Анализ_успешности_игр.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Анализ успешности игр. Сборный проект 1. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Описание данных](#descriptionid)\n",
    "2. [Предобработка данных](#preprocid)\n",
    "    1. [Замена типов данных. Обработка пропусков](#typesid)\n",
    "    2. [Обработка пропусков и дупликатов](#nansduplicatesid)\n",
    "3. [Исследовательский анализ данных](#analysisifd)\n",
    "4. [Портрет пользователя](#customerid)\n",
    "    1. [Популярные платформы](#platformsid)\n",
    "    2. [Популрные жанры](#genresid)\n",
    "    3. [Рейтинг ERSB](#ersbid)\n",
    "5. [Провекра гипотез](#hypothesisid)\n",
    "6. [Общий вывод](#conclusionid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для анализа были предоставлены данные о продажах компьютерных игр за 2016 год. \n",
    "\n",
    "**Цель проекта:** выявить закономерности, определяющие успешность компьютерных игр в целях планирования рекламных кампаний. \n",
    "\n",
    "## Описание данных<a id='descriptionid'></a>\n",
    "Для начала откроем файл и посмотрим на данные.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats as st\n",
    "from math import factorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th>Genre</th>\n",
       "      <th>NA_sales</th>\n",
       "      <th>EU_sales</th>\n",
       "      <th>JP_sales</th>\n",
       "      <th>Other_sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>41.36</td>\n",
       "      <td>28.96</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.45</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>15.68</td>\n",
       "      <td>12.76</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.29</td>\n",
       "      <td>82.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>15.61</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name Platform  Year_of_Release         Genre  NA_sales  \\\n",
       "0                Wii Sports      Wii           2006.0        Sports     41.36   \n",
       "1         Super Mario Bros.      NES           1985.0      Platform     29.08   \n",
       "2            Mario Kart Wii      Wii           2008.0        Racing     15.68   \n",
       "3         Wii Sports Resort      Wii           2009.0        Sports     15.61   \n",
       "4  Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing     11.27   \n",
       "\n",
       "   EU_sales  JP_sales  Other_sales  Critic_Score User_Score Rating  \n",
       "0     28.96      3.77         8.45          76.0          8      E  \n",
       "1      3.58      6.81         0.77           NaN        NaN    NaN  \n",
       "2     12.76      3.79         3.29          82.0        8.3      E  \n",
       "3     10.93      3.28         2.95          80.0          8      E  \n",
       "4      8.89     10.22         1.00           NaN        NaN    NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'C:/Python/datasets/'\n",
    "if os.path.exists(path):\n",
    "    df = pd.read_csv(path + 'games.csv')\n",
    "else:\n",
    "    df = pd.read_csv('/datasets/games.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В таблице присутствуют следующие столбцы: \n",
    "\n",
    "* Name — название игры\n",
    "* Platform — платформа\n",
    "* Year_of_Release — год выпуска\n",
    "* Genre — жанр игры\n",
    "* NA_sales — продажи в Северной Америке (миллионы проданных копий)\n",
    "* EU_sales — продажи в Европе (миллионы проданных копий)\n",
    "* JP_sales — продажи в Японии (миллионы проданных копий)\n",
    "* Other_sales — продажи в других странах (миллионы проданных копий)\n",
    "* Critic_Score — оценка критиков (максимум 100)\n",
    "* User_Score — оценка пользователей (максимум 10)\n",
    "* Rating — рейтинг от организации ESRB (англ. Entertainment Software Rating Board). Эта ассоциация определяет рейтинг компьютерных игр и присваивает им подходящую возрастную категорию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16715 entries, 0 to 16714\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Name             16713 non-null  object \n",
      " 1   Platform         16715 non-null  object \n",
      " 2   Year_of_Release  16446 non-null  float64\n",
      " 3   Genre            16713 non-null  object \n",
      " 4   NA_sales         16715 non-null  float64\n",
      " 5   EU_sales         16715 non-null  float64\n",
      " 6   JP_sales         16715 non-null  float64\n",
      " 7   Other_sales      16715 non-null  float64\n",
      " 8   Critic_Score     8137 non-null   float64\n",
      " 9   User_Score       10014 non-null  object \n",
      " 10  Rating           9949 non-null   object \n",
      "dtypes: float64(6), object(5)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Много пропусков наблюдается в столбцах с отзывами пользователей и критиков, а также в столбце рейтинга. Они могут быть связаны с недостатком данных в исходном источнике. Их замена в данном контексте неразумна. \n",
    "\n",
    "Также, пропуски наблюдаются в столбце с датой выпуска. Такие пропуски могут быть следствием ошибок при вводе данных или результатом недостатка данных. Учитывая, что их относительно немного, возможно их игнорирование, однако, этот вопрос требует дополнительного анализа. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Предобработка данных<a id='preprocid'></a>\n",
    "### Замена типов данных. Обработка пропусков<a id='typesid'></a>\n",
    "Для начала приведем все названия столбцов к нижнему реестру."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'Name':'name', 'Platform':'platform', 'Year_of_Release':'year_of_release', 'Genre':'genre', 'NA_sales':'na_sales', \n",
    "    'EU_sales':'eu_sales', 'JP_sales':'jp_sales', 'Other_sales':'other_sales', 'Critic_Score':'critic_score',\n",
    "    'User_Score':'user_score', 'Rating':'rating_age'}\n",
    "df.rename(columns = d, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Затем посмотрим на типы данных.\n",
    "\n",
    "В столбце с годом выпуска наблюдаются пропуски, поэтому тип данных - float. В столбцах по продажам - float64, который можно заменить на float16. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41.36"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['na_sales'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['na_sales'] = df['na_sales'].astype('float16')\n",
    "df['eu_sales'] = df['eu_sales'].astype('float16')\n",
