Games_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "from scipy import stats\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "      <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>16710</td>\n",
       "      <td>Samurai Warriors: Sanada Maru</td>\n",
       "      <td>PS3</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16711</td>\n",
       "      <td>LMA Manager 2007</td>\n",
       "      <td>X360</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16712</td>\n",
       "      <td>Haitaka no Psychedelica</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16713</td>\n",
       "      <td>Spirits &amp; Spells</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16714</td>\n",
       "      <td>Winning Post 8 2016</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</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",
       "<p>16715 rows × 11 columns</p>\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",
       "...                              ...      ...              ...           ...   \n",
       "16710  Samurai Warriors: Sanada Maru      PS3           2016.0        Action   \n",
       "16711               LMA Manager 2007     X360           2006.0        Sports   \n",
       "16712        Haitaka no Psychedelica      PSV           2016.0     Adventure   \n",
       "16713               Spirits & Spells      GBA           2003.0      Platform   \n",
       "16714            Winning Post 8 2016      PSV           2016.0    Simulation   \n",
       "\n",
       "       NA_sales  EU_sales  JP_sales  Other_sales  Critic_Score User_Score  \\\n",
       "0         41.36     28.96      3.77         8.45          76.0          8   \n",
       "1         29.08      3.58      6.81         0.77           NaN        NaN   \n",
       "2         15.68     12.76      3.79         3.29          82.0        8.3   \n",
       "3         15.61     10.93      3.28         2.95          80.0          8   \n",
       "4         11.27      8.89     10.22         1.00           NaN        NaN   \n",
       "...         ...       ...       ...          ...           ...        ...   \n",
       "16710      0.00      0.00      0.01         0.00           NaN        NaN   \n",
       "16711      0.00      0.01      0.00         0.00           NaN        NaN   \n",
       "16712      0.00      0.00      0.01         0.00           NaN        NaN   \n",
       "16713      0.01      0.00      0.00         0.00           NaN        NaN   \n",
       "16714      0.00      0.00      0.01         0.00           NaN        NaN   \n",
       "\n",
       "      Rating  \n",
       "0          E  \n",
       "1        NaN  \n",
       "2          E  \n",
       "3          E  \n",
       "4        NaN  \n",
       "...      ...  \n",
       "16710    NaN  \n",
       "16711    NaN  \n",
       "16712    NaN  \n",
       "16713    NaN  \n",
       "16714    NaN  \n",
       "\n",
       "[16715 rows x 11 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('games.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                object\n",
       "Platform            object\n",
       "Year_of_Release    float64\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          object\n",
       "Rating              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.lower() # changing column names to lowercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "      <td>1764</td>\n",
       "      <td>Kaboom!</td>\n",
       "      <td>2600</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>546</td>\n",
       "      <td>Missile Command</td>\n",
       "      <td>2600</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1968</td>\n",
       "      <td>Defender</td>\n",
       "      <td>2600</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6300</td>\n",
       "      <td>Bridge</td>\n",
       "      <td>2600</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6875</td>\n",
       "      <td>Checkers</td>\n",
       "      <td>2600</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16373</td>\n",
       "      <td>PDC World Championship Darts 2008</td>\n",
       "      <td>PSP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>43.0</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E10+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16405</td>\n",
       "      <td>Freaky Flyers</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16448</td>\n",
       "      <td>Inversion</td>\n",
       "      <td>PC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16458</td>\n",
       "      <td>Hakuouki: Shinsengumi Kitan</td>\n",
       "      <td>PS3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16522</td>\n",
       "      <td>Virtua Quest</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16715 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    name platform  year_of_release  \\\n",
       "1764                             Kaboom!     2600           1980.0   \n",
       "546                      Missile Command     2600           1980.0   \n",
       "1968                            Defender     2600           1980.0   \n",
       "6300                              Bridge     2600           1980.0   \n",
       "6875                            Checkers     2600           1980.0   \n",
       "...                                  ...      ...              ...   \n",
       "16373  PDC World Championship Darts 2008      PSP              NaN   \n",
       "16405                      Freaky Flyers       GC              NaN   \n",
       "16448                          Inversion       PC              NaN   \n",
       "16458        Hakuouki: Shinsengumi Kitan      PS3              NaN   \n",
       "16522                       Virtua Quest       GC              NaN   \n",
       "\n",
       "              genre  na_sales  eu_sales  jp_sales  other_sales  critic_score  \\\n",
       "1764           Misc      1.07      0.07       0.0         0.01           NaN   \n",
       "546         Shooter      2.56      0.17       0.0         0.03           NaN   \n",
       "1968           Misc      0.99      0.05       0.0         0.01           NaN   \n",
       "6300           Misc      0.25      0.02       0.0         0.00           NaN   \n",
       "6875           Misc      0.22      0.01       0.0         0.00           NaN   \n",
       "...             ...       ...       ...       ...          ...           ...   \n",
       "16373        Sports      0.01      0.00       0.0         0.00          43.0   \n",
       "16405        Racing      0.01      0.00       0.0         0.00          69.0   \n",
       "16448       Shooter      0.01      0.00       0.0         0.00          59.0   \n",
       "16458     Adventure      0.01      0.00       0.0         0.00           NaN   \n",
       "16522  Role-Playing      0.01      0.00       0.0         0.00          55.0   \n",
       "\n",
       "      user_score rating  \n",
       "1764         NaN    NaN  \n",
       "546          NaN    NaN  \n",
       "1968         NaN    NaN  \n",
       "6300         NaN    NaN  \n",
       "6875         NaN    NaN  \n",
       "...          ...    ...  \n",
       "16373        tbd   E10+  \n",
       "16405        6.5      T  \n",
       "16448        6.7      M  \n",
       "16458        NaN    NaN  \n",
       "16522        5.5      T  \n",
       "\n",
       "[16715 rows x 11 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('year_of_release', inplace = True) # Sorting on Year\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                object\n",
       "platform            object\n",
       "year_of_release    float64\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          object\n",
       "rating              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data types of following columns have been changed:\n",
    "\n",
    "* __`year_of_release`__: This column represents the year in which a specific game of a platform is released. As it is not full `date` so we would avoid converting it to a `datetime` datatype. The default datatype of this column has been set to `float` which is not favourable for the processing that we need to perform. So the datatype of this column is converted to `int`.\n",
    "\n",
    "$$$$\n",
    "\n",
