveltal', 'Zygarde', 'Diancie', 'Hoopa', 'Volcanion', 'Silvally', 'Cosmog', 'Cosmoem', 'Solgaleo', 'Lunala', 'Necrozma', 'Magearna', 'Marshadow', 'Zeraora', 'Zacian', 'Zamazenta', 'Deoxys-Attack', 'Deoxys-Defense', 'Deoxys-Speed', 'Giratina-Origin', 'Shaymin-Sky', 'Tornadus-Therian', 'Thundurus-Therian', 'Landorus-Therian', 'TapuKoko', 'TapuLele', 'TapuBulu', 'TapuFini', 'Type:Null']\n"
     ]
    }
   ],
   "source": [
    "legend_list_smogon.extend(['deoxys-attack', 'deoxys-defense', 'deoxys-speed'])\n",
    "legend_list_smogon.extend(['giratina-origin'])\n",
    "legend_list_smogon.extend(['shaymin-sky'])\n",
    "legend_list_smogon.extend(['tornadus-therian'])\n",
    "legend_list_smogon.extend(['thundurus-therian'])\n",
    "legend_list_smogon.extend(['landorus-therian'])\n",
    "\n",
    "try:\n",
    "    legend_list_smogon.remove('meltan')\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "try:\n",
    "    legend_list_smogon.remove('melmetal')\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "# smogon pokemon names are captialized\n",
    "legend_list_smogon = [str.title(name) for name in legend_list_smogon]\n",
    "\n",
    "try:\n",
    "    legend_list_smogon.remove('Tapu-Koko')\n",
    "    legend_list_smogon.extend(['TapuKoko'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "try:\n",
    "    legend_list_smogon.remove('Tapu-Lele')\n",
    "    legend_list_smogon.extend(['TapuLele'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "try:\n",
    "    legend_list_smogon.remove('Tapu-Bulu')\n",
    "    legend_list_smogon.extend(['TapuBulu'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "try:\n",
    "    legend_list_smogon.remove('Tapu-Fini')\n",
    "    legend_list_smogon.extend(['TapuFini'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "\n",
    "try:\n",
    "    legend_list_smogon.remove('Type-Null')\n",
    "    legend_list_smogon.extend(['Type:Null'])\n",
    "except ValueError:\n",
    "    print('Error')\n",
    "    \n",
    "print(legend_list_smogon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Smogon's name formatting is somewhat different than the format we use elsewhere in this notebook. We are doing some manual editing for entries that differ to match the naming used by Smogon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Smogon Competitive Usage Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "APqKfds8MJyI"
   },
   "source": [
    "## Visualizing Smogon Usage Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1969
    },
    "colab_type": "code",
    "id": "sojCmvSF_URY",
    "outputId": "f626337a-2a19-45e2-ee6c-075a5ae125fc"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Raw Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Legendary</td>\n",
       "      <td>2319636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Normal</td>\n",
       "      <td>1848922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Type  Raw Usage\n",
       "0  Legendary    2319636\n",
       "1     Normal    1848922"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert string to number\n",
    "smogon_df['Raw'] = smogon_df['Raw'].astype(int)\n",
    "\n",
    "# using legend_list_smogon, split dataframe\n",
    "smogon_legend_df = smogon_df[smogon_df['Pokemon'].str.contains('|'.join(legend_list_smogon))].copy()\n",
    "smogon_legend_df['Rank'] = smogon_legend_df['Rank'].astype(int)\n",
    "smogon_normal_df = smogon_df[-smogon_df['Pokemon'].str.contains('|'.join(legend_list_smogon))].copy()\n",
    "smogon_normal_df['Rank'] = smogon_normal_df['Rank'].astype(int)\n",
    "\n",
    "# plot raw usage of legendaries vs normal\n",
    "smogon_usage_overall_graph_data = [\n",
    "  ['Legendary', smogon_legend_df['Raw'].sum()],\n",
    "  ['Normal', smogon_normal_df['Raw'].sum()]\n",
    "]\n",
    "smogon_usage_overall_graph_df = pd.DataFrame(smogon_usage_overall_graph_data, columns = ['Type', 'Raw Usage'])\n",
    "ax_smogon_usage_overall = smogon_usage_overall_graph_df.plot.bar(x = 'Type', y = 'Raw Usage', \n",
    "  title = 'Smogon Ubers Overall Combined Raw Usage', legend = False, color = ['orange', 'blue'])\n",
    "ax_smogon_usage_overall.set_xlabel('Type')\n",
    "ax_smogon_usage_overall.set_ylabel('Raw Usage')\n",
    "plt.show()\n",
    "smogon_usage_overall_graph_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the overall combined raw usage of legendary against normal Pokémon, we can see that legendary Pokémon are somewhat more popular in team compositions by a difference of 22.58%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Legendary</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Normal</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Type   Usage\n",
       "0  Legendary  2900.0\n",
       "1     Normal   321.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smogon_graph_data_usage = [\n",
    "  ['Legendary', smogon_legend_df['Raw'].median()],\n",
    "  ['Normal', smogon_normal_df['Raw'].median()]\n",
    "]\n",
    "smogon_usage_graph_df = pd.DataFrame(smogon_graph_data_usage, columns = ['Type', 'Usage'])\n",
    "ax_smogon_usage = smogon_usage_graph_df.plot.bar(x = 'Type', y = 'Usage', title = 'Smogon Ubers Median Raw Usage',\n",
    "  legend = False, color = ['orange', 'blue'])\n",
    "ax_smogon_usage.set_xlabel('Type')\n",
    "ax_smogon_usage.set_ylabel('Usage')\n",
    "plt.show()\n",
    "smogon_usage_graph_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we graph the median usage. The median has been chosen over the mean as it is more robust to potential outliers. Comparing the two usage medians, legendary usage appears to be ahead by a staggering amount. We should graph each individual usage to see what is happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot raw usage against rank\n",
    "plt.scatter(smogon_legend_df['Rank'], smogon_legend_df['Raw'], color = 'orange')\n",
    "plt.scatter(smogon_normal_df['Rank'], smogon_normal_df['Raw'], color = 'blue')\n",
    "plt.title('Smogon Ubers Rank vs. Raw Usage')\n",
    "plt.xlabel('Rank')\n",
    "plt.ylabel('Raw Usage')\n",
    "plt.xticks([1, *range(100, len(smogon_df), 100)])\n",
    "plt.legend(['Legendary', 'Normal'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we plot the raw usage of each Pokémon against their rank. It is apparent that usage is exponential, meaning only a certain amount of Pokémon are widely used and the rest quickly fall off in usage. In addition, at first glance it would appear that legendary Pokémon seem to dominate the upper ranks. It is hard to tell for sure without a further breakdown since the dots overlap each other. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Legendary:\n",
      " count       123.000000\n",
      "mean      18858.829268\n",
      "std       40850.295869\n",
      "min          25.000000\n",
      "25%         818.000000\n",
      "50%        2900.000000\n",
      "75%       17505.500000\n",
      "max      274400.000000\n",
      "Name: Raw, dtype: float64 \n",
      "\n",
      "Normal:\n",
      " count      834.000000\n",
      "mean      2216.932854\n",
      "std       6011.370972\n",
      "min          1.000000\n",
      "25%         63.000000\n",
      "50%        321.000000\n",
      "75%       1701.750000\n",
      "max      68805.000000\n",
      "Name: Raw, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Legendary:\\n', smogon_legend_df['Raw'].describe(), '\\n\\nNormal:\\n', smogon_normal_df['Raw'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the statistics and graph of all legendary and normal Pokémon, our data is subject to extreme skew due to the amount of Pokémon that remain relatively unused. Thus, we will visualize the most used Pokémon to make it easier to analyze. We decided on the top 10% usage of all Pokémon, which rounds up to 96."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Just the Top 10%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Legendary</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Normal</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Type  Count\n",
       "0  Legendary     45\n",
       "1     Normal     51"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get top x% used\n",
    "smogon_top_count = math.ceil(len(smogon_df) * 0.1) # used for the cutoffs\n",
    "smogon_legend_top_df = (smogon_legend_df[smogon_legend_df['Rank'] <= smogon_top_count])\n",
    "smogon_normal_top_df = (smogon_normal_df[smogon_normal_df['Rank'] <= smogon_top_count])\n",
    "\n",
    "smogon_num_legend_top = smogon_legend_top_df['Rank'].size\n",
    "smogon_num_normal_top = smogon_normal_top_df['Rank'].size\n",
    "smogon_num_graph_data = [\n",
    "  ['Legendary', smogon_num_legend_top],\n",
    "  ['Normal', smogon_num_normal_top]\n",
    "]\n",
    "smogon_num_graph_top_df = pd.DataFrame(smogon_num_graph_data, columns = ['Type', 'Count'])\n",
    "ax_smogon_num_top = smogon_num_graph_top_df.plot.pie(x = 'Type', y = 'Count', title = 'Smogon Ubers Top 10% ({}) Used Composition'.format(smogon_top_count),\n",
    "  legend = False, colors = ['orange', 'blue'], labels = ['Legendary', 'Normal'])\n",