    "df['jp_sales'] = df['jp_sales'].astype('float16')\n",
    "df['other_sales'] = df['other_sales'].astype('float16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16715 entries, 0 to 16714\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   name             16713 non-null  object \n",
      " 1   platform         16715 non-null  object \n",
      " 2   year_of_release  16446 non-null  float64\n",
      " 3   genre            16713 non-null  object \n",
      " 4   na_sales         16715 non-null  float16\n",
      " 5   eu_sales         16715 non-null  float16\n",
      " 6   jp_sales         16715 non-null  float16\n",
      " 7   other_sales      16715 non-null  float16\n",
      " 8   critic_score     8137 non-null   float64\n",
      " 9   user_score       10014 non-null  object \n",
      " 10  rating_age       9949 non-null   object \n",
      "dtypes: float16(4), float64(2), object(5)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обработка пропусков и дупликатов<a id='nansduplicatesid'></a>\n",
    "Из таблицы info видно, что в таблице присутствуют пропуски. Посмотрим отдельно на данные каждого столбца. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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_age</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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      "text/plain": [
       "       name  platform  year_of_release  genre  na_sales  eu_sales  jp_sales  \\\n",
       "659    True     False            False   True     False     False     False   \n",
       "14244  True     False            False   True     False     False     False   \n",
       "\n",
       "       other_sales  critic_score  user_score  rating_age  \n",
       "659          False          True        True        True  \n",
       "14244        False          True        True        True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().query('name == True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таких строк всего две. Можно заменить его на \"unknown\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'unknown'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'] = df['name'].fillna('unknown')\n",
    "df.loc[14244, 'name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Далее посмотрим на пропуски в столбце year_of_release:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "269"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year_of_release'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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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_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Madden NFL 2004</td>\n",
       "      <td>PS2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sports</td>\n",
       "      <td>4.261719</td>\n",
       "      <td>0.260010</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.709961</td>\n",
       "      <td>94.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>FIFA Soccer 2004</td>\n",
       "      <td>PS2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.589844</td>\n",
       "      <td>2.359375</td>\n",
       "      <td>0.040009</td>\n",
       "      <td>0.509766</td>\n",
       "      <td>84.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>LEGO Batman: The Videogame</td>\n",
       "      <td>Wii</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Action</td>\n",
       "      <td>1.799805</td>\n",
       "      <td>0.970215</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.290039</td>\n",
       "      <td>74.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>E10+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>wwe Smackdown vs. Raw 2006</td>\n",
       "      <td>PS2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>1.570312</td>\n",
       "      <td>1.019531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.409912</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Space Invaders</td>\n",
       "      <td>2600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>2.359375</td>\n",
       "      <td>0.140015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16373</th>\n",
       "      <td>PDC World Championship Darts 2008</td>\n",
       "      <td>PSP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.0</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E10+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16405</th>\n",
       "      <td>Freaky Flyers</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16448</th>\n",
       "      <td>Inversion</td>\n",
       "      <td>PC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16458</th>\n",
       "      <td>Hakuouki: Shinsengumi Kitan</td>\n",
       "      <td>PS3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16522</th>\n",
       "      <td>Virtua Quest</td>\n",
       "      <td>GC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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  4.261719  0.260010  0.010002     0.709961          94.0   \n",
       "377          Sports  0.589844  2.359375  0.040009     0.509766          84.0   \n",
       "456          Action  1.799805  0.970215  0.000000     0.290039          74.0   \n",
       "475        Fighting  1.570312  1.019531  0.000000     0.409912           NaN   \n",
       "609         Shooter  2.359375  0.140015  0.000000     0.029999           NaN   \n",
       "...             ...       ...       ...       ...          ...           ...   \n",
       "16373        Sports  0.010002  0.000000  0.000000     0.000000          43.0   \n",
       "16405        Racing  0.010002  0.000000  0.000000     0.000000          69.0   \n",
       "16448       Shooter  0.010002  0.000000  0.000000     0.000000          59.0   \n",
       "16458     Adventure  0.010002  0.000000  0.000000     0.000000           NaN   \n",
       "16522  Role-Playing  0.010002  0.000000  0.000000     0.000000          55.0   \n",
       "\n",
       "      user_score rating_age  \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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['year_of_release'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на столбец user_score. Почему тип данного столбца object?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6701"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['user_score'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Изначально количество пропущенных значений - 6701."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['8', nan, '8.3', '8.5', '6.6', '8.4', '8.6', '7.7', '6.3', '7.4',\n",