    "* __`user_score`__: This column represents the score given to the game according to the user's perspective. The default datatype for this column has been set to `object` as it contains **tbd** in it. We would need to convert this column to `float` if we want to perform any processing on it. Before converting it to `float`, we would need to remove all the rows which contain the word __tbd__ in this column. As the removing rows with `tbd` also means losing important dat of other columns, so we will not perform this operation until we need process this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>183</td>\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",
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       "    <tr>\n",
       "      <td>377</td>\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",
       "      <td>456</td>\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",
       "      <td>475</td>\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",
       "      <td>609</td>\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",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16373</td>\n",
       "      <td>PDC World Championship Darts 2008</td>\n",
       "      <td>PSP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>43.0</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E10+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16405</td>\n",
       "      <td>Freaky Flyers</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16448</td>\n",
       "      <td>Inversion</td>\n",
       "      <td>PC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16458</td>\n",
       "      <td>Hakuouki: Shinsengumi Kitan</td>\n",
       "      <td>PS3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16522</td>\n",
       "      <td>Virtua Quest</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>269 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    name platform  year_of_release  \\\n",
       "183                      Madden NFL 2004      PS2              NaN   \n",
       "377                     FIFA Soccer 2004      PS2              NaN   \n",
       "456           LEGO Batman: The Videogame      Wii              NaN   \n",
       "475           wwe Smackdown vs. Raw 2006      PS2              NaN   \n",
       "609                       Space Invaders     2600              NaN   \n",
       "...                                  ...      ...              ...   \n",
       "16373  PDC World Championship Darts 2008      PSP              NaN   \n",
       "16405                      Freaky Flyers       GC              NaN   \n",
       "16448                          Inversion       PC              NaN   \n",
       "16458        Hakuouki: Shinsengumi Kitan      PS3              NaN   \n",
       "16522                       Virtua Quest       GC              NaN   \n",
       "\n",
       "              genre  na_sales  eu_sales  jp_sales  other_sales  critic_score  \\\n",
       "183          Sports      4.26      0.26      0.01         0.71          94.0   \n",
       "377          Sports      0.59      2.36      0.04         0.51          84.0   \n",
       "456          Action      1.80      0.97      0.00         0.29          74.0   \n",
       "475        Fighting      1.57      1.02      0.00         0.41           NaN   \n",
       "609         Shooter      2.36      0.14      0.00         0.03           NaN   \n",
       "...             ...       ...       ...       ...          ...           ...   \n",
       "16373        Sports      0.01      0.00      0.00         0.00          43.0   \n",
       "16405        Racing      0.01      0.00      0.00         0.00          69.0   \n",
       "16448       Shooter      0.01      0.00      0.00         0.00          59.0   \n",
       "16458     Adventure      0.01      0.00      0.00         0.00           NaN   \n",
       "16522  Role-Playing      0.01      0.00      0.00         0.00          55.0   \n",
       "\n",
       "      user_score rating  \n",
       "183          8.5      E  \n",
       "377          6.4      E  \n",
       "456          7.9   E10+  \n",
       "475          NaN    NaN  \n",
       "609          NaN    NaN  \n",
       "...          ...    ...  \n",
       "16373        tbd   E10+  \n",
       "16405        6.5      T  \n",
       "16448        6.7      M  \n",
       "16458        NaN    NaN  \n",
       "16522        5.5      T  \n",
       "\n",
       "[269 rows x 11 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['year_of_release'].isna()] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following 4 columns have missing values:\n",
    "\n",
    "* __`year_of_release`__: As we can see in the above output that there are `269` rows whose `year_of_release` data is not available. We don;t have enough information to just assume that the missing data belongs to `2016`, so the optimum way to deal with this would be to remove all the rows where the value for `year_of_release` column is missing.\n",
    "$$$$\n",
    "* __`critic_score`__: This is a complicated case as we cannot simply assume values for it because it will effect the results that are based on this column. Removing the rows with missing values in `critic_score` column would also not be feasible as important data in other columns might get lost. The optimum decision would be to leave it as it is as the availability of missing values will not affect our results based on this column.\n",
    "$$$$\n",
    "* __`user_score`__(Column with __tbd__ value): This is a complicated case as we cannot simply assume values for it because it will effect the results that are based on this column. Removing the rows with missing values in `user_score` column would also not be feasible as important data in other columns might get lost. The optimum decision would be to leave it as it is as the availability of missing values will not affect our results based on this column. We would however remove the rows where the value of this column is __tbd__. This value means that we are still waiting for a rating on this from a users perspective and as a users perspective is not involved in this row of data, we can manage to remove this. However, we would only perform this operation when specific functions need to be performed on this column, otherwise we would use the complete data.\n",
    "$$$$\n",
    "* __`rating`__: This is a complicated case as we cannot simply assume values for it because it will effect the results that are based on this column. Removing the rows with missing values in `rating` column would also not be feasible as important data in other columns might get lost. The optimum decision would be to leave it as it is as the availability of missing values will not affect our results based on this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1764</td>\n",
       "      <td>Kaboom!</td>\n",
       "      <td>2600</td>\n",
       "      <td>1980</td>\n",
       "      <td>Misc</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>546</td>\n",
       "      <td>Missile Command</td>\n",
       "      <td>2600</td>\n",
       "      <td>1980</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1968</td>\n",
       "      <td>Defender</td>\n",
       "      <td>2600</td>\n",
       "      <td>1980</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6300</td>\n",
       "      <td>Bridge</td>\n",
       "      <td>2600</td>\n",
       "      <td>1980</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6875</td>\n",
       "      <td>Checkers</td>\n",
       "      <td>2600</td>\n",
       "      <td>1980</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.23</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",
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       "    <tr>\n",
       "      <td>15198</td>\n",
       "      <td>Psycho-Pass: Mandatory Happiness</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>T</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15210</td>\n",
       "      <td>Atelier Firis: The Alchemist of the Mysterious...</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10371</td>\n",
       "      <td>WWE 2K17</td>\n",
       "      <td>X360</td>\n",
       "      <td>2016</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tbd</td>\n",
       "      <td>T</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12352</td>\n",
       "      <td>Utawarerumono: Futari no Hakuoro</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16714</td>\n",
       "      <td>Winning Post 8 2016</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16446 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    name platform  \\\n",
       "1764                                             Kaboom!     2600   \n",
       "546                                      Missile Command     2600   \n",
       "1968                                            Defender     2600   \n",
       "6300                                              Bridge     2600   \n",
