Integrated project 1_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integrated Project 1\n",
    "# *Comparative analysis for an advertising campaign in the video game industry*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Goal\" data-toc-modified-id=\"Goal-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Goal</a></span></li><li><span><a href=\"#Hypotheses\" data-toc-modified-id=\"Hypotheses-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Hypotheses</a></span></li><li><span><a href=\"#Description-of-the-data\" data-toc-modified-id=\"Description-of-the-data-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Description of the data</a></span></li><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Imports</a></span></li><li><span><a href=\"#Library-version-check-and-update\" data-toc-modified-id=\"Library-version-check-and-update-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Library version check and update</a></span></li><li><span><a href=\"#Input-data\" data-toc-modified-id=\"Input-data-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Input data</a></span></li><li><span><a href=\"#Descriptive-statistics\" data-toc-modified-id=\"Descriptive-statistics-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Descriptive statistics</a></span></li><li><span><a href=\"#Preprocessing\" data-toc-modified-id=\"Preprocessing-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Preprocessing</a></span><ul class=\"toc-item\"><li><span><a href=\"#Column-names\" data-toc-modified-id=\"Column-names-8.1\"><span class=\"toc-item-num\">8.1&nbsp;&nbsp;</span>Column names</a></span></li><li><span><a href=\"#Missing-values\" data-toc-modified-id=\"Missing-values-8.2\"><span class=\"toc-item-num\">8.2&nbsp;&nbsp;</span>Missing values</a></span><ul class=\"toc-item\"><li><span><a href=\"#Name\" data-toc-modified-id=\"Name-8.2.1\"><span class=\"toc-item-num\">8.2.1&nbsp;&nbsp;</span>Name</a></span></li><li><span><a href=\"#Release-year\" data-toc-modified-id=\"Release-year-8.2.2\"><span class=\"toc-item-num\">8.2.2&nbsp;&nbsp;</span>Release year</a></span></li><li><span><a href=\"#Scores\" data-toc-modified-id=\"Scores-8.2.3\"><span class=\"toc-item-num\">8.2.3&nbsp;&nbsp;</span>Scores</a></span></li></ul></li><li><span><a href=\"#Duplicates\" data-toc-modified-id=\"Duplicates-8.3\"><span class=\"toc-item-num\">8.3&nbsp;&nbsp;</span>Duplicates</a></span></li><li><span><a href=\"#Data-type-change\" data-toc-modified-id=\"Data-type-change-8.4\"><span class=\"toc-item-num\">8.4&nbsp;&nbsp;</span>Data type change</a></span></li><li><span><a href=\"#Total-sales-calculation\" data-toc-modified-id=\"Total-sales-calculation-8.5\"><span class=\"toc-item-num\">8.5&nbsp;&nbsp;</span>Total sales calculation</a></span></li></ul></li><li><span><a href=\"#EDA\" data-toc-modified-id=\"EDA-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>EDA</a></span><ul class=\"toc-item\"><li><span><a href=\"#All-platforms\" data-toc-modified-id=\"All-platforms-9.1\"><span class=\"toc-item-num\">9.1&nbsp;&nbsp;</span>All platforms</a></span><ul class=\"toc-item\"><li><span><a href=\"#Dynamics-in-game-realizations\" data-toc-modified-id=\"Dynamics-in-game-realizations-9.1.1\"><span class=\"toc-item-num\">9.1.1&nbsp;&nbsp;</span>Dynamics in game realizations</a></span></li><li><span><a href=\"#Total-sales-per-platform\" data-toc-modified-id=\"Total-sales-per-platform-9.1.2\"><span class=\"toc-item-num\">9.1.2&nbsp;&nbsp;</span>Total sales per platform</a></span></li><li><span><a href=\"#Dynamics-in-game-realizations-per-platform\" data-toc-modified-id=\"Dynamics-in-game-realizations-per-platform-9.1.3\"><span class=\"toc-item-num\">9.1.3&nbsp;&nbsp;</span>Dynamics in game realizations per platform</a></span></li><li><span><a href=\"#Most-popular-platforms-selection\" data-toc-modified-id=\"Most-popular-platforms-selection-9.1.4\"><span class=\"toc-item-num\">9.1.4&nbsp;&nbsp;</span>Most popular platforms selection</a></span></li></ul></li><li><span><a href=\"#Top-platforms\" data-toc-modified-id=\"Top-platforms-9.2\"><span class=\"toc-item-num\">9.2&nbsp;&nbsp;</span>Top platforms</a></span><ul class=\"toc-item\"><li><span><a href=\"#Differences-in-sales-between-platforms\" data-toc-modified-id=\"Differences-in-sales-between-platforms-9.2.1\"><span class=\"toc-item-num\">9.2.1&nbsp;&nbsp;</span>Differences in sales between platforms</a></span></li><li><span><a href=\"#Correlation-between-sales-and-reviews\" data-toc-modified-id=\"Correlation-between-sales-and-reviews-9.2.2\"><span class=\"toc-item-num\">9.2.2&nbsp;&nbsp;</span>Correlation between sales and reviews</a></span></li></ul></li><li><span><a href=\"#Games-sold-on-several-platforms\" data-toc-modified-id=\"Games-sold-on-several-platforms-9.3\"><span class=\"toc-item-num\">9.3&nbsp;&nbsp;</span>Games sold on several platforms</a></span></li><li><span><a href=\"#Games'-genre-analysis-in-terms-of-sales\" data-toc-modified-id=\"Games'-genre-analysis-in-terms-of-sales-9.4\"><span class=\"toc-item-num\">9.4&nbsp;&nbsp;</span>Games' genre analysis in terms of sales</a></span></li></ul></li><li><span><a href=\"#User-profile-per-region\" data-toc-modified-id=\"User-profile-per-region-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>User profile per region</a></span><ul class=\"toc-item\"><li><span><a href=\"#Top-platforms-per-region\" data-toc-modified-id=\"Top-platforms-per-region-10.1\"><span class=\"toc-item-num\">10.1&nbsp;&nbsp;</span>Top platforms per region</a></span></li><li><span><a href=\"#Top-genres-per-region\" data-toc-modified-id=\"Top-genres-per-region-10.2\"><span class=\"toc-item-num\">10.2&nbsp;&nbsp;</span>Top genres per region</a></span></li><li><span><a href=\"#ESRB-ratings-per-region\" data-toc-modified-id=\"ESRB-ratings-per-region-10.3\"><span class=\"toc-item-num\">10.3&nbsp;&nbsp;</span>ESRB ratings per region</a></span></li></ul></li><li><span><a href=\"#Statistical-hypotheses-testing\" data-toc-modified-id=\"Statistical-hypotheses-testing-11\"><span class=\"toc-item-num\">11&nbsp;&nbsp;</span>Statistical hypotheses testing</a></span><ul class=\"toc-item\"><li><span><a href=\"#Average-user-ratings-of-the-Xbox-One-and-PC-platforms-are-the-same\" data-toc-modified-id=\"Average-user-ratings-of-the-Xbox-One-and-PC-platforms-are-the-same-11.1\"><span class=\"toc-item-num\">11.1&nbsp;&nbsp;</span>Average user ratings of the Xbox One and PC platforms are the same</a></span><ul class=\"toc-item\"><li><span><a href=\"#Step-1:-the-null-and-alternative-hypotheses\" data-toc-modified-id=\"Step-1:-the-null-and-alternative-hypotheses-11.1.1\"><span class=\"toc-item-num\">11.1.1&nbsp;&nbsp;</span>Step 1: the null and alternative hypotheses</a></span></li><li><span><a href=\"#Step-2:-Set-the-criteria-for-a-decision\" data-toc-modified-id=\"Step-2:-Set-the-criteria-for-a-decision-11.1.2\"><span class=\"toc-item-num\">11.1.2&nbsp;&nbsp;</span>Step 2: Set the criteria for a decision</a></span></li><li><span><a href=\"#Step-3:-Compute-the-test-statistic\" data-toc-modified-id=\"Step-3:-Compute-the-test-statistic-11.1.3\"><span class=\"toc-item-num\">11.1.3&nbsp;&nbsp;</span>Step 3: Compute the test statistic</a></span></li><li><span><a href=\"#Step-4:-Make-a-decision\" data-toc-modified-id=\"Step-4:-Make-a-decision-11.1.4\"><span class=\"toc-item-num\">11.1.4&nbsp;&nbsp;</span>Step 4: Make a decision</a></span></li></ul></li><li><span><a href=\"#Average-user-ratings-for-the-Action-and-Sports-genres-are-the-same\" data-toc-modified-id=\"Average-user-ratings-for-the-Action-and-Sports-genres-are-the-same-11.2\"><span class=\"toc-item-num\">11.2&nbsp;&nbsp;</span>Average user ratings for the Action and Sports genres are the same</a></span><ul class=\"toc-item\"><li><span><a href=\"#Step-1:-the-null-and-alternative-hypotheses\" data-toc-modified-id=\"Step-1:-the-null-and-alternative-hypotheses-11.2.1\"><span class=\"toc-item-num\">11.2.1&nbsp;&nbsp;</span>Step 1: the null and alternative hypotheses</a></span></li><li><span><a href=\"#Step-2:-Set-the-criteria-for-a-decision\" data-toc-modified-id=\"Step-2:-Set-the-criteria-for-a-decision-11.2.2\"><span class=\"toc-item-num\">11.2.2&nbsp;&nbsp;</span>Step 2: Set the criteria for a decision</a></span></li><li><span><a href=\"#Step-3:-Compute-the-test-statistic\" data-toc-modified-id=\"Step-3:-Compute-the-test-statistic-11.2.3\"><span class=\"toc-item-num\">11.2.3&nbsp;&nbsp;</span>Step 3: Compute the test statistic</a></span></li><li><span><a href=\"#Step-4:-Make-a-decision\" data-toc-modified-id=\"Step-4:-Make-a-decision-11.2.4\"><span class=\"toc-item-num\">11.2.4&nbsp;&nbsp;</span>Step 4: Make a decision</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Goal\n",