       "       '8.2', '9', '7.9', '8.1', '8.7', '7.1', '3.4', '5.3', '4.8', '3.2',\n",
       "       '8.9', '6.4', '7.8', '7.5', '2.6', '7.2', '9.2', '7', '7.3', '4.3',\n",
       "       '7.6', '5.7', '5', '9.1', '6.5', 'tbd', '8.8', '6.9', '9.4', '6.8',\n",
       "       '6.1', '6.7', '5.4', '4', '4.9', '4.5', '9.3', '6.2', '4.2', '6',\n",
       "       '3.7', '4.1', '5.8', '5.6', '5.5', '4.4', '4.6', '5.9', '3.9',\n",
       "       '3.1', '2.9', '5.2', '3.3', '4.7', '5.1', '3.5', '2.5', '1.9', '3',\n",
       "       '2.7', '2.2', '2', '9.5', '2.1', '3.6', '2.8', '1.8', '3.8', '0',\n",
       "       '1.6', '9.6', '2.4', '1.7', '1.1', '0.3', '1.5', '0.7', '1.2',\n",
       "       '2.3', '0.5', '1.3', '0.2', '0.6', '1.4', '0.9', '1', '9.7'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['user_score'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>year_of_release</th>\n",
       "      <th>genre</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>critic_score</th>\n",
       "      <th>user_score</th>\n",
       "      <th>rating_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Zumba Fitness</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>3.449219</td>\n",
       "      <td>2.589844</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.660156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>Namco Museum: 50th Anniversary</td>\n",
       "      <td>PS2</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>2.080078</td>\n",
       "      <td>1.349609</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.540039</td>\n",
       "      <td>61.0</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E10+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>Zumba Fitness 2</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>1.509766</td>\n",
       "      <td>1.030273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.270020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tbd</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>uDraw Studio</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>1.650391</td>\n",
       "      <td>0.569824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.199951</td>\n",
       "      <td>71.0</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>Frogger's Adventures: Temple of the Frog</td>\n",
       "      <td>GBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2.150391</td>\n",
       "      <td>0.180054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>73.0</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16695</th>\n",
       "      <td>Planet Monsters</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.0</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16697</th>\n",
       "      <td>Bust-A-Move 3000</td>\n",
       "      <td>GC</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Puzzle</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.0</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16698</th>\n",
       "      <td>Mega Brain Boost</td>\n",
       "      <td>DS</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Puzzle</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16704</th>\n",
       "      <td>Plushees</td>\n",
       "      <td>DS</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tbd</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16706</th>\n",
       "      <td>Men in Black II: Alien Escape</td>\n",
       "      <td>GC</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tbd</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2424 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           name platform  year_of_release  \\\n",
       "119                               Zumba Fitness      Wii           2010.0   \n",
       "301              Namco Museum: 50th Anniversary      PS2           2005.0   \n",
       "520                             Zumba Fitness 2      Wii           2011.0   \n",
       "645                                uDraw Studio      Wii           2010.0   \n",
       "657    Frogger's Adventures: Temple of the Frog      GBA              NaN   \n",
       "...                                         ...      ...              ...   \n",
       "16695                           Planet Monsters      GBA           2001.0   \n",
       "16697                          Bust-A-Move 3000       GC           2003.0   \n",
       "16698                          Mega Brain Boost       DS           2008.0   \n",
       "16704                                  Plushees       DS           2008.0   \n",
       "16706             Men in Black II: Alien Escape       GC           2003.0   \n",
       "\n",
       "            genre  na_sales  eu_sales  jp_sales  other_sales  critic_score  \\\n",
       "119        Sports  3.449219  2.589844       0.0     0.660156           NaN   \n",
       "301          Misc  2.080078  1.349609       0.0     0.540039          61.0   \n",
       "520        Sports  1.509766  1.030273       0.0     0.270020           NaN   \n",
       "645          Misc  1.650391  0.569824       0.0     0.199951          71.0   \n",
       "657     Adventure  2.150391  0.180054       0.0     0.070007          73.0   \n",
       "...           ...       ...       ...       ...          ...           ...   \n",
       "16695      Action  0.010002  0.000000       0.0     0.000000          67.0   \n",
       "16697      Puzzle  0.010002  0.000000       0.0     0.000000          53.0   \n",
       "16698      Puzzle  0.010002  0.000000       0.0     0.000000          48.0   \n",
       "16704  Simulation  0.010002  0.000000       0.0     0.000000           NaN   \n",
       "16706     Shooter  0.010002  0.000000       0.0     0.000000           NaN   \n",
       "\n",
       "      user_score rating_age  \n",
       "119          tbd          E  \n",
       "301          tbd       E10+  \n",
       "520          tbd          T  \n",
       "645          tbd          E  \n",
       "657          tbd          E  \n",
       "...          ...        ...  \n",
       "16695        tbd          E  \n",
       "16697        tbd          E  \n",
       "16698        tbd          E  \n",
       "16704        tbd          E  \n",
       "16706        tbd          T  \n",
       "\n",