    "plt.show()\n",
    "smogon_num_graph_top_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Legendary in Top 10%:\n",
      " count        45.000000\n",
      "mean      48429.933333\n",
      "std       56661.093534\n",
      "min        8907.000000\n",
      "25%       15831.000000\n",
      "50%       23644.000000\n",
      "75%       53916.000000\n",
      "max      274400.000000\n",
      "Name: Raw, dtype: float64 \n",
      "\n",
      "Normal in Top 10%:\n",
      " count       51.000000\n",
      "mean     20206.274510\n",
      "std      14432.625164\n",
      "min       8051.000000\n",
      "25%       9928.000000\n",
      "50%      15080.000000\n",
      "75%      24082.500000\n",
      "max      68805.000000\n",
      "Name: Raw, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Legendary in Top 10%:\\n', smogon_legend_top_df['Raw'].describe(), \\\n",
    "      '\\n\\nNormal in Top 10%:\\n', smogon_normal_top_df['Raw'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the statistcs of the top 10%, we can see this is somewhat more reasonable than analyzing them all. In the top 10%, there are more normal Pokémon than legendary Pokémon by a difference of 12.50%. However, the difference in usage is still quite large."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Legendary</td>\n",
       "      <td>23644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Normal</td>\n",
       "      <td>15080.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Type    Usage\n",
       "0  Legendary  23644.0\n",
       "1     Normal  15080.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the makeup of the top x% used\n",
    "smogon_num_graph_data_usage = [\n",
    "  ['Legendary', smogon_legend_top_df['Raw'].median()],\n",
    "  ['Normal', smogon_normal_top_df['Raw'].median()]\n",
    "]\n",
    "smogon_usage_top_graph_df = pd.DataFrame(smogon_num_graph_data_usage, columns = ['Type', 'Usage'])\n",
    "ax_smogon_usage_top = smogon_usage_top_graph_df.plot.bar(x = 'Type', y = 'Usage', title = 'Smogon Ubers Top 10% ({}) Median Raw Usage'.format(smogon_top_count),\n",
    "  legend = False, color = ['orange', 'blue'])\n",
    "ax_smogon_usage_top.set_xlabel('Type')\n",
    "ax_smogon_usage_top.set_ylabel('Usage')\n",
    "plt.show()\n",
    "smogon_usage_top_graph_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the medians have been graphed instead of the mean as the maximum legendary usage is quite large, and the median is more robust in this scenario. Looking at just the top 10% medians of raw usage, legendary Pokémon are still used far more than normal Pokémon by a difference of 50.84%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of Usage Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The usage of legendary Pokémon compared to normal Pokémon is undoubtedly larger. This is especially true at the very top - the difference between the most used legendary Pokémon far surpass the normal Pokémon. With this usage difference, the fact that there are ten more normal Pokémon than legendary in the top 10% may be surprising. However, it is important to note that there are vastly more normal Pokémon than legendary. Smogon has 838 entries that are classified as normal, compared to 119 that are classified as legendary. With that many more normal Pokémon, there are bound to be quite a few normal Pokémon that can be considered a valuable addition to the team. Regardless, this large difference in usage between legendary and normal Pokémon is significant and must be due to some kind of attribute differences legendary Pokémon possess. In the next part, we will look at the objective data of legendary Pokémon against normal Pokemon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3: Physical Attributes Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Weight vs. Height\n",
    "\n",
    "Using a scatter plots, we can examine the relationship between the height and weight of all Pokémon. The plots below show how most Pokémon's measurements concentrate toward the bottom left corner, whereas there are only a few outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-legendaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Height (decimetres)'), Text(0.5, 0, 'Weight (hectograms)')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[~df['Pokemon'].isin(legend_list)][['Height', 'Weight']].plot.scatter('Weight', 'Height').set(xlabel='Weight (hectograms)', ylabel='Height (decimetres)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first glance, we can see in the scatter plot that the bulk of Non-Legendary Pokémons’ Height and Weight are concentrated in the bottom left. This indicates that Non-Legendaries tend to be smaller in size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Legendaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Height (decimetres)'), Text(0.5, 0, 'Weight (hectograms)')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Pokemon'].isin(legend_list)][['Height', 'Weight']].plot.scatter('Weight', 'Height', c='orange').set(xlabel='Weight (hectograms)', ylabel='Height (decimetres)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this scatterplot, the first thing to note is that there is significantly less Legendary Pokémons than Non-Legendaries. Additionally, we can see that the Height and Weight for Legendaries vary extremely - with most being in the larger range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Height (decimetres)'), Text(0.5, 0, 'Weight (hectograms)')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[~df['Pokemon'].isin(legend_list)][['Height', 'Weight']].plot('Weight', 'Height', kind='scatter')\n",
    "df[df['Pokemon'].isin(legend_list)][['Height', 'Weight']].plot('Weight', 'Height', kind='scatter', c='orange', ax=ax).set(xlabel='Weight (hectograms)', ylabel='Height (decimetres)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining both scatter plots shows that although Legendary Pokémons are larger in size, there are exceptions to this case. An outlier example being that there is a Non-Legendary that is the tallest out of all Pokémon. Another irrational outlier is that there is a Legendary that is short but is the heaviest Pokémon overall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Means of Weight and Height\n",
    "\n",
    "Legendary mean: maroon  \n",
    "Regular mean: cyan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Legendary Pokemon average weight:  1716.66 ; average height:  20.66\n",
      "Regular Pokemon average weight:  516.59 ; average height:  10.79\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Height (decimetres)'), Text(0.5, 0, 'Weight (hectograms)')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# legendary average height\n",
    "l_h_mean = df[df['Pokemon'].isin(legend_list)]['Height'].mean()\n",
    "\n",
    "# legendary average weight\n",
    "l_w_mean = df[df['Pokemon'].isin(legend_list)]['Weight'].mean()\n",
    "\n",
    "# regular average height\n",
    "r_h_mean = df[~df['Pokemon'].isin(legend_list)]['Height'].mean()\n",
    "\n",
    "# regular avaerage weight\n",
    "r_w_mean = df[~df['Pokemon'].isin(legend_list)]['Weight'].mean()\n",
    "\n",
    "# calculate means\n",
    "df_l_mean = pd.DataFrame({\"Weight\":[l_w_mean], \"Height\":[l_h_mean]})\n",
    "df_r_mean = pd.DataFrame({\"Weight\":[r_w_mean], \"Height\":[r_h_mean]})\n",
    "print('Legendary Pokemon average weight: ', round(df_l_mean.loc[0]['Weight'], 2), '; average height: ', round(df_l_mean.loc[0]['Height'], 2))\n",
    "print('Regular Pokemon average weight: ', round(df_r_mean.loc[0]['Weight'], 2), '; average height: ', round(df_r_mean.loc[0]['Height'], 2))\n",
    "\n",
    "# plot\n",
    "ax = df[~df['Pokemon'].isin(legend_list)][['Height', 'Weight']].plot('Weight', 'Height', kind='scatter')\n",
    "df[df['Pokemon'].isin(legend_list)][['Height', 'Weight']].plot('Weight', 'Height', kind='scatter', c='orange', ax=ax)\n",
    "df_l_mean.plot.scatter('Weight', 'Height', c=\"maroon\", s=140, ax = ax)\n",
    "df_r_mean.plot.scatter('Weight', 'Height', c=\"cyan\", s=140, ax = ax).set(xlabel='Weight (hectograms)', ylabel='Height (decimetres)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the mean Height and Weight data to display the combined average Height & Weight of Legendary and Non-Legendary, individually, amongst all Pokémons. This data also includes outliers but they do not affect the data substantially, as there are many other data points we are analyzing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally from the plot above, we can conclude that the average legendary Pokémon is likely to be taller and heavier than the average non-legendary Pokémon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets identify what typings legendary Pokemon typically are. First we need to separate the legendary data from the overall dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a list of Pokémon Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "legendsDf = df[df['Pokemon'].isin(legend_list)]\n",