    "Prepare a report for the online store Ice to identify patterns that determine whether a game will succeed or not. This will allow us to spot potential big winners and plan advertising campaigns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypotheses\n",
    "1. Average user ratings of the Xbox One and PC platforms are the same.\n",
    "2. Average user ratings for the Action and Sports genres are the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Description of the data\n",
    "\n",
    "The Entertainment Software Rating Board (ESRB) evaluates a game's content and assigns an age rating such as Teen or Mature.\n",
    "\n",
    "— Name\n",
    "\n",
    "— Platform\n",
    "\n",
    "— Year_of_Release\n",
    "\n",
    "— Genre\n",
    "\n",
    "— NA_sales (North American sales in USD million)\n",
    "\n",
    "— EU_sales (sales in Europe in USD million)\n",
    "\n",
    "— JP_sales (sales in Japan in USD million)\n",
    "\n",
    "— Other_sales (sales in other countries in USD million)\n",
    "\n",
    "— Critic_Score (maximum of 100)\n",
    "\n",
    "— User_Score (maximum of 10)\n",
    "\n",
    "— Rating (ESRB)\n",
    "    - E: everyone;\n",
    "    - E10+: everyone 10+;\n",
    "    - T: Teen;\n",
    "    - M: Mature 17+;\n",
    "    - A: Adults only 18+;\n",
    "    - RT: rating pending.\n",
    "\n",
    "Data for 2016 may be incomplete."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy import stats as st\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "import warnings\n",
    "if not sys.warnoptions:\n",
    "       warnings.simplefilter(\"ignore\")\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Library version check and update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "version_dict = {pd:'1.0.1', np:'1.18.1', scipy:'1.6.0', matplotlib:'3.1.4'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_value(my_key):\n",
    "    \"\"\"\n",
    "    If the val can be found in the dictinary.values() list, \n",
    "    returns the key of the dictionary item in which the val was found.\n",
    "    \"\"\"\n",
    "    wrong_val = []\n",
    "    \n",
    "    for key, value in version_dict.items(): \n",
    "        try:\n",
    "            if key == my_key: \n",
    "                return value\n",
    "        except:\n",
    "            wrong_val.append(key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: to be able to run this code correctly, please install the version 3.1.4 of <module 'matplotlib' from '/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py'>\n"
     ]
    }
   ],
   "source": [
    "for lib in version_dict.keys():\n",
    "    if lib.__version__ != get_value(lib):\n",
    "        print(\"Warning: to be able to run this code correctly, please install the version\", get_value(lib), 'of', lib)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df = pd.read_csv('games.csv')\n",
    "except:\n",
    "    df = pd.read_csv('/datasets/games.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\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>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th>Genre</th>\n",
       "      <th>NA_sales</th>\n",
       "      <th>EU_sales</th>\n",
       "      <th>JP_sales</th>\n",
       "      <th>Other_sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>41.36</td>\n",
       "      <td>28.96</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.45</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>15.68</td>\n",
       "      <td>12.76</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.29</td>\n",
       "      <td>82.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>15.61</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tetris</td>\n",
       "      <td>GB</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Puzzle</td>\n",
       "      <td>23.20</td>\n",
       "      <td>2.26</td>\n",
       "      <td>4.22</td>\n",
       "      <td>0.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Super Mario Bros.</td>\n",
       "      <td>DS</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>11.28</td>\n",
       "      <td>9.14</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2.88</td>\n",
       "      <td>89.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Wii Play</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>13.96</td>\n",
       "      <td>9.18</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.84</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Super Mario Bros. Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>14.44</td>\n",
       "      <td>6.94</td>\n",
       "      <td>4.70</td>\n",
       "      <td>2.24</td>\n",
       "      <td>87.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Duck Hunt</td>\n",
       "      <td>NES</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>26.93</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name Platform  Year_of_Release         Genre  \\\n",
       "0                 Wii Sports      Wii           2006.0        Sports   \n",
       "1          Super Mario Bros.      NES           1985.0      Platform   \n",
       "2             Mario Kart Wii      Wii           2008.0        Racing   \n",
       "3          Wii Sports Resort      Wii           2009.0        Sports   \n",
       "4   Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing   \n",
       "5                     Tetris       GB           1989.0        Puzzle   \n",
       "6      New Super Mario Bros.       DS           2006.0      Platform   \n",
       "7                   Wii Play      Wii           2006.0          Misc   \n",
       "8  New Super Mario Bros. Wii      Wii           2009.0      Platform   \n",
       "9                  Duck Hunt      NES           1984.0       Shooter   \n",
       "\n",
       "   NA_sales  EU_sales  JP_sales  Other_sales  Critic_Score User_Score Rating  \n",
       "0     41.36     28.96      3.77         8.45          76.0          8      E  \n",
       "1     29.08      3.58      6.81         0.77           NaN        NaN    NaN  \n",
       "2     15.68     12.76      3.79         3.29          82.0        8.3      E  \n",
       "3     15.61     10.93      3.28         2.95          80.0          8      E  \n",
       "4     11.27      8.89     10.22         1.00           NaN        NaN    NaN  \n",
       "5     23.20      2.26      4.22         0.58           NaN        NaN    NaN  \n",
       "6     11.28      9.14      6.50         2.88          89.0        8.5      E  \n",
       "7     13.96      9.18      2.93         2.84          58.0        6.6      E  \n",
       "8     14.44      6.94      4.70         2.24          87.0        8.4      E  \n",
       "9     26.93      0.63      0.28         0.47           NaN        NaN    NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes for preprocessing:\n",
    "- We see \"NaN\"s in the last 3 columns;\n",
    "- `Year_of_Release` data type should be changed to integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16715 entries, 0 to 16714\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Name             16713 non-null  object \n",
      " 1   Platform         16715 non-null  object \n",
      " 2   Year_of_Release  16446 non-null  float64\n",
      " 3   Genre            16713 non-null  object \n",
      " 4   NA_sales         16715 non-null  float64\n",
      " 5   EU_sales         16715 non-null  float64\n",
      " 6   JP_sales         16715 non-null  float64\n",
      " 7   Other_sales      16715 non-null  float64\n",
      " 8   Critic_Score     8137 non-null   float64\n",
      " 9   User_Score       10014 non-null  object \n",
      " 10  Rating           9949 non-null   object \n",
      "dtypes: float64(6), object(5)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes for data preprocessing:\n",
    "- The data set contains almost 17k observations and 11 columns;\n",
    "- `Name`, `Year_of_Release`, `Genre`, `Critic_Score`, `User_Score` and `Rating` columns contain missing values;\n",
    "- `Critic_Score` data type should be changed to integer (as there is no need for floating points in this column);\n",
    "- `User_Score` data type should be changed from object to float;\n",