       "6875                                            Checkers     2600   \n",
       "...                                                  ...      ...   \n",
       "15198                   Psycho-Pass: Mandatory Happiness      PSV   \n",
       "15210  Atelier Firis: The Alchemist of the Mysterious...      PSV   \n",
       "10371                                           WWE 2K17     X360   \n",
       "12352                   Utawarerumono: Futari no Hakuoro      PSV   \n",
       "16714                                Winning Post 8 2016      PSV   \n",
       "\n",
       "       year_of_release         genre  na_sales  eu_sales  jp_sales  \\\n",
       "1764              1980          Misc      1.07      0.07      0.00   \n",
       "546               1980       Shooter      2.56      0.17      0.00   \n",
       "1968              1980          Misc      0.99      0.05      0.00   \n",
       "6300              1980          Misc      0.25      0.02      0.00   \n",
       "6875              1980          Misc      0.22      0.01      0.00   \n",
       "...                ...           ...       ...       ...       ...   \n",
       "15198             2016     Adventure      0.00      0.01      0.01   \n",
       "15210             2016  Role-Playing      0.00      0.00      0.02   \n",
       "10371             2016        Sports      0.05      0.04      0.00   \n",
       "12352             2016          Misc      0.00      0.00      0.06   \n",
       "16714             2016    Simulation      0.00      0.00      0.01   \n",
       "\n",
       "       other_sales  critic_score user_score rating  Total_sales  \n",
       "1764          0.01           NaN        NaN    NaN         1.15  \n",
       "546           0.03           NaN        NaN    NaN         2.76  \n",
       "1968          0.01           NaN        NaN    NaN         1.05  \n",
       "6300          0.00           NaN        NaN    NaN         0.27  \n",
       "6875          0.00           NaN        NaN    NaN         0.23  \n",
       "...            ...           ...        ...    ...          ...  \n",
       "15198         0.00          64.0        7.3      T         0.02  \n",
       "15210         0.00           NaN        NaN    NaN         0.02  \n",
       "10371         0.01           NaN        tbd      T         0.10  \n",
       "12352         0.00           NaN        NaN    NaN         0.06  \n",
       "16714         0.00           NaN        NaN    NaN         0.01  \n",
       "\n",
       "[16446 rows x 12 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['year_of_release'].notnull()].copy() # Removing rows where value of year_of_release column is NaN\n",
    "\n",
    "df.year_of_release = df.year_of_release.astype('int64') # Converting datatype of year_of_release column to int\n",
    "\n",
    "df['Total_sales'] = df['na_sales'] + df['eu_sales'] + df['jp_sales'] + df['other_sales'] # Computing the Total_sales column\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['year_of_release'] != 2016.0][['name', 'platform', 'year_of_release', 'Total_sales']].to_csv('test.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3\n",
    "\n",
    "### Number of games released every year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>year_of_release</th>\n",
       "      <th>platform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1980</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1981</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1983</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1984</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1985</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1986</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1987</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1988</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1989</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1990</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1991</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1992</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1993</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1994</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1995</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1996</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1997</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1998</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1999</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2000</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>2001</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>2002</td>\n",
       "      <td>829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>2003</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>2004</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>2005</td>\n",
       "      <td>939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>2006</td>\n",
       "      <td>1006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>2007</td>\n",
       "      <td>1197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>2008</td>\n",
       "      <td>1427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>2009</td>\n",
       "      <td>1426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>2010</td>\n",
       "      <td>1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>2011</td>\n",
       "      <td>1136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>2012</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>2013</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>2014</td>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>2015</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>2016</td>\n",
       "      <td>502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year_of_release  platform\n",
       "0              1980         9\n",
       "1              1981        46\n",
       "2              1982        36\n",
       "3              1983        17\n",
       "4              1984        14\n",
       "5              1985        14\n",
       "6              1986        21\n",
       "7              1987        16\n",
       "8              1988        15\n",
       "9              1989        17\n",
       "10             1990        16\n",
       "11             1991        41\n",
       "12             1992        43\n",
       "13             1993        62\n",
       "14             1994       121\n",
       "15             1995       219\n",
       "16             1996       263\n",
       "17             1997       289\n",
       "18             1998       379\n",
       "19             1999       338\n",
       "20             2000       350\n",
       "21             2001       482\n",
       "22             2002       829\n",
       "23             2003       775\n",
       "24             2004       762\n",
       "25             2005       939\n",
       "26             2006      1006\n",
       "27             2007      1197\n",
       "28             2008      1427\n",
       "29             2009      1426\n",
       "30             2010      1255\n",
       "31             2011      1136\n",
       "32             2012       653\n",
       "33             2013       544\n",
       "34             2014       581\n",
       "35             2015       606\n",
       "36             2016       502"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_count_byYear = df.groupby('year_of_release').count()['platform']\n",
    "game_count_byYear.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graphical representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Games Released')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "game_count_byYear.plot.bar(figsize = (15, 8))\n",
    "plt.xlabel('Year', size=15)\n",
    "plt.ylabel('Games Released', size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "#### According to the above graph, it looks like the rise of games and gaming patforms started in the late 90's era. It can be seen that in the 80's not many games were released but the tide really changed in the 90's and in the 21'st century. The data for every period mentioned in the graph would not be significant in predicting a marketing campaign for 2017.\n",
    "\n",
    "\n",
    "## Look how sales vary from platform to platform.\n",
    "\n",
    "### Platform with greatest total sales\n",
    "\n",
    "##### We will be choosing those platforms whose Total sales value will exceed the value of `100.0`. This will be our assumed benchmark for greatest total sales as no specific number is mentioned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>platform</th>\n",