    "\n",
    "legend_types = list(legendsDf['Type 1']) + list(legendsDf['Type 2'])\n",
    "types = list(df['Type 1']) + list(df['Type 2'])\n",
    "\n",
    "legendTypeCount = pd.Series(legend_types).value_counts().append(pd.Series([0],['poison']))\n",
    "regularTypeCount = pd.Series(types).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Pokemon</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Stat Total</th>\n",
       "      <th>ATK Sum</th>\n",
       "      <th>DEF Sum</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>articuno</td>\n",
       "      <td>True</td>\n",
       "      <td>580</td>\n",
       "      <td>265</td>\n",
       "      <td>315</td>\n",
       "      <td>17</td>\n",
       "      <td>554</td>\n",
       "      <td>flying</td>\n",
       "      <td>ice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>zapdos</td>\n",
       "      <td>True</td>\n",
       "      <td>580</td>\n",
       "      <td>315</td>\n",
       "      <td>265</td>\n",
       "      <td>16</td>\n",
       "      <td>526</td>\n",
       "      <td>flying</td>\n",
       "      <td>electric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>moltres</td>\n",
       "      <td>True</td>\n",
       "      <td>580</td>\n",
       "      <td>315</td>\n",
       "      <td>265</td>\n",
       "      <td>20</td>\n",
       "      <td>600</td>\n",
       "      <td>flying</td>\n",
       "      <td>fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>mewtwo</td>\n",
       "      <td>True</td>\n",
       "      <td>680</td>\n",
       "      <td>394</td>\n",
       "      <td>286</td>\n",
       "      <td>20</td>\n",
       "      <td>1220</td>\n",
       "      <td>psychic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>800</td>\n",
       "      <td>necrozma</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "      <td>313</td>\n",
       "      <td>287</td>\n",
       "      <td>24</td>\n",
       "      <td>2300</td>\n",
       "      <td>psychic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>801</td>\n",
       "      <td>magearna</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "      <td>290</td>\n",
       "      <td>310</td>\n",
       "      <td>10</td>\n",
       "      <td>805</td>\n",
       "      <td>fairy</td>\n",
       "      <td>steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>802</td>\n",
       "      <td>marshadow</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "      <td>340</td>\n",
       "      <td>260</td>\n",
       "      <td>7</td>\n",
       "      <td>222</td>\n",
       "      <td>ghost</td>\n",
       "      <td>fighting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>807</td>\n",
       "      <td>zeraora</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "      <td>357</td>\n",
       "      <td>243</td>\n",
       "      <td>15</td>\n",
       "      <td>445</td>\n",
       "      <td>electric</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID    Pokemon  Legendary  Stat Total  ATK Sum  DEF Sum  Height  Weight  \\\n",
       "143  144   articuno       True         580      265      315      17     554   \n",
       "144  145     zapdos       True         580      315      265      16     526   \n",
       "145  146    moltres       True         580      315      265      20     600   \n",
       "149  150     mewtwo       True         680      394      286      20    1220   \n",
       "..   ...        ...        ...         ...      ...      ...     ...     ...   \n",
       "799  800   necrozma       True         600      313      287      24    2300   \n",
       "800  801   magearna       True         600      290      310      10     805   \n",
       "801  802  marshadow       True         600      340      260       7     222   \n",
       "806  807    zeraora       True         600      357      243      15     445   \n",
       "\n",
       "       Type 1    Type 2  \n",
       "143    flying       ice  \n",
       "144    flying  electric  \n",
       "145    flying      fire  \n",
       "149   psychic       NaN  \n",
       "..        ...       ...  \n",
       "799   psychic       NaN  \n",
       "800     fairy     steel  \n",
       "801     ghost  fighting  \n",
       "806  electric       NaN  \n",
       "\n",
       "[68 rows x 10 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legendsDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kind of hard to read, so lets just get the counts instead of individual pokemon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "psychic    21\n",
       "flying     10\n",
       "dragon     10\n",
       "steel       8\n",
       "           ..\n",
       "ice         3\n",
       "dark        2\n",
       "bug         1\n",
       "poison      0\n",
       "Length: 18, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legendTypeCount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "legendPlot = legendTypeCount.plot(kind='bar', title='Legendary Pokemon Typing', yticks=range(0,25,5), color=\"orange\")\n",
    "xstuff = legendPlot.set_xlabel(\"Typing\")\n",
    "ystuff = legendPlot.set_ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that psychic dominate the typings for legendary Pokemon, doubling the counts for any other type. From there it slowly declines until we get to poison, where we can see 0 Pokemon have poison typing. Surprisingly, dragon isnt the most frequent typing, which somewhat subverted our expectations but is reasonable once we thought back onto what legendary Pokemon there were."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "water     131\n",
       "normal    109\n",
       "flying     98\n",
       "grass      97\n",
       "         ... \n",
       "dark       46\n",
       "dragon     45\n",
       "ghost      43\n",
       "ice        34\n",
       "Length: 18, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regularTypeCount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalPlot = regularTypeCount.plot(kind='bar', title='All Pokemon Typing', yticks=range(0,150,10),  color=\"blue\")\n",
    "xstuff = normalPlot.set_xlabel(\"Typing\")\n",
    "ystuff = normalPlot.set_ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now with regular pokemon, we can see that its a lot more evenly spread out. Water has a plurality of the typings but is not as dominant as psychic was for legendaries. Poison finds itself near the middle of the type distribution, suggesting that the lack of a legendary Pokemon being poison typed may be intentional rather than through sheer chance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "typeCompDf = legendTypeCount.to_frame()\n",
    "typeCompDf = typeCompDf.merge(regularTypeCount.to_frame(), left_index=True, right_index=True)\n",
    "typeCompDf.columns = ['Legendary','Normal']\n",
    "typeCompDf=typeCompDf.reindex(columns=['Normal','Legendary'])\n",
    "normFirst = regularTypeCount.to_frame()\n",
    "normFirst = normFirst.merge(legendTypeCount.to_frame(), left_index = True, right_index = True)\n",
    "normFirst.columns = ['Normal','Legendary']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets compare the two side-by-side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Normal</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>psychic</th>\n",
       "      <td>82</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flying</th>\n",
       "      <td>98</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dragon</th>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>steel</th>\n",
       "      <td>47</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ice</th>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dark</th>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bug</th>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poison</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Normal  Legendary\n",
       "psychic      82         21\n",
       "flying       98         10\n",
       "dragon       45         10\n",
       "steel        47          8\n",
       "...         ...        ...\n",
       "ice          34          3\n",
       "dark         46          2\n",
       "bug          77          1\n",
       "poison       66          0\n",
       "\n",
       "[18 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typeCompDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Normal</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>water</th>\n",
       "      <td>131</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal</th>\n",
       "      <td>109</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flying</th>\n",
       "      <td>98</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grass</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dark</th>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dragon</th>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ghost</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ice</th>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Normal  Legendary\n",
       "water      131          8\n",