    "- Column names should all be converted to lower case letters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Year_of_Release</th>\n",
       "      <th>NA_sales</th>\n",
       "      <th>EU_sales</th>\n",
       "      <th>JP_sales</th>\n",
       "      <th>Other_sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16446.000000</td>\n",
       "      <td>16715.000000</td>\n",
       "      <td>16715.000000</td>\n",
       "      <td>16715.000000</td>\n",
       "      <td>16715.000000</td>\n",
       "      <td>8137.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2006.484616</td>\n",
       "      <td>0.263377</td>\n",
       "      <td>0.145060</td>\n",
       "      <td>0.077617</td>\n",
       "      <td>0.047342</td>\n",
       "      <td>68.967679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.877050</td>\n",
       "      <td>0.813604</td>\n",
       "      <td>0.503339</td>\n",
       "      <td>0.308853</td>\n",
       "      <td>0.186731</td>\n",
       "      <td>13.938165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1980.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>41.360000</td>\n",
       "      <td>28.960000</td>\n",
       "      <td>10.220000</td>\n",
       "      <td>10.570000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year_of_Release      NA_sales      EU_sales      JP_sales  \\\n",
       "count     16446.000000  16715.000000  16715.000000  16715.000000   \n",
       "mean       2006.484616      0.263377      0.145060      0.077617   \n",
       "std           5.877050      0.813604      0.503339      0.308853   \n",
       "min        1980.000000      0.000000      0.000000      0.000000   \n",
       "25%        2003.000000      0.000000      0.000000      0.000000   \n",
       "50%        2007.000000      0.080000      0.020000      0.000000   \n",
       "75%        2010.000000      0.240000      0.110000      0.040000   \n",
       "max        2016.000000     41.360000     28.960000     10.220000   \n",
       "\n",
       "        Other_sales  Critic_Score  \n",
       "count  16715.000000   8137.000000  \n",
       "mean       0.047342     68.967679  \n",
       "std        0.186731     13.938165  \n",
       "min        0.000000     13.000000  \n",
       "25%        0.000000     60.000000  \n",
       "50%        0.010000     71.000000  \n",
       "75%        0.030000     79.000000  \n",
       "max       10.570000     98.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes for data preprocessing:\n",
    "- `Year_of_Release` ranges from 1980 to 2016, an average game was released in 2006, while most of them - in 2007. Since we don't see a big difference between mean and medium values while the standard deviation is quite low, we can assume the distribution of this variable is close to normal;\n",
    "- All sales variables have a minimum value of 0, it should be checked and corrected if needed. They all looks like a very much positively skewed distributions - even the third quartile is less than 1 million USD while the max value is from 11 to 42 millions. These distributions should also be further analyzed with box plots for outliers check;\n",
    "- `Critic_Score` ranges from 13 to 98. The average score is close to 67, the medium value is around 71, standard deviation is reasonable - 14, hence we can assume that this distribution is close to normal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing\n",
    "#### Column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name               0.000120\n",
       "platform           0.000000\n",
       "year_of_release    0.016093\n",
       "genre              0.000120\n",
       "na_sales           0.000000\n",
       "eu_sales           0.000000\n",
       "jp_sales           0.000000\n",
       "other_sales        0.000000\n",
       "critic_score       0.513192\n",
       "user_score         0.400897\n",
       "rating             0.404786\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()/len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
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       "    }\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>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>year_of_release</th>\n",
       "      <th>genre</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>critic_score</th>\n",
       "      <th>user_score</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GEN</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14244</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GEN</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name platform  year_of_release genre  na_sales  eu_sales  jp_sales  \\\n",
       "659    NaN      GEN           1993.0   NaN      1.78      0.53      0.00   \n",
       "14244  NaN      GEN           1993.0   NaN      0.00      0.00      0.03   \n",
       "\n",
       "       other_sales  critic_score user_score rating  \n",
       "659           0.08           NaN        NaN    NaN  \n",
       "14244         0.00           NaN        NaN    NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['name'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two games are quite old, most of data is missing and sales are very low. We will exclude these 2 observations from our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16713, 11)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(subset=['name'], axis=0)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Release year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>year_of_release</th>\n",
       "      <th>genre</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>critic_score</th>\n",
       "      <th>user_score</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Madden NFL 2004</td>\n",
       "      <td>PS2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sports</td>\n",
       "      <td>4.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.71</td>\n",
       "      <td>94.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>FIFA Soccer 2004</td>\n",
       "      <td>PS2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.51</td>\n",
       "      <td>84.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>LEGO Batman: The Videogame</td>\n",
       "      <td>Wii</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Action</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>74.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>E10+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>wwe Smackdown vs. Raw 2006</td>\n",
       "      <td>PS2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Space Invaders</td>\n",
       "      <td>2600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           name platform  year_of_release     genre  na_sales  \\\n",
       "183             Madden NFL 2004      PS2              NaN    Sports      4.26   \n",
       "377            FIFA Soccer 2004      PS2              NaN    Sports      0.59   \n",
       "456  LEGO Batman: The Videogame      Wii              NaN    Action      1.80   \n",
       "475  wwe Smackdown vs. Raw 2006      PS2              NaN  Fighting      1.57   \n",
       "609              Space Invaders     2600              NaN   Shooter      2.36   \n",
       "\n",
       "     eu_sales  jp_sales  other_sales  critic_score user_score rating  \n",
       "183      0.26      0.01         0.71          94.0        8.5      E  \n",
       "377      2.36      0.04         0.51          84.0        6.4      E  \n",
       "456      0.97      0.00         0.29          74.0        7.9   E10+  \n",
       "475      1.02      0.00         0.41           NaN        NaN    NaN  \n",
       "609      0.14      0.00         0.03           NaN        NaN    NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['year_of_release'].isnull()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to our previous analysis, `year_of_release`'s distribution is close to normal. We will then replace the missing values with the mean per genre and platform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year_of_release'] = df.groupby(['platform','genre'])['year_of_release'].apply(lambda x: x.fillna(x.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year_of_release'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's change the data type to integer as years are not float type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year_of_release'] = np.floor(df['year_of_release']).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Scores\n",