       "[2424 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['user_score'] == 'tbd']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Скорее всего, tbd - \"to be determined or decided or declared\". Таких строк - 2424. Поэтому, можно заменить данные значения на NaN. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
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       "      <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_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>41.375000</td>\n",
       "      <td>28.953125</td>\n",
       "      <td>3.769531</td>\n",
       "      <td>8.453125</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>29.078125</td>\n",
       "      <td>3.580078</td>\n",
       "      <td>6.808594</td>\n",
       "      <td>0.770020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>15.679688</td>\n",
       "      <td>12.757812</td>\n",
       "      <td>3.789062</td>\n",
       "      <td>3.289062</td>\n",
       "      <td>82.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>15.609375</td>\n",
       "      <td>10.929688</td>\n",
       "      <td>3.279297</td>\n",
       "      <td>2.949219</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>11.273438</td>\n",
       "      <td>8.890625</td>\n",
       "      <td>10.218750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16710</th>\n",
       "      <td>Samurai Warriors: Sanada Maru</td>\n",
       "      <td>PS3</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16711</th>\n",
       "      <td>LMA Manager 2007</td>\n",
       "      <td>X360</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16712</th>\n",
       "      <td>Haitaka no Psychedelica</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16713</th>\n",
       "      <td>Spirits &amp; Spells</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16714</th>\n",
       "      <td>Winning Post 8 2016</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</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      41.375000  28.953125   3.769531     8.453125          76.0          8   \n",
       "1      29.078125   3.580078   6.808594     0.770020           NaN        NaN   \n",
       "2      15.679688  12.757812   3.789062     3.289062          82.0        8.3   \n",
       "3      15.609375  10.929688   3.279297     2.949219          80.0          8   \n",
       "4      11.273438   8.890625  10.218750     1.000000           NaN        NaN   \n",
       "...          ...        ...        ...          ...           ...        ...   \n",
       "16710   0.000000   0.000000   0.010002     0.000000           NaN        NaN   \n",
       "16711   0.000000   0.010002   0.000000     0.000000           NaN        NaN   \n",
       "16712   0.000000   0.000000   0.010002     0.000000           NaN        NaN   \n",
       "16713   0.010002   0.000000   0.000000     0.000000           NaN        NaN   \n",
       "16714   0.000000   0.000000   0.010002     0.000000           NaN        NaN   \n",
       "\n",
       "      rating_age  \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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_nan(row):\n",
    "    if row['user_score'] == 'tbd':\n",
    "        new = np.nan\n",
    "    else:\n",
    "        new = row['user_score']\n",
    "    return new\n",
    "df['new'] = df.apply(to_nan, axis = 1)\n",
    "df['user_score'] = df['new']\n",
    "del df['new']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16715 entries, 0 to 16714\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   name             16715 non-null  object \n",
      " 1   platform         16715 non-null  object \n",
      " 2   year_of_release  16446 non-null  float64\n",
      " 3   genre            16713 non-null  object \n",
      " 4   na_sales         16715 non-null  float16\n",
      " 5   eu_sales         16715 non-null  float16\n",
      " 6   jp_sales         16715 non-null  float16\n",
      " 7   other_sales      16715 non-null  float16\n",
      " 8   critic_score     8137 non-null   float64\n",
      " 9   user_score       7590 non-null   object \n",
      " 10  rating_age       9949 non-null   object \n",
      "dtypes: float16(4), float64(2), object(5)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['user_score'] = df['user_score'].astype('float16')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, все ли названия игр представлены одной строкой, или же имеются дупликаты. Но для начала приведем все названия в столбцах platform и name к нижнему реестру:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['platform'] = df['platform'].str.lower()\n",
    "df['name'] = df['name'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11560"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['name'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Явно, огромное число названий представлено в таблице несколько раз. Это связано с тем, что некоторые игры представлены на нескольких платформах. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6631</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5422</th>\n",
       "      <td>madden nfl 07</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5211</th>\n",
       "      <td>lego marvel super heroes</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7791</th>\n",
       "      <td>ratatouille</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3114</th>\n",
       "      <td>fifa 14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4394</th>\n",
       "      <td>indy 500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4395</th>\n",
       "      <td>indy racing 2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4397</th>\n",
       "      <td>indycar series 2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4398</th>\n",
       "      <td>infamous</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11559</th>\n",
       "      <td>¡shin chan flipa en colores!</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11560 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               name  platform\n",
       "6631    need for speed: most wanted        12\n",
       "5422                  madden nfl 07         9\n",
       "5211       lego marvel super heroes         9\n",
       "7791                    ratatouille         9\n",
       "3114                        fifa 14         9\n",