       "      <th>Total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>PS2</td>\n",
       "      <td>1233.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>X360</td>\n",
       "      <td>961.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>PS3</td>\n",
       "      <td>931.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>Wii</td>\n",
       "      <td>891.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>DS</td>\n",
       "      <td>802.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>PS</td>\n",
       "      <td>727.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>PS4</td>\n",
       "      <td>314.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>GBA</td>\n",
       "      <td>312.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>PSP</td>\n",
       "      <td>289.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3DS</td>\n",
       "      <td>257.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>PC</td>\n",
       "      <td>255.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>GB</td>\n",
       "      <td>254.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>XB</td>\n",
       "      <td>251.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>NES</td>\n",
       "      <td>251.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>N64</td>\n",
       "      <td>218.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>SNES</td>\n",
       "      <td>200.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>GC</td>\n",
       "      <td>196.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>XOne</td>\n",
       "      <td>159.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   platform  Total_sales\n",
       "16      PS2      1233.56\n",
       "28     X360       961.24\n",
       "17      PS3       931.34\n",
       "26      Wii       891.18\n",
       "4        DS       802.78\n",
       "15       PS       727.58\n",
       "18      PS4       314.14\n",
       "6       GBA       312.88\n",
       "19      PSP       289.53\n",
       "2       3DS       257.81\n",
       "13       PC       255.76\n",
       "5        GB       254.43\n",
       "29       XB       251.57\n",
       "11      NES       251.05\n",
       "10      N64       218.01\n",
       "23     SNES       200.04\n",
       "7        GC       196.73\n",
       "30     XOne       159.32"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = df.groupby('platform')['Total_sales'].sum().reset_index().sort_values('Total_sales', ascending=False).copy()\n",
    "df_platform = df_test[df_test['Total_sales'] > 100.0]\n",
    "df_platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "platform\n",
       "PS2     1233.56\n",
       "X360     961.24\n",
       "PS3      931.34\n",
       "Wii      891.18\n",
       "DS       802.78\n",
       "PS       727.58\n",
       "PS4      314.14\n",
       "GBA      312.88\n",
       "PSP      289.53\n",
       "3DS      257.81\n",
       "PC       255.76\n",
       "GB       254.43\n",
       "XB       251.57\n",
       "NES      251.05\n",
       "N64      218.01\n",
       "SNES     200.04\n",
       "GC       196.73\n",
       "XOne     159.32\n",
       "Name: Total_sales, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = df.groupby('platform')['Total_sales'].sum().sort_values(ascending=False).copy()\n",
    "df_platform = df_test[df_test > 100.0]\n",
    "df_platform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graphical representation of the data distribution based on each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3f10d4710>"
      ]
     },
     "execution_count": 595,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Constructing a KDE plot which is just a smoothed out version of the histogram which is also visible.\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.distplot(df_platform['Total_sales'], hist=True, kde=True, \n",
    "             bins=int(180/5), color = 'darkblue', \n",
    "             hist_kws={'edgecolor':'black'},\n",
    "             kde_kws={'linewidth': 4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd3ee6b5850>]"
      ]
     },
     "execution_count": 596,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Constructing a normal distribution plot\n",
    "\n",
    "vals = df_platform['Total_sales']\n",
    "v_mean = np.mean(vals)\n",
    "v_std = np.std(vals)\n",
    "pdf = stats.norm.pdf(vals, v_mean, v_std)\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(vals, pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### According to the above two plots, it is evidently clear that sales data for the platforms with greatest total sales is not normally distributed. The data is positively skewed which means that data conatains large data points and mean value is greater than the median. This can be considered a good point as positively skewed sales represent good behaviour.\n",
    "\n",
    "\n",
    "\n",
    "### Plaforms which were popular but now have zero sales (Now = 2016) (Popular = Sales > 0.5)\n",
    "\n",
    "##### For this, we will assume that any platform is popular if its total sales are greater than `0.5`. The `now` time period will be considered `2016`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following are the platforms which were once famous and now have zero sales:\n",
      "\n",
      " ['2600' 'NES' 'GB' 'SNES' 'GEN' 'SCD' 'PS' 'SAT' 'N64' 'DC' 'PS2' 'XB'\n",
      " 'WS' 'GC' 'GBA' 'DS' 'PSP']\n"
     ]
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df_2016 = df2[df2['year_of_release'] == 2016]['platform'].unique()\n",
    "df2 = df2[(df2['Total_sales'] >= 0.5) & (df2['platform'].isin(df_2016) == False)]\n",
    "print(\"The following are the platforms which were once famous and now have zero sales:\\n\\n\", df2['platform'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determining data from which period should be chosen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following relevant periods are chosen:\n",
      "\n",
      " [2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016]\n",
      "\n",
      "\n",
      "The following relevant platforms in the above time periods are chosen:\n",
      "\n",
      " ['WiiU' 'PS3' 'PS4' 'PSV' 'XOne' '3DS' 'X360' 'Wii']\n"
     ]
    }
   ],
   "source": [
    "df_filter = df[df['year_of_release'] == 2016]['platform'].unique()\n",
    "df_filter = np.delete(df_filter, 3) # Removing PC platform\n",
    "relevant_years = df[df['platform'].isin(df_filter)]['year_of_release'].unique()\n",
    "print(\"The following relevant periods are chosen:\\n\\n\", relevant_years)\n",
    "print(\"\\n\\nThe following relevant platforms in the above time periods are chosen:\\n\\n\", df_filter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The platform `PC` was also selected during our processing but was ignored as it was an extreme outlier. If `PC` was included then all the other time periods would also get selected which would not present any relevant data for the rest of the selected platforms and it will become redundant. That's why `PC` paltform was ignored from our finalized dataset.\n",
    "\n",
    "### Selecting relevant data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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",
       "      <th>Total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5969</td>\n",
       "      <td>NBA Live 06</td>\n",
       "      <td>X360</td>\n",
       "      <td>2005</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>E</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6558</td>\n",
       "      <td>Peter Jackson's King Kong: The Official Game o...</td>\n",
       "      <td>X360</td>\n",
       "      <td>2005</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>T</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3374</td>\n",
       "      <td>PGR3 - Project Gotham Racing 3</td>\n",
       "      <td>X360</td>\n",
       "      <td>2005</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5779</td>\n",
       "      <td>Tiger Woods PGA Tour 06</td>\n",
       "      <td>X360</td>\n",
       "      <td>2005</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>71.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>E</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8663</td>\n",
       "      <td>Amped 3</td>\n",
       "      <td>X360</td>\n",
       "      <td>2005</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>72.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>T</td>\n",
       "      <td>0.16</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15198</td>\n",