    "\n",
    "There are some games for which all 3 scorings are missing. These games were released on old consoles that are practically not used any more. Besides, our goal is to build a prognosis for 2017, so the data from the 80s is not going to be very useful. We will keep these values they way they are and make a decision how to deal with them later if we need it.\n",
    "\n",
    "For the rest of missing values, we will fill them based on the score column that is available. If the correlation between `critic_score` and `user_score` is high enough, then we can just pick a certain game, look for games with similar user score (plus/minus 0.5 points) -- get an array of such games and then simply take median value for `critic_score`. \n",
    "\n",
    "First, it will be necessary to change `user_score` data type from object to float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['8', nan, '8.3', '8.5', '6.6', '8.4', '8.6', '7.7', '6.3', '7.4',\n",
       "       '8.2', '9', '7.9', '8.1', '8.7', '7.1', '3.4', '5.3', '4.8', '3.2',\n",
       "       '8.9', '6.4', '7.8', '7.5', '2.6', '7.2', '9.2', '7', '7.3', '4.3',\n",
       "       '7.6', '5.7', '5', '9.1', '6.5', 'tbd', '8.8', '6.9', '9.4', '6.8',\n",
       "       '6.1', '6.7', '5.4', '4', '4.9', '4.5', '9.3', '6.2', '4.2', '6',\n",
       "       '3.7', '4.1', '5.8', '5.6', '5.5', '4.4', '4.6', '5.9', '3.9',\n",
       "       '3.1', '2.9', '5.2', '3.3', '4.7', '5.1', '3.5', '2.5', '1.9', '3',\n",
       "       '2.7', '2.2', '2', '9.5', '2.1', '3.6', '2.8', '1.8', '3.8', '0',\n",
       "       '1.6', '9.6', '2.4', '1.7', '1.1', '0.3', '1.5', '0.7', '1.2',\n",
       "       '2.3', '0.5', '1.3', '0.2', '0.6', '1.4', '0.9', '1', '9.7'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['user_score'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apart from missing values, we see some 'tbd'. It basically means that the rating is missing, so let's replace these values with \"NaN\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['user_score'] == 'tbd', 'user_score'] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check the correlation between the two columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5808778320767239"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['critic_score'] = (df['critic_score'].astype(float))\n",
    "df['user_score'] = df['user_score'].astype(float)\n",
    "df['user_score'].corr(df['critic_score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is quite high, so we can use the approach mentioned above. We will fill missing values in the `user_score` column using the `critic_score` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['user_score'] = df.groupby('critic_score')['user_score'].apply(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['user_score'] = df.groupby('rating')['user_score'].apply(lambda x: x.fillna(x.median()))\n",
    "df['critic_score'] = df.groupby('rating')['critic_score'].apply(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's see if there are any games that are sold on different platforms. If the rating is filled on one of the platforms we can fill missing rating on other platforms as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['user_score'] = df.groupby('name')['user_score'].apply(lambda x: x.fillna(x.mean()))\n",
    "df['critic_score'] = df.groupby('name')['critic_score'].apply(lambda x: x.fillna(x.mean()))\n",
    "df['rating'] = df.groupby('name')['rating'].apply(lambda x: x.fillna(x.mode()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                  0\n",
       "platform              0\n",
       "year_of_release       0\n",
       "genre                 0\n",
       "na_sales              0\n",
       "eu_sales              0\n",
       "jp_sales              0\n",
       "other_sales           0\n",
       "critic_score       6337\n",
       "user_score         6337\n",
       "rating             6764\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will keep the rest of missing values for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Duplicates\n",
    "Let's check if any rows are duplicated in any data frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data type change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                object\n",
       "platform            object\n",
       "year_of_release      int64\n",
       "genre               object\n",
       "na_sales           float64\n",
       "eu_sales           float64\n",
       "jp_sales           float64\n",
       "other_sales        float64\n",
       "critic_score       float64\n",
       "user_score         float64\n",
       "rating              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All variables now have correct data types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total sales calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['total_sales'] = df['na_sales'] + df['eu_sales'] + df['jp_sales'] + df['other_sales']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All platforms\n",
    "##### Dynamics in game realizations"
   ]
  },
  {
   "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": [
    "df.hist('year_of_release')\n",
    "plt.title('Number of games released per year')\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('frequency');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most games in this data set were released around 2009-2012. The number of games released before the 21st century is not very significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Total sales per platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "df.groupby('platform')['total_sales'].sum().sort_values(ascending=False).plot(kind='bar')\n",
    "plt.title('Total sales per platform, USD million')\n",
    "plt.ylabel('total sales');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 6 platforms that stand out in terms of total sales in all regions: 'PS2','X360','PS3','Wii','DS','PS'. Let's build separate distributions for each of them to see how many games were released each year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dynamics in game realizations per platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_platform_year = df[df['platform'].isin(['PS2','X360','PS3','Wii','DS','PS'])].groupby([\"platform\",\"year_of_release\"]).size().unstack()\n",
    "\n",
    "plt.figure(figsize=(14,10))\n",
    "g = sns.heatmap(\n",
    "    df_platform_year, \n",
    "    square=True, # make cells square\n",
    "    cbar_kws={'fraction' : 0.02}, # shrink colour bar\n",
    "    cmap='OrRd', # use orange/red colour map\n",
    "    linewidth=1 # space between cells\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of these 6 platforms only 3 still have sales - 'X360', 'PS3' and 'Wii' (very low amount from 2012 to 2016). The other 3 platforms used to be very popular but now (in 2016) have zero sales. On average it takes around 10 years for new platforms to appear and for the old ones to fade away. If we would like to build a prognosis for 2017, we need to identify those platforms that are still popular in 2016. The middle of a platform's lifespan when it's on the peak of popularity is 5 years from the beginning, so we will take data for the past 5 years to build a prognosis for 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2893, 12)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = df[df['year_of_release'] >= 2012]\n",
    "df_final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Most popular platforms selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "df_final.groupby('platform')['total_sales'].sum().sort_values(ascending=False).plot(kind='bar')\n",
    "plt.title('Total sales per platform, USD million')\n",
    "plt.ylabel('total sales');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this data frame the following 5 platforms are leading in sales: 'PS4', 'PS3', 'X360', '3DS', 'XOne'. Let's choose them for further analysis.\n",
    "\n",
    "We will now build a box plot for the global sales of all games, broken down by platform in order to see if there are any differences in sales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top platforms\n",
