diegotf30/proyecto-final.ipynb

## proyecto-final.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fase 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>Critic_Count</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>User_Count</th>\n",
       "      <th>Developer</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>41.36</td>\n",
       "      <td>28.96</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.45</td>\n",
       "      <td>82.53</td>\n",
       "      <td>76.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.68</td>\n",
       "      <td>12.76</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.29</td>\n",
       "      <td>35.52</td>\n",
       "      <td>82.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.61</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>32.77</td>\n",
       "      <td>80.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>New Super Mario Bros.</td>\n",
       "      <td>DS</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>11.28</td>\n",
       "      <td>9.14</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2.88</td>\n",
       "      <td>29.80</td>\n",
       "      <td>89.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Wii Play</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>13.96</td>\n",
       "      <td>9.18</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.84</td>\n",
       "      <td>28.92</td>\n",
       "      <td>58.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16667</td>\n",
       "      <td>E.T. The Extra-Terrestrial</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>NewKidCo</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Fluid Studios</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16677</td>\n",
       "      <td>Mortal Kombat: Deadly Alliance</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>Midway Games</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>81.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Criterion Games</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16696</td>\n",
       "      <td>Metal Gear Solid V: Ground Zeroes</td>\n",
       "      <td>PC</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>Konami Digital Entertainment</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>Kojima Productions</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16700</td>\n",
       "      <td>Breach</td>\n",
       "      <td>PC</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>Destineer</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Atomic Games</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16706</td>\n",
       "      <td>STORM: Frontline Nation</td>\n",
       "      <td>PC</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Strategy</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>SimBin</td>\n",
       "      <td>E10+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6825 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Name Platform  Year_of_Release     Genre  \\\n",
       "0                             Wii Sports      Wii           2006.0    Sports   \n",
       "2                         Mario Kart Wii      Wii           2008.0    Racing   \n",
       "3                      Wii Sports Resort      Wii           2009.0    Sports   \n",
       "6                  New Super Mario Bros.       DS           2006.0  Platform   \n",
       "7                               Wii Play      Wii           2006.0      Misc   \n",
       "...                                  ...      ...              ...       ...   \n",
       "16667         E.T. The Extra-Terrestrial      GBA           2001.0    Action   \n",
       "16677     Mortal Kombat: Deadly Alliance      GBA           2002.0  Fighting   \n",
       "16696  Metal Gear Solid V: Ground Zeroes       PC           2014.0    Action   \n",
       "16700                             Breach       PC           2011.0   Shooter   \n",
       "16706            STORM: Frontline Nation       PC           2011.0  Strategy   \n",
       "\n",
       "                          Publisher  NA_Sales  EU_Sales  JP_Sales  \\\n",
       "0                          Nintendo     41.36     28.96      3.77   \n",
       "2                          Nintendo     15.68     12.76      3.79   \n",
       "3                          Nintendo     15.61     10.93      3.28   \n",
       "6                          Nintendo     11.28      9.14      6.50   \n",
       "7                          Nintendo     13.96      9.18      2.93   \n",
       "...                             ...       ...       ...       ...   \n",
       "16667                      NewKidCo      0.01      0.00      0.00   \n",
       "16677                  Midway Games      0.01      0.00      0.00   \n",
       "16696  Konami Digital Entertainment      0.00      0.01      0.00   \n",
       "16700                     Destineer      0.01      0.00      0.00   \n",
       "16706                       Unknown      0.00      0.01      0.00   \n",
       "\n",
       "       Other_Sales  Global_Sales  Critic_Score  Critic_Count  User_Score  \\\n",
       "0             8.45         82.53          76.0          51.0        80.0   \n",
       "2             3.29         35.52          82.0          73.0        83.0   \n",
       "3             2.95         32.77          80.0          73.0        80.0   \n",
       "6             2.88         29.80          89.0          65.0        85.0   \n",