       "...                             ...       ...\n",
       "4394                       indy 500         1\n",
       "4395               indy racing 2000         1\n",
       "4397            indycar series 2005         1\n",
       "4398                       infamous         1\n",
       "11559  ¡shin chan flipa en colores!         1\n",
       "\n",
       "[11560 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_names = df.pivot_table(index = 'name', values = 'platform', aggfunc = 'count').reset_index()\n",
    "df_names.sort_values(by = 'platform', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для примера посмотрим срезы по игре в сводной таблице df_names, с наибольим числом платформ, чтобы проверить, есть ли пропущенные значения в столбце с датой выпуска:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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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_age</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>ps2</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>2.029297</td>\n",
       "      <td>1.790039</td>\n",
       "      <td>0.080017</td>\n",
       "      <td>0.469971</td>\n",
       "      <td>82.0</td>\n",
       "      <td>9.101562</td>\n",
       "      <td>T</td>\n",
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       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>ps3</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.709961</td>\n",
       "      <td>1.459961</td>\n",
       "      <td>0.059998</td>\n",
       "      <td>0.580078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>x360</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.620117</td>\n",
       "      <td>0.779785</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.150024</td>\n",
       "      <td>83.0</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>T</td>\n",
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       "    <tr>\n",
       "      <th>1591</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>x360</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.130005</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>83.0</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>xb</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.529785</td>\n",
       "      <td>0.459961</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049988</td>\n",
       "      <td>83.0</td>\n",
       "      <td>8.796875</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>psv</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.330078</td>\n",
       "      <td>0.449951</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.219971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3581</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>gc</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.429932</td>\n",
       "      <td>0.109985</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.101562</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5972</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>pc</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.229980</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040009</td>\n",
       "      <td>82.0</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6273</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>wiiu</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.130005</td>\n",
       "      <td>0.119995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6410</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>ds</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.239990</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.101562</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6473</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>gba</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.189941</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.296875</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11715</th>\n",
       "      <td>need for speed: most wanted</td>\n",
       "      <td>pc</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.059998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>82.0</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name platform  year_of_release   genre  \\\n",
       "253    need for speed: most wanted      ps2           2005.0  Racing   \n",
       "523    need for speed: most wanted      ps3           2012.0  Racing   \n",
       "1190   need for speed: most wanted     x360           2012.0  Racing   \n",
       "1591   need for speed: most wanted     x360           2005.0  Racing   \n",
       "1998   need for speed: most wanted       xb           2005.0  Racing   \n",
       "2048   need for speed: most wanted      psv           2012.0  Racing   \n",
       "3581   need for speed: most wanted       gc           2005.0  Racing   \n",
       "5972   need for speed: most wanted       pc           2005.0  Racing   \n",
       "6273   need for speed: most wanted     wiiu           2013.0  Racing   \n",
       "6410   need for speed: most wanted       ds           2005.0  Racing   \n",
       "6473   need for speed: most wanted      gba           2005.0  Racing   \n",
       "11715  need for speed: most wanted       pc           2012.0  Racing   \n",
       "\n",
       "       na_sales  eu_sales  jp_sales  other_sales  critic_score  user_score  \\\n",
       "253    2.029297  1.790039  0.080017     0.469971          82.0    9.101562   \n",
       "523    0.709961  1.459961  0.059998     0.580078           NaN         NaN   \n",
       "1190   0.620117  0.779785  0.010002     0.150024          83.0    8.500000   \n",
       "1591   1.000000  0.130005  0.020004     0.099976          83.0    8.500000   \n",
       "1998   0.529785  0.459961  0.000000     0.049988          83.0    8.796875   \n",
       "2048   0.330078  0.449951  0.010002     0.219971           NaN         NaN   \n",
       "3581   0.429932  0.109985  0.000000     0.020004          80.0    9.101562   \n",
       "5972   0.020004  0.229980  0.000000     0.040009          82.0    8.500000   \n",
       "6273   0.130005  0.119995  0.000000     0.020004           NaN         NaN   \n",
       "6410   0.239990  0.010002  0.000000     0.020004          45.0    6.101562   \n",
       "6473   0.189941  0.070007  0.000000     0.000000           NaN    8.296875   \n",