       "      <td>Psycho-Pass: Mandatory Happiness</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>T</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15210</td>\n",
       "      <td>Atelier Firis: The Alchemist of the Mysterious...</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10371</td>\n",
       "      <td>WWE 2K17</td>\n",
       "      <td>X360</td>\n",
       "      <td>2016</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tbd</td>\n",
       "      <td>T</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12352</td>\n",
       "      <td>Utawarerumono: Futari no Hakuoro</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16714</td>\n",
       "      <td>Winning Post 8 2016</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5551 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    name platform  \\\n",
       "5969                                         NBA Live 06     X360   \n",
       "6558   Peter Jackson's King Kong: The Official Game o...     X360   \n",
       "3374                      PGR3 - Project Gotham Racing 3     X360   \n",
       "5779                             Tiger Woods PGA Tour 06     X360   \n",
       "8663                                             Amped 3     X360   \n",
       "...                                                  ...      ...   \n",
       "15198                   Psycho-Pass: Mandatory Happiness      PSV   \n",
       "15210  Atelier Firis: The Alchemist of the Mysterious...      PSV   \n",
       "10371                                           WWE 2K17     X360   \n",
       "12352                   Utawarerumono: Futari no Hakuoro      PSV   \n",
       "16714                                Winning Post 8 2016      PSV   \n",
       "\n",
       "       year_of_release         genre  na_sales  eu_sales  jp_sales  \\\n",
       "5969              2005        Sports      0.27      0.00      0.00   \n",
       "6558              2005        Action      0.20      0.03      0.00   \n",
       "3374              2005        Racing      0.49      0.03      0.03   \n",
       "5779              2005        Sports      0.28      0.00      0.00   \n",
       "8663              2005        Sports      0.13      0.02      0.00   \n",
       "...                ...           ...       ...       ...       ...   \n",
       "15198             2016     Adventure      0.00      0.01      0.01   \n",
       "15210             2016  Role-Playing      0.00      0.00      0.02   \n",
       "10371             2016        Sports      0.05      0.04      0.00   \n",
       "12352             2016          Misc      0.00      0.00      0.06   \n",
       "16714             2016    Simulation      0.00      0.00      0.01   \n",
       "\n",
       "       other_sales  critic_score user_score rating  Total_sales  \n",
       "5969          0.02          64.0        5.6      E         0.29  \n",
       "6558          0.02          80.0        7.5      T         0.25  \n",
       "3374          0.05           NaN        NaN    NaN         0.60  \n",
       "5779          0.02          71.0        7.4      E         0.30  \n",
       "8663          0.01          72.0        7.1      T         0.16  \n",
       "...            ...           ...        ...    ...          ...  \n",
       "15198         0.00          64.0        7.3      T         0.02  \n",
       "15210         0.00           NaN        NaN    NaN         0.02  \n",
       "10371         0.01           NaN        tbd      T         0.10  \n",
       "12352         0.00           NaN        NaN    NaN         0.06  \n",
       "16714         0.00           NaN        NaN    NaN         0.01  \n",
       "\n",
       "[5551 rows x 12 columns]"
      ]
     },
     "execution_count": 599,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = df[(df['year_of_release'].isin(relevant_years)) & (df['platform'].isin(df_filter))]\n",
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which platforms are leading in sales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "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>platform</th>\n",
       "      <th>Total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>X360</td>\n",
       "      <td>961.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>PS3</td>\n",
       "      <td>931.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Wii</td>\n",
       "      <td>891.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>PS4</td>\n",
       "      <td>314.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3DS</td>\n",
       "      <td>257.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>XOne</td>\n",
       "      <td>159.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>WiiU</td>\n",
       "      <td>82.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>PSV</td>\n",
       "      <td>53.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  platform  Total_sales\n",
       "6     X360       961.24\n",
       "1      PS3       931.34\n",
       "4      Wii       891.18\n",
       "2      PS4       314.14\n",
       "0      3DS       257.81\n",
       "7     XOne       159.32\n",
       "5     WiiU        82.19\n",
       "3      PSV        53.81"
      ]
     },
     "execution_count": 600,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.groupby(['platform'])['Total_sales'].sum().reset_index().sort_values('Total_sales', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The above table shows which of our chosen platforms in our chosen time periods have the higest and lowest total sales.\n",
    "\n",
    "### Which platforms are growing or shrinking?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total_sales')"
      ]
     },
     "execution_count": 601,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.groupby(['year_of_release', 'platform'])['Total_sales'].sum().unstack().fillna(0).plot(figsize = (15, 8))\n",
    "plt.ylabel('Total_sales')\n",
    "# PS4, XOne, 3DS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The above line graph clearly shows which of the choosen platforms in our choosen time periods are growing and shrinking in sales. According to the above graph, the platforms that are currently in the growth phase are`PS4`, `XOne`, `3DS` and are growing in sales. The rest of the rremaining platforms are shrinking in sales according to the above graph.\n",
    "\n",
    "### Box plot for the global sales of all games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Value of X360 is 0.7808610885458966\n",
      "Mean Value of PS3 is 0.7136704980842895\n",
      "Mean Value of Wii is 0.6929860031104199\n",
      "Mean Value of 3DS is 0.5035351562499999\n",
      "Mean Value of PSV is 0.1254312354312356\n",
      "Mean Value of WiiU is 0.5591156462585036\n",
      "Mean Value of XOne is 0.6450202429149801\n",
      "Mean Value of PS4 is 0.8013775510204074\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = df_final.groupby(['platform', 'name'])['Total_sales'].sum()\n",
    "for name in df_final['platform'].unique():\n",
    "    plt.figure(figsize=(15,8))\n",
    "    plt.xlabel('Total Sales', size=12)\n",
    "    plt.title(name, size = 30)\n",
    "    sns.boxplot(res[name])\n",
    "    print(\"Mean Value of\", name, \"is\", res[name].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The difference is sales is quite visible and significant in the above graphs. Every platform has a lot of positive outliers but it can also be seen that most of the outliers are clustered close to the maximum value. The average sales values for most of the platforms are pretty close except for the `PSV` platform. These similarities indicate that we were right in selecting these platforms and time periods to compute results for 2017 as they are all closely connected in terms of their statistical features.\n",
    "\n",
    "### Choosing PS4 as a platform to draw a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:1847: UserWarning: JointGrid annotation is deprecated and will be removed in a future release.\n",
      "  warnings.warn(UserWarning(msg))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fd3ed958510>"
      ]
     },
     "execution_count": 603,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for column critic_score with column Total_sales\n",
    "\n",
    "res2_c = df_final[df_final['platform'] == 'PS4'][['name', 'critic_score', 'Total_sales']]\n",