       "7             2.84         28.92          58.0          41.0        66.0   \n",
       "...            ...           ...           ...           ...         ...   \n",
       "16667         0.00          0.01          46.0           4.0        24.0   \n",
       "16677         0.00          0.01          81.0          12.0        88.0   \n",
       "16696         0.00          0.01          80.0          20.0        76.0   \n",
       "16700         0.00          0.01          61.0          12.0        58.0   \n",
       "16706         0.00          0.01          60.0          12.0        72.0   \n",
       "\n",
       "       User_Count           Developer Rating  \n",
       "0           322.0            Nintendo      E  \n",
       "2           709.0            Nintendo      E  \n",
       "3           192.0            Nintendo      E  \n",
       "6           431.0            Nintendo      E  \n",
       "7           129.0            Nintendo      E  \n",
       "...           ...                 ...    ...  \n",
       "16667        21.0       Fluid Studios      E  \n",
       "16677         9.0     Criterion Games      M  \n",
       "16696       412.0  Kojima Productions      M  \n",
       "16700        43.0        Atomic Games      T  \n",
       "16706        13.0              SimBin   E10+  \n",
       "\n",
       "[6825 rows x 16 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"vg_with_sales.csv\")\n",
    "df = data.dropna() # Remove rows with NaN\n",
    "df = df.astype({'User_Score': float})    # Cast User Score\n",
    "df['User_Score'] = df['User_Score'] * 10 # Making User Score based on 100 \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.hist(column = \"Critic_Score\")\n",
    "df_publishers = df.groupby('Publisher')\n",
    "#print(gb_publisher.mean()['Global_Sales'])\n",
    "#gb_publisher.mean()['Global_Sales'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Video Game Genre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Sales per Genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genre = df.groupby('Genre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f84f56ac668>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_genre['Global_Sales'].sum().plot.bar(figsize=(7, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avg Score per Genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f84f35df898>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Agd1ToiRJfU+HAjgz7wBGt3PT2K4tR5KklYMrYUmSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVED/jtwpImYBzwPzgVczc3RErAdMBIYBs4ADM/Pp7ilTkqS+ZXmugHfOzC0zc3R9fAIwOTM3BSbXx5IkqQM60wS9DzCh/nwCsG/ny5EkaeXQ0QBO4OqImB4RR9bnhmTmo/XnjwFDurw6SZL6qA71AQM7ZObDEbEhcE1E/Kn5xszMiMj2HlgH9pEAG2+8caeKlSSpr+jQFXBmPlx/nANcCowBHo+IjQDqj3OW8tjxmTk6M0cPHjy4a6qWJKmXW2YAR8QaEbFm43Ngd+Bu4ApgXH23ccDl3VWkJEl9TUeaoIcAl0ZE4/4XZOakiJgKXBgRhwMPAQd2X5mSJPUtywzgzHwAGNnO+SeBsd1RlCRJfZ0rYUmSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAQawJEkFGMCSJBVgAEuSVIABLElSAR0O4IjoFxG3R8SV9fHwiLg1Iv4aERMj4nXdV6YkSX3L8lwBfwG4r+n4FOAHmflW4Gng8K4sTJKkvqxDARwRQ4EPAGfWxwHsAlxc32UCsG93FChJUl/U0SvgHwL/ASyoj9cHnsnMV+vj2cAb23tgRBwZEdMiYtrcuXM7VawkSX3FMgM4IvYC5mTm9BV5gswcn5mjM3P04MGDV+RLSJLU5/TvwH22B/aOiH8DBgJrAf8LrBMR/eur4KHAw91XpiRJfcsyr4Az8z8zc2hmDgMOAqZk5iHAtcAB9d3GAZd3W5WSJPUxnZkHfDzwpYj4K1Wf8FldU5IkSX1fR5qgF8rM64Dr6s8fAMZ0fUmSJPV9roQlSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBywzgiBgYEbdFxJ0RcU9EfLM+Pzwibo2Iv0bExIh4XfeXK0lS39CRK+B5wC6ZORLYEnh/RGwLnAL8IDPfCjwNHN59ZUqS1LcsM4Cz8kJ9OKD+l8AuwMX1+QnAvt1SoSRJfVCH+oAjol9E3AHMAa4B/gY8k5mv1neZDbyxe0qUJKnv6VAAZ+b8zNwSGAqMAd7R0SeIiCMjYlpETJs7d+4KlilJUt+yXKOgM/MZ4FrgPcA6EdG/vmko8PBSHjM+M0dn5ujBgwd3qlhJkvqKjoyCHhwR69SfrwbsBtxHFcQH1HcbB1zeXUVKktTX9F/2XdgImBAR/agC+8LMvDIi7gV+GRHfAW4HzurGOiVJ6lOWGcCZORMY1c75B6j6gyVJ0nJyJSxJkgowgCVJKsAAliSpAANYkqQCDGBJkgowgCVJKsAAliSpAANYkqQCDGBJkgowgCVJKsAAliSpAANYkqQCDGBJkgowgCVJKsAAliSpAANYkqQCDGBJkgowgCVJKsAAliSpAANYkqQCDGBJkgowgCVJKsAAliSpAANYkqQCDGBJkgowgCVJKsAAliSpAANYkqQCDGBJkgowgCVJKsAAliSpAANYkqQCDGBJkgowgCVJKsAAliSpAANYkqQCDGBJkgowgCVJKsAAliSpAANYkqQCDGBJkgowgCVJKsAAliSpAANYkqQCDGBJkgpYZgBHxJsi4tqIuDci7omIL9Tn14uIayLi/vrjut1friRJfUNHroBfBY7NzM2AbYGjI2Iz4ARgcmZuCkyujyVJUgcsM4Az89HMnFF//jxwH/BGYB9gQn23CcC+3VWkJEl9zXL1AUfEMGAUcCswJDMfrW96DBjSpZVJktSHdTiAI2IQcAlwTGY+13xbZiaQS3nckRExLSKmzZ07t1PFSpLUV3QogCNiAFX4np+Zv6pPPx4RG9W3bwTMae+xmTk+M0dn5ujBgwd3Rc2SJPV6HRkFHcBZwH2Z+X+abroCGFd/Pg64vOvLkySpb+rfgftsDxwK3BURd9TnvgKcDFwYEYcDDwEHdk+JkiT1PcsM4My8CYil3Dy2a8uRJGnl4EpYkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklSAASxJUgEGsCRJBRjAkiQVYABLklTAMgM4Is6OiDkRcXfTufUi4pqIuL/+uG73lilJUt/SkSvgc4D3tzl3AjA5MzcFJtfHkiSpg5YZwJl5A/BUm9P7ABPqzycA+3ZxXZIk9Wkr2gc8JDMfrT9/DBiytDtGxJERMS0ips2dO3cFn06SpL6l04OwMjOBfI3bx2fm6MwcPXjw4M4+nSRJfcKKBvDjEbERQP1xTteVJElS37eiAXwFMK7+fBxwedeUI0nSyqEj05B+AfwBeHtEzI6Iw4GTgd0i4n5g1/pYkiR1UP9l3SEzD17KTWO7uBZJklYaroQlSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBnQrgiHh/RPw5Iv4aESd0VVGSJPV1KxzAEdEPOB3YE9gMODgiNuuqwiRJ6ss6cwU8BvhrZj6Qmf8Cfgns0zVlSZLUt3UmgN8I/KPpeHZ9TpIkLUNk5oo9MOIA4P2Z+an6+FBgm8z8XJv7HQkcWR++Hfjzipe70AbAE13wdbpaK9ZlTR1jTR3XinVZU8dYU8d1VV2bZObg9m7o34kv+jDwpqbjofW5xWTmeGB8J55nCRExLTNHd+XX7AqtWJc1dYw1dVwr1mVNHWNNHdcTdXWmCXoqsGlEDI+I1wEHAVd0TVmSJPVtK3wFnJmvRsTngKuAfsDZmXlPl1UmSVIf1pkmaDLzt8Bvu6iW5dGlTdpdqBXrsqaOsaaOa8W6rKljrKnjur2uFR6EJUmSVpxLUUqSVIABLElSAQawJEkFdGoQ1souIgI4BHhzZn4rIjYGXp+ZtxUureVExNHA+Zn5TH28LnBwZv64YE39gA8Aw2j6XcjM/1