       "11715  0.000000  0.059998  0.000000     0.020004          82.0    8.500000   \n",
       "\n",
       "      rating_age  \n",
       "253            T  \n",
       "523          NaN  \n",
       "1190           T  \n",
       "1591           T  \n",
       "1998           T  \n",
       "2048         NaN  \n",
       "3581           T  \n",
       "5972           T  \n",
       "6273         NaN  \n",
       "6410           E  \n",
       "6473           E  \n",
       "11715          T  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('name == \"need for speed: most wanted\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</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_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>madden nfl 07</td>\n",
       "      <td>ps2</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>3.630859</td>\n",
       "      <td>0.239990</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.609863</td>\n",
       "      <td>84.0</td>\n",
       "      <td>8.203125</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>madden nfl 07</td>\n",
       "      <td>x360</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>1.660156</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.130005</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>madden nfl 07</td>\n",
       "      <td>xb</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.970215</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>83.0</td>\n",
       "      <td>8.703125</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2479</th>\n",
       "      <td>madden nfl 07</td>\n",
       "      <td>psp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.770020</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040009</td>\n",
       "      <td>78.0</td>\n",
       "      <td>6.601562</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3229</th>\n",
       "      <td>madden nfl 07</td>\n",
       "      <td>gc</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.479980</td>\n",
       "      <td>0.130005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>82.0</td>\n",
       "      <td>9.203125</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838</th>\n",
       "      <td>madden nfl 07</td>\n",
       "      <td>ps3</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.469971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.040009</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4.199219</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4006</th>\n",
       "      <td>madden nfl 07</td>\n",
       "      <td>wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.459961</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040009</td>\n",
       "      <td>81.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7372</th>\n",
       "      <td>madden nfl 07</td>\n",
       "      <td>ds</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.199951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14889</th>\n",
       "      <td>madden nfl 07</td>\n",
       "      <td>gba</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9.296875</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name platform  year_of_release   genre  na_sales  eu_sales  \\\n",
       "241    madden nfl 07      ps2           2006.0  Sports  3.630859  0.239990   \n",
       "972    madden nfl 07     x360           2006.0  Sports  1.660156  0.000000   \n",
       "2035   madden nfl 07       xb           2006.0  Sports  0.970215  0.029999   \n",
       "2479   madden nfl 07      psp              NaN  Sports  0.770020  0.029999   \n",
       "3229   madden nfl 07       gc           2006.0  Sports  0.479980  0.130005   \n",
       "3838   madden nfl 07      ps3           2006.0  Sports  0.469971  0.000000   \n",
       "4006   madden nfl 07      wii           2006.0  Sports  0.459961  0.000000   \n",
       "7372   madden nfl 07       ds           2006.0  Sports  0.199951  0.000000   \n",
       "14889  madden nfl 07      gba           2006.0  Sports  0.020004  0.010002   \n",
       "\n",
       "       jp_sales  other_sales  critic_score  user_score rating_age  \n",
       "241    0.010002     0.609863          84.0    8.203125          E  \n",
       "972    0.010002     0.130005          80.0    6.000000          E  \n",
       "2035   0.000000     0.029999          83.0    8.703125          E  \n",
       "2479   0.000000     0.040009          78.0    6.601562          E  \n",
       "3229   0.000000     0.020004          82.0    9.203125          E  \n",
       "3838   0.010002     0.040009          76.0    4.199219          E  \n",
       "4006   0.000000     0.040009          81.0    8.000000          E  \n",
       "7372   0.000000     0.020004          70.0    6.500000          E  \n",
       "14889  0.000000     0.000000          68.0    9.296875          E  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('name == \"madden nfl 07\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что хоть для платформы madden nfl 07 игры на всех платформах были выпущенны в один и тот же год, первая платформа, не содержит дупликатов по платформам, но при этом год выпуска разных платформ разный. Таким образом, мы не можем заменить дупликаты годом выпуска для других платформ, на которых представлена игра. \n",
    "\n",
    "Общее количество игр, представленных на более чем одной платформе:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2806"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_names.query('platform > 1'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\" frozen: olaf's quest\", '007: quantum of solace',\n",
       "       '007: the world is not enough', ..., 'zumba fitness',\n",
       "       'zumba fitness core', 'zumba fitness: world party'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#игры с более чем одной платформой:\n",
    "pl_mult = df_names.query('platform > 1')['name'].unique()\n",
    "pl_mult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Всего типов платформ в таблице: 31\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['wii', 'nes', 'gb', 'ds', 'x360', 'ps3', 'ps2', 'snes', 'gba',\n",
       "       'ps4', '3ds', 'n64', 'ps', 'xb', 'pc', '2600', 'psp', 'xone',\n",
       "       'wiiu', 'gc', 'gen', 'dc', 'psv', 'sat', 'scd', 'ws', 'ng', 'tg16',\n",
       "       '3do', 'gg', 'pcfx'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Всего типов платформ в таблице:', len(df['platform'].unique()))\n",