    "sns.jointplot('critic_score', 'Total_sales', res2_c, kind = 'scatter').annotate(stats.pearsonr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The above is a scatter plot between `critic_score` and `Total_sales` and on the top is the correlation score is displayed. The correlation score for them is `0.41` which means that the two columns are positively correlated. It means a good or higher `critic_score` will fetch higher sales for the `PS4` platform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:5208: 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",
      "  self[name] = value\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fd3ed49c790>"
      ]
     },
     "execution_count": 604,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for column user_score with column Total_sales\n",
    "\n",
    "df_final2 = df_final[df_final['user_score'] != 'tbd']\n",
    "df_final2.user_score = df_final2.user_score.astype('float64')\n",
    "res2_u = df_final2[df_final2['platform'] == 'PS4'][['name', 'user_score', 'Total_sales']]\n",
    "sns.jointplot('user_score', 'Total_sales', res2_u, kind = 'scatter').annotate(stats.pearsonr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The above is a scatter plot between `user_score` and `Total_sales` and on the top is the correlation score is displayed. The correlation score for them is `-0.032` which means that the two columns display a very loose negative correlation. As the correlation score is very close to `0`, so the two columns wont display any correlated behaviour(not negative or positive). This suggests that no matter what the value of `user_score` is, it ont affect the sales of `PS4` platform according to the provided data. \n",
    "\n",
    "### General distribution of games by genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Game_count')"
      ]
     },
     "execution_count": 605,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.groupby(['platform', 'genre'])['name'].count().unstack().fillna(0).plot.bar(figsize = (20, 10))\n",
    "plt.xlabel('Platform', size=20)\n",
    "plt.ylabel('Game_count', size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The above graph clearly displays which platform released how much game of which genre over the course of our chosen time periods.\n",
    "\n",
    "\n",
    "### Most profitable games with high and low sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total_sales')"
      ]
     },
     "execution_count": 606,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.groupby(['genre'])['Total_sales'].sum().reset_index().sort_values('Total_sales').plot.bar(x='genre', y='Total_sales', figsize=(15,8))\n",
    "plt.xlabel('Genre', size=20)\n",
    "plt.ylabel('Total_sales', size=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### According to the above graph, the most profitable genres are related to competetive type gaming genres such as `Action`, `Shooter`, and `Sports`. We can generalize a conclusion based on our statement that games where users have to play against some other user or users are considered most profitable ones and the rest of the games can be considered less profitble according to the provided data. It can also be noted that games which are available on widely used devices also fetch higher sales than the games which require specific platforms for the games to work.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Step 4\n",
    "\n",
    "## Top five platforms for each region based on their sales\n",
    "\n",
    "### Graphical representation of the top 5 platforms based on sales in the `na` region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.groupby(['platform'])['na_sales'].sum().reset_index().sort_values('na_sales', ascending=False).head().set_index('platform').plot.bar()\n",
    "plt.xlabel('Top 5 Platforms', size=15)\n",
    "plt.ylabel('Sales', size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphical representation of the top 5 platforms based on sales in the `eu` region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.groupby(['platform'])['eu_sales'].sum().reset_index().sort_values('eu_sales', ascending=False).head().set_index('platform').plot.bar()\n",
    "plt.xlabel('Top 5 Platforms', size=15)\n",
    "plt.ylabel('Sales', size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphical representation of the top 5 platforms based on sales in the `jp` region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 609,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAb40lEQVR4nO3de7xVdZ3/8ddbQJEA5aaiNB6caNSgo0ngJcnCmVDLO5P++qWYE0mmWVriPH79NHWSStNSs4FwpH48ECxHnEEhM7ExRhQUAi8joKhHQY94Q/GC8vn9sdaR7eGwzn2vtc9+Px+P89h73fb6nAV7vc/6ftdFEYGZmdn27JB3AWZmVmwOCjMzy+SgMDOzTA4KMzPL5KAwM7NMDgozM8vUPe8COtrAgQOjpqYm7zLMzCrK0qVLX4qIQU1N63JBUVNTw5IlS/Iuw8ysokh6envT3PRkZmaZHBRmZpbJQWFmZpm6XB+FmVWfzZs3U1dXx9tvv513KYXXs2dPhgwZQo8ePVq8jIPCzCpeXV0dffr0oaamBkl5l1NYEcGGDRuoq6tj6NChLV7OTU9mVvHefvttBgwY4JBohiQGDBjQ6iOvsgaFpBslvShpZcm4/pLukrQqfe2XjpekX0haLemvkj5VzlrNrLI4JFqmLdup3EcUNwHjGo2bDNwdEcOAu9NhgKOAYenPROCGMtVoZmYlytpHERF/llTTaPRxwBHp+xnAQuDCdPxvInmy0v2SdpU0OCLWdXadNZPndfYqmrV2yjF5l2BWsTr6O9zS7+Ohhx7KokWLOnTdpS655BJ69+7NBRdc0GnraEoR+ih2b9j5p6+7peP3Ap4tma8uHWdmVkidGRJ5KkJQbE9TDWlNPrdV0kRJSyQtqa+v7+SyzMya1rt3bxYuXMiYMWM44YQT2H///TnrrLPYsmVLk/O///77TJgwgeHDhzNixAiuvvpqAKZNm8anP/1pamtrOemkk9i0adM2y65Zs4Zx48Zx0EEHcfjhh/P4448DcMsttzB8+HBqa2sZM2ZMh/xeRQiKFyQNBkhfX0zH1wEfLZlvCPB8Ux8QEVMjYmREjBw0qMl7WpmZlc0DDzzAVVddxYoVK1izZg233nprk/MtW7aM5557jpUrV7JixQrOOOMMAE488UQefPBBli9fzn777cf06dO3WXbixIlce+21LF26lCuvvJJvfvObAFx66aUsWLCA5cuXc/vtt3fI71OEoLgdOD19fzowt2T8aenZTwcDr5Wjf8LMrL1GjRrFPvvsQ7du3Tj11FO57777mpxvn3324cknn+Scc85h/vz59O3bF4CVK1dy+OGHM2LECGbOnMkjjzzyoeXeeOMNFi1axPjx4znggAP4xje+wbp1ye7xsMMOY8KECUybNo3333+/Q36fsnZmS5pF0nE9UFIdcDEwBZgj6UzgGWB8OvsdwNHAamATcEY5azUza6vGp6Bu75TUfv36sXz5chYsWMD111/PnDlzuPHGG5kwYQK33XYbtbW13HTTTSxcuPBDy23ZsoVdd92VZcuWbfOZv/rVr1i8eDHz5s3jgAMOYNmyZQwYMKBdv09Zjygi4tSIGBwRPSJiSERMj4gNETE2Ioalry+n80ZEnB0RfxsRIyLC9w43s4rwwAMP8NRTT7FlyxZmz57NZz7zmSbne+mll9iyZQsnnXQSl112GQ899BAAGzduZPDgwWzevJmZM2dus1zfvn0ZOnQot9xyC5Bccb18+XIg6bsYPXo0l156KQMHDuTZZ5/dZvnW8i08zKzLyev08oYjh0MOOYTJkyezYsWKDzq2m/Lcc89xxhlnfNDZfcUVVwBw2WWXMXr0aPbee29GjBjBxo0bt1l25syZTJo0icsvv5zNmzdzyimnUFtby/e+9z1WrVpFRDB27Fhqa2vb/Xs5KMzMOsCGDRvo378/AL169WL27NnNLlNbW/vBUUSpSZMmMWnSpG3GX3LJJR+8Hzp0KPPnz99mnu11nLdHETqzzcwq2vPPP88hhxxS9gvhysVHFGZm7bTnnnvyxBNPfDB8xBFHbDPP6NGjeeeddz407re//S0jRozo7PLazUFhZlYGixcvzruENnPTk5l1Cclt4aw5bdlODgozq3g9e/Zkw4YNDotmNDy4qGfPnq1azk1PZlbxhgwZQl1dHb7XW/MaHoXaGg4KM6t4PXr0aNWjPa113PRkZmaZHBRmZpbJQWFmZpkcFGZmlslBYWZmmRwUZmaWyUFhZmaZHBRmZpbJQWFmZpkcFGZmlslBYWZmmRwUZmaWyUFhZmaZHBRmZpbJQWFmZpkcFGZmlslBYWZmmRwUZmaWyUFhZmaZHBRmZpbJQWFmZpkcFGZmlslBYWZmmQoTFJK+I+kRSSslzZLUU9JQSYslrZI0W9KOeddpZlZtChEUkvYCzgVGRsRwoBtwCvBj4OqIGAa8ApyZX5VmZtWpEEGR6g7sLKk70AtYB3we+F06fQZwfE61mZlVrUIERUQ8B1wJPEMSEK8BS4FXI+K9dLY6YK98KjQzq16FCApJ/YDjgKHAnsBHgKOamDW2s/xESUskLamvr++8Qs3MqlD3vAtIHQk8FRH1AJJuBQ4FdpXUPT2qGAI839TCETEVmAowcuTIJsPE2qZm8ry8S2DtlGPyLsGsqhXiiIKkyelgSb0kCRgLPArcA5ycznM6MDen+szMqlYhgiIiFpN0Wj8ErCCpaypwIfBdSauBAcD03Io0M6tSRWl6IiIuBi5uNPpJYFQO5ZiZWaoQRxRmZlZcDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAwM7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAwM7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAwM7NMhXlmtlnR1Uyel3cJrJ1yTN4lWBXyEYWZmWVyUJiZWSYHhZmZZXJQmJlZJgeFmZllclCYmVkmB4WZmWVyUJiZWSYHhZmZZXJQmJlZJgeFmZllclCYmVmmwgSFpF0l/U7S45Iek3SIpP6S7pK0Kn3tl3edZmbVpl1BIWlfScdL2rMDavk5MD8i9gVqgceAycDdETEMuDsdNjOzMmpxUEj6V0m/Khn+MrACuBV4XNKhbS1CUl9gDDAdICLejYhXgeOAGelsM4Dj27oOMzNrm9YcUYwD/lwyfBkwC9gTWJAOt9U+QD3wb5IelvRrSR8Bdo+IdQDp627tWIeZmbVBa4JiN+BZAEnDgI8BP4mI9cBU4MB21NEd+BRwQ0QcCLxJK5qZJE2UtETSkvr6+naUYWZmjbUmKF4Gdk/fHwmsj4iV6bCAbu2oow6oi4jF6fDvSILjBUmDAdLXF5taOCKmRsTIiBg5aNCgdpRhZmaNtSYo7gQulXQ2yV/7c0qmDQfWtrWI9KjkWUl/l44aCzwK3A6cno47HZjb1nWYmVnbtOaZ2ecDVwNnkfRVXFwy7QRgfjtrOQeYKWlH4EngDJIgmyPpTOAZYHw712FmZq3U4qCIiNeAr21n2uHtLSQilgEjm5g0tr2fbWZmbdeaIwoAJO0PHAR8FLgxItZL+hjwQkRs7OgCzcwsXy0OCkm9gRuBk4HN6bLzgfXAj0iahi7ohBrNzCxHrenM/hlwKElTUB+SM50a3EFynYWZmXUxrWl6OhH4dkTcI6nxqbBPA3t3XFlmZlYUrTmi2BnYsJ1pfYD321+OmZkVTWuC4kHgtO1MOxlY1P5yzMysaFrT9PR/gD9K+iNwCxDA0ZK+QxIUYzqhPjMzy1mLjygi4j6SjuydgOtIOrN/SHJDvyMj4sFOqdDMzHLVqusoIuIvwOGSdgb6Aa9GxKZOqczMzAqh1RfcAUTEW8BbHVyLmZkVUGZQSPpJKz4rIuLCdtZjZmYF09wRRWtuwheAg8LMrIvJDIqIGFquQszMrJhacx2FmZlVoVZ1ZksScBjwcaBn4+kR8csOqsvMzAqiNXeP3R24G9ifpD+i4aaAUTKbg8LMrItpTdPTVcBrJM+hEDAaqAF+AKwiOcowM7MupjVNT58Fvg2sS4cVEc8AP5K0A8nRxBc6uD4zM8tZa44odgXqI2IL8DqwW8m0RSTPqjAzsy6mNUHxFDA4ff8I8JWSaV8CXu6ooszMrDha0/Q0D/gHYA5wOTBXUh3JY1H/Bl9sZ2bWJbU4KCLiopL3d0o6DDiB5DTZuyLizk6oz8zMctammwICpLcV963Fzcy6uDYFhaRewJnAvsB64DcR8XRHFmZmZsXQ3N1jrwK+FBEfLxnXh+RIYhjwCrALcL6kURHxRGcWa2Zm5dfcWU+fA/5fo3EXkFxc9/WIGAjsCawlufDOzMy6mOaCogZY2mjcScCjEXEjQETUk1y1fViHV2dmZrlrLii6A283DEjqD+wH/KnRfGuBPTq0MjMzK4TmguIJ4IiS4S+mrwsazbcbvuDOzKxLau6sp+uAaZJ2AV4AziW5QvsPjeb7B2Blx5dnZmZ5a+4JdzdJGgycTXKvp4eAsyNic8M8kgYBxwE/7MxCzcwsH81eRxERVwBXZEyvx/0TZmZdVqEehSqpm6SHJf1nOjxU0mJJqyTNlrRj3jWamVWbQgUFyfMuHisZ/jFwdUQ0XNx3Zi5VmZlVscIEhaQhwDHAr9NhAZ8HfpfOMgM4Pp/qzMyqV2GCArgG+D6wJR0eALwaEe+lw3XAXnkUZmZWzQoRFJK+CLwYEaVXgauJWWM7y0+UtETSkvr6+k6p0cysWhUiKEhu/3GspLXAzSRNTtcAu0pqODNrCPB8UwtHxNSIGBkRIwcNGlSOes3MqkYhgiIiLoqIIRFRA5wC/CkivgLcA5ycznY6MDenEs3MqlYhgiLDhcB3Ja0m6bOYnnM9ZmZVp81PuOssEbEQWJi+fxIYlWc9ZmbVruhHFGZmljMHhZmZZXJQmJlZJgeFmZllclCYmVkmB4WZmWVyUJiZWSYHhZmZZXJQmJlZJgeFmZllclCYmVkmB4WZmWVyUJiZWSYHhZmZZXJQmJlZJgeFmZllclCYmVkmB4WZmWVyUJiZWSYHhZmZZXJQmJlZJgeFmZllclCYmVkmB4WZmWVyUJiZWSYHhZmZZXJQmJlZJgeFmZllclCYmVkmB4WZmWVyUJiZWSYHhZmZZSpEUEj6qKR7JD0m6RFJ307H95d0l6RV6Wu/vGs1M6s23fMuIPUecH5EPCSpD7BU0l3ABODuiJgiaTIwGbgwxzrNDKiZPC/vElg75Zi8S6gahTiiiIh1EfFQ+n4j8BiwF3AcMCOdbQZwfD4VmplVr0IERSlJNcCBwGJg94hYB0mYALvlV5mZWXUqVFBI6g38HjgvIl5vxXITJS2RtKS+vr7zCjQzq0KFCQpJPUhCYmZE3JqOfkHS4HT6YODFppaNiKkRMTIiRg4aNKg8BZuZVYlCBIUkAdOBxyLiZyWTbgdOT9+fDswtd21mZtWuKGc9HQZ8FVghaVk67p+BKcAcSWcCzwDjc6rPzKxqFSIoIuI+QNuZPLactZiZ2YcVounJzMyKy0FhZmaZHBRmZpbJQWFmZpkK0ZltZlapquG+Vz6iMDOzTA4KMzPL5KAwM7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAwM7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAwM7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAwM7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAwM7NMDgozM8tU+KCQNE7S/0haLWly3vWYmVWbQgeFpG7A9cBRwP7AqZL2z7cqM7PqUuigAEYBqyPiyYh4F7gZOC7nmszMqkrRg2Iv4NmS4bp0nJmZlYkiIu8atkvSeOALEfFP6fBXgVERcU6j+SYCE9PBvwP+p6yFNm0g8FLeRRSEt0XC22Erb4utirIt9o6IQU1N6F7uSlqpDvhoyfAQ4PnGM0XEVGBquYpqCUlLImJk3nUUgbdFwtthK2+LrSphWxS96elBYJikoZJ2BE4Bbs+5JjOzqlLoI4qIeE/St4AFQDfgxoh4JOeyzMyqSqGDAiAi7gDuyLuONihUU1jOvC0S3g5beVtsVfhtUejObDMzy1/R+yjMzCxnDgozM8vkoDAzs0yF78wuOkmfBp6NiPXp8GnAScDTwCUR8XKe9ZWbpF7At4AAriU5pflE4HHg0oh4I8fyykbSNRFxnqT/INkWHxIRx+ZQVi4kPQrMBG6OiDV511M0kp6IiI/nXUcWd2a3k6SHgCMj4mVJY0juR3UOcACwX0ScnGuBZSZpDsltV3YmuUr+MWAO8CVgj4j4ao7llY2kgyJiqaTPNjU9Iu4td015kVRL8gfDP5JcgTwLmBMR21w829VJ2sjWPxyUvvYCNgEREX1zKawZDop2krQ8ImrT99cD9RFxSTq8LCIOyLO+cmv4nSUJWAcMjohIh5dHxCdzLtFyJOlg4MskR92rgVkRMS3fqspH0rXALsD3IuKFdNxTETE038qyuY+i/bpJamjCGwv8qWRa1TbtRfIXyB3pa8Nw1fxVkh5ZIWmFpL+W/KyQ9Ne868tLRNwfEd8BTgP6AdflXFJZpfep+zkwS9K5knagAr4XVbsj60CzgHslvQS8BfwXgKSPAa/lWVhOlkjqHRFvRMTXGkZK+ltgY451ldvDaf/VCcDmvIspgnR7nEpyNLGW5EKzW/KsKQ9pk+SRJH159wI9cy6pWW566gDp4fRg4A8R8WY67uNA74h4KNfiCkSSokr+w0m6EjgU2Bf4K7AI+Avw31V4gsOPSJqbXiHpw7s5IuryraoYJA0GDkzvQFFYDooOImkEyU4B4LGIWJlnPXnxWWAflt7MciRJaByS/rwaEVXzpEZJF5P0RTyRdy15q9Tvh/so2knSLpIWArcB/wv4CjBX0j2SCnkGQyf7V+BdgPQssCnAb0ia4Qp/T5tOsDPQl6QDcxeS2+QvzrWi8rsDeL1hQNJpkuZK+oWk/jnWlYeK/H74iKKdJP2C5B/++xGxJR23A8l/gJ0bP2Spq/NZYAlJU4FPkPTLLAbuB+6PiFdyLSwHPoV8q0r9frgzu/2OBD7ZEBIAEbFF0j8DK/IrKzfdJHWPiPdIzgKbWDKtmv6//Q2wE7AKeI7kIVyv5lpRfrqVNKl8GZgaEb8Hfi9pWY515aEivx+FLayCvJv+o39I+iyNd/IoKGc+CwyIiHHptSOfIOmfOB8YLullkg7ti3MtsLwqcufYSSry+1Ft/0idoaekA9l6lWUDkfxFWVUi4l8k3c3Ws8Aa2jZ3IGluqBrp775S0qskO4HXgC8Co4BqCoqK3Dl2hkr9friPop3SjuwtfDgoomE4Ij6XQ1m5kdQTOAv4GEnT2/Smjri6OknnkhxJHEZyHcVfgP9OX1eUNlVWA59CnqjU74eDogNIGg1siYgHJX0CGEdyimyhz43uDJJmk+wY/ws4Cng6Ir6db1XlJ+lnpNdORMS6vOvJU6XuHDtDpX4/HBTtlJ4jfhRJM95dJM0K95J0ci+IiH/Jsbyyk7QiIkak77sDD0TEp3Iuy3JUqTvHzlCp3w/3UbTfySSn+e0ErAeGRMTrkn5KclpkVQUFJberSDv086zFimH/kp3jdOCBnOvJU0V+PxwU7fdeRLwPbJK0JiJeB4iItyRVVTt0qlZSw8VVAnZOh0WBb6Nsnaoid46dpCK/Hw6K9ntXUq+I2AQc1DBS0i4kndxVJSK65V2DFU5F7hw7Q6V+P9xH0U6SdoqIba6XkDSQ5FkM1XjRnZl1IQ4KMzPL5JsCmplZJgeFmZllclBYriRFC36OKEMdU9qybklnNZr/BUl3SPpkyTxTJLXqQT2Sekm6RNLwJqaNkPQXSZvSde7Rms82ay2f9WR5O6Tk/c4kzxy/HJhXMv7RMtVSDxzbaFxL1/0Z4H1gL5L7OC2UtG9EvNjGWnqln/M40PghWFeTbKsvApuADW1ch1mLOCgsVxFxf8N7Sb3Tt2tKx5fRu+1Y7+KG21Kkt85eTXJL7Ws7qrgS+wK/iYg/tedD0rvb7tjUWXtmpdz0ZBVD0khJC9Mmlw2SZqSnITdM3zdtihkv6WZJb0haL+mictYZEWtInuhW09R0SX0l3SDpifR3eVLSzxuCMr03Un06+6ySZq2DJQXJUctF6bj5JZ/7HUmrJb2TfvbZjdY7RVKdpM8peZjQO8Cxksaln/VZSfPSmh5P5+sh6Zp0e9c18Zm1ku6S9Eq6vR+R9PUO2pRWEA4KqwhKHkJ/D9ANOAX4LvAFYH56z5xS1wAvkTyLeAbwI0lntmA1u0l6WdJmSUslfamNtfYH+pDc0qUpfUiaqS4iuffRD4GjgZnp9HdIbiwJ8AO2Pmv7kfT1ZeCm9P156TrPAa4Cfk/SfDYXuE7SeY3WvQvwa+CX6ToeLpk2DbgbOAF4Mf2sX7J1m/9H+pkHpOvcgaSJ8E2SxwAfB9yQrsO6kojwj38K8QP0JrlF+4QmpjXs/D9SMm5MOv8J6fC+6fDtjZb9LfBUM+ueQLLTPYJkh/cHkivrj2lmubPSde5E0pS7N/DvJI/H3S+dZwpQl/EZ3Uke6LMF2D0dNzD93FOamH89cHnJcA+SHfsNjea7kaT/ontJHQF8odF849LxF5aM+1Q67o5GdW4AfpgOD0nnGZb3/x3/dO6PjyisUowi2Wm92TAiIv5MstP8TKN5/73R8K1AjaTdtvfhEXFTRFwTEQsjYi7JX/oPk/xF3xJvk9zTaC3JcyhOj4jHtjezpK9JWi7pzXS5P5Lc0mJYC9dXaigwCLil0fjZQH9gv5Jxm0nuctyUu0ver05fP+gHiaQP5mmSpi+AF0i2/7S0uW9QG2q3CuCgsEoxmGTH1NgLJDvDUo3PNGoYHtzSlUVyo8fbgANbuMjBwEiSI4o9ImLW9maUdCowneR29CcDo0madgB6trTGEg2/V+Pt0zBcun3qY/sPTSp9pve7TYxrGN8TICI2A3+fzjMDWJ/2IY1oRe1WAXzWk1WKdUBTRwS7k7TZl2o8X8NwZz5AaGm0/GE844F7I+LchhGS+rVj3Q2/124k/RgNdk9fS7dPh96zJyJWAsdL2hH4LPATkr6Mmo5cj+XLRxRWKRYDR0vq1TBC0uHAHsB9jeY9odHwiSQPy2nxNQ2SugHHA0vbVm6mnUk6rEt9pdFww1/0LTnCeIrkLKnxjcb/I0mfwnabwDpKRLwbEXcBvwD2lvSRzl6nlY+PKKxS/BT4J+BOSVcC/Ug6Z5eS/AVb6iBJ16bjxwL/G5iY9eGSFgGzSC5w6wNMInkg1dEd+Ds0uAv4qaTvk/SDHEujfpZIHn61DjhF0iqSYFnW1FFLRGyWdBnwc0mvkZwddiRwBvDdVhzptIqkUSRnbM0hCauBwPkk15S8mbWsVRYHhVWEiHhe0ueBK0l2TG8B/0nTO8LzSP6avpXk1M0fRMS0ZlbxJMkpt4OB94AlwLj0r+SOdi1JX8YFJEcMdwKnkTwqtNTXgR+TdDLvlNbW5Cm3EXGtpB7At0h21s8A50bEdZ1Qf4PngFeA/5vW9gpJp/zkTlyn5cC3GbcuQ9K+JM0sfx8Rf8y7HrOuwn0UZmaWyUFhZmaZ3PRkZmaZfERhZmaZHBRmZpbJQWFmZpkcFGZmlslBYWZmmRwUZmaW6f8DcWBOxV4I2NIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.groupby(['platform'])['jp_sales'].sum().reset_index().sort_values('jp_sales', ascending=False).head().set_index('platform').plot.bar()\n",
    "plt.xlabel('Top 5 Platforms', size=15)\n",
    "plt.ylabel('Sales', size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total market share"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Market share for the selected time periods and platforms is 3651.0299999999997\n"
     ]
    }
   ],
   "source": [
    "tms = df_final['Total_sales'].sum()\n",
    "print(\"Total Market share for the selected time periods and platforms is\", tms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Market share occupied by the `na` region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.515342245886785"
      ]
     },
     "execution_count": 611,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_ms = df_final['na_sales'].sum()\n",
    "na_ms / tms *100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Market share occupied by the `eu` region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.388402724710563"
      ]
     },
     "execution_count": 612,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eu_ms = df_final['eu_sales'].sum()\n",
    "eu_ms / tms *100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Market share occupied by the `jp` region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.538960238617596"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jp_ms = df_final['jp_sales'].sum()\n",
    "jp_ms / tms *100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### According to the above results, it is concluded that out of the `100%` market share of `3651`, the `na` region occupies `49.5%`, the `eu` region occupies `31.4%`, and the `jp` region occupies `8.5%`. The remaining market share is occupied by the `other_sales` column in the original dataset.\n",
    "\n",
    "\n",
    "## Top five genres for each region based on their sales\n",
    "\n",
    "### Graphical representation of the top 5 genres based on sales in the `na` region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.groupby(['genre'])['na_sales'].sum().reset_index().sort_values('na_sales', ascending=False).head().set_index('genre').plot.bar()\n",
    "plt.xlabel('Top 5 Genres', size=15)\n",