    "##### Differences in sales between platforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_top_platforms = df[df['platform'].isin(['PS4', 'PS3', 'X360', '3DS', 'XOne'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_top_platforms.boxplot(by='platform', column='total_sales')\n",
    "plt.suptitle('')\n",
    "plt.ylim(-5, 20)\n",
    "plt.title('Differences in sales between platforms')\n",
    "plt.gcf().set_size_inches(10, 5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All box plots look similar, there are no significant differences in sales. All distributions are positively skewed. Let's calculate the median and average values for each platform. "
   ]
  },
  {
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>total_sales</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platform</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3DS</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.498077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PS3</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.705973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PS4</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.801378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X360</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.769746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOne</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.645020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              median        mean\n",
       "         total_sales total_sales\n",
       "platform                        \n",
       "3DS             0.12    0.498077\n",
       "PS3             0.27    0.705973\n",
       "PS4             0.20    0.801378\n",
       "X360            0.28    0.769746\n",
       "XOne            0.22    0.645020"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top_platforms.pivot_table(index='platform', values='total_sales', aggfunc=['median','mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean values are much higher than medians which makes sense as the distributions are positively skewed as we noticed earlier.\n",
    "\n",
    "Now let's take a look at how user and professional reviews affect sales for the most popular platform (according to sales) - 'PS4'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Correlation between sales and reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": [
    "ps4 = df_final[df_final['platform']=='PS4']\n",
    "ps4.plot.scatter(y='total_sales', x='user_score', alpha=.15)\n",
    "plt.title('Link between sales and user reviews for \"PS4\"');"
   ]
  },
  {
   "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps4.plot.scatter(y='total_sales', x='critic_score', alpha=.15)\n",
    "plt.title('Link between sales and professional reviews for \"PS4\"');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson correlation coefficient between sales and user reviews: -4%\n"
     ]
    }
   ],
   "source": [
    "print('Pearson correlation coefficient between sales and user reviews: {:.0%}'.format(ps4['total_sales'].corr(ps4['user_score'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson correlation coefficient between sales and professional reviews: 39%\n"
     ]
    }
   ],
   "source": [
    "print('Pearson correlation coefficient between sales and professional reviews: {:.0%}'.format(ps4['total_sales'].corr(ps4['critic_score'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the scatter plots and Pearson coefficients, there is almost no linear connection between sales and user reviews and quite a weak positive linear connection between game sales and professional reviews for the 'PS4' platform."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Games sold on several platforms\n",
    "\n",
    "Let's compare total sales of the 10 top games sold by the 3 top platforms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df_top_platforms.pivot_table(index=['name','platform'], values='total_sales', aggfunc='sum')\n",
    "                        .sort_values(by='total_sales', ascending=False).unstack(1).dropna()[0:10].plot(kind='bar', figsize=(15,5)))\n",
    "plt.title('Comparison of sales on different platforms')\n",
    "plt.xlabel('name of a game')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('total sales, million USD');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no significant difference between the amounts of total sales each platform made for the same games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Games' genre analysis in terms of sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "df_final.groupby('genre')['genre'].count().sort_values(ascending=False).plot(kind='bar')\n",
    "plt.title('Distribution of games by genre')\n",
    "plt.xlabel('genre of a game')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('frequency');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Action games are by far the most popular in this data set. Now let's see if they are the most profitable as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": [
    "df_final.groupby('genre')['total_sales'].sum().sort_values(ascending=False).plot(kind='bar')\n",
    "plt.title('Total sales of games by genre')\n",
    "plt.xlabel('genre of a game')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('total sales, million USD');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can confirm that action games are the most profitable as well as popular. It looks like, generally, more active games (action, shooter, sports) have high sales, while more calm and intellectual games (puzzle, strategy) have low sales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User profile per region\n",
    "#### Top platforms per region\n",
    "\n",
    "First, let's identify the top 5 platforms in terms of sales in every region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_top_platforms = df_final.pivot_table(index='platform', values='na_sales', aggfunc=['sum']).reset_index()\n",
    "na_top_platforms.columns = ['platform','total_na_sales']\n",
    "na_top_platforms['market_share'] = na_top_platforms['total_na_sales']/(na_top_platforms['total_na_sales']).sum()\n",
    "na_top_platforms = na_top_platforms.sort_values(by='total_na_sales', ascending=False).reset_index(drop=True)\n",
    "\n",
    "others_sales = na_top_platforms.loc[5:,'total_na_sales'].sum()\n",
    "others_share = na_top_platforms.loc[5:,'market_share'].sum()\n",
    "\n",
    "na_top_platforms = na_top_platforms[0:5].append({'platform':'others', 'total_na_sales':others_sales, 'market_share':others_share}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "eu_top_platforms = df_final.pivot_table(index='platform', values='eu_sales', aggfunc=['sum']).reset_index()\n",
    "eu_top_platforms.columns = ['platform','total_eu_sales']\n",
    "eu_top_platforms['market_share'] = eu_top_platforms['total_eu_sales']/(eu_top_platforms['total_eu_sales']).sum()\n",
    "eu_top_platforms = eu_top_platforms.sort_values(by='total_eu_sales', ascending=False).reset_index(drop=True)\n",
    "\n",
    "others_sales = eu_top_platforms.loc[5:,'total_eu_sales'].sum()\n",
    "others_share = eu_top_platforms.loc[5:,'market_share'].sum()\n",
    "\n",
    "eu_top_platforms = eu_top_platforms[0:5].append({'platform':'others', 'total_eu_sales':others_sales, 'market_share':others_share}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "jp_top_platforms = df_final.pivot_table(index='platform', values='jp_sales', aggfunc=['sum']).reset_index()\n",
    "jp_top_platforms.columns = ['platform','total_jp_sales']\n",
    "jp_top_platforms['market_share'] = jp_top_platforms['total_jp_sales']/(jp_top_platforms['total_jp_sales']).sum()\n",
    "jp_top_platforms = jp_top_platforms.sort_values(by='total_jp_sales', ascending=False).reset_index(drop=True)\n",
    "\n",
    "others_sales = jp_top_platforms.loc[5:,'total_jp_sales'].sum()\n",
    "others_share = jp_top_platforms.loc[5:,'market_share'].sum()\n",
    "\n",
    "jp_top_platforms = jp_top_platforms[0:5].append({'platform':'others', 'total_jp_sales':others_sales, 'market_share':others_share}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5)) #ax1,ax2,ax3 refer to our 3 pies\n",
    "# 1,2 denotes 1 row, 2 columns - if you want to stack vertically, it would be 2,1\n",
    "\n",
    "fig.suptitle('Top platforms per region in terms of sales', fontsize=15)\n",
    "\n",
    "\n",
    "labels = na_top_platforms['platform']\n",
    "sizes = na_top_platforms['market_share']\n",
    "explode = (0.1, 0, 0, 0, 0, 0)  # only \"explode\" the 1st slice\n",