OqpoaIWD0zXypdR0NEbNXO6WeBhzLz1Z6uByAi1mvn9POZ+UqPF8PC19O5mXlIied/LRGxHUu+zs8tVlCT+m/BmzJzZuE6tsjMu3r0OXvLIKyIGAwcwZIvok8WrOknwAJgl8x8Z/1Cujozty5VU13X80DbH+yzwDTg2Mx8oEBNd2Tmlm3O3Z6Zo3q6lqbn/y3wMnAX1c8RgMz8ZsGatgPOBAZl5sYRMRI4KjM/W6qmuq4/AlsBM4EANgfuAdYGPpOZVxeoaRbVYkBP1zWtAzwGPA4ckZnTC9R0E9Xfg3/19HMvTUScB7wFuAOYX5/OzPx8wZquA/am+ls+HZgD3JyZXypY043AqsA5VBcLz3b3c/amK+DLgRuB37PoRVTaNpm5VUTcDpCZT9eLkpT2Q6q1uS+g+sN0ENUv4AzgbOB9BWrqFxGR9Tu++mqh9PdqaGaOKFxDWz8A9qBe1CYz74yIncqWBMAjwOGNuf71zmffAv4D+BXQ4wEMXANcnJlX1TXtDnwI+BnwY2CbAjU9ANwcEVcALzZOFm5VGQ1slq11tbV2Zj4XEZ+iajX4RkQUvQLOzB0jYlPgk8D0iLgN+FlmXtNdz9mbAnj1zDy+dBFtvFIHSSNUBtN0JVXQ3pk5sul4fH0FenxEfKVQTZOAiRHx0/r4qPpcSb+LiN1LXL29lsz8R9W7sVArvOF8W/NCO5l5b0S8IzMfaFNrT9o2M49oqunqiDg1M4+KiFUL1fS3+t8qwJqFamjrbuD1wKOlC2nSPyI2Ag4E/qt0MQ2ZeX9EfJWqtfBHwKi6q/Ermfmrrn6+3hTAV0bEv9WLf7SKHwGXAhtGxEnAAcBXy5YEwEsRcSBwcX18AFVTKyzZNN1TjqfalOMz9fE1VE2tJf0RuDQiVgFeoWotyMxcq2BN/6iboTMiBgBfAO4rWE/DPXWXyy/r448A99ZBV6TPFXg0Io5vU9Pj9ZviIm+EG90XLdaHvwHVz+o2YF7jZGbuXa4kvkm1iuJNmTk1It4M3F+wHiJiBHAY1biQa4APZuaMiHgD8Aeqlp6ufc7WapVYurpfcw3gXyz6hS/9x5KIeAcwluqP9+TMLP7Hsn4x/y/wHqrA/SPwRarNMt6dmTcVLK8xeGZoCwy6eJBqD+u7WqV5LiI2oPrZ7Ur1mroa+EJmPlm4rtWAzwI71KdupmrmfZmqdeqFAjVtAHyjTU3fpBrvsHFm/rVATe8BzqKF+vAj4r3tnc/M63u6loaI2D4zb17WuR6u6Xqqn91FmfnPNrcdmpnndflztsjfnV6nfpd9T2a+o3QtvcFSBl3ckplfLFjTDcD7MrMVug3UB0TErVQtTlc0BhhGxN2ZuXnhuoYAjcGht2XmnML1zMjMrZZ1rq/rTU3QRMTeQGNAynWZeWWpWjJzfkT8OSI2zsy/l6qjPa04YpwWHHRBNWDmuoj4HYs3zfX4gJmIOI3X6B4oOWIVqqsT4ERgExZ/Tb25YE1vA45jydf5LqVqqp+/pfrw6+6o7wPXUbWqnBYRX87Mi1/zgd1Ty3uA7YDBEdE84nktqk19iomIu1j67JHvdEcrVK8J4Ig4meod3Pn1qS/UTRb/WbCsdan6xm5j8RGPJftWoDVHjLfioIsH63+vo/yI7GmFn39ZzqLqxphO67ymLgLOoBpL0Co1tWIf/n8BWzeueus36L9n0RiRnvQ6YBBV9jQPUnuOquWgpN9RvY4uqI8PAlanmtp2DvDBrn7CXhPAwL8BWzaaCyNiAnA7UDKAv1bwuV9LK44Y/xYtNOii7kJYMzOPK1VDs8ycABARO1I1zS8MlKUsgtHTns3M35Uuoo1XM/MnpYto49NUffhvpBpzcTVV33lJq7Rpcn6SQqsgZub19VzpESXn2y/Frm2awO9qNItHxMe641s6ObgAABNdSURBVAl7UwBDNdH+qfrztUsWAmUHMSxDy40Yz8yLqK5YGscPUM3ZLFXP/LpZtdVcBUyNiA83/dE8k2oRjJKujYjvU40EbW6un1GuJH4dEZ+lmonQXNNTS39It3t725Ww6tdZscFFwKSIuAr4RX38EcpsIwss/N17Q6nnfw39ImJM1isZRsTWLGoW75bV3nrNIKyIOBg4GbiWqh9jJ+CEzJxYsKbmFadeBwwAXmyBkdmNEePzKDy9JiL+IzO/t7Q+zsKr8fyE6krlIhbvQujy6QbLUdPtVC0r36da+OKW0iuG1XVd287pLNnfWo9ibysL90u35OCiiPgQ0HjDeWNmXlq4nlb83duaaqGiQfWp54FPUa349oHMvLCrn7PXXAFn5i/qkbSNkXzHZ+ZjBUsiMxf2YdSTtfcBti1XUaW5rhbQ6P9qxT7OgVTNcc0hknTDfL/lkJl5ZUT8mWrhkrMpN3e7uaidS9fQVmYOL11DQysPLgLIzEuAS0rX0aTlfvcycyqwRUSsXR83L0XZ5eELveAKuF5t509L6wcr3AS2hJJXK73te6UlNb9+ImINqmUV98/MIm+WI+JjmfnzNqGyUKER47tk5pSI2H8pNfX4H/F6ru37qPqAz2i66Xng15nZ4+MdIuKmzNwhllwbvhUWnGk59VSt/wbekJl7RrXc6nsy86zues7ecAX8JaoVlP6nnduSxd9B9ag2fwBWoVpz9eWl3L0ntNz3Kqo1cZeq5IjxiBgKnEZT0xzVohezS9VENVcagMx8ETgwql22Slmj/thKrSrvBabQ/qjUIldR9XiQ6yPinMx8KCIG1ed7fIGSppp2qD+20s8OaNnfvXOo3vA2Zmn8BZhINQOgW7T8FXBDRAzMzJeXda6Ha/pZ0+GrwCzg/7XAJPeW+V5FxFzgH1QDQG6leve9UOHVeK6hmnLQWOHmY8AhmblbwZpeoZoecnjWSxm2Qh9iK4qIfs2jxVtBRGxO9XpqbJX4BDAuM+8uWNN5mXnoss71cE2t+Ls3NTO3btMKtcQubl2pyFD0FXRLB8/1pDMz87D63xGZeRKwaeGaoLW+V68HvkK1fd3/ArsBT2Tm9S0winxwZv4sM1+t/50DDC5c091UVwM3RcRb6nPFdjtoiIgJEbFO0/G6df90SfdHxPfrpsJWMR74UmZukpmbAMfW50p6V/NBRPQH3l2oloZW/N17MSLWZ9HmOttSLcTRbVq+CToiXk81Wm61iBjFoj9Ga1FNki7pNJacHtLeuR7Rit+r+gplEtVUiFWBg6lWn/pmZv7fEjU1ebKe39eYnnEw1cCQkjIzfxwRd1JNszmeFhiERTVv85nGQVZbbxYdmQ2MpFos4cyoNtQ4G/hlZj5XsKY1MnPhiPHMvK7uy+9xEfGfVG9+V4uIxvckqNbTL/2moBV/975EtQ3oWyLiZqo3BB/uzids+SboiBgHfIKqf3Uqi0LlOWBCoQEXjRGPx1Dt39qwFrBfLr4VYE/W1fy9ah51/DxwTqkh/nXwfoDql2wY1Yv87Mx8uEQ9TXVtQvWGqbFpxS3A57Pg0qJtmr82ohp9+e7MLPpms35D8L7MfLo+Xg+4PjO3KFlXQz0I6gKqtQIuBr6dZTZjuJRq3+3mptV3Z+Z+PV1LU03fzbIrBi6hze8eVPOkS//urUq1EtbbqXLmz1SLmMx7zQd25jlbPYAbIuJD9VD64lpxxGOzFvtenUvV/PxbqquTYn1hDRFxSlZ7I384qwVCWkZEbJSZjzYd9we2y8wbCpZFRHyc6mrqIqo/TgcAJ2U37BCzHDX1o3pjdxjVG7vzqJaq3RH478x8W4Ga1qXakamxQ9ONwImNNy6l1HVtSjX9B4DSr6lWU2IOd28K4P8GvtdoBqtfUMdmZrH9dyNik8x8qNTzL039Tu5DLLlI/bcK1LKARRPtW2IqRFSLro8Aprfa4KaI+Hp750v87NqKiHcBjfnAUzLz3sL1PEC1MM9ZmXlLm9t+lGUXeVmT6vVdbBR0Uy2folqTeihwB9VaBX/IsouoNLZM3Zbq78IfgC9mtUJeT9fS6Lr7OfBRFu+6OyO7cce7lu8DbrJnZn6lcVD3Qf0bUCyAgVUjYjwtthsL1WYMz1ItnN9tzScdkZmtONBvEvA0MKjuGwuqPwKtMD/yxabPBwJ7UX4xfwAy8556VPtAgCi/E9iIpQVcqfCNiC2Ac6lHQUdE8VHQVOG7NfDHzNw5qj3M/7tgPVB1F5wONJrmD6LqD96mQC17UHXdDQWa57U/T9Xq02160xXwTKodPebVx6sB0zLzXa/9yG6t6U6qJujFdojJzOmlagJaYv/R3iAiLs/MfUrX8Vrq1oyrMvN9hevYm2p++Ruo9nLeBLiv8O/fQOBwqlG+zU2rxbbdjIhbgP9qDMSKiPdRNYdvV7CmxvSaO4BtMnNeRNxT+Gc3MzNHtDl3Z6nxM/Xz93jXXW+6Aj4fmFzPvQ2qdywTilbUmruxANwSEVtk5l2lC2lxf2p7otE/XKKYpVid6p15ad+mai78fWaOioidqQYYlXQe1c9wD6rdtg6hfGtBy4yCbjK7nkJ2GXBNRDwNlO46+11EnAD8kqr16SPAb+vBfUU21MjMSyLiAyz5hq7bun96zRUwQES8H9iV6gf2HPD6zDy6YD0nUl0NtMRuLLFoQ+n+VAMuHqjrajStjniNh690ljLoYol35j1cU/Om4P2opkJ8q/SUrYiYlpmj61afUZm5oAWuWG6v3wzMzMwRUe2/e2NmFluPvRVHQTerB5CuDUzKzH8VrKOxkUbjtd481z2zwIYaEXEG1Rvenal2IDsAuC0zD++u5+xNV8AAj1P9wD5MtZF66ZG+4+qPX246l0Cp3Vj2KvS8vUpEfIZqj9a31F0bDWtSdts4WPxn+CrweGZ2y1Zoy+mZqJZXvAE4PyLmsHh/dQmv1B+fiWoFqseADQvWA/BJqlHQjSl/N9bnelzjarKNRqvYIBZt7dpjotpx6B9Zb6RRT538ENUqgieWunipbVe/kZuZmd+MiP8BunUP7Ja/Ao6It1HNHz2Yalm3icBx9SozasdSfvGez8xX2jm/0olqt5N1ge8CJzTd9HzB1ouBVNPa3kr1R/KsFglegMbGEP+kWj3vEKqrqPMzs9jiCfXo3kuoRrT/jCpUvp6ZZ7zmA1cS9VVmY3BhW6WuMmdQbXz/VETsRNUE/e/AlsA7M/OAnq6pqbbbMnNMRPwR2J9qYZB7MvOt3facvSCAF1C9izy8MbE+Ih4o8eJpKyJWp1o9ZePMPDIiNqXakPvKwnXNAt5ENdI3qBYneIyqBeGI0oPEWk1EbMjifT49PrI3IiZSXdHdCOwJPJSZX+jpOjqqXnnq4Mw8v3QtraS+YDiO1psZ0RKauy0i4nRgbmaeWB9367rLHajta1SLg4ylGqGdVGv7tzs1sCv0hibo/amGqF8bEZOo3jEVXxu39jOqEdCNEY4PUy1UUDSAgWuAizPzKoCI2J2qmednwI8pM9S/5UTEB6mmHSw2spc2a+f2kM2