       "normal     109          5\n",
       "flying      98         10\n",
       "grass       97          4\n",
       "...        ...        ...\n",
       "dark        46          2\n",
       "dragon      45         10\n",
       "ghost       43          4\n",
       "ice         34          3\n",
       "\n",
       "[18 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normFirst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overallPlot = typeCompDf.plot(kind='bar', title =\"Type Distribution\")\n",
    "xstuff = overallPlot.set_xlabel(\"Typing\")\n",
    "ystuff = overallPlot.set_ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normFirstPlot = normFirst.plot(kind='bar', title =\"Type Distribution\")\n",
    "xstuff = normFirstPlot.set_xlabel(\"Typing\")\n",
    "ystuff = normFirstPlot.set_ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through these two graphs, we can see that there only seems to be a small correlation between the typing for all Pokemon in general, and the typing for legendary Pokemon (seen in flying, water, and psychic for both graphs). This suggests that legendary Pokemon are made without this distribution in mind, and more made to appeal to the abstract idea of legendary that most people view as dragons or psychic beings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4: Pokémon Stats Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display plots directly in the notebook instead of in a new window\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure libraries\n",
    "# The seaborn library makes plots look nicer\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "\n",
    "# Don't display too many rows/cols of DataFrames\n",
    "pd.options.display.max_rows = 9\n",
    "pd.options.display.max_columns = 10\n",
    "\n",
    "# Round decimals when displaying DataFrames\n",
    "pd.set_option('precision', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Pokemon</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Stat Total</th>\n",
       "      <th>ATK Sum</th>\n",
       "      <th>DEF Sum</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>False</td>\n",
       "      <td>318</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>7</td>\n",
       "      <td>69</td>\n",
       "      <td>poison</td>\n",
       "      <td>grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ivysaur</td>\n",
       "      <td>False</td>\n",
       "      <td>405</td>\n",
       "      <td>202</td>\n",
       "      <td>203</td>\n",
       "      <td>10</td>\n",
       "      <td>130</td>\n",
       "      <td>poison</td>\n",
       "      <td>grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>venusaur</td>\n",
       "      <td>False</td>\n",
       "      <td>525</td>\n",
       "      <td>262</td>\n",
       "      <td>263</td>\n",
       "      <td>20</td>\n",
       "      <td>1000</td>\n",
       "      <td>poison</td>\n",
       "      <td>grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>charmander</td>\n",
       "      <td>False</td>\n",
       "      <td>309</td>\n",
       "      <td>177</td>\n",
       "      <td>132</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>naganadel</td>\n",
       "      <td>False</td>\n",
       "      <td>540</td>\n",
       "      <td>321</td>\n",
       "      <td>219</td>\n",
       "      <td>36</td>\n",
       "      <td>1500</td>\n",
       "      <td>dragon</td>\n",
       "      <td>poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>805</td>\n",
       "      <td>stakataka</td>\n",
       "      <td>False</td>\n",
       "      <td>570</td>\n",
       "      <td>197</td>\n",
       "      <td>373</td>\n",
       "      <td>55</td>\n",
       "      <td>8200</td>\n",
       "      <td>steel</td>\n",
       "      <td>rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>806</td>\n",
       "      <td>blacephalon</td>\n",
       "      <td>False</td>\n",
       "      <td>570</td>\n",
       "      <td>385</td>\n",
       "      <td>185</td>\n",
       "      <td>18</td>\n",
       "      <td>130</td>\n",
       "      <td>ghost</td>\n",
       "      <td>fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>807</td>\n",
       "      <td>zeraora</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "      <td>357</td>\n",
       "      <td>243</td>\n",
       "      <td>15</td>\n",
       "      <td>445</td>\n",
       "      <td>electric</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>807 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID      Pokemon  Legendary  Stat Total  ATK Sum  DEF Sum  Height  \\\n",
       "0      1    bulbasaur      False         318      159      159       7   \n",
       "1      2      ivysaur      False         405      202      203      10   \n",
       "2      3     venusaur      False         525      262      263      20   \n",
       "3      4   charmander      False         309      177      132       6   \n",
       "..   ...          ...        ...         ...      ...      ...     ...   \n",
       "803  804    naganadel      False         540      321      219      36   \n",
       "804  805    stakataka      False         570      197      373      55   \n",
       "805  806  blacephalon      False         570      385      185      18   \n",
       "806  807      zeraora       True         600      357      243      15   \n",
       "\n",
       "     Weight    Type 1  Type 2  \n",
       "0        69    poison   grass  \n",
       "1       130    poison   grass  \n",
       "2      1000    poison   grass  \n",
       "3        85      fire     NaN  \n",
       "..      ...       ...     ...  \n",
       "803    1500    dragon  poison  \n",
       "804    8200     steel    rock  \n",
       "805     130     ghost    fire  \n",
       "806     445  electric     NaN  \n",
       "\n",
       "[807 rows x 10 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_poke = pd.read_csv(\"statsDF.csv\")\n",
    "df_poke"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping columns that are irrelevant to this section and splitting the dataframe into two dataframes for Legendary and NonLegendary Pokemon:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Pokemon</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>ATK Sum</th>\n",
       "      <th>DEF Sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>articuno</td>\n",
       "      <td>True</td>\n",
       "      <td>265</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>zapdos</td>\n",
       "      <td>True</td>\n",
       "      <td>315</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>moltres</td>\n",
       "      <td>True</td>\n",
       "      <td>315</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>mewtwo</td>\n",
       "      <td>True</td>\n",
       "      <td>394</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>800</td>\n",
       "      <td>necrozma</td>\n",
       "      <td>True</td>\n",
       "      <td>313</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>801</td>\n",
       "      <td>magearna</td>\n",
       "      <td>True</td>\n",
       "      <td>290</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>802</td>\n",
       "      <td>marshadow</td>\n",
       "      <td>True</td>\n",
       "      <td>340</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>807</td>\n",
       "      <td>zeraora</td>\n",
       "      <td>True</td>\n",
       "      <td>357</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID    Pokemon  Legendary  ATK Sum  DEF Sum\n",
       "143  144   articuno       True      265      315\n",
       "144  145     zapdos       True      315      265\n",
       "145  146    moltres       True      315      265\n",
       "149  150     mewtwo       True      394      286\n",
       "..   ...        ...        ...      ...      ...\n",
       "799  800   necrozma       True      313      287\n",
       "800  801   magearna       True      290      310\n",
       "801  802  marshadow       True      340      260\n",
       "806  807    zeraora       True      357      243\n",
       "\n",
       "[68 rows x 5 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_AD = df_poke.drop(columns = [\"Height\",\"Weight\",\"Stat Total\",\"Type 1\",\"Type 2\"])\n",
    "df_LegsEnd = df_AD[df_AD[\"Legendary\"] == True]\n",
    "df_LegsDontEnd = df_AD[df_AD[\"Legendary\"] == False]\n",
    "df_LegsEnd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Pokemon Distribution by Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data is organized distinctly, let's visualize the pokemon in scatterplots with their offensive stat totals on the x-axis and their defensive stat totals on the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_LegsEnd.plot.scatter(x=\"ATK Sum\", y=\"DEF Sum\", c=\"orange\")\n",
    "plt.title(\"Legendary Offensive vs. Defensive Stat Distribution\", pad=(25))\n",
    "df_LegsDontEnd.plot.scatter(x=\"ATK Sum\", y=\"DEF Sum\", c=\"b\")\n",