    "\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax1.set_title('NA region');\n",
    "\n",
    "\n",
    "labels = eu_top_platforms['platform']\n",
    "sizes = eu_top_platforms['market_share']\n",
    "explode = (0.1, 0, 0, 0, 0, 0)  # only \"explode\" the 1st slice\n",
    "\n",
    "ax2.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax2.axis('equal');  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax2.set_title('EU region')\n",
    "\n",
    "\n",
    "labels = jp_top_platforms['platform']\n",
    "sizes = jp_top_platforms['market_share']\n",
    "explode = (0.1, 0, 0, 0, 0, 0)  # only \"explode\" the 1st slice\n",
    "\n",
    "ax3.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax3.axis('equal');  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax3.set_title('JP region');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In each region different platforms prevail - most games are sold on 'X360' platform in the NA region, on 'PS4' platform in the EU region and on '3DS' in the JP region.\n",
    "\n",
    "In the NA region, second place belongs to 'PS4' and third - to 'PS3'. The differences between the 3 platforms are not very big.\n",
    "\n",
    "In the EU region, second place belongs to 'PS3' and third - to 'X360'. The differences between the 3 platforms are not very big either.\n",
    "\n",
    "Interestingly, the biggest platform of the NA region is almost not represented at all in Japan. Instead, second place belongs to 'PS3' and third - to 'PSV'.\n",
    "\n",
    "Most popular platforms fill up a quarter of all sales in NA and EU regions and almost a half in Japan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top genres per region\n",
    "\n",
    "Next, let's look at the top 5 genres in terms of sales in every region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_top_genres = df_final.pivot_table(index='genre', values='na_sales', aggfunc=['sum']).reset_index()\n",
    "na_top_genres.columns = ['genre','total_na_sales']\n",
    "na_top_genres['market_share'] = na_top_genres['total_na_sales']/(na_top_genres['total_na_sales']).sum()\n",
    "na_top_genres = na_top_genres.sort_values(by='total_na_sales', ascending=False).reset_index(drop=True)\n",
    "\n",
    "others_sales = na_top_genres.loc[5:,'total_na_sales'].sum()\n",
    "others_share = na_top_genres.loc[5:,'market_share'].sum()\n",
    "\n",
    "na_top_genres = na_top_genres[0:5].append({'genre':'others', 'total_na_sales':others_sales, 'market_share':others_share}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "eu_top_genres = df_final.pivot_table(index='genre', values='eu_sales', aggfunc=['sum']).reset_index()\n",
    "eu_top_genres.columns = ['genre','total_eu_sales']\n",
    "eu_top_genres['market_share'] = eu_top_genres['total_eu_sales']/(eu_top_genres['total_eu_sales']).sum()\n",
    "eu_top_genres = eu_top_genres.sort_values(by='total_eu_sales', ascending=False).reset_index(drop=True)\n",
    "\n",
    "others_sales = eu_top_genres.loc[5:,'total_eu_sales'].sum()\n",
    "others_share = eu_top_genres.loc[5:,'market_share'].sum()\n",
    "\n",
    "eu_top_genres = eu_top_genres[0:5].append({'genre':'others', 'total_eu_sales':others_sales, 'market_share':others_share}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "jp_top_genres = df_final.pivot_table(index='genre', values='jp_sales', aggfunc=['sum']).reset_index()\n",
    "jp_top_genres.columns = ['genre','total_jp_sales']\n",
    "jp_top_genres['market_share'] = jp_top_genres['total_jp_sales']/(jp_top_genres['total_jp_sales']).sum()\n",
    "jp_top_genres = jp_top_genres.sort_values(by='total_jp_sales', ascending=False).reset_index(drop=True)\n",
    "\n",
    "others_sales = jp_top_genres.loc[5:,'total_jp_sales'].sum()\n",
    "others_share = jp_top_genres.loc[5:,'market_share'].sum()\n",
    "\n",
    "jp_top_genres = jp_top_genres[0:5].append({'genre':'others', 'total_jp_sales':others_sales, 'market_share':others_share}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5)) #ax1,ax2,ax3 refer to our 3 pies\n",
    "# 1,2 denotes 1 row, 2 columns - if you want to stack vertically, it would be 2,1\n",
    "\n",
    "fig.suptitle('Top genres per region in terms of sales', fontsize=15)\n",
    "\n",
    "\n",
    "labels = na_top_genres['genre']\n",
    "sizes = na_top_genres['market_share']\n",
    "explode = (0.1, 0, 0, 0, 0, 0)  # only \"explode\" the 1st slice\n",
    "\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax1.set_title('NA region');\n",
    "\n",
    "\n",
    "labels = eu_top_genres['genre']\n",
    "sizes = eu_top_genres['market_share']\n",
    "explode = (0.1, 0, 0, 0, 0, 0)  # only \"explode\" the 1st slice\n",
    "\n",
    "ax2.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax2.axis('equal');  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax2.set_title('EU region')\n",
    "\n",
    "\n",
    "labels = jp_top_genres['genre']\n",
    "sizes = jp_top_genres['market_share']\n",
    "explode = (0.1, 0, 0, 0, 0, 0)  # only \"explode\" the 1st slice\n",
    "\n",
    "ax3.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax3.axis('equal');  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax3.set_title('JP region');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Action games are the most popular in both the NA and EU regions, while role-playing games prevail in Japan (with action games on the second place).\n",
    "\n",
    "The NA and EU regions are similar: shooter games come second in both regions, the difference in percentage is not very big - around 7-8%. Third are sport games. Let's see which platform has the most sports games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "platform\n",
       "PS3     63\n",
       "PS4     46\n",
       "X360    44\n",
       "Name: genre, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[df_final['genre']=='Sports'].groupby('platform')['genre'].count().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'PS3' has the most sports games and it explains why it occupies third and second places in the NA and EU regions, respectively.\n",
    "\n",
    "The JP region differs: more than 30% of games are role-playing. Let's check which platform has the most role-playing games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "platform\n",
       "PSV    85\n",
       "3DS    80\n",
       "PS3    61\n",
       "Name: genre, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[df_final['genre']=='Role-Playing'].groupby('platform')['genre'].count().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we expected, 'PSV' and '3DS' platforms release the most role-playing games, that's why they are the biggest in Japan in terms of sales.\n",
    "\n",
    "The rest of genres that we summarized under \"others\" takes up a little less than a quarter of all sales, so we can conclude that different players prefer different kinds of games, so the variety of genres is appreciated by users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ESRB ratings per region\n",
    "\n",
    "Finally, let's analyze the ESRB ratings in terms of sales in every region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_ratings = df_final.pivot_table(index='rating', values='na_sales', aggfunc=['sum']).reset_index()\n",
    "na_ratings.columns = ['rating','total_na_sales']\n",
    "na_ratings['market_share'] = na_ratings['total_na_sales']/(na_ratings['total_na_sales']).sum()\n",
    "na_ratings = na_ratings.sort_values(by='total_na_sales', ascending=False).reset_index(drop=True)\n",
    "\n",
    "others_sales = na_ratings.loc[4:,'total_na_sales'].sum()\n",
    "others_share = na_ratings.loc[4:,'market_share'].sum()\n",
    "\n",
    "na_ratings = na_ratings[0:4].append({'rating':'others', 'total_na_sales':others_sales, 'market_share':others_share}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "eu_ratings = df_final.pivot_table(index='rating', values='eu_sales', aggfunc=['sum']).reset_index()\n",
    "eu_ratings.columns = ['rating','total_eu_sales']\n",
    "eu_ratings['market_share'] = eu_ratings['total_eu_sales']/(eu_ratings['total_eu_sales']).sum()\n",
    "eu_ratings = eu_ratings.sort_values(by='total_eu_sales', ascending=False).reset_index(drop=True)\n",
    "\n",
    "others_sales = eu_ratings.loc[4:,'total_eu_sales'].sum()\n",
    "others_share = eu_ratings.loc[4:,'market_share'].sum()\n",
    "\n",