yXlUqIs4CbitQwxIiYi3gaKp5kldQvbaOpgqZO6kGR/Z0Te1ujdiQBbZIbHIR1cyIM2maGVFSfaW5hEILcfSLiP51685Yqt3bGorlUf2GcnK9zsQlEXElMDAX3xO4y7V8AGfmZcBldRPYPlTLP24YET8BLs3MqwuW95bM/EhEHFzX+lJEtMKbg20z84jGQWZeHRGnZuZR9bQWVb5D64zsXdg9kJmvtsbLCKgGEz1NtVDCp6jmRQawb2beUaimltter0krzoxoHqMyEBhDdeFQ4qr8F1TbNj5B1aVxI0BEvJVu3vjgtdSDCk8HRtXH8+iBNRRaPoAbstob9QLggnoVrA8DxwMlA/hf9Xzkxu4Zb6Hwwhe1R6NaxP+X9fFHgMejWrpvQbmyWs4rmflkRKwSEatk5rUR8cNCtYyMxRfMbyygX3pxkDc3XZmfCTxK1eVSbBvQzPxmXc8GmflEqTqaNY27+HVEfJYWmRlRP/di+yZHxJuAIq/zzDwpIiYDGwFX56I+0FWo+oJLmhwRHwJ+1VRXt2r5PuBWVjft/hewGdUbge2BT2TmdYXr2gD4BovWo72ZamTms1R/PHt8kfpWFBG/B/alGoy1AVUz9NYlF01oNW2narU3datATXtRdae8QvWG8sBssxRlgZraDnha7A9rK4xZaahb6e7JzFbaxrG4iHgeWINq9sHL9MCbXwO4k6LaP3Jbqh/WH1vlHbmWre7WaPyitcTI3lYTEfNZNN0ogNWAlyi7lvdMqtD9U0RsQ7VG/Ht7uo42NY2hml7zaH3cMtNrIuI0Fr0hWIVqxPGszCy9kMpKzwDuhIj4NVWz+BV1E3npen6YmcfUdS3xg83MvQuUJXWpFr0qb+XpNeOaDl+lCt/S891bTkRMzsyxyzrXlXpNH3CLOpWqf/XkiJhK9Ut3ZcH+scbqO6cWev5eoW5qaq+5sHR/qzpmwzYjoRc7LjQKul/TVe5HgPFZrSt8SVRrMBeTmaWX7G1p9Rz81YEN6vFFzbshvbE7n9sA7oTMvJ5qRF8/qhGFRwBnU/3gSpjbVJeWIjNbeRStlu3/sfhI6LbHJbTk9BpY2Gf+bappdv3xjWZbR1HNrnkD1ejwhueBbl0C1gDupHoU9Aep3vVuRdkNIi6rayAiLsnMDxWspWW1WXVqJnB2K606pdfWGAXdYlpyek3th1TrKdzVU6N7e5lbgAuBAzLztDb99xd05xPbB9wJEXEh1Zy6SVRLZF6fmcWm+US9OH3bz7W43rbqlJatRfqBt2XR9JoX63NvAwZl5oyCdV0LjC35t6mVley/9wq4c86iWo6vJVa8YfGBV76zWrqWXHVKnVJ85ZLM/GM75/5SopY2/oNqq7/rWXxucskVw1pJsf57A3gFRMT+TYf7tF21KDN/RRmNxRyaF3IA+3zaatVVp7TiflO6gBZ2EvAC1SpYrytcSysq1n9vAK+YxsoyG1KtAz2lPt6Zqj+hSABnZr8Sz9sLteqqU1pOEbEJsGlmfrUej9E/M58vXVeLeUNmbl66iBZWrP/ePuBOiIhrgI83Tb7fCDgnM/coW5nU90XEEVRXK+tl5lvq3cjO6M55m71RRHyPar3zksv2trRS/fcGcCdExH2Z+c6m41WAezPzHQXLklYKdf/cGODWpsGHdzX691VpWmJxHlX3iy09LcIm6M6ZHBFXUTVhQNWB77tMqWfMy8x/NfrwI6I/Dj5cgvPeW5cB3AmZ+bmI2A9o7Ld5C/D6giVJK5PrI+IrVH34uwGfBX5duKaWERHvqNfLbnd6VsmpUarYBN1JETEK+CjV9ogPApdkZreuniJpYZfP4cDuVM2qVwFnuthEJSLGZ+aR9TzghoXfm8wssR+wmhjAK6DunD+4/vcE1SIcx2XmJkULk6RavUPT3zPzsfq4ZXZoUsUAXgERsYBqqPrhjb11I+KBVtrzU+qrIuIuXqOvNzNH9GA5LauVd2hSxT7gFbM/cBBwbURMonphu5qD1DP2Kl1AL9GyOzSpskrpAnqjzLwsMw8C3gFcS7WTxoYR8ZOI2L1sdVLflpkPNf4BLwNb1P/+WZ9TpV89MhyqFZ6mNN3mxVcLMIA7ITNfzMwLMvODwFDgduD4wmVJK4WIOJBqHe8PAwcCt0aEzaqLNFZ4upzW26FJ2AcsqZeKiDuB3TJzTn08mGrFp5FlK2sdrbpDkyo2Q0jqrVZphG/tSWzVW0wL79AkDGBJvdekdlai+13BeqTlYhO0pF6r3hp0h/rwxsy8tGQ90vIwgCX1CfXKWAdn5vmla5E6wv4SSb1KRKwVEf8ZEf83InaPyueAB6hGQ0u9glfAknqVelrN08AfqOa3bki1EM4XMtMFJtRrGMCSepXmPX8joh/wKLBxZr5ctjJp+dgELam3eaXxSWbOB2YbvuqNvAKW1KtExHzgxcYhsBrwUv15ZuZapWqTlocBLElSATZBS5JUgAEsSVIBBrDUi0TEkIi4ICIeiIjpEfGHiNivdF2Slp8BLPUSERHAZcANmfnmzHw3cBDVVpid/dr9Ovs1JC0fA1jqPXYB/pWZZzRO1BvTnxYR/SLi+xExNSJmRsRRABHxvoi4LiIujog/RcT5dZATEbMi4pSImAF8OCLeEhGT6ivrGyPiHWX+m9LKwd2QpN7jXcDS9nA9HHg2M7eOiFWBmyPi6vq2UfVjHwFuBrYHbqpvezIztwKIiMnApzPz/ojYBvgxVehL6gYGsNRLRcTpVDsB/Qt4CBgREQfUN68NbFrfdltmzq4fcwcwjEUBPLE+PwjYDriovkAGWLX7/xfSyssAlnqPe4APNQ4y8+iI2ACYBvwd+PfMvKr5ARHxPmBe06n5LP5731jQYhXgmczcshvqltQO+4Cl3mMKMDAiPtN0bvX641XAZyJiAEBEvC0i1ujoF87M54AHI+LD9eMjIkZ2Ud2S2mEAS71EVsvW7Qu8NyIejIjbgAnA8cCZwL3AjIi4G/gpy9/CdQhweETcSXW1vU+XFS9pCS5FKUlSAV4BS5JUgAEsSVIBBrAkSQUYwJIkFWAAS5JUgAEsSVIBBrAkSQUYwJIkFfD/ASTvUrPYwfhCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_genre[['Critic_Score', 'User_Score']].mean().plot.bar(figsize=(8, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Platform Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_platform = df.groupby('Platform')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sales per Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f84f2d42f28>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_platform[['NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales']].sum().plot.bar(figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average Sales per Platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f84f2d85a20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_platform['Global_Sales'].sum().plot.pie(figsize=(7, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average Score per Platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f84f2ccb208>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HxKcjYlpETJs/f37T9kqS+oieBNRcYG5mTqmnr6EKrD9GxNYA9e953b04Mydm5pjMHDNo0KBWtFmS1AesMKAy8yngDxHx7nrWeOA+4AbgqHreUcD1bWmhJKlP6t/D5/0tcGVErAs8BBxNFW5XRcQngUeBj7SniZKkvqhHAZWZM4Ex3Swa39rmSJJUcSQJSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpEMKElSkQwoSVKRDChJUpF6HFAR0S8i7o6In9bTwyJiSkQ8GBGTImLd9jVTktTXrMwe1EnA/Z2mzwXOz8ztgGeAT7ayYZKkvq1HARURQ4APAv9eTwcwDrimfsrlwCHtaKAkqW/q6R7UBcDngUX19BbAs5n5ej09F3hri9smSerDVhhQEXEgMC8zp/dmBRHx6YiYFhHT5s+f35sSkqQ+qCd7ULsDB0XEI8APqQ7tfR3YNCL6188ZAjze3Yszc2JmjsnMMYMGDWpBkyVJfcEKAyoz/z4zh2TmUOAwYHJmHg7cAny4ftpRwPVta6Ukqc9pch3U3wGnRsSDVOekLm1NkyRJgv4rfsobMvNW4Nb68UPA2NY3SZIkR5KQJBXKgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFWmFARURb4uIWyLivoi4NyJOqudvHhE3RcQD9e/N2t9cSVJf0ZM9qNeB0zJzOLArcHxEDAfOAG7OzHcCN9fTkiS1xAoDKjOfzMwZ9eMXgPuBtwIHA5fXT7scOKRdjZQk9T0rdQ4qIoYCo4EpwODMfLJe9BQwuKUtkyT1aT0OqIjYCPgRcHJmPt95WWYmkMt53acjYlpETJs/f36jxkqS+o4eBVREDKAKpysz89p69h8jYut6+dbAvO5em5kTM3NMZo4ZNGhQK9osSeoDetKLL4BLgfsz82udFt0AHFU/Pgq4vvXNkyT1Vf178JzdgSOA2RExs573f4FzgKsi4pPAo8BH2tNESVJftMKAysw7gFjO4vGtbY4kSRVHkpAkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVqVFARcQHIuK3EfFgRJzRqkZJktTrgIqIfsDFwAHAcOCjETG8VQ2TJPVtTfagxgIPZuZDmfkX4IfAwa1pliSpr2sSUG8F/tBpem49T5KkxiIze/fCiA8DH8jMT9XTRwC7ZOYJXZ73aeDT9eS7gd/2cBVbAn/qVeOsbW1rW9vaa0rtbTNzUHcL+jdowOPA2zpND6nnLSUzJwITV7Z4REzLzDG9b561rW1ta1t7Ta7d5BDfVOCdETEsItYFDgNuaNogSZKgwR5UZr4eEScA/w30Ay7LzHtb1jJJUp/W5BAfmfmfwH+2qC1drfRhQWtb29rWtvbaU7vXnSQkSWonhzqSJBXJgJIkFWmtD6iI6IiIZfrYR8SgiOhYHW1qKiJ2bvj6te49kdYWEbF5RGy+uttRgiICKiJ2joi3dJo+MiKuj4gLW/AfdSGwZzfz9wDOb1I4IjaIiAGdpt8dEadExKFN6i5nXcMj4ksR8SBwScNy7XxPtouI3buZv3tEvKNh7bZ9TiKiX0QcV7/Hu3dZ9oWGtTeIiM9HxOn1xsEnIuKGiPhqRGzUpPZy1ve7FtV5Z/3+zomIH0REy0aKiYj7IuILTT8T3dS9oP79k/o9XuqnYe3Nu/xsFhHRonZvExE/jIj5wBTg1xExr543tGHtd0bEdyLiaxExJCL+KyJeioh7mm7sdlpHRMTHI+KL9fQ2ETG2ceHMXO0/wAxg8/rxXsATwN8AXwKuaVh7+pssu7dh7duAd9aPtwMWABcBNwNfacH7MhT4e2AWMJ3qyuyhLajbzvfkp8CIbuaPAH5S8Ofk34HvAyfX7/XXOq+3Ye2rgH8Fvll/Nr5BtYHwL8AVDWu/ADxf/7xQ/yxcPL9h7duBY6lGgDkduLbpZ69T7VHAV4DfA78GTgH+qgV1d6p/v7+7n4a1HwYeqn8v/pkP/KLp3yXwK+D/AP06zetHdX3p/zSsfQfVaD6foxpM4X8DHcC+wJQW/X9eQjV4+P319GbA1MZ1W/WBa/iPu6fT44uBszpNz2xY+/7eLOth7dmdHn8JuLh+vG7nZb2s/SvgXuAfeCMEH27R+93O92S5H8oWvCft/JzM6vS4P1U32WuB9YC7G9aeWf8O4Cne6D0bndfby9oXAt8FBnea16rPycwu042C+k3WsyvVnvtjwC3Ase1YT7t+gEOBGxvWeKA3y1b2/5FqgO/l/h83WMeM+vfdnebd07RuEYf4gH4RsfiarPHA5E7LGl2rBczrblez3rWd37B25z7644CbALIa3X1Rw9p/BDYGBgOLzxe16pqAdr4nm77JsvUb1m7n52TdxQ8y8/XM/DQws15HSw7DZfVX+5/178XTjf5PM/NE4OvADyLixIhYp2nNTjoiYnRE7BgROwLrd5luicz8n8w8BTiS6vPzjd7Wioir6t+zI2JWp5/ZETGrRU1eSmZeC2zVsMz0iPhmROwSEX9V/+wSEd8E7m5Yu/N30fNvsqyJ16K6BVO15VWd425cu+kfdav8APhlRPwJ+DPVoQUiYjvguYa1TweuiojvUB26ARhD9cdwWMPasyLiPKrd5u2AnwNExJt9SfdIZh4SEQOpts7Oioh3AptGxNjM/HXD8st7T46iOszQxLSIODYz/63zzIj4VKd19VY7PyfTIuIDmXnj4hmZ+U8R8QTNz/lNi4iNMvPFzDxm8cz6/MsLDWuTmdMjYh/gBOCXVIdvWuEp4GvLmU6qjbJG6o2ij1Idqn0Y+DZwdYOSd9cbXxOA15q2ryfq84hNN/aPBD4J/CNv3BXicarh4y5tWPu9dTgH8I5OQR3A2xvWXuxC4MfAVhFxNvBhoNG5WyjoQt2I2BXYGvh5Zr5Uz3sXsFFmzmhYezDwWWD7eta9wDcyc17DuusDJ1G1+7LMvKeevxvwjsy8okn9Luvaiio8DgO2ycy3reAlPal3PNV7krzxnjTag6rf6x8Df2Hp8FsXmJCZTzWs37bPyeoQEZEt/COMiK2B0VmN8lKsiPhnqs/zAqp7yU3KzLktqHsesBvwHmA2cCdwF3BXZi5oWPvUbmZvBhxE9bfzb90sX+0iYts3WbxNZt7eovW8h+rIRgA3Z+b9jWuWElAAETGC6oMF1bmQOS2uPwig6Zfwqq7dzbq2zcxHG7z+YGBIZl5cT/+a6jBiAp/PzGta0Ma96bRBkJmT3+z5PazZAXyGam91NnBpZr7etG5de2fgD4sDNCKOpNqqf5TqXFevv9ys3W3tLwI/yMwHeltjBfXXpdow2g346/rn2czs9V2/I+LMLrMSeBq4LTNn97ZuXXsC8MvMXFB/l5wH7AjcB5zWJLwj4iHgW8C/ZubCet5gqo4778kWjWheH+IbTKcjc5n5WKOirThB1oITbAOBW6l69PwYuK5+fAuwScPaAZxFdW5lQf0zH/hiC9odwJmdaj/Twtpb1rVPpDoHcgkwB7ge2K5h7TuBt3WanglsDmxDteXTpHYHVU+4bwDHAf1b+DmZBHyvrnsd8PUW1m5nD0FrL1t7Z+AtnaaPrD/bFy5eZ8P6A4EP1G39BTAN+I9WfV5a/QPc1+nxJKpejUOATwA3Nay9GdWh09lUh2VPotrIOB5Yp0Xt/1uqXsb3UvU6nk3DDkCZ5fTiu5Bqi2GdTvPWAb4KXNSw9qlUnReGdZr3dqpR2E8puPbPgX+m6rZ+H9V5o/dQdfu9tWHtqV2mv9HpcdMurV1D5IIWfk4695rsTwt7ldHeHoLWXrZ2W8KPqvflncCNVOdzDgA2a9Fn5IL690+ozg0t9dOw9m87PZ7eZVmretqdRNVxYS7VEZTGNTvVfhDYopU1M8sJqPvoZku7/hJq2u35bmDLbuYPonn34XbWvqf+HcBjXZY1/XJ48E2W/b5h7XaGyIw3m25Ye87izyDwG2Cvzsus3fLabQm/OpimAd+huvZnBPWpjBZ8Rtp5jdW3gX+i6un6r1TnawH2pjr016T2pnX9mcB+wAXUe1OteF/qddzS3Xd4059SevH9Jbs5l5DVPadebVh7QGYuc+vhzJwfnUaBKLD2wrpW1r3WOmvafXPKcnraHUd10WQTS3pO1f9/DcstZVRELO4mG1Tdnp+vH2dmbtKgdjt7CFp7Wf0ion/9dz+eKkwWa3Kfug9E9aF7H9X5p9OA7SNiAfCrzOx6Hmllak+vf/+ytzXexAnA/wN+W0+fEhEvUe2tHdGw9gyqi8SPr9/vn0fEDsA3I+LRzPxow/pQXcB8a0T8DFjynZ2ZX1v+S1asiE4SEfEbqq6mXb/NAvheZr63Qe0ZmdntNRtvtqyA2s9SjVQRVKMO3LZ4EbBHZm7WoPZWVIffXqX68ALsRHVR6iGZ+ccGtRcCL3Vq6/rAy7QmRNqqzT1Jrb103f8H/C+q8xbbADvWG2PbAZdn5jLDZfViHUOA3amC6kCqQ1C9vgQkIq7KzI9ExGyWvtZs8Wd7ZKMGv7GegVR7I0+3qN6QXE4ni+42VHu5jm6DPzP/sVHdQgLqVqq9gs4BlYunM3PvBrU7f2EutQjoyMxe7+m0ufb7u5m9+D8rWrEVFxHjqLY0oUU97dZUbe4haO3u67c8/CLiRKpA2o1qb/6uTj+zM7PXRx8i4u+phqpaQDfXWGWDnrV1/bfUdZ6qe/LtSXVuao24U3l9PRiZ+WLLapYQUAARsQuwKDOnRsT7qHrg3J+FX8/RLivoCv53mdnkYkZ1ERGTqL50bqc6sf5oZp5k7bbVbljbA1YAAATlSURBVEv4RcTXqK99yswnm9brUrud11gdB5xBtXF7LlXvvTlUAzh/NTObXqzbNhHxWaq2b1jPehE4NzO/2bh2CQFV7x4eQHXs+SZgLFW3832B/87Ms1df61aPiLgTOCwz/1BPz6Q6Vr8hVXfZ8auzfWubiJidmSPqx/2BXzc5RGvtFdZuW/i1W5uusZoN7EJ1SPxRqktJnoqIzYBbMnOH5i1vvahG+t8NOCEzH6rnvZ1q+K0pmfnlJvVL6STxYWAHqnMgT1HtOTxfb7FMAfpcQAHrLg6n2h31MemnI2LD5b1IvdbOzh3WXtbwTuF3Kc0756xK6wObUF1rNZCqi3yjC3WB1zPzZeDliPh91hdHZ+YzEbH69yKW7whgVGa+snhGZj4UER8B7gHWioB6PasrnBf/5zwPkJl/johWDWa4plmqE0RmntBpcpmbDaqxdvYQtPay2hl+bRERE6nO2b5AteF8F9VtWZ5pQflFETEgM18DPthpnR0Uct++5cjO4dRpZku+u0sJqL9ExAb1FsROi2fWvVn6akC1syu4usjMftZedbVpb/i1yzZUR3keoBrIdS7wbItqn0zdCapLj7stgMZDj7XR4xExPjNv7jyz7oDV+BxgKeeg1svMZa53iogtga2z4ThXa6J2dgWX1DtdrrHajWq8ycbXWMUqGi+v1eoObddT3RSx8+DQuwMHN+2BWERAafnsCi6Vpw3XWG0GnFPXO4lqBIxTqYZ7u6RJ9/h2iohtgHnAx3jje+o+4Epg52w4UroBJUk90M5rrDqt4ySquws/Aey6vAtsS9HuPb+ST75JUkmGUt1McZfMfEdmHpGZl2TmPU3DKSI2jYhvA0dTXQN6DfBf9RGUku0EvAOYGRHj6oD9NfArqsuFGnEPSpJWs3pP5JtUI6a/Xs/boZ7XqvHy2qZde37uQUnS6rdXZp7XeTSNzJyZmbsBxZ53bveen3tQkqReafeenwElSeqVdo+UbkBJkorkOShJUpEMKElSkQwoqQciYmFEzIyIORFxdURsUM9/05uz1b2cPttl3r9ExL0R8S/tbLO0pvMclNQDEfFiZm5UP74SmJ6ZX+s8fzmvGwr8NDO37zTvOWDzxVfe92Dd/Vt5J1tpTVHKaObSmuR2YGTnGfXtrq+nuk3KAOALmXk91fhq76hvOHkT8G5gI2B6RHyF6rYNlwFbAvOBozPzsYj4DvAKMBq4sx7pexjwdqpRtU8BdqW62d/jwIfqWzVIaw0DSloJ9Z1lDwBu7LLoFWBCfaPNLYH/iYgbqG6FvX3nO6LWe1071I9/AlyemZdHxDHAhcAh9VOHALtl5sKIOItqSJm9geFUQ8n8TWZ+PiJ+THUPoeva86+WVg8DSuqZ9eu9IKj2oC7tsjyAf46IvajuYfZWYHAP6v41cGj9+Aqq0asXu7rLYcD/yszX6tuD9+ONkJxNNU6ctFYxoKSe+XPnvaBuHE51p+Od6hB5BOhouM6Xuky/CpCZiyLitXzjBPIi/FvWWshefFJrDATm1eG0N7BtPf8FYOM3ed1dwGH148Op9s4kYUBJrXIlMKY+/HYk8BuAzHyaqpPDnOV0K/9b4OiImAUcQXWzOknYzVySVCj3oCRJRTKgJElFMqAkSUUyoCRJRTKgJElFMqAkSUUyoCRJRTKgJElF+v+pqrMUJByIkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_platform[['User_Score', 