    "plt.ylabel('Sales', size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphical representation of the top 5 genres based on sales in the `eu` region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 615,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FRcc1pmVmZlZFtZAomqIyZVH2QGmUpBmSZixfvryVwzIzqy+1kCheLVQppffL0vJGoHfRcb2AJeWeICImRMSAiBjQrVu3Vg3WzKze1MLI7DuBkcC49P6OovIzJd0CHACsKlRRmZnVinoYpV7VRCHpZuBgoKukRuAHJAniNkmnAC8Dw9PD7wIOAxYA7wDfqGasZmaWqGqiiIgRTewaUubYIFmr28zMclQLbRRmZlbDnCjMzCyTE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJlZJicKMzPL5ERhZmaZnCjMzCyTE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJlZJicKMzPL5ERhZmaZnCjMzCyTE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJlZJicKMzPL5ERhZmaZnCjMzCyTE4WZmWXaOu8ACiQtBN4E1gJrImKApB2BW4EGYCFwbET8Pa8YzczqUa1dURwSEf0iYkC6fR4wPSL6ANPTbTMzq6JaSxSljgQmpo8nAkflGIuZWV2qpUQRwH2SZkoalZb1iIilAOl999yiMzOrUzXTRgEMioglkroD0yT9tbknpollFMBuu+3WWvGZmdWlmrmiiIgl6f0yYAqwP/CqpJ4A6f2yJs6dEBEDImJAt27dqhWymVldqIlEIWlbSV0Kj4GhwFzgTmBkethI4I58IjQzq1+1UvXUA5giCZKYfhMR90h6ErhN0inAy8DwHGM0M6tLNZEoIuJFYJ8y5SuAIdWPyMzMCmqi6snMzGqXE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJlZJicKMzPL5ERhZmaZnCjMzCyTE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJlZJicKMzPL5ERhZmaZnCjMzCyTE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJlZJicKMzPL5ERhZmaZnCjMzCyTE4WZmWVyojAzs0xOFGZmlqlNJApJh0p6TtICSeflHY+ZWT2p+UQhqQNwFfBFYG9ghKS9843KzKx+1HyiAPYHFkTEixHxD+AW4MicYzIzqxuKiLxjyCTpq8ChEXFqun0CcEBEnFl0zChgVLr5SeC5qge6sa7Aa3kHUSP8Waznz2I9fxbr1cJnsXtEdCu3Y+tqR7IZVKZsg+wWEROACdUJp3kkzYiIAXnHUQv8Waznz2I9fxbr1fpn0RaqnhqB3kXbvYAlOcViZlZ32kKieBLoI2kPSR8CjgPuzDkmM7O6UfNVTxGxRtKZwL1AB+C6iHgm57Cao6aqwnLmz2I9fxbr+bNYr6Y/i5pvzDYzs3y1haonMzPLkROFmZllcqIwM7NMThTWqiTtIOnTeceRB0kdJN2YdxxmW6rmez21FZK6Ad8EGij6XCPi5LxiyoukB4Avk3wOs4Hlkh6MiO/kGliVRcRaSd0kfSidfqbuSToDuCkiXk+3dwBGRMQv8o2s+iT1L1O8ClgUEWuqHU8W93qqEEmPAQ8DM4G1hfKI+G1uQeVE0l8iYl9JpwK9I+IHkuZERN1dWUi6BuhPMvbn7UJ5RFyRW1A5kjQ7IvqVlP0lIvbNK6a8SHqc5P/GHJIZKPqmj3cCvhUR9+UY3gZ8RVE5H4mIc/MOokZsLakncCwwJu9gcrYkvW0FdMk5llqwlSRF+gs1nR36QznHlJeFwCmFcWHprNijgR8CtwNOFO3QHyQdFhF35R1IDbiQZIDkIxHxpKSPAfNzjikXEXEhgKRtI+LtTR1fB+4FbpN0Ncmcbd8C7sk3pNzsWTx4OCKelbRvRLwolZviLj+ueqoQSW8C2wL/AFanxRERH80vqnxIGhQRj26qrB5I+ixwLdA5InaTtA9wWkScnnNouZC0FclMz58nqW65D/ifiFibeWI7JOlWYCXJ0gkAXyOZRfYEkh9ZA/OKrZQThVWcpFkR0X9TZfVA0hPAV4E7C/XwkuZGRN98I8ufpB2BXhExJ+9Y8iBpG+B04F9IkuYjwC+A90iqst/KMbwNuOqpgiR9GRicbj4QEX/IM55qS389Hwh0k1Tcw+mjJPN01aWIWFxSlVB3v54L3CNuvYh4F7g8vZWqmSQBHkdRMZLGAWcDz6a3s9OyevIhoDPJl0CXotsbJL+q69FiSQcCIelDks4B5uUdVI62i4g3gKOBX0XEfiTVUHVH0iBJ0yQ9L+nFwi3vuMpx1VOFSJoD9IuIdel2B+Av9dYlNH3ft0ZEvSaGDUjqCvyUDevkz4qIlbkGlhNJTwNDgYnAmLSzQ712nf4r8B9s3KV+RW5BNcFVT5W1PUnjFMB2eQaSl3SQ2Y55x1FDPhkRXy8ukDQIqLuG/dRY3COuYFVE3J13EM3hK4oKkTQCGAfcT/LLcTBwfkTcknliOyTpcqAPMIkNB5ndnltQOXHDvjUlrZruQDJm4v1CeUTMyi2oJviKokIi4ua0oW4gSaI4NyJeyTeq3OwIrAA+V1QWJH8QdcEN+xuS9J8RcamkKylZ8x4gIs7KIay8HZDeF6+VHWz4d1MTnCi2kKQ9I+KvRfO2NKb3u0japRZ/HbS2iPhG3jHUgNKG/YJ6bdgvNODPyDWKGhIRh+QdQ3O56mkLSZoQEaMk3V9md0REzf06aG2SegFXAoNIfiE9ApwdEY2ZJ7ZDknaPiEWSupD8f6ipbo9WfZKOj4gbS640P1CL84D5imILRcSo9OEXI+K94n2SOuUQUi34FfAbYHi6fXxa9oXcIspPF0l/IamOQ9JrwMiImJtvWNUl6c6s/RHx5WrFUgO2Te/bzNxfvqKoEDdartfEDKEbldWDdFbhMRFxf7p9MPCjiDgw18CqTNJyYDFwM/AESTveByLiwTzisubxgLstJGlnSfsB20jaV1L/9HYw8JGcw8vLa5KOTxfu6SDpeJLG7Xq0bSFJAETEA6z/RVlPdga+RzKV9k9Jri5fi4gH6zVJSJooafui7R0kXZdnTE1x1dOWGwacBPQiGYpf+KX0BskfRj06Gfg58JN0+9G0rB69KOn7wA3p9vHASznGk4t00r97gHskfRgYATwgaWxEXJlvdLn5dGEBJ4CI+LukmlyXw1VPFSLpmHpcpMiypSu4Xcj6id8eAi6IiL/nGlgO0gRxOEmSaCBZzOm6iPhbnnHlRdJTwMGF/wvpQNUHI+Kf841sY76iqJz9JE0vWeLxuxHxXznHVXXpaNufAp8h6fX0Z+A/IqIm57FpTemXwFmSPgqsq9deT5ImklQ73Q1cWG+N+U24HHhM0uR0ezhwcY7xNMlXFBVSbjnHOm7Mfhy4iqThEuA44N8j4oCmz2qfJP0zcD1pryegXns9rWP9KP3iLx1Rp+u2AEj6FHAIyecwPSKezTmksnxFUTkdJH04It6HD+aa/3DOMeVFEXFD0faNks7MLZp8XQN8p6TX0wSSUdt1IyLccaaMiHgm7RHWCUDSbhHxcs5hbcSJonJuBKZL+lW6/Q2SGTLr0f2SziNZuStIVu6aWpgssM5mTt2o15Okeuz1ZCXS9WsuB3YBlgG7k4xg/1SecZXjqqcKknQo66eT/jvQMyLOyDeq6pNU6NVT+M9V3Gc+IuJjVQ4pN5KmALPYsNfTgIg4Kr+orBakjdmfA/4YEftKOgQYUTSIt2b4crCyXgHWAccAQ6izBWokDZS0c0TsERF7kPT2mQv8HtgvLa+bJJE6GehGMiHilPSx58IygNXp2hNbSdoqvfKsyUGprnraQpI+QdJYO4JkUNmtJFdqbWbCrwq6hnS1MkmDgUuAfyf5zz+BOpwMr9DrKe84rCa9LqkzSZfpmyQtA9bkHFNZrnraQmlvjoeBUyJiQVr2Yh3+ckbSUxGxT/r4KmB5RFyQbtfrFB6fAM4hGTfwwQ+zepws0jaUtlW9S1Kz83WSxc5u8gp37dMxJFcU90u6h6QBV9mntFsdJG0dEWtIqt6K61rr9f/aJOBq4H8oWu7SLCIK3YXXARPTZYSPA27KL6ry6vWPt2IiYgowJf11cBTJGrg9JI0HpkTEfbkGWF03Aw+mM6S+S3KlhaSPA6vyDCxHayJifN5BWO1IB1+eAexKMjp9Wro9GphNDSYKVz21grQb6HDga/VWxSDpM0BP4L7CL6a0+qVzPS3iVLRu+FkkXR+nsOFyl/XURdiKSLqDpFfkn0muvHcgWejq7IiYnWdsTXGiMGsFaRfhYH015AZ/aPXYhmUJSU8X5nNKq5teA3aLiDfzjaxprnoyax1fAxZHxFIASSNJ2rMWAhfkF5bVgNWFBxGxVtJLtZwkwFcUZq1C0izg8xGxMu0qfAvruwrvFRF111XYEpLWsn7eKwHbAO9Qw/Ne+YrCrHV0KGqH+BowIZ2G/reSarIe2qojIjrkHUNLeWS2WevoIKnwQ2wI8Keiff6BZm2K/8OatQ53FbZ2w20UZq3EXYWtvXCiMDOzTG6jMDOzTE4UZmaWyYnCap6kaMbt4CrEMW5LXlvSEEm/l7RM0mpJr0r6naShrRy62RZxrydrCz5b9Hgbkq6mFwFTi8qrtSj9cuDLJWWbfG1J5wLjSGaTPQN4lWQJzGOAeyQdFBGPVjhWs4pworCaFxGPFx6nC70AvFBcXkX/aOnrSvosySJO50fEuJLdt0g6iGSSuFYnaZuIeLcar2Xth6uerF2RNEDSA5LekbRC0kRJXYv275lWFw2XdIuktyS9Iun8VgzrLOBl4NJyOyPi4YjY4KpE0r9JmifpfUkvSfp2yf5bJD0i6TBJz6Tv40FJnyw6plP6Xs+U9PN0TMeTRfu/KmmWpPckLZF0cTpJXWF/g6TbJS2X9K6k+ZK+X6HPxNoQJwprNyT1BO4HCgvAfAcYRlK1U3r1/N8ks3YeA0wEfiTplGa8THdJK9M2hpmSvtSMcwYDf4yIdc18H99P47sNOBy4FrhU0qklh36cpAruAuB4oDfJQL9SY4Dt02POSV/jRJJlex8mqUq7hCShXVh03m+ArsCpwGHA/yep+rN6ExG++dZmbkBnkim7Tyqzr/Dlv21R2eD0+K+k23um23eWnHsD8NImXvsk4NvAwcCRwH0kq5MdnnGOSFa2+0GZ8q2Lblul5TuSjOQ+t+T4S4GXi7ZvAf4B7F5Udlz63hrS7U7p9p9LnqsDsBQYX1J+OvAW8NE0vtXAF/L+N/ct/5uvKKw92R+4K9YvMUlEPAS8AvxLybFTSrZvBxokdW/qySPi1xHx3xHxQETcAXwR+AuQVR1Tdj0KkjWSVxfdxqblB5F8wU+StHXhBkwHekvqUfQcz0fEoqLtQvVVr5LXmlqy3RfYucxr/AnYlmR22wCeAi6TdKKk0ue0OuJEYe1JT5LeRKVeJfmlXmxZE9s9m/tiEbEW+B2wb8Yx60gSVekX7V3AwPS2oqi80J7yAhsmknvS8t5Fx75e8pz/SO87lZSXfiaF15he8hrzSl7jaOBp4GfA4rSqbTBWd9zrydqTpUC5K4IeQOnSo6XHFbaXVjoo4CHgC5KU/lInkinIVwJIWlN0bCHOoZTvCTWvTNmmlF7NFF5jJOW79r6QxvgycELawH0A8EPgD5J6RcQbmxGHtVFOFNaePAEcL+kjEfEOQNr1dGfgkZJjvwL8qmj7aGBRRJReaTQp/QI9Cpi5iUN/RrImxTnAZZs49hGSK4OdI2Jac2NpoadJxoPsHhHXb+rg9MrpMUkXkVRP9aJ641asBjhRWHtyGUkPnbsl/Zhk0fpxJF/kvy85dj9JV6blQ0h6BI3KenJJj5H0Kvor0AX4N5IV6w7LOi8i/izpeyQ9lwYAk0mqo7YH/pWkWuyt9Njlki4GxqdTkj9C8nf6SeDAiDi2GZ9DpohYI2k08EtJO5I0yq8B/okkgR5GUj31W+BGYD7wEWA00JhuWx1xorB2IyKWSPoc8GOSrqXvAn8AvhMRa0oO/zZwLEkj9tvA9yPil5t4iRdJutz2JPlinQEc2pxf/hExTtKM9HXHA9uRVAE9Cfw/ki/lwrFjJS0m6a56Lskymc+RdFetiIiYKGklcD5wWvp+FpAkznUkies5kvfbO91+DDgtIlaXfVJrtzzNuNUVSXuS1PN/ISL+mHc8Zm2Bez2ZmVkmJwozM8vkqiczM8vkKwozM8vkRGFmZpmcKMzMLDK+8EcAAAAYSURBVJMThZmZZXKiMDOzTE4UZmaW6f8ADw/cJ0KykFIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.groupby(['genre'])['eu_sales'].sum().reset_index().sort_values('eu_sales', ascending=False).head().set_index('genre').plot.bar()\n",
    "plt.xlabel('Top 5 Genres', size=15)\n",
    "plt.ylabel('Sales', size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphical representation of the top 5 genres based on sales in the `jp` region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.groupby(['genre'])['jp_sales'].sum().reset_index().sort_values('jp_sales', ascending=False).head().set_index('genre').plot.bar()\n",
    "plt.xlabel('Top 5 Genres', size=15)\n",
    "plt.ylabel('Sales', size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do ESRB rating affect sales?\n",
    "\n",
    "### ESRB rating for `na` region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total na_sales without ESRB rating in na region is 178.51999999999998\n"
     ]
    },
    {
     "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(['rating'])['na_sales'].sum().reset_index().sort_values('na_sales', ascending=False).set_index('rating').plot.bar()\n",
    "plt.xlabel('Ratings', size=15)\n",
    "plt.ylabel('Sales', size=15)\n",
    "\n",
    "print(\"Total na_sales without ESRB rating in na region is\", df_final[df_final['rating'].isna()]['na_sales'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For `na` region, the sales with ESRB rating are quite higher than the sales without any rating. So we can assume that in the `na` region, the ESRB rating does affect sales.\n",
    "\n",
    "\n",
    "### ESRB rating for `eu` region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total eu_sales without ESRB rating in eu region is 154.29000000000002\n"
     ]
    },
    {
     "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(['rating'])['eu_sales'].sum().reset_index().sort_values('eu_sales', ascending=False).set_index('rating').plot.bar()\n",
    "plt.xlabel('Ratings', size=15)\n",
    "plt.ylabel('Sales', size=15)\n",
    "\n",
    "print(\"Total eu_sales without ESRB rating in eu region is\", df_final[df_final['rating'].isna()]['eu_sales'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For `eu` region, the sales with ESRB rating are quite higher than the sales without any rating. So we can assume that in the `eu` region, the ESRB rating does affect sales.\n",
    "\n",
    "\n",
    "### ESRB rating for `jp` region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total jp_sales without ESRB rating in jp region is 126.12\n"
     ]
    },
    {
     "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(['rating'])['jp_sales'].sum().reset_index().sort_values('jp_sales', ascending=False).set_index('rating').plot.bar()\n",
    "plt.xlabel('Ratings', size=15)\n",
    "plt.ylabel('Sales', size=15)\n",
    "\n",
    "print(\"Total jp_sales without ESRB rating in jp region is\", df_final[df_final['rating'].isna()]['jp_sales'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For `jp` region, the sales with ESRB rating are quite lower than the sales without any rating. So we can assume that in the `jp` region, the ESRB rating does not affect sales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The End!"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}