    "eu_ratings = eu_ratings[0:4].append({'rating':'others', 'total_eu_sales':others_sales, 'market_share':others_share}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "jp_ratings = df_final.pivot_table(index='rating', values='jp_sales', aggfunc=['sum']).reset_index()\n",
    "jp_ratings.columns = ['rating','total_jp_sales']\n",
    "jp_ratings['market_share'] = jp_ratings['total_jp_sales']/(jp_ratings['total_jp_sales']).sum()\n",
    "jp_ratings = jp_ratings.sort_values(by='total_jp_sales', ascending=False).reset_index(drop=True)\n",
    "\n",
    "others_sales = jp_ratings.loc[4:,'total_jp_sales'].sum()\n",
    "others_share = jp_ratings.loc[4:,'market_share'].sum()\n",
    "\n",
    "jp_ratings = jp_ratings[0:4].append({'rating':'others', 'total_jp_sales':others_sales, 'market_share':others_share}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5)) #ax1,ax2,ax3 refer to our 3 pies\n",
    "# 1,2 denotes 1 row, 2 columns - if you want to stack vertically, it would be 2,1\n",
    "\n",
    "fig.suptitle('ESRB ratings per region in terms of sales', fontsize=15)\n",
    "\n",
    "\n",
    "labels = na_ratings['rating']\n",
    "sizes = na_ratings['market_share']\n",
    "explode = (0.1, 0, 0, 0, 0)  # only \"explode\" the 1st slice\n",
    "\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax1.set_title('NA region');\n",
    "\n",
    "\n",
    "labels = eu_ratings['rating']\n",
    "sizes = eu_ratings['market_share']\n",
    "explode = (0.1, 0, 0, 0, 0)  # only \"explode\" the 1st slice\n",
    "\n",
    "ax2.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax2.axis('equal');  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax2.set_title('EU region')\n",
    "\n",
    "\n",
    "labels = jp_ratings['rating']\n",
    "sizes = jp_ratings['market_share']\n",
    "explode = (0.1, 0, 0, 0, 0)  # only \"explode\" the 1st slice\n",
    "\n",
    "ax3.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax3.axis('equal');  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax3.set_title('JP region');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for the ESRB rating, the tendencies in the NA and EU regions are the same, while Japan shows again a different picture.\n",
    "\n",
    "**The NA and EU**:\n",
    "\n",
    "Games with rating \"M\" (Mature 18+) are sold the most - almost a half of all market -, second are games with rating \"E\" (Everyone) (a little under 30% of all sales), third are games with rating \"E10+\" (Everyone 10+) - around 15% of all sales, and forth are games with rating \"T\" (Teens 17+) - also around 15% of all sales. The rest of ratings occupy less than 1% of all sales (EC - probably it was abandoned as there is no description on the official website and RP - ratings pending).\n",
    "\n",
    "**Japan**:\n",
    "\n",
    "Games with rating \"E\" (Everyone) are sold the most - a little more than 30% of all-, second are games with rating \"T\" (teens 17+) - also around 30% of all sales, third are games with rating \"M\" (Mature 18+) - around 25% of all sales, and forth are games with rating \"E10+\" - 10% of all sales. The rest of ratings occupy less than 1% of all sales (EC - probably it was abandoned as there is no description on the official website and RP - ratings pending).\n",
    "\n",
    "This gives us an idea of the age distribution of the target audience in the gaming industry per region.\n",
    "\n",
    "**Summarizing this section, a typical user in each region has the following characteristics**:\n",
    "- NA region: a Mature person (18+) playing mostly action games on the \"X360\" platform;\n",
    "- EU region: a Mature person (18+) playing mostly action games on the \"PS4\" platform;\n",
    "- JP region: a person of any age playing mostly role-playing games on the \"3DS\" platform."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical hypotheses testing\n",
    "#### Average user ratings of the Xbox One and PC platforms are the same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 1: the null and alternative hypotheses\n",
    "**H0:** The means of two statistical populations are equal. In our case it means that the average user score of the \"XOne\" and \"PC\" platforms is the same.\n",
    "\n",
    "**H1:** The means of two statistical populations are not equal. In our case it means that the average user score of the \"XOne\" and \"PC\" platforms differs, although we do not specify here which one is more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 2: Set the criteria for a decision\n",
    "\n",
    "In behavioral science, the level of significance is typically set at 5% and we will choose this criteria as well. When the probability of obtaining a sample mean is less than 5% if the null hypothesis were true, then we reject the value stated in the null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 3: Compute the test statistic\n",
    "\n",
    "In order to test our hypothesis that the means of two statistical populations are equal based on samples taken from them, we will apply the method `scipy.stats.ttest_ind()`. \n",
    "\n",
    "The method takes the following parameters: \n",
    "\n",
    "- `array1`, `array2` are arrays containing the samples. We will use the `monthly_profit` variables we calculated earlier for both plans;\n",
    "- `equal_var` is an optional parameter that specifies whether or not the variances of the populations should be considered equal. To set this parameter let's test whether the variances of our samples are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_1 = df_final[df_final['platform'] == 'XOne'].dropna()['user_score']\n",
    "sample_2 = df_final[df_final['platform'] == 'PC'].dropna()['user_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=10.413474411034404, pvalue=0.0013628728621040526)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.levene(sample_1, sample_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is very low (much less than 1%) suggests that the populations do not have equal variances. Hence we will set the `equal_var` parameter to False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  0.17787582723979845\n",
      "We retain the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "alpha = .05 # critical statistical significance level\n",
    "                        # if the p-value is less than alpha, we reject the hypothesis\n",
    "\n",
    "results = st.ttest_ind(\n",
    "                        sample_1, \n",
    "                        sample_2, \n",
    "                        equal_var=False)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if (results.pvalue < alpha):\n",
    "        print(\"We reject the null hypothesis\")\n",
    "else:\n",
    "        print(\"We retain the null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 4: Make a decision\n",
    "\n",
    "Based on the results of the test statistic we **failed to reach significance**: the decision is to **retain the null hypothesis**. The equality of samples' means is associated with a high probability of occurrence (more than 5%) when the null hypothesis is true. It means that the average user ratings of the Xbox One and PC platforms are indeed the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average user ratings for the Action and Sports genres are the same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 1: the null and alternative hypotheses\n",
    "**H0:** The means of two statistical populations are equal. In our case it means that the average user score of the \"Action\" games and \"Sports\" games is the same.\n",
    "\n",
    "**H1:** The means of two statistical populations are not equal. In our case it means that the average user score of the \"Action\" games and \"Sports\" games differs, although we do not specify here which one is more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 2: Set the criteria for a decision\n",
    "\n",
    "In behavioral science, the level of significance is typically set at 5% and we will choose this criteria as well. When the probability of obtaining a sample mean is less than 5% if the null hypothesis were true, then we reject the value stated in the null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 3: Compute the test statistic\n",
    "\n",
    "In order to test our hypothesis that the means of two statistical populations are equal based on samples taken from them, we will apply the method `scipy.stats.ttest_ind()`. \n",
    "\n",
    "The method takes the following parameters: \n",