    "plt.title(\"NonLegendary Offensive vs. Defensive Stat Distribution\", pad=(25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the two scatterplots above, we can observe the distribution of offensive and defensive stats on the Legendary and NonLegendary Pokemon separately. In the first scatterplot, we can already see that Legendary Pokemon tend to lean in the upper range for both offsensive and defensive stats, with the exception of a couple of outliers. In the second scatterplot, we can observe that the majority of the NonLegendary Pokemon cluster in a lower range than the Legendary Pokemon. \n",
    "\n",
    "\n",
    "Now let's see how the two groups look in the same scatterplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df_LegsDontEnd.plot.scatter(x=\"ATK Sum\", y=\"DEF Sum\", c=\"b\")\n",
    "b = df_LegsEnd.plot.scatter(x=\"ATK Sum\", y=\"DEF Sum\", c=\"orange\", ax = a)\n",
    "plt.title(\"Offensive vs. Defensive Stat Distribution\", pad=(25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After stacking the two scatterplots together we can observe that the majority of the Legendary Pokemon (yellow) tend to have greater stats all around. Except for a few exceptions, almost all of the Legendary Pokemon data points are in the upper right quadrant of the scatterplot such that they either have great defensive stats or greater offensive stats or even both. Only a handful of the NonLegendary Pokemon data points are in the same class as the Legendary Pokemon.\n",
    "\n",
    "However, despite that distinct difference between the two categories of Pokemon, they both seem to have populations that do not heavily skew towards either axis.\n",
    "\n",
    "\n",
    "Now, to visualize their stats as a whole and comparing their distributions, let's create another data frame to handle the \"Stat Total\" data and drop irrelevant columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Pokemon</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Stat Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>False</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ivysaur</td>\n",
       "      <td>False</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>venusaur</td>\n",
       "      <td>False</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>charmander</td>\n",
       "      <td>False</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>naganadel</td>\n",
       "      <td>False</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>805</td>\n",
       "      <td>stakataka</td>\n",
       "      <td>False</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>806</td>\n",
       "      <td>blacephalon</td>\n",
       "      <td>False</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>807</td>\n",
       "      <td>zeraora</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>807 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID      Pokemon  Legendary  Stat Total\n",
       "0      1    bulbasaur      False         318\n",
       "1      2      ivysaur      False         405\n",
       "2      3     venusaur      False         525\n",
       "3      4   charmander      False         309\n",
       "..   ...          ...        ...         ...\n",
       "803  804    naganadel      False         540\n",
       "804  805    stakataka      False         570\n",
       "805  806  blacephalon      False         570\n",
       "806  807      zeraora       True         600\n",
       "\n",
       "[807 rows x 4 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total = df_poke.drop(columns = [\"Height\",\"Weight\",\"DEF Sum\",\"ATK Sum\",\"Type 1\",\"Type 2\"])\n",
    "df_total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we'll separate the data frame into two parts: Legendary and NonLegendary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Pokemon</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Stat Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>articuno</td>\n",
       "      <td>True</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>zapdos</td>\n",
       "      <td>True</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>moltres</td>\n",
       "      <td>True</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>mewtwo</td>\n",
       "      <td>True</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>800</td>\n",
       "      <td>necrozma</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>801</td>\n",
       "      <td>magearna</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>802</td>\n",
       "      <td>marshadow</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>807</td>\n",
       "      <td>zeraora</td>\n",
       "      <td>True</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID    Pokemon  Legendary  Stat Total\n",
       "143  144   articuno       True         580\n",
       "144  145     zapdos       True         580\n",
       "145  146    moltres       True         580\n",
       "149  150     mewtwo       True         680\n",
       "..   ...        ...        ...         ...\n",
       "799  800   necrozma       True         600\n",
       "800  801   magearna       True         600\n",
       "801  802  marshadow       True         600\n",
       "806  807    zeraora       True         600\n",
       "\n",
       "[68 rows x 4 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_legtotal = df_total[df_AD[\"Legendary\"] == True]\n",
    "df_legtotal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Pokemon</th>\n",
       "      <th>Legendary</th>\n",
       "      <th>Stat Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>False</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ivysaur</td>\n",
       "      <td>False</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>venusaur</td>\n",
       "      <td>False</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>charmander</td>\n",
       "      <td>False</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>803</td>\n",
       "      <td>poipole</td>\n",
       "      <td>False</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>naganadel</td>\n",
       "      <td>False</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>805</td>\n",
       "      <td>stakataka</td>\n",
       "      <td>False</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>806</td>\n",
       "      <td>blacephalon</td>\n",
       "      <td>False</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>739 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID      Pokemon  Legendary  Stat Total\n",
       "0      1    bulbasaur      False         318\n",
       "1      2      ivysaur      False         405\n",
       "2      3     venusaur      False         525\n",
       "3      4   charmander      False         309\n",
       "..   ...          ...        ...         ...\n",
       "802  803      poipole      False         420\n",
       "803  804    naganadel      False         540\n",
       "804  805    stakataka      False         570\n",
       "805  806  blacephalon      False         570\n",
       "\n",
       "[739 rows x 4 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nontotal = df_total[df_AD[\"Legendary\"] == False]\n",
    "df_nontotal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now with the data separated, let's visualize the distributions using histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_legtotal[\"Stat Total\"].hist(bins = 25)\n",
    "plt.xlabel(\"Stat Total\")\n",
    "plt.ylabel(\"Pokemon\")\n",
    "plt.title(\"Legendary Total Stat Distribution\", pad=(25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this first histogram, we plotted the distribution of Legendary Pokemon with respect to the total sum of all their core stats. Most notably, we observe that a large number of Legendary Pokemon have stat totals of around 600 and even a significant number around 700. Furthermore, we can observe that there is an outlier data point with a stat total of around 200. Altogether, the histogram shows that Legendary Pokemon have stat totals that skews to the right of the histogram, with notably high stats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_nontotal[\"Stat Total\"].hist(bins = 25)\n",
    "plt.xlabel(\"Stat Total\")\n",
    "plt.ylabel(\"Pokemon\")\n",
    "plt.title(\"NonLegendary Total Stat Distribution\", pad=(25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this second histogram, we plotted the distribution of NonLegendary Pokemon with respect to the total sum of all their core stats. Here, we can observe that the distribution is not quite normal with its two peaks at roughly 300 and 500 and a fall-off on either end that do not have any extremeley explicit outliers. The distribution seems to be wide and does not skew in either direction in particular.\n",
    "\n",