'Critic_Score']].mean().plot.bar(figsize=(7, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f84f2c22da0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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seV9eVe7laJ/hZR4Wj1MojejuRP8ogK6w72FwU4dXeXlZ9SnJC7upvClvpLtiZndkikbEAm/HhXRnXCY5kcGv+z8GTmBwN3CpL+8EJgGSvIDByWV/BmwCPtxjriXPMzHbcWBV/bRbfgXw0ap6X3eLtSt7zCVVVf2yW34JcH5VbQW2JvnLHnMtee6BtyPTlk8FtgBUlXf9Vt+S5OBuZ2It3Wez84ieMi0L7oG348tJLgS2M7jzyZcBkqwG7t3dC6UR+yCDb4F3AtdU1QRAkpMYfF41Ih6F0ogkYXBxoNXAhVV1c7f+JOAxHkaoPiU5ksEJZd+d+lbY7VwcUFU/7sZPrKptPcZccizwJSbJN6vqaX3nkGZK8u2qenLfOZYS58CXHuccta/K3JtoT1jgS49fqbSv8rO5wCxwSWqUBb70+DVV+yqPllpgFnhDkuyf5NI5NnvlooSRZkjy4qnLPXTjQ5OcMTWuqqf2k2zpssAbUlX3M7ic7KN2s83VixhJmu5tVfWLqUF33fq39ZhnyfNEnvbcDVyVZDODC1oBUFWv6y+SBMy+Q2jHjJD/cdvzme5P2tdMJHk/8KFu/Bpga495ljxP5GlQd7PYx1XVtX1nkaYkOQh4K/DsbtVm4B1Vdc+uX6X5sMAbk+SFwHuBlVV1THdp2b+vqhfN8VJJS4wF3pgkWxlcjfCyqjqpW3d1VT2p32RarpJ8sKrekOSzzHKyjjsXo+MceHt+U1W/GFzb6kFeUlZ9+lj3+N5eUyxDFnh7tiV5GbB/kmOB1wHf6DmTlrHu5g0AJ1bVedOfS/J6Bjfg1gh4HHh7Xgs8Efg1cAGDazC/oddE0sC6Wdb9yWKHWE6cA29Ykv2Bg6rqzr6zaPlKcjbwMuAU4GvTnjoEeKCq1vYSbBlwCqUxST4B/AVwP/At4JFJzquqf+g3mZaxbzC4884RwPumrb8L+F4viZYJ98Abk+TKqjoxycuBJwPnAlur6nd6jiZpkTkH3p4DkhwAnAFsqqrf4HWW1aMkX+8e70py57S/u5I4vTdCTqG058PA/zH4avrVJEcz+CFT6kVVndI9HtJ3luXGKZRGJHnj9CGDve5J4OvAT6rqvl6CSTz4g/q2qjqu7yzLiVMo7Thk2t/B3eM48HngzB5zSVOXOr42yeP6zrKcuAfeuCSHA1/ybt/qW5KvAicBV/DwSx17Kv2IOAfeuKr6eWacVy8tpiSPB1YxuBLhdM9gcHihRsQCb1ySZwG3951Dy9oHgTdX1VXTVyb5OfAu4PxeUi0DFngjklzFzocLHg78FHjV4ieSHrRqZnkDVNVVSdYsfpzlwwJvxwtmjAu4zYvlax9w6G6eO3DRUixDFngjqurGvjNIuzCR5NVV9a/TVyb5c7yl2kh5FIqkeUmyCrgYuJeHCnscWAm8uKpu6SvbUmeBS1oQ3Q/qU3eG2lZVX+4zz3JggUtSozwTU5IaZYFLUqMscElqlAUuSY36f5Z5tYIY7373AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['User_Score', 'Critic_Score']].mean().plot.bar()"
   ]
  },
  {
   "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",
       "        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>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>Critic_Count</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>User_Count</th>\n",
       "      <th>Developer</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Action</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "      <td>1630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Adventure</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fighting</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Misc</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Platform</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Puzzle</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Racing</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Shooter</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "      <td>864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Simulation</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sports</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Strategy</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name  Platform  Year_of_Release  Publisher  NA_Sales  EU_Sales  \\\n",
       "Genre                                                                          \n",
       "Action        1630      1630             1630       1630      1630      1630   \n",
       "Adventure      248       248              248        248       248       248   \n",
       "Fighting       378       378              378        378       378       378   \n",
       "Misc           384       384              384        384       384       384   \n",
       "Platform       403       403              403        403       403       403   \n",
       "Puzzle         118       118              118        118       118       118   \n",
       "Racing         581       581              581        581       581       581   \n",
       "Role-Playing   712       712              712        712       712       712   \n",
       "Shooter        864       864              864        864       864       864   \n",
       "Simulation     297       297              297        297       297       297   \n",
       "Sports         943       943              943        943       943       943   \n",
       "Strategy       267       267              267        267       267       267   \n",
       "\n",
       "              JP_Sales  Other_Sales  Global_Sales  Critic_Score  Critic_Count  \\\n",
       "Genre                                                                           \n",
       "Action            1630         1630          1630          1630          1630   \n",
       "Adventure          248          248           248           248           248   \n",
       "Fighting           378          378           378           378           378   \n",
       "Misc               384          384           384           384           384   \n",
       "Platform           403          403           403           403           403   \n",
       "Puzzle             118          118           118           118           118   \n",
       "Racing             581          581           581           581           581   \n",
       "Role-Playing       712          712           712           712           712   \n",
       "Shooter            864          864           864           864           864   \n",
       "Simulation         297          297           297           297           297   \n",
       "Sports             943          943           943           943           943   \n",
       "Strategy           267          267           267           267           267   \n",
       "\n",
       "              User_Score  User_Count  Developer  Rating  \n",