    "\n",
    "- `array1`, `array2` are arrays containing the samples. We will use the `monthly_profit` variables we calculated earlier for both plans;\n",
    "- `equal_var` is an optional parameter that specifies whether or not the variances of the populations should be considered equal. To set this parameter let's test whether the variances of our samples are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_3 = df_final[df_final['genre'] == 'Action'].dropna()['user_score']\n",
    "sample_4 = df_final[df_final['genre'] == 'Sports'].dropna()['user_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=9.406709133497195, pvalue=0.0022382935563650197)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.levene(sample_3, sample_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is very low (much less than 1%) suggests that the populations do not have equal variances. Hence we will set the `equal_var` parameter to False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  2.0282047387359358e-09\n",
      "We reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "alpha = .05 # critical statistical significance level\n",
    "                        # if the p-value is less than alpha, we reject the hypothesis\n",
    "\n",
    "results = st.ttest_ind(\n",
    "                        sample_3, \n",
    "                        sample_4, \n",
    "                        equal_var=False)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if (results.pvalue < alpha):\n",
    "        print(\"We reject the null hypothesis\")\n",
    "else:\n",
    "        print(\"We retain the null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 4: Make a decision\n",
    "\n",
    "Based on the results of the test statistic we reach **significance**: the decision is to **reject the null hypothesis**. The equality of samples' means is associated with a low probability of occurrence (less than 5%) when the null hypothesis is true. It means that the average user ratings of the Action and Sports games are not the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "In this report we have **analyzed video games players' behavior in order to identify patterns that determine whether a game succeeds or not and to test two statistical hypotheses**.\n",
    "\n",
    "First of all, we have familiarized ourselves with the data by performing the descriptive statistics. Based on that analysis, in the preprocessing step we converted all column names to lower case letters, made sure all variables had appropriate data types, checked for duplicates and filled a part of missing values. For the rest of missing values, we've decided to keep them as they are because we didn't find any appropriate way to fill them all and we don't want to distort our data by false assumptions. Finally, we have calculated total sales for all regions.\n",
    "\n",
    "**In the following section we have performed an exploratory data analysis and reached the following conclusions**:\n",
    "- Most games in this data set were released around 2009-2012, although the number of games released before the 21st century is not very significant;\n",
    "- There are 5 platforms that stand out in terms of total sales in all regions: 'PS4', 'PS3', 'X360', '3DS', 'XOne';\n",
    "- On average it takes around 10 years for new platforms to appear and for the old ones to fade away, so we decided to take data for the past 5 years to build a prognosis for 2017 in order to make sure the chosen platforms are still popular in 2017;\n",
    "- There are no significant differences in sales for the top 5 platforms. All 5 distributions have long tails of large positive numbers;\n",
    "- Based on the scatter plots and Pearson coefficients, there is almost no linear connection between sales and user reviews and quite a weak linear connection between game sales and professional reviews for the 'PS4' platform.\n",
    "- We didn't find any significant differences between the amounts of total sales each platform made for the same games.\n",
    "- Action games are the most profitable as well as popular. It looks like, generally, more active games (action, shooter, sports) have high sales, while more calm and intellectual games (puzzle, strategy) have low sales.\n",
    "\n",
    "**Next, we analyzed top 5 platforms and genres in each region and then compared them**.\n",
    "\n",
    "We found that in each region different platforms prevail - most games are sold on 'X360' platform in the NA region, on 'PS4' platform in the EU region and on '3DS' in the JP region.\n",
    "\n",
    "In the NA region, second place belongs to 'PS4' and third - to 'PS3'. In the EU region, second place belongs to 'PS3' and third - to 'X360'. The differences between the 3 platforms are not very big in both regions. Interestingly, the biggest platform of the NA region is almost not represented at all in Japan. Instead, second place belongs to 'PS3' and third - to 'PSV'.\n",
    "Most popular platforms fill up a quarter of all sales in NA and EU regions and almost a half in Japan.\n",
    "\n",
    "Action games are the most popular in both the NA and EU regions, while role-playing games prevail in Japan (with action games on the second place).\n",
    "\n",
    "The NA and EU regions are similar: shooter games come second in both regions, the difference in percentage is not very big - around 7-8%. Third are sport games. 'PS3' released the most sports games in the last 5 years and it explains why it occupies the third and second places in the NA and EU regions, respectively.\n",
    "\n",
    "The JP region differs: more than 30% of games are role-playing. 'PS3' and '3DS' platforms release the most role-playing games, that's why they are the biggest in Japan in terms of sales.\n",
    "\n",
    "The rest of genres that we summarized under \"others\" takes up a little less than a quarter of all sales, so we can conclude that different players prefer different kinds of games, so the variety of genres is appreciated by users.\n",
    "\n",
    "We also checked whether the ESRB ratings affect sales in individual regions and concluded that the tendencies in the NA and EU regions are the same, while Japan shows again a different picture.\n",
    "\n",
    "**The NA and EU**:\n",
    "\n",
    "Games with rating \"M\" (Mature 18+) are sold the most - almost a half of all market -, second are games with rating \"E\" (Everyone) (a little under 30% of all sales), third are games with rating \"E10+\" (Everyone 10+) - around 15% of all sales, and forth are games with rating \"T\" (Teens 17+) - also around 15% of all sales. The rest of ratings occupy less than 1% of all sales (EC - probably it was abandoned as there is no description on the official website and RP - ratings pending).\n",
    "\n",
    "**Japan**:\n",
    "\n",
    "Games with rating \"E\" (Everyone) are sold the most - a little more than 30% of all-, second are games with rating \"T\" (teens 17+) - also around 30% of all sales, third are games with rating \"M\" (Mature 18+) - around 25% of all sales, and forth are games with rating \"E10+\" - 10% of all sales. The rest of ratings occupy less than 1% of all sales (EC - probably it was abandoned as there is no description on the official website and RP - ratings pending).\n",
    "\n",
    "This gives us an idea of the age distribution of the target audience in the gaming industry per region.\n",
    "\n",
    "**Summarizing this section, a typical user in each region has the following characteristics**:\n",
    "- NA region: a Mature person (18+) playing mostly action games on the \"X360\" platform;\n",
    "- EU region: a Mature person (18+) playing mostly action games on the \"PS4\" platform;\n",
    "- JP region: a person of any age playing mostly role-playing games on the \"3DS\" platform.\n",
    "\n",
    "**Next step was statistical hypotheses testing. We tested two hypotheses**:\n",
    "1. Average user ratings of the 'Xbox One' and 'PC' platforms are the same.\n",
    "2. Average user ratings for the Action and Sports genres are the same.\n",
    "\n",
    "In the first case we have **retained the null** - the average user ratings of the 'Xbox One' and 'PC' platforms are indeed the same.\n",
    "\n",
    "In the second case we have **rejected the null** - the average user ratings of the Action and Sports games are not the same."
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