    "Now let's see how they compare together on the same histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAFACAYAAACWdPreAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3deVhU1f8H8PewDCoYIuGGG+J3UEAFRQ0F3HABxQ2XFJdcUkzzp+KWlUqZGpamuKXivuRSrkUguYVlhaDl8tVMFEXDBFFZBIHz+4MvkyMgcxlm0/freXge59wz937O3HE+995z7zkyIYQAERFROZnoOwAiIjJuTCRERKQRJhIiItIIEwkREWmEiYSIiDTCREJERBphIiHJZs+eDScnpzL/Zs+eLXnd9+7dw5MnTyS/b9CgQejRo0eZ9fLz87F9+3YMGDAA7u7ucHd3R0BAAFasWIGMjIxi9XNycpCSkiI5nqJtJScnv7DOX3/9Vexza9q0KTw8PDBo0CDs3LkTBQUFKu85deoUnJyc8O2330qO6datW2rVa9++PcaMGVPq64rw/L6eOnUqmjVrVqHbIN0w03cAZHwGDx4MT09P5euzZ89i9+7dGDx4MFq1aqUsr1+/vqT1xsTEYObMmYiKikKlSpUqLN5nTZ06FT/88AP8/f3Rt29fyGQy/P7771i7di2ioqLw1VdfoWrVqgCAmzdvYuzYsZgyZQp69uwpaTvp6ekYNWoU/Pz8MG7cuDLrv/HGG+jfvz+AwgSUnp6OEydOIDQ0FD///DNWrFgBmUwGAGjSpAnCwsLg5uYmKaYvvvgCR48eVSsBzZ07F6+99pqk9UtR0r4OCgpCly5dtLZN0h4mEpKs6Ei+SH5+Pnbv3g03Nzf06dOn3Os9d+4cMjMzKyLEEv3888+IiorC3LlzERQUpCwPCgqCp6cnZs2ahU2bNmHy5MkAChNJUlJSubaVmpqKS5cuwc/PT636DRo0KPbZjR49GosWLcLmzZuxbds2jBgxAgBQo0aNcn3OP/30E/Lz89Wq2717d8nrl6Kkfe3h4aHVbZL28NIWvTISEhIAAF5eXsWW9enTBzY2Njh37pyuw3qh6dOno27dutiwYUOxS1xEhoKJhHTi559/xvDhw+Hm5gZ3d3eMHj0a8fHxyuVTp07F+vXrART+0D97Pf7IkSMYOnQoWrVqBVdXV/j6+mLZsmV4+vSppBgsLS0BALt378bzIwPJZDKcOnUKGzduBADs2rULb7/9NgBg2rRpKtfuf//9d0ycOBGenp5wcXFB+/btMWPGDNy7dw9AYR+Gv78/AODzzz+Hk5MT/vnnH0mxFjE3N0ePHj2QkpKCq1evKtf/fB/JTz/9hCFDhqBVq1Zwd3fH0KFDcerUKeXy9u3b4/z580hMTISTkxPWrVuHnJwcODk5YeXKlRg7dixcXV3Rp08fFBQUlNonsnPnTnTu3BnNmzfHm2++iZ9//llleWnve7a8tH1dUh/JrVu3MG3aNLRt2xbNmjVD37598c0336jUmTp1Kvr06YOEhAQMGTIELVq0QPv27bF48WLk5uaq/VlT+TGRkNZFRkZi1KhRSE1NxaRJkxAcHIwbN25gxIgRyh+7oKAgdOrUCUDh9fmxY8cCALZv346QkBDY2tpixowZmDFjBuzs7LB27Vps2rRJUhw9evSAlZUVIiIi0K1bNyxZsgSxsbHIzs4GAMjlcmVdT09P5Q9cUFAQFi1aBAC4cOECgoKCcPfuXQQHB+PDDz+Ep6cnDh8+jDlz5gAo7MOYMWMGAMDf3x9hYWEa9Tf85z//AQD897//LXH51atXMWHCBJiZmSEkJATTpk3Do0ePEBwcjN9//x1A4Wdav3592NnZISwsTPlZA0BERAQA4IMPPsCAAQNgYlLyz0J8fDyWLFmCgIAATJkyBampqRg7dizi4uIktae0ff28xMREDBgwAD/++COGDBmCGTNmoEqVKnjvvfewfPlylbopKSl4++230aRJE7z//vto1qwZNm3ahDVr1kiKjcpJEGno66+/FgqFQnz99dfFluXk5Ih27doJX19fkZmZqSxPTU0Vnp6eonPnziI/P18IIcSSJUuEQqEQ9+7dU9br3LmzGDZsWLF1enp6isDAQGXZwIEDRffu3cuM9bfffhOdOnUSCoVC+efq6ireeecdcfHiRZW6J0+eFAqFQhw5ckRZNnv2bNGyZUvx+PFjlbrvvPOOcHJyUrbx2rVrQqFQiC+//PKF8RTV+/DDD0utc+zYMaFQKMSmTZtKjCs8PFwoFAqVmFJSUkS3bt3Erl27lGXPf0ZPnjwRCoVCtG3btlh72rVrJ0aPHq3yWqFQiJ9++klZlpqaKlq2bCkGDx5c6vtKKy9pX0+ZMkW4uroqX0+YMEE4OzuLK1euKMvy8vLEqFGjRJMmTcSNGzeU71MoFGL37t3Kevn5+aJLly6iS5cuxWKhisczEtKq8+fP4/79+xg+fDiqVKmiLK9evTqGDBmC27dvl3qkDRSezaxatUqlLC0tDa+99hqysrIkx+Ph4YGoqCisXr0aAwcORJ06dZCbm4uYmBgMGDAAUVFRL3z/J598gqNHj8LKykpZ9vjxY1SqVAlCCOXZTUXKy8sDAOVdW8+rVasWACA0NBSXL18GUNghHxUVhTfffLPM9bu7u6u0pzQuLi4qd+tVr14dPXv2xLlz5/Dw4cMy3y9Fbm4ufvzxR3Tq1AkKhUJZbmpqivHjx6OgoADHjx9Xec+zt3+bmJjAyckJ9+/fr9C4qGS8a4u06vbt2wAABweHYsscHR0BAHfu3IGzs3OJ75fL5Thz5gwiIyORmJiImzdvIi0trdR1qsPc3BxdunRR3mp67do1bNu2DV999RVCQ0PRqVMnlctczzIxMUFqairWrFmDq1ev4tatW7hz546yz0UbHeLp6ekACn+4S9K7d2/88MMPOHToEA4dOoSaNWuiY8eO6Nevn8rddaWxtbVVK45GjRoVK6tXrx6EELhz5w6sra3VWo86/vnnH+Tm5r7we/PsMzrm5ubFLh/K5XK171IjzfCMhLRKvGC6m6IfXXNz81LrLFy4EG+//TauXr0KZ2dnTJkyBYcPHy7Xg2vLly/H/v37i5U3btwYoaGh6N+/P1JTU3Hz5s1S1xEdHa384a5Tpw6GDx+O7du3Y9SoUZLjUdelS5cAFPa9lEQul2PNmjU4cOAA3nnnHdSoUQN79uzBm2++ic2bN5e5/tL6RJ5X0hlR0f4tax1Sf9Clfm9KO1sj3eAZCWmVvb09AOD69evw9vZWWZaYmAjg30szz0tMTMSWLVswaNAgfPzxxyrLUlNTYWFhISmWffv2wcrKCv369StxeVGn9osehvzoo4/wn//8B3v27FGpt2fPHkmxqCsvLw8xMTGoX7++Mr7n3b59G/fu3UPLli3RtGlT/N///R+Sk5MxfPhwRERE4K233qqQWEp6Sv/GjRswMTFR7mcTE5Nid0rl5ubi8ePHkrZVo0YNmJub4/r168WWFX1vateuLWmdpD08IyGtcnd3h42NDbZv367Sp/Hw4UPs3r0b9vb2ymvgpqamAP494iy6pFN0KaPI0aNHcefOHclHuQEBAbh+/bryFt9nZWdn49ChQ1AoFKhXr16J8Qgh8OjRI9StW1clidy6dQvHjh0D8O+R9/PvLa8VK1bg3r17pd7ZBAArV67E6NGjVfoD7O3tUaNGDWUcQOGP/IuO9MuSkJCAa9euKV+npKTgu+++g6enp7KPxc7ODn/99ZdKMomKilL28xQp6/ORy+Xw8vLCiRMnlLc9F9XfsGEDTExM0KFDh3K3hSoWz0hIq+RyOd5//33MmDEDAwYMQGBgIPLz87Fnzx48ePAAq1evVl6WsLGxAQCsX78eXl5eaNu2LWrUqIFVq1YhMzMTdnZ2OHfuHA4cOAALCwvJT8FPmjQJv/32Gz799FP88MMP6NSpE2xsbHDnzh0cPHgQqamp2LJli7J+UZ/E/v378eTJEwwYMABeXl44duwYPvroIzRt2hRJSUnYs2eP8oezKKaitkRHR8PW1hZ+fn4v7NC+efMmDh48CKDwxzItLQ0nT57EL7/8gp49e2LQoEGlvnfYsGH49ttvERQUhEGDBsHKygqnT59GQkKC8jbkovZcuHABmzdvRqtWrVQ6sdXx2muv4a233sLIkSMBFN6aLZPJMGvWLGWdnj17IiwsDOPGjYO/vz8SExOxb9++YmcPz+/rjh07FtvejBkzEBcXh6CgIAQFBcHW1hbR0dH49ddfERwcLHkIHtIeJhLSuoCAAFhbW2Pt2rUIDw+HmZkZ3N3dsWTJEpXO4KK+h6+++gpxcXE4cOAA1q9fj8WLF2PTpk2QyWSoV68eQkNDkZ6ejs8++wx//vlnqZd8nlelShXs2LEDO3fuxPfff4/169cjMzMTtra2aNeuHSZMmKDy49SkSRMMHjwYhw8fRkJCAry9vbFgwQKEhYUhKioK33zzDWrVqoVBgwbB29sbw4cPx5kzZ+Do6Ahra2u8++672LJlCz755BM0btz4hR3fZ86cwZkzZwAUHq3b2dmhfv36WLBgAQIDA1/YB+Dq6oqNGzdi1apVWL9+PbKysuDg4IDQ0FCVu7bGjx+Pv/76C5999hnefPNNlSSjDl9fXzg4OGDLli149OgR3NzcMGvWLDg5OSnrjBw5Eo8fP8Y333yDjz/+GM7OzlizZg1Wrlypsq7n93VJicTR0RF79uzBF198gZ07dyInJweNGzfGp59+ir59+0qKnbRLJjQ51yUiolce+0iIiEgjTCRERKQRJhIiItIIEwkREWmEiYSIiDTCREJERBphIiEiIo0wkRARkUaYSIiISCNMJEREpBEmEiIi0ggTCRERaYSJhIiINMJEQkREGmEiISIijTCREBGRRphIiIhII0wkRESkESYSIiLSCBMJERFphImEiIg0wkRCREQaYSIhIiKNMJEQEZFGzPQdgK4JISCE7rYnkxVtV3fb1CW2z7ixfcZLl22TyQBZ0QZL8AomEiA1NUNn27O2rgwAePgwW2fb1CW2z7ixfcZLl22ztbXCC/IIL20REZFmmEiIiEgjTCRERKQRJhIiItIIEwkREWmEiYSIiDTCREJERBp55Z4jIXqZWFpawMxM/ePBvLwCZGbmaDEiehUxkRAZMTMzE+TmCyQmPyyzroO9NeQSkg6RuphIiIxcYvJDzFlzusx6Cye0h1P9ajqIiF41PDwhIiKNMJEQEZFGmEiIiEgjTCRERKQRJhIiItKI3hPJrl274OfnBzc3NwQEBODQoUMqy2NjYxEYGIgWLVqgc+fO2Lhxo54iJSKikug1kezevRvz589Hx44dsXr1arRr1w4zZsxAZGQkACA+Ph7BwcFo1KgRwsPDERAQgLCwMEREROgzbCIieoZenyPZv38/2rZti1mzZgEA2rVrhwsXLmDnzp3w8/PDihUr4OzsjCVLlgAAfHx8kJeXh7Vr12L48OGQy+X6DJ+IiKDnM5KcnBxYWlqqlFWrVg3p6enIyclBXFwcunXrprK8e/fuePToEeLj43UZKhERlUKvZyQjRozAhx9+iMjISHh7eyM2NhYnTpzA1KlTcevWLTx9+hQODg4q72nQoAEAIDExEW+88Ybkbcpk/851rAtmZqYAdLtNXWL79KsoPin1n22LobdPUy9z+3TZthfN1w7oOZH07NkTZ86cwZQpU5Rl/fr1w9ixY5GQkAAAsLKyUnlP0RlMRkaG7gIlIqJS6TWRTJgwAQkJCXjvvffg7OyM8+fPY/Xq1bCysoK/vz8AQFZKKjQxKd9VOSGAhw+zyx2zVEVHC7rcpi6xffol9Wg0Ly9fpS2G3j5Nvczt02XbbG2tXnhWordEEh8fj9jYWCxatAj9+/cHALRp0wavvfYa5s6diwEDBgAofuZR9Lpq1aq6DZiIiEqkt872O3fuAABatmypUu7h4QEAuHz5MkxNTZGUlKSyvOj1830nRESkH3pLJEWJ4LffflMpP3fuHACgUaNG8PDwQHR0NIQQyuVRUVGoWrUqXF1ddRcsERGVSm+XtlxcXODr64uFCxciMzMTTZs2xYULF7Bq1Sr4+PigRYsWmDBhAkaNGoWpU6eiX79+SEhIQEREBEJCQlC58st3FwYRkTHSa2f7smXLsHLlSmzevBmpqamwt7fH6NGjMW7cOACAp6cnwsPDsWLFCkycOBE1a9bEzJkzMXr0aH2GTUREz9BrIpHL5Zg2bRqmTZtWap2uXbuia9euOoyKiIik0PugjUREZNyYSIiISCNMJEREpBEmEiIi0ggTCRERaYSJhIiINMJEQkREGmEiISIijTCREBGRRphIiIhII0wkRESkESYSIiLSCBMJERFpRK+j/5JxsrS0gJlZ4TGImZkpgBfPHZ6XV4DMzBydxEalq/26JczMTFX21Yv2H/cbqYuJhCQzMzNBbr5AYvLDMus62FtDbsYTX0NQycIMWTl53G9U4ZhIqFwSkx9izprTZdZbOKE9nOpX00FEpA7uN9IGHnIQEZFGmEiIiEgjTCRERKQRJhIiItIIEwkREWmEiYSIiDTCREJERBphIiEiIo0wkRARkUaYSIiISCNMJEREpBEmEiIi0ggTCRERaYSJhIiINMJEQkREGmEiISIijTCREBGRRphIiIhII0wkRESkEb0nkt9++w1DhgxBixYt4OXlhY8//hiZmZnK5bGxsQgMDESLFi3QuXNnbNy4UY/REhHR8/SaSM6dO4dRo0bBzs4Oa9aswcSJE3Ho0CF88MEHAID4+HgEBwejUaNGCA8PR0BAAMLCwhAREaHPsImI6Blm+tz4Z599Bjc3NyxfvhwymQzt2rVDQUEBNm3ahOzsbKxYsQLOzs5YsmQJAMDHxwd5eXlYu3Ythg8fDrlcrs/wiYgIejwjSUtLQ1xcHIYMGQKZTKYsDwoKQkxMDExMTBAXF4du3bqpvK979+549OgR4uPjdR0yERGVQG+J5OrVqxBCwNraGlOmTIGbmxtatWqFefPm4cmTJ7h16xaePn0KBwcHlfc1aNAAAJCYmKiPsImI6Dl6u7SVlpYGAJg9eza6du2KNWvW4MqVK/jiiy+Qk5ODwYMHAwCsrKxU3mdpaQkAyMjIKNd2ZTLA2rqyBpFLY2ZmCkC329S2ojZJqW+s7Tf0/Sd1X0hdt6G2W12Gvv80ocu2PXPRqORYtB5BKZ4+fQoAaNmyJebNmwcA8PT0hBACn376KQYNGgQAKpe9nmViovcbzoiICHpMJEVnFj4+PirlXl5eWLx4Mf744w8Axc88il5XrVq1XNsVAnj4MLtc7y2PoqMFXW5T26QeAeXl5Rtt+w19/2nzaNSY91sRQ99/mtBl22xtrV54VqK3w/qGDRsCAHJzc1XKi85U6tatC1NTUyQlJaksL3r9fN8JERHph94SiaOjI+zt7fHdd9+plB8/fhxmZmZwd3eHh4cHoqOjIYRQLo+KikLVqlXh6uqq65CJiKgE5bq0derUKRw+fBj3799Hfn5+seUymQxbtmx54TpkMhmmT5+OadOmYfr06ejfvz8uXLiANWvWYPjw4ahevTomTJiAUaNGYerUqejXrx8SEhIQERGBkJAQVK788nWeEREZI8mJZMeOHViwYAEAwNbWVqOHAv39/SGXy7Fq1SqMHz8etra2mDhxIsaPHw+gsPM9PDwcK1aswMSJE1GzZk3MnDkTo0ePLvc2iYioYklOJFu3bkWTJk2wfv16vP766xoH4OvrC19f31KXd+3aFV27dtV4O6QftV+3lHQbaV5eATIzc7QcFZWF+42kkJxI7t69izlz5lRIEqGXXyULM2Tl5CEx+WGZdR3srSE3423dhoD7jaSQnEjq16+P+/fvayMWekklJj/EnDWny6y3cEJ7ONWvpoOISB3cb6QuyYcR48aNw7Zt2/Dnn39qIx4iIjIyks9Izp49C0tLS/Tp0wcODg6oXr16safP1blri4iIXg6SE8mPP/4IAKhVqxays7ORnJxc4UEREZHxkJxIjh07po04iIjISJV7rK38/HxcuHABycnJkMvlqF27NlxcXCoyNiIiMgLlSiTHjx9HaGgoUlJSlMOXyGQy1KhRA/PmzUPnzp0rNEgiIjJckhNJXFwc3n33Xdja2mLq1KlwdHSEEALXr1/Hzp07MXnyZGzduhUtW7bURrxERGRgJCeS8PBw2NvbY9++fcWGch86dCgCAwOxZs0arF+/vsKCJCIiwyX5OZLff/8dAwcOLHE+ECsrKwwYMADnz5+vkOCIiMjwVfi4BjKZTDmnCBERvfwkX9pq0aIF9u3bh6FDh6JKlSoqyzIyMrB37140a9aswgIk0iVLSwuYPTNuVFnzYnOwQqJyJJJJkyZhxIgR6NWrF4YNG6ac6bCosz0lJQWhoaEVHSeRTpiZmSA3X3CwQiIJJCcSDw8PhIeH46OPPkJYWJhyeBQhBOzs7LBs2TK88cYbFR4oka7oe7DC58+KXqTojIlIn8r1HEmXLl3QsWNHXLx4Ebdv3wYA2Nvbw8XFBWZm5X7GkYgg7azI2aG6DiIierFy/+qbmpqiefPmaN68eUXGQ0RQ/6xo1wJ/HURD9GKSE0lubi7Wr1+P06dP459//kFBQUGxOjKZDDExMRUSIBERGTbJieSTTz7B7t27UatWLdjb28PEhJ2NRESvMsmJ5OjRo+jVqxc+++wzbcRDRERGRvLpRH5+Plq3bq2NWIiIyAhJTiTdu3fH0aNHtRELEREZIcmXtmbNmoVx48bhzTffhK+vL2xtbYtNtQsAffv2rZAAiYjIsJVrzvZLly4hOzsb586dK7GOTCZjIiEiekVITiRhYWGoUqUKZsyYAQcHB5ia8slaIqJXmeREkpSUhBkzZmDo0KHaiIeIiIyM5M52BwcHPH78WBuxEBGREZKcSCZPnowtW7bg5MmTJT7VTkRErxbJl7b27t0LuVyO4OBgWFhYoFq1asX6SThEChHRq0NyIsnMzETDhg2V85AQEdGrTXIi2bZtmzbiICIiI1XuYeTz8vLwxx9/4O7du2jTpg0qVaqE/Px8WFtbV2R8RERk4Mo1dG9kZCQ6duyIoUOHIiQkBH/++SfOnj2LDh06YMOGDRUdIxERGTDJiSQ2NhYhISFo2LAhZs2aBSEEAKBu3bpQKBT4/PPPcfDgwQoPlIiIDJPkRLJq1Sq4urpi69at6NOnj7Lc0dERO3fuhLu7O7Zs2VKhQRIRkeGSnEguX76Mnj17ljihlZmZGXr16oXExMQKCY6IiAyf5ERibm6OvLy8Upenp6fD3Ny8XMFMmjQJXbt2VSmLjY1FYGAgWrRogc6dO2Pjxo3lWjcREWmH5ETSpk0b7Nu3Dzk5OcWW3bt3Dzt37kSrVq0kB3Lw4MFi85zEx8cjODgYjRo1Qnh4OAICAhAWFoaIiAjJ6yciIu2QfPvvtGnTMHjwYPTu3Rs+Pj6QyWT44YcfcOLECezfvx+5ubmYPHmypHWmpKTgk08+Qa1atVTKV6xYAWdnZyxZsgQA4OPjg7y8PKxduxbDhw+HXC6XGj4REVUwyWckjo6O2LFjB2rUqIFt27ZBCIHt27djy5YtqF+/PjZv3oymTZtKWucHH3yA9u3bw9PTU1mWk5ODuLg4dOvWTaVu9+7d8ejRI8THx0sNnYiItKBcDyQ6OTlh27ZtSE9PR1JSEgoKCmBvbw87OzvJ69q7dy8uXryII0eOICwsTFl+69YtPH36FA4ODir1GzRoAABITEzEG2+8UZ7wiYioAklOJAkJCXB3dwcAVKtWDdWqVVNZnpycjNDQUKxbt67MdSUnJ2PRokVYtGgRqlevrrKsaKh6KysrlXJLS0sAQEZGhtTQAQAyGWBtXblc7y0PM7PCAS11uU1tK2qTttatz89Katu0Ea82P19t0fd+K83L+P+viC7bVsJs6iokX9oaM2YMfv3112Ll+fn5WLduHXr16oXY2Ngy1yOEwJw5c9ChQwd07969xOUASpwPHkCJtx8TEZHuST4jcXR0xLhx4xAeHg5vb28AhWcpc+fOxZ9//okmTZpg3rx5Za5nx44duHLlCg4fPqy8nbgoeeTl5aFq1aoAip95FL0uWi6VEMDDh9nlem95FB0t6HKb2qbNI6C8vHy9flZS26aNeI3x6Fnf+600L+P/vyK6bJutrdULz0okJ5ItW7Zg4sSJmDhxIj766CPEx8dj3759sLS0xPvvv4+goCC1zhaioqLw4MEDeHl5FVvm4uKC+fPnw9TUFElJSSrLil4/33dCRET6ITmRVKlSBevWrUNISAhmz54NmUyG3r17Y+bMmbC1tVV7PaGhocjMzFQpW7VqFS5fvoyVK1eibt26iIyMRHR0NEaOHKm8xBUVFYWqVavC1dVVauhERKQF5bpry9zcHMuXL8e8efOwb98+tGrVSlISAYBGjRoVK6tWrRrkcjmaNWsGAJgwYQJGjRqFqVOnol+/fkhISEBERARCQkJQubLxnf4TEb2MykwkXbp0eeHygoICzJ8/H19++aWyrKKm2vX09ER4eDhWrFiBiRMnombNmpg5cyZGjx6t8bqJiKhilJlI6tSpo9FyKRYvXlysrGvXrsXG3yIyBLVft5R022teXgEyM4sPLURk7MpMJJxal6hklSzMkJWTh8Tkh2XWdbC3htyMt6zTy6ncU+3m5+fjwoULSE5OhlwuR61atdgBTq+cxOSHmLPmdJn1Fk5oD6f61cqsR2SMypVIjh8/jtDQUKSkpKg8OFijRg3MmzcPnTt3rtAgiYjIcElOJHFxcXj33Xdha2uLqVOnwtHREUIIXL9+HTt37sTkyZOxdetWtGzZUhvxEhGRgZGcSMLDw2Fvb499+/YVe7p86NChCAwMxJo1a7B+/foKC5K0z9LSAmZqXsPX1lhQ7LwmMk6SE8nvv/+OiRMnljhEiZWVFQYMGMAkYoTMzEyQmy/U6jh2dqheZp3yYOc1kXEqd2d7aWQyGZ4+fVrRqyUdULfjeNcCf73HwM5rIsMh+ZCuRYsW2LdvH7Kysooty8jIwN69e5VPphMR0ctP8hnJpEmTMGLECPTq1W0opIsAABsWSURBVAvDhg1Dw4YNAUDZ2Z6SkoLQ0NCKjpOIiAyU5ETi4eGB8PBwfPTRRwgLC1MOpiiEgJ2dHZYtW8aZC4mIXiFqJ5KnT5/i2rVryMvLQ7t27XDs2DFcvHgRt2/fBgDY29vDxcUFZmYV3u1CREQGTK1f/c2bN2PVqlXKSaXkcjmGDh2KkJAQNG/eXKsBEhGRYSszkRw4cACLFy+Gvb09+vTpAxMTE/zyyy/YvHkz8vPzMWfOHF3ESUREBqrMRLJz5064ublhy5YtsLCwAFDYHzJ16lTs3r0b06dPh1wu13qgRERkmMq8/fevv/5CQECAMokAhc+KvPXWW8jNzcX169e1GiARERm2MhNJdnZ2iU+x161bF0IIPHr0SCuBERGRcSgzkRQUFChv8X2WqWnheEv5+fkVHxURERkNDlZEREQaUev23/T0dNy5c0el7OHDwoH10tLSii0DKnYKXiIiMlxqJZKFCxdi4cKFJS6bPn16sTKZTIZLly5pFhkRERmFMhNJv379dBEHEREZqTITyaJFi3QRB9FLTcqkXdqaOIxIWzgwFpEOSJm0S1sThxFpCxMJkY4YwsRhRNrAREJGifO7ExkOJhIySpzfnchwMJGQ0eL87kSGgYdpRESkESYSIiLSCBMJERFphImEiIg0ws52ItIIb8UmJhIi0ghvxSYmEiLSmCHdim1paQGz/yWronHLyjpb4lmSZphIiOilYmZmAtOCHOSmJEKd+VvlNR0AMwutx/UyYyIhopdObkoi7m6fp1bd2sNCYVq7iZYjernp9WJlQUEBdu3ahYCAALi7u8PX1xeLFi1CRkaGss4ff/yB4cOHw93dHV5eXli6dCmePn2qx6iJiOhZej0j2bBhA7744guMGTMGnp6eSExMxIoVK3Dt2jVERETg5s2beOutt+Du7o4vvvgCf/31F5YtW4aMjAzMnTtXn6ETEdH/6C2RCCGwYcMGDB48GCEhIQCAdu3awcbGBlOnTsXly5exfft2VK1aFatXr4ZcLkeHDh1QqVIlLFiwAOPHj0fNmjX1Fb5ePNuJqC52IhKRtuktkWRmZqJ3797w8/NTKW/UqBEAICkpCadPn0anTp0gl8uVy3v06IHQ0FDExsYiMDBQpzHrm5mZCXLzhVq3WQK81ZKIdENvicTKygoffPBBsfKYmBgAgKOjI+7evQsHBweV5dWrV4eVlRUSExN1EqehUfc2S4Cj3hKRbhjUXVvnz5/HunXr4Ovri9deew1AYcJ5nqWlpUqHvBQyWdn3lFckde9jl7Iuqe951ecJr/26JczNTWFrW/y79DyZTAcBveKkPAVf3vWrc9vv8+/R5e9CRajI35aylPX/wmASydmzZxEcHIy6detiwYIFyM3NBQDISmiBEAImJrxkQ+qpZGGGzCecL51IWwwikXz33XeYPXs2GjZsiA0bNsDGxgaZmZkAUOKZR1ZWFqpWrVqubQkBPHyYrVG8UhQdLVTENstz5JGXl6/Wto3taEwqzpduONT9TpaXNv+fGJKK/G0pi62t1QvPSvR+WL9p0yZMmzYNbm5u2LFjB2rUqAGg8PJVzZo1cfPmTZX6qampyMjIKNZ3QkRE+qHXM5K9e/di8eLF8Pf3x6effqpydxYAtG/fHsePH8fMmTOVy6KiomBqaoo2bdroI2SjImVU1pe5j4SMm9Tb3svTR0Ka0VsiSU1NxSeffAJ7e3sEBQXh0qVLKsvr16+PsWPH4ttvv8W4ceMwcuRI3LhxA0uXLsWgQYNQp04dPUVuPKSMysq+ATJUz46dpQ7zek21HBE9T2+J5Mcff0R2djaSk5MRFBRUbHlYWBj69OmDjRs3IiwsDJMnT4aNjQ1GjRqFd999Vw8RGyf2DdDLQMrYWQ1Ctmo5Gnqe3hJJ37590bdv3zLreXh4YM+ePTqIqOJIHcaaT58TkTEziLu2XjZSnkDn0+dEZOyYSLTEkCb6ISLSJh4KExGRRphIiIhII0wkRESkESYSIiLSCBMJERFphImEiIg0wkRCREQa4XMkREQGoDyDUwohtBiR+phIiIgMgNTBKU1rOkAmr6LlqNTDREJEOiNlagOgcBy6V4mUwSlrDwuFRT0XLUekHiYSItIZKVMbcBw648FEQkQ6xXHoXj5M90REpBEmEiIi0ggTCRERaYSJhIiINMJEQkREGmEiISIijTCREBGRRvgciZ5JedLXzMxUBxEREUnDRKJnUp70dXaoroOIiIikYSIxAOo+6btrgb8OoiEikoZ9JEREpBEmEiIi0ggTCRERaYSJhIiINMLOdiLSubF9XNGojvUL6zjYWytvec/XRVBUbkwkRKRzjepYw7lu5RdPK3vvb+QAqFSvqc7iovJhIiEivVB3WtkGIVt1EA1pgolETZaWFjBTc9pPPoFORID03w1jvYTHRKImMzMT5OYLPoFORGozMzOBaUHOiy/h/Y+5EV/CYyKRgE+gE5FUr8IlPN7+S0REGmEiISIijRhFIjly5Ah69uyJ5s2bw8/PDwcOHNB3SERE9D8Gn0giIyMxffp0tG/fHqtWrUKbNm0wa9YsfP/99/oOjYiIYASd7UuXLoWfnx/mzJkDAPD29sbDhw+xfPly9OjRQ8/Rla2sJ3gry03hYG+NhRPaK8uu33mIDQcv6CI8guo+Kml/PM9Q9s/YPq5qxVukstwUtV+31EostV+3VDsGB/v//X+497dWYtGn52/31eYtveY2tSCTQa1J8Yrk5RUgMzOnwmMx6ERy69YtJCUlYdq0aSrl3bt3R2RkJG7duoV69erpKTr1lPUEb+7tv2EKoLF54Wt5TQfdBUcAVPfR8/vjeYa0fxrVsQaeZsP0XmKp8ap4WhmVLORaiaWShVnZT6rj3+97pXpN8UQrkejX848JONhbQ1tPlcnklVGQk4V8NW4tBv733TWz0E4sQgihlTVXgJMnT2LcuHE4ePAgmjRpoiy/dOkS+vXrh/Xr18PHx0fSOjVtbkFB2e83MZEp6xb9G6JAvQ3IDP5q48tLnX1kiPvHkL5bUmOR8plrY93P1teml6CdMpms1GUGfUby+PFjAICVlZVKuaVl4el5RkaG5HW+6MNQh6mp+u9XqSvj0+4Gz1j3kSHFLTUWKfW1uW5te8nbaYCHV/8qOnt4/se/qNzExKDDJyJ6JRj0L3HVqlUBFD/zyMzMVFlORET6Y9CJxMGhsGMzKSlJpfzmzZsqy4mISH8MOpE0aNAAdevWLfbMSHR0NBo2bIg6deroKTIiIipi0J3tADBx4kS89957sLa2RseOHXHs2DFERkZi2bJl+g6NiIhg4Lf/Fvnqq6+wceNG3L17F/Xq1cO4cePQt29ffYdFREQwkkRCRESGy6D7SIiIyPAxkRARkUaYSIiISCNMJEREpBEmEiIi0ggTSTkUFBRg165dCAgIgLu7O3x9fbFo0SKVoVz++OMPDB8+HO7u7vDy8sLSpUvx9OlTlfXcuHEDwcHB8PDwQNu2bTFv3rxyDURZ0YQQ2Lx5M7p3747mzZujd+/eOHz4sEqd2NhYBAYGokWLFujcuTM2btxYbD3qfAaGYNKkSejatatKmTG3Ly8vD82bN4eTk5PKn7u7u7KOMbcPAH777TcMGTIELVq0gJeXFz7++GPl0EmA8bbvl19+Kbbfnv3bv38/AANsnyDJvvzyS9G0aVPx2WefidOnT4vt27eLNm3aiNGjRwshhLhx44Zo2bKlGDNmjDhx4oSIiIgQrq6uIjQ0VLmO9PR04ePjIwIDA0VMTIzYvXu38PDwEOPGjdNXs5TWrFkjmjZtKlavXi1++uknsXjxYqFQKMS3334rhBDi7NmzwsXFRUyfPl2cPHlSLF26VDg5OYkNGzYo16HOZ2AIDhw4IBQKhfD19VWWGXv7rl69KhQKhdi/f79ISEhQ/p0/f14IYfztS0hIEC4uLuLdd98Vp0+fFjt37hQeHh5iypQpQgjjbt/jx49V9llCQoKIj48Xfn5+okOHDiI1NdUg28dEIlFBQYFo3bq1mD9/vkr5t99+KxQKhbh06ZKYM2eO6NChg8jJyVEu37Fjh2jatKn4+++/hRBCrFq1Sri5uYm0tDRlnRMnTgiFQiHOnTunm8aUIDc3V7Ru3Vp89NFHKuXDhg0TQ4YMEUIIMXLkSDFw4ECV5WFhYcLDw0PZZnU+A337+++/RevWrYWPj49KIjH29h06dEg0adJEZGVllbjc2NsXFBQkgoKCREFBgbJs+/btokuXLiIrK8vo2/e8zZs3iyZNmih/Fwyxfby0JVFmZiZ69+6NXr16qZQ3atQIQOEAk6dPn0anTp0gl/87G12PHj2Qn5+P2NhYAMDp06fRunVr2NjYKOt4eXnB0tISJ0+e1EFLSmZqaopt27Zh3LhxKuXm5ubIyclBTk4O4uLi0K1bN5Xl3bt3x6NHjxAfHw8Aan0G+vbBBx+gffv28PT0VJa9DO27fPky6tevj8qVi0/BauztS0tLQ1xcHIYMGaIyvURQUBBiYmJgYmJi1O173v3797F8+XLlZTxD3X9MJBJZWVnhgw8+QKtWrVTKY2JiAACOjo64e/dusZGJq1evDisrKyQmFk6Lef369WJ1TE1NUbduXWUdfTAxMYGTkxNq1qwJIQTu37+PdevW4aeffsLgwYNx69YtPH36tFjsDRo0AAAkJiYiOztbrc9An/bu3YuLFy/iww8/VCl/Gdp35coVyOVyjBkzBu7u7mjdujXmzp2LjIwMo2/f1atXIYSAtbU1pkyZAjc3N7Rq1Qrz5s3DkydPjL59z1uxYgVMTEwwZcoUAIb7/TT4QRuNwfnz57Fu3Tr4+vritddeA1B8VkegcGbHos70x48fl1lH36KjozF58mQAQMeOHdG7d29cvnwZwItnrSxtZsuievpuX3JyMhYtWoRFixahevXqKsvUmZXT0Nv33//+FxkZGRg4cCCCg4Nx4cIFhIeHIzExEdOmTQNgvO1LS0sDAMyePRtdu3bFmjVrcOXKFXzxxRfIycnB4MGDARhv+56VlpaGAwcOYPTo0crfFUP9fjKRaOjs2bMIDg5G3bp1sWDBAuTm5gIoeUpfIYTKrI7q1NEnZ2dnbN++HVeuXMHy5csxbtw45ZFRaVMWm5iYlDqzJaD/9gkhMGfOHHTo0AHdu3cvcTlgvO0DgGXLlsHa2hpOTk4AgNatW8PW1hYzZszA6dOnARhv+4ruOmrZsiXmzZsHAPD09IQQAp9++ikGDRoEwHjb96w9e/agoKAAI0aMUJYZ6veTiUQD3333HWbPno2GDRtiw4YNsLGxUd6CWFLWz8rKUs7qaGVlVWKdzMxM2NvbazdwNdWrVw/16tVD69atYWVlhVmzZim/pM/HXvS6atWqyiOhsj4DfdixYweuXLmCw4cPIy8vD8C//znz8vJKnZXTWNoHAG3atClW1rFjR5XXxtq+oiNvHx8flXIvLy8sXrwYf/zxBwDjbd+zoqKi4O3trXLWbKjfT8NIvUZo06ZNmDZtGtzc3LBjxw7UqFEDQOEXvWbNmspZHIukpqYiIyNDed3SwcGhWJ38/Hzcvn1brzM/pqen48CBA0hJSVEpd3Z2BgDcvn0bpqamxWatLHrt4OCg9megD1FRUXjw4AG8vLzg4uICFxcXHDhwAElJSXBxcUFcXJxRty81NRV79+7FrVu3VMqfPHkCALC1tTXq9jVs2BAAlGf+RYrOVOrWrWvU7SuSkpKCS5cuwc/PT6W8fv36Btk+JpJy2Lt3LxYvXgw/Pz9s2LChWIZv3749jh8/rvJlj4qKgqmpqfJosX379vjll1+Qnp6urBMbG4usrCy0a9dONw0pQUFBAWbPno3du3erlBddEmnWrBk8PDwQHR2tPJIHCttXtWpVuLq6AlDvM9CH0NBQ7Nu3T+WvU6dOqFWrFvbt24cePXoYdftkMhnmzp2L7du3q5R/9913MDU1Rbt27Yy6fY6OjrC3t8d3332nUn78+HGYmZnB3d3dqNtX5Pz58wBQ7KYeCwsLg2yf6fz58+dX+FpfYqmpqRg7dixq1qyJkJAQpKam4u+//1b+yeVyODs7Y+PGjYiLi4O1tTVOnDiBJUuWYODAgQgICAAANG7cGF999RViYmJga2uL+Ph4zJ8/H23btsX48eP11r7KlSsjLS0NW7duhZmZGXJzc3Hw4EGsXLkS/fv3R2BgIGrVqoW1a9fir7/+QuXKlXHgwAGsX78e7777Ltq2bQug8MiorM9AH2xsbFCzZk2Vv9jYWNy7dw/Tp09H5cqVjbp9lStXRnp6Onbs2IGCggIUFBTg4MGDWLFiBYYOHYqAgACjbp9MJoOdnR02bdqEGzduwMrKCpGRkVi1ahWGDx+Orl27GnX7ikRGRuKPP/7AzJkziy0zyPZV+JMpL7n9+/cLhUJR6t+BAweEEEL89ttvYuDAgcLV1VV4e3uLzz//XOTm5qqs68qVK2LkyJGiefPmwtPTU3z44Yfi8ePH+miWitzcXLFu3TrRrVs34erqKnx9fcWXX34p8vPzlXWio6NFr169hIuLi+jcubOIiIgoth51PgNDMGvWLJUHEoUw7vYV7b/u3bsLV1dX0aVLl5du/x09elT07dtXuLq6ig4dOojVq1e/VO2bN2+e8Pb2LnW5obWPMyQSEZFG2EdCREQaYSIhIiKNMJEQEZFGmEiIiEgjTCRERKQRJhIiItIIx9qiV1Zubi42b96MI0eOICkpCSYmJmjYsCF69OiBkSNHwsLCQqV+RkYGcnNzi40YrO62Hjx4gJo1a5a4/JdfflEZnO9Frly5Imnb+fn5+PvvvyWP4bZr1y7Mnz8fu3fvhpubm6T30quFiYReSXl5eRgzZgzOnTuHvn37YvDgwcjPz0dcXByWLl2KY8eOYevWrcqJgS5cuIAJEybgs88+Uz49rK7k5GSMHj0a48ePR//+/Uus4+joiLCwMJWymTNnolGjRggODi5fI1E4dtqoUaPg5+dXbLIyoorCREKvpMjISPz6668IDw9XmW1uxIgR2LBhA5YsWYJ9+/Zh6NChAAonVLp37165tnX79m3cuHHjhXVef/119OnTR6Vs5syZJZZLkZqaWuLgf0QViX0k9EpKSEgAUDi43fOCgoJgbm6Oc+fO6TosIqPEREKvpKJ5LZ4f5RgoHPgwPj5eeakpPDwc7733HoDCM5bOnTsr6/78888YO3Ys2rZtCxcXF3h7e2Pu3Ll49OgRAOCbb75R9n289957ysmmKkJqaio+/PBDeHl5wdXVFX5+fti4cSMKCgoAAKdOnYK/vz8A4PPPP4eTkxP++ecfAIVTPU+fPl353rZt2+Kdd97B9evXKyw+enVw9F96JdnZ2WHv3r04deoUoqKicO/ePchkMtSoUQOmpqYwNTVV1rW2toYQAhcvXkRwcDAGDBiARo0aITY2FmPHjoWdnR2GDRsGHx8f5Obm4vDhw0hNTYWvry+srKxQpUoVxMXFYfDgwRg5ciSaNGmiVowrV66Evb19if0qaWlpGDRoEM6ePYvAwED06tVLOerv7du30bVrV1SuXBmvv/46fvrpJ/j7++Ptt9+Gi4sL7t+/j/79++PRo0cICgpCt27dYG1tje+//x6nT5/G0KFDIZPJcOHCBZw4cQIDBw5ErVq1Kuyzp5eQVoaCJDICx48fF56eniqjN7u5uYlp06aJ69evq9T9+uuvhUKhEGfOnFGWjRkzRnTq1Enk5OSo1B00aJBwd3dXvj5z5oxQKBTi66+/lhSfQqEQw4YNK3HZggULhEKhECdPnlQpf++994RCoRA///yzEEKIa9euCYVCIb788ktlnfDwcNGkSRORlJSk8t5PPvlEKBQK8eeffwohhNi5c6dQKBQiISFBUtz06uGlLXpldezYEcePH8eyZcvQp08f2NnZISsrC0eOHEGfPn3w66+/vvD9X375Jb7++mvlnV0A8ODBA1hZWSErK0ursR87dgzOzs7Fppx95513AAAxMTGlvnfSpEk4ffo06tWrpyzLzs6GmVnhvTfajp1ePrxri15pFhYW8Pf3V/YlXLx4ERs3bsSRI0cwb948REZGlvpeU1NT3Lp1C8uXL8e1a9eQlJRUbIpibRBC4M6dO2jRokWxZXXr1kWlSpVw586dF64jOzsbn3/+OS5duoSkpCQkJycjPz8fAJR9LETq4hkJvXKysrKwbNkyREdHF1vm4uKCzz//HD4+Prh+/ToePHhQ6nq++uorDBw4EGfOnEHDhg0xduxY7NmzR+sz7IkXTCEkhIAQAubm5qXWOXfuHPz9/fH111/DxsYGAwcOxPr165U3FBBJxTMSeuVYWFggIiIC7u7uKs+QPKtx48b48ccfUalSpRKX5+TkYPHixWjbti02btyovCwEAMuXL9dK3EVMTExQu3btEu+wSk5ORk5ODmrXrl3q+xcvXgwrKyt8++23qFatmrI8Li5OK/HSy49nJPTKMTU1hb+/P3799VccPHiw2PL09HRERUWhXbt2qFy5MoDCH2/g38s+T548QXZ2Nho2bKiSRC5fvqzsW8nLy1Nu79n3VoROnTrh8uXLOHXqlEr5unXrABT2/5S27QcPHuD1119XSSLp6enKz6IobiJ18YyEXkmzZ8/G77//jpkzZ+LQoUPw9vaGlZUVkpKS8M033+Dp06eYO3eusn7R+Fq7du3C/fv3ERAQgBYtWuCbb76BlZUVHBwc8Oeff2Lv3r3KpJOZmQlra2vY2NgAAA4dOgQhBPr166eSfMrjnXfewQ8//ICJEyciKCgI9erVQ2xsLI4dO4ZevXrhjTfeAADltqOjo2Fraws/Pz/4+Phg69atCAkJwRtvvIGUlBTs3btXeRkvMzNTo9jo1cPnSOiVVLlyZfTv3x+VKlXC5cuXERMTg5iYGCQnJ6NLly5YunSpyiCHderUQWJiIk6cOIHTp09jxIgR6NixI5KTk3H06FGcOHECDx8+xJAhQ/Dmm28iMjISzZo1Q+PGjVG9enU8fPgQJ0+exI8//ohevXrB2tq6zBhf9BxJlSpV4Ofnh7S0NERHR+PYsWOQyWQYP348ZsyYAZlMBgCoVKkSZDIZTp06hRMnTsDb2xsBAQHIysrCqVOnEB0djdu3b8Pb2xsLFy7Erl27YGdnBy8vLz5HQmqTiRf13BEREZWBfSRERKQRJhIiItIIEwkREWmEiYSIiDTCREJERBphIiEiIo0wkRARkUaYSIiISCNMJEREpBEmEiIi0sj/A88uHBjmg2OrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a1 = df_nontotal[\"Stat Total\"].hist(bins = 25)\n",
    "b1 = df_legtotal[\"Stat Total\"].hist(bins = 25, ax = a1)\n",
    "plt.title(\"Total Stat Distribution\", pad=(25))\n",
    "plt.xlabel(\"Stat Total\")\n",
    "plt.ylabel(\"Pokemon\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this histogram, the two categories of Pokemon are plotted together with blue representing the NonLegendary Pokemon and orange representing the Legendary Pokemon. In this histogram, the data just barely overlaps and we chose to have the Legendary population at the front because it has a smaller population and infringes less in the overlap. Here we see that the vast majority of the Legendary Pokemon distribution is significantly right skewed are almost completely to the right of the NonLegendary Pokemon distribution.\n",
    "\n",
    "To further examine the magnitude of their difference let's get their averages amost the entire population and visualize the comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "405.32205683355886\n",
      "601.9705882352941\n"
     ]
    }
   ],
   "source": [
    "numNon = len(df_nontotal[\"Stat Total\"])\n",
    "numLeg = len(df_legtotal[\"Stat Total\"])\n",
    "sumNon = df_nontotal.sum(axis = 0)[3]\n",
    "sumLeg = df_legtotal.sum(axis = 0)[3]\n",
    "avgNon = sumNon / numNon\n",
    "avgLeg = sumLeg / numLeg\n",
    "print(avgNon)\n",
    "print(avgLeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "202.68606224627877\n",
      "202.6359945872801\n",
      "306.1470588235294\n",
      "295.8235294117647\n"
     ]
    }
   ],
   "source": [
    "sumNonA = df_LegsDontEnd.sum(axis = 0)[3]\n",
    "sumNonD = df_LegsDontEnd.sum(axis = 0)[4]\n",
    "sumLegA = df_LegsEnd.sum(axis = 0)[3]\n",
    "sumLegD = df_LegsEnd.sum(axis = 0)[4]\n",
    "avgNonA = sumNonA / numNon\n",
    "avgNonD = sumNonD / numNon\n",
    "avgLegA = sumLegA / numLeg\n",
    "avgLegD = sumLegD / numLeg\n",
    "\n",
    "print(avgNonA)\n",
    "print(avgNonD)\n",
    "print(avgLegA)\n",
    "print(avgLegD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({'stats':['NonLegendary', 'Legendary'], 'val':[avgNon, avgLeg]})\n",
    "ax = df.plot.bar(x='stats', y='val', rot=0)\n",
    "plt.title(\"Legendary vs NonLegendary Total Stats\", pad=(25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've calculated the average NonLegendary stat total to be 405 and the average Legendary stat total to be 602. For their offsensive and defensive breakdowns we have 203 and 203, and 306 and 296 respectively. As shown in the bar graph, Legendary Pokemon are found to have approximately 50% greater stats than NonLegendary Pokemon, truly earning them a \"legendary\" status.\n",
    "\n",
    "Now with their average offensive and defensive stats, let's revisit the scatterplot and add in points to show where their averages lie:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_avgLeg = pd.DataFrame({\"ID\":[\"999\"], \"Pokemon\":[\"Average Legendary\"], \"Legendary\":[\"True\"], \"ATK Sum\":[avgLegA], \"DEF Sum\":[avgLegD]})\n",
    "df_avgNon = pd.DataFrame({\"ID\":[\"999\"], \"Pokemon\":[\"Average NonLegendary\"], \"Legendary\":[\"False\"], \"ATK Sum\":[avgNonA], \"DEF Sum\":[avgNonD]})\n",
    "\n",
    "aaa = df_LegsDontEnd.plot.scatter(x=\"ATK Sum\", y=\"DEF Sum\", c=\"b\")\n",
    "bbb = df_LegsEnd.plot.scatter(x=\"ATK Sum\", y=\"DEF Sum\", c=\"orange\", ax = aaa)\n",
    "ccc = df_avgNon.plot.scatter(x=\"ATK Sum\", y=\"DEF Sum\", c=\"cyan\", s=140, ax = aaa)\n",
    "ddd = df_avgLeg.plot.scatter(x=\"ATK Sum\", y=\"DEF Sum\", c=\"maroon\", s=140, ax = aaa)\n",
    "plt.title(\"Offensive vs. Defensive Stat Distribution With Average\", pad=(25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After adding a big cyan data point to represent the average NonLegendary Pokemon and a big maroon data point to represent the average Legendary Pokemon, we can easily see how Legendary Pokemon tend to be roughly 50% further from the origin (0,0) point in the scatterplot.\n",
    "\n",
    "\n",
    "With such a well-defined difference in stats in favor of Legendary Pokemon, it's no surprise they are referred to as such and tend to be more limited and significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "Usage-wise, legendary Pokémon are significantly more popular than other non-legendary Pokémon, despite being outnumbered in count. Overall, legendary Pokémon usage is significantly higher compared to the non-legendary counterparts, despite the fact that non-legendary Pokémon outnumber them in count. This can be seen due to their increased base stats that we analyzed next.\n",
    "\n",
    "A limitation of our usage analysis is that the only data we have readily available to us is the usage data of Pokémon used in online battles against other players. The Pokémon franchise spans multiple mediums: anime, movies, manga, video games, card games, and more. Gauging the popularity of the 800+ Pokémon species across the millions of fans of the various mediums would be a huge task that would involve a lengthy data collection process that would be beyond the scope of this project. Analyzing only the competitive usage allows us to compare popularity with the objective statistics of legendary and non-legendary Pokémon.\n",
    "\n",
    "In terms of height and weight, the average legendary Pokémon stands 2.1 metres tall and weighs 171.7 kilograms, while the average non-legendary Pokémon stands 1.1 metres tall and weighs 51.7 kilograms. We can conclude that the average legendary Pokémon is likely to be about 2 times taller and at least 3 times heavier than the average non-legendary Pokémon.\n",
    "\n",
    "Typingwise, we see that while legendary Pokémon do appear often in the most populated typing, there are some significant outliers (Psychic and Poison). An interesting tidbit is that Psychic Pokémon were extremely strong in the early generations, only to grow progressively weaker as typing with strong matchups were released; Psychic still maintains a subjectively strong moveset however. Dragon is also weighted highly, being only weak to Ice and other Dragon types, and deals at least full damage to all types except Steel (and the newly released Fairy type). This lead us to believe that the developers tried to make legendary Pokémon relatively strong or above average versus the majority of Pokémon, and improve their image of being “legendary”. \n",
    "\n",
    "As we predicted, legendary Pokémon have higher average attack and defense stats than non-legendary Pokémon. Looking at the Pokémon’s total stats, an amalgamation of all 6 of their stat distributions, we can see that legendary Pokémon are, on average, 50% stronger than non-legendary Pokémon. Another interesting observation is that across both non-legendary and legendary Pokémon, the total attack and defense are about equal. This indicates that both Pokémon categories are fairly evenly distributed among those that specialize in attack and those in defense. We are unsure if this is an intentional design choice by the creators, but it is an interesting result nonetheless. "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "FinalProject.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}