       "Genre                                                    \n",
       "Action              1630        1630       1630    1630  \n",
       "Adventure            248         248        248     248  \n",
       "Fighting             378         378        378     378  \n",
       "Misc                 384         384        384     384  \n",
       "Platform             403         403        403     403  \n",
       "Puzzle               118         118        118     118  \n",
       "Racing               581         581        581     581  \n",
       "Role-Playing         712         712        712     712  \n",
       "Shooter              864         864        864     864  \n",
       "Simulation           297         297        297     297  \n",
       "Sports               943         943        943     943  \n",
       "Strategy             267         267        267     267  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Genre').count() # \"Freq table\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6825.000000\n",
       "mean        0.777590\n",
       "std         1.963443\n",
       "min         0.010000\n",
       "25%         0.110000\n",
       "50%         0.290000\n",
       "75%         0.750000\n",
       "max        82.530000\n",
       "Name: Global_Sales, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Global_Sales'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fase 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import anderson\n",
    "from scipy.stats import kstest\n",
    "from scipy.stats import gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printAnderson(data):\n",
    "    # normality test\n",
    "    result = anderson(data)\n",
    "    print('Statistic: %.3f' % result.statistic)\n",
    "    p = 0\n",
    "    for i in range(len(result.critical_values)):\n",
    "        sl, cv = result.significance_level[i], result.critical_values[i]\n",
    "        if result.statistic < result.critical_values[i]:\n",
    "            print('%.3f: %.3f, data looks normal (fail to reject H0)' % (sl, cv))\n",
    "        else:\n",
    "            print('%.3f: %.3f, data does not look normal (reject H0)' % (sl, cv))\n",
    "            \n",
    "def printKS(data):\n",
    "    statistic, p = kstest(data, 'norm')\n",
    "    print('Statistic: %.3f' % statistic)\n",
    "    print('P Value: %.3f' % p)\n",
    "\n",
    "def printAndersonAndKS(data):\n",
    "    printAnderson(data)\n",
    "    print(\"----------------\")\n",
    "    printKS(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistic: 1222.089\n",
      "15.000: 0.576, data does not look normal (reject H0)\n",
      "10.000: 0.656, data does not look normal (reject H0)\n",
      "5.000: 0.787, data does not look normal (reject H0)\n",
      "2.500: 0.917, data does not look normal (reject H0)\n",
      "1.000: 1.091, data does not look normal (reject H0)\n",
      "----------------\n",
      "Statistic: 0.504\n",
      "P Value: 0.000\n"
     ]
    }
   ],
   "source": [
    "data = df['Global_Sales']\n",
    "printAndersonAndKS(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistic: 63.418\n",
      "15.000: 0.576, data does not look normal (reject H0)\n",
      "10.000: 0.656, data does not look normal (reject H0)\n",
      "5.000: 0.787, data does not look normal (reject H0)\n",
      "2.500: 0.917, data does not look normal (reject H0)\n",
      "1.000: 1.091, data does not look normal (reject H0)\n",
      "----------------\n",
      "Statistic: 1.000\n",
      "P Value: 0.000\n"
     ]
    }
   ],
   "source": [
    "data = df['Critic_Score']\n",
    "printAndersonAndKS(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistic: 154.317\n",
      "15.000: 0.576, data does not look normal (reject H0)\n",
      "10.000: 0.656, data does not look normal (reject H0)\n",
      "5.000: 0.787, data does not look normal (reject H0)\n",
      "2.500: 0.917, data does not look normal (reject H0)\n",
      "1.000: 1.091, data does not look normal (reject H0)\n",
      "----------------\n",
      "Statistic: 1.000\n",
      "P Value: 0.000\n"
     ]
    }
   ],
   "source": [
    "data = df['User_Score']\n",
    "printAndersonAndKS(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<scipy.stats._distn_infrastructure.rv_frozen at 0x7f84f5728748>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df['Global_Sales']\n",
    "gamma(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Fase 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/diegotf/.local/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "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>Global_Sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>Critic_Count</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>User_Count</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>82.53</td>\n",
       "      <td>76.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>6824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>35.52</td>\n",
       "      <td>82.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>6823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>32.77</td>\n",
       "      <td>80.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>6822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>29.80</td>\n",
       "      <td>89.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>6821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>28.92</td>\n",