    "df['platform'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также, отметим, что нет каких-то дупликатов или ошибок в названиях платформ. \n",
    "\n",
    "Видно, что некоторые игры представлены в таблице более одного раза, таких - 2806. **Это может быть связано с тем, что одна игра представлена в нескольких типах платформ. Однако, возможны и дупликаты - одно и то же название, одна и та же платформа.** Чтобы это выяснить, сгруппируем данные по всем показателям и сравним колличество строк с колличеством строк в таблице df.query('name in @pl_mult')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007: quantum of solace</td>\n",
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       "      <th>1</th>\n",
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       "      <td>pc</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.300781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>ps3</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>T</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.509766</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.429932</td>\n",
       "      <td>0.189941</td>\n",
       "      <td>6.601562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>T</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.280029</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.290039</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>x360</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>T</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.509766</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.819824</td>\n",
       "      <td>0.140015</td>\n",
       "      <td>7.101562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5979</th>\n",
       "      <td>zumba fitness</td>\n",
       "      <td>x360</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>E</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.449951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.740234</td>\n",
       "      <td>0.180054</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5980</th>\n",
       "      <td>zumba fitness core</td>\n",
       "      <td>wii</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>E10+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.059998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5981</th>\n",
       "      <td>zumba fitness core</td>\n",
       "      <td>x360</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>E10+</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.049988</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.699219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5982</th>\n",
       "      <td>zumba fitness: world party</td>\n",
       "      <td>wii</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.109985</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5983</th>\n",
       "      <td>zumba fitness: world party</td>\n",
       "      <td>xone</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>E</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.049988</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.170044</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>6.199219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5984 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            name platform  year_of_release   genre rating_age  \\\n",
       "0         007: quantum of solace       ds           2008.0  Action          T   \n",
       "1         007: quantum of solace       pc           2008.0  Action          T   \n",
       "2         007: quantum of solace      ps3           2008.0  Action          T   \n",
       "3         007: quantum of solace      wii           2008.0  Action          T   \n",
       "4         007: quantum of solace     x360           2008.0  Action          T   \n",
       "...                          ...      ...              ...     ...        ...   \n",
       "5979               zumba fitness     x360           2010.0  Sports          E   \n",
       "5980          zumba fitness core      wii           2012.0    Misc       E10+   \n",
       "5981          zumba fitness core     x360           2012.0    Misc       E10+   \n",
       "5982  zumba fitness: world party      wii           2013.0    Misc          E   \n",
       "5983  zumba fitness: world party     xone           2013.0    Misc          E   \n",
       "\n",
       "      critic_score  eu_sales  jp_sales  na_sales  other_sales  user_score  \n",
       "0             65.0  0.010002  0.000000  0.109985     0.010002         NaN  \n",
       "1             70.0  0.010002  0.000000  0.010002     0.000000    6.300781  \n",
       "2             65.0  0.509766  0.020004  0.429932     0.189941    6.601562  \n",
       "3             54.0  0.280029  0.010002  0.290039     0.070007    7.500000  \n",
       "4             65.0  0.509766  0.010002  0.819824     0.140015    7.101562  \n",
       "...            ...       ...       ...       ...          ...         ...  \n",
       "5979          42.0  0.449951  0.000000  1.740234     0.180054    5.500000  \n",
       "5980           NaN  0.059998  0.000000  0.000000     0.010002         NaN  \n",
       "5981          77.0  0.049988  0.000000  0.000000     0.000000    6.699219  \n",
       "5982           NaN  0.099976  0.000000  0.109985     0.020004         NaN  \n",
       "5983          73.0  0.049988  0.000000  0.170044     0.020004    6.199219  \n",
       "\n",
       "[5984 rows x 11 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Таблица, в которой содержатся только данные по играм с более, чем одной платформой:\n",
    "duplicated_names = (df.query('name in @pl_mult')\n",
    "                    .pivot_table(index = ['name', 'platform', 'year_of_release', 'genre', 'rating_age'], \n",
    "                                                                  values = ['user_score', 'na_sales', 'eu_sales', \n",
    "                                                                            'jp_sales', 'other_sales', 'critic_score'], \n",
    "                                                                  aggfunc = 'mean').reset_index()\n",
    "                   )\n",
    "duplicated_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Её длина - 5968 строк (после группировки некоторые строки объединились (строки, для которых название, платформа, год выпуска, жанр, возрастная категория одинаковы: index = ['name', 'platform', 'year_of_release', 'genre', 'rating_age']). \n",