       "      <td>58.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>6820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Global_Sales  Critic_Score  Critic_Count  User_Score  User_Count    id\n",
       "0         82.53          76.0          51.0        80.0       322.0  6824\n",
       "2         35.52          82.0          73.0        83.0       709.0  6823\n",
       "3         32.77          80.0          73.0        80.0       192.0  6822\n",
       "6         29.80          89.0          65.0        85.0       431.0  6821\n",
       "7         28.92          58.0          41.0        66.0       129.0  6820"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We want to predict global sales based on scores\n",
    "fts = [\"Global_Sales\", \"Critic_Score\", \"Critic_Count\", \"User_Score\", \"User_Count\"]\n",
    "trees_df = df[fts]\n",
    "trees_df[\"id\"] = trees_df.groupby(fts).ngroup()\n",
    "trees_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of features before encoding: (6825, 6)\n",
      "Shape of features after one-hot encoding: (6825, 6)\n"
     ]
    }
   ],
   "source": [
    "features = pd.get_dummies(trees_df)\n",
    "print('Shape of features before encoding:', trees_df.shape)\n",
    "print('Shape of features after one-hot encoding:', features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labels are the values we want to predict\n",
    "labels = np.array(features['Global_Sales'])\n",
    "features = features.drop('Global_Sales', axis = 1)\n",
    "feature_list = list(features.columns) # Saving feature names for later use\n",
    "features = np.array(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape: (5118, 5)\n",
      "Training Labels Shape: (5118,)\n",
      "Testing Features Shape: (1707, 5)\n",
      "Testing Labels Shape: (1707,)\n"
     ]
    }
   ],
   "source": [
    "print('Training Features Shape:', train_features.shape)\n",
    "print('Training Labels Shape:', train_labels.shape)\n",
    "print('Testing Features Shape:', test_features.shape)\n",
    "print('Testing Labels Shape:', test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'id'"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove id from data sets & feature list\n",
    "id_idx = feature_list.index(\"id\")\n",
    "train_ids = train_features[:, id_idx]\n",
    "test_ids = test_features[:, id_idx]\n",
    "train_features = np.delete(train_features, id_idx, axis = 1)\n",
    "test_features = np.delete(test_features, id_idx, axis = 1)\n",
    "feature_list.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape: (5118, 4)\n",
      "Testing Features Shape: (1707, 4)\n"
     ]
    }
   ],
   "source": [
    "print('Training Features Shape:', train_features.shape)\n",
    "print('Testing Features Shape:', test_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                      n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the model we are using\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Instantiate model \n",
    "rf = RandomForestRegressor(n_estimators= 1000, random_state=42)\n",
    "\n",
    "# Train the model on training data\n",
    "rf.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.67 degrees.\n"
     ]
    }
   ],
   "source": [
    "# Use the forest's predict method on the test data\n",
    "predictions = rf.predict(test_features)\n",
    "\n",
    "# Calculate the absolute errors\n",
    "errors = abs(predictions - test_labels)\n",
    "\n",
    "# Print out the mean absolute error (mae)\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: -350.44 %.\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean absolute percentage error (MAPE)\n",
    "mape = 100 * (errors / test_labels)\n",
    "\n",
    "# Calculate and display accuracy\n",
    "accuracy = 100 - np.mean(mape)\n",
    "print('Accuracy:', round(accuracy, 2), '%.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('User_Count', 0.45),\n",
       " ('Critic_Count', 0.22),\n",
       " ('Critic_Score', 0.19),\n",
       " ('User_Score', 0.14)]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get numerical feature importances\n",
    "importances = list(rf.feature_importances_)\n",
    "# List of tuples with variable and importance\n",
    "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n",
    "# Sort the feature importances by most important first\n",
    "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "\n",
    "# Make a bar chart\n",
    "plt.pie(importances, labels=feature_list)\n",
    "\n",
    "# Axis labels and title\n",
    "plt.title('Variable Importances')\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Real and Predicted Global Sales')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uscores = features[:, feature_list.index('User_Score')]\n",
    "# Dataframe with true values and dates\n",
    "predictions_data = pd.DataFrame(data = {'User_Score': tuscores, 'Global_Sales': predictions})\n",
    "predictions_data[\"id\"] = test_ids\n",
    "\n",
    "# Plot the predicted values\n",
    "plt.plot(predictions_data[\"id\"], predictions_data['Global_Sales'], 'ro', label = 'prediction')\n",
    "# Plot the actual values\n",
    "plt.plot(labels, 'b-', label = 'actual')\n",
    "plt.legend()\n",
    "\n",
    "plt.xlabel('# of games')\n",
    "plt.ylabel('Global Sales (Millions USD)')\n",
    "plt.title('Real and Predicted Global Sales')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}