    "\n",
    "Длина же несгруппированных данных (те же игры с несколькими платормами): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7961"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.query('name in @pl_mult'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "То есть в результате группировки таблица потеряла строк:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1977"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.query('name in @pl_mult')) - len(duplicated_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Код ревьюера\n",
    "df.duplicated(['name', 'platform', 'year_of_release', 'genre']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получается, что 1997 названий в таблице - дупликаты. Поэтому можно взять среднее по показателям рейтинга и продаж. \n",
    "\n",
    "Сначала удалим срез данных с играми из таблицы duplicated_names, а затем добавим всю таблицу к df, таким образом мы получим данные, в которых дупликаты объеденены по медиане. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16715 entries, 0 to 16714\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   name             16715 non-null  object \n",
      " 1   platform         16715 non-null  object \n",
      " 2   year_of_release  16446 non-null  float64\n",
      " 3   genre            16713 non-null  object \n",
      " 4   na_sales         16715 non-null  float16\n",
      " 5   eu_sales         16715 non-null  float16\n",
      " 6   jp_sales         16715 non-null  float16\n",
      " 7   other_sales      16715 non-null  float16\n",
      " 8   critic_score     8137 non-null   float64\n",
      " 9   user_score       7590 non-null   float16\n",
      " 10  rating_age       9949 non-null   object \n",
      "dtypes: float16(5), float64(2), object(4)\n",
      "memory usage: 946.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8754 entries, 0 to 16712\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   name             8754 non-null   object \n",
      " 1   platform         8754 non-null   object \n",
      " 2   year_of_release  8631 non-null   float64\n",
      " 3   genre            8754 non-null   object \n",
      " 4   na_sales         8754 non-null   float16\n",
      " 5   eu_sales         8754 non-null   float16\n",
      " 6   jp_sales         8754 non-null   float16\n",
      " 7   other_sales      8754 non-null   float16\n",
      " 8   critic_score     3070 non-null   float64\n",
      " 9   user_score       2717 non-null   float16\n",
      " 10  rating_age       3841 non-null   object \n",
      "dtypes: float16(5), float64(2), object(4)\n",
      "memory usage: 564.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df = df.query('name not in @pl_mult')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16715"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7961 + 8754 \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь в нашей таблице нет строк с повторяющимися именами. Мы можем добавить таблицу duplicated_names, которая содержит средние значения по дупликатам в получившуюся df. Предыдущая ячейка - подсчет строк. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'platform', 'year_of_release', 'genre', 'na_sales', 'eu_sales',\n",
       "       'jp_sales', 'other_sales', 'critic_score', 'user_score', 'rating_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'platform', 'year_of_release', 'genre', 'rating_age',\n",
       "       'critic_score', 'eu_sales', 'jp_sales', 'na_sales', 'other_sales',\n",
       "       'user_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicated_names.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сделаем одинаковый порядок столбцов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicated_names = duplicated_names.reindex(columns = ['name', 'platform', 'year_of_release', \n",
    "                                                       'genre', 'na_sales', 'eu_sales','jp_sales', \n",
    "                                                       'other_sales', 'critic_score', 'user_score', 'rating_age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8754 entries, 0 to 16712\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   name             8754 non-null   object \n",
      " 1   platform         8754 non-null   object \n",
      " 2   year_of_release  8631 non-null   float64\n",
      " 3   genre            8754 non-null   object \n",
      " 4   na_sales         8754 non-null   float16\n",
      " 5   eu_sales         8754 non-null   float16\n",
      " 6   jp_sales         8754 non-null   float16\n",
      " 7   other_sales      8754 non-null   float16\n",
      " 8   critic_score     3070 non-null   float64\n",
      " 9   user_score       2717 non-null   float16\n",
      " 10  rating_age       3841 non-null   object \n",
      "dtypes: float16(5), float64(2), object(4)\n",
      "memory usage: 564.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5984 entries, 0 to 5983\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   name             5984 non-null   object \n",
      " 1   platform         5984 non-null   object \n",
      " 2   year_of_release  5984 non-null   float64\n",
      " 3   genre            5984 non-null   object \n",
      " 4   na_sales         5984 non-null   float16\n",
      " 5   eu_sales         5984 non-null   float16\n",
      " 6   jp_sales         5984 non-null   float16\n",
      " 7   other_sales      5984 non-null   float16\n",
      " 8   critic_score     4921 non-null   float64\n",
      " 9   user_score       4741 non-null   float16\n",
      " 10  rating_age       5984 non-null   object \n",
      "dtypes: float16(5), float64(2), object(4)\n",
      "memory usage: 339.1+ KB\n"
     ]
    }
   ],
   "source": [
    "display(df.info())\n",
    "duplicated_names.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14738 entries, 0 to 14737\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   name             14738 non-null  object \n",
      " 1   platform         14738 non-null  object \n",
      " 2   year_of_release  14615 non-null  float64\n",
      " 3   genre            14738 non-null  object \n",
      " 4   na_sales         14738 non-null  float16\n",
      " 5   eu_sales         14738 non-null  float16\n",
      " 6   jp_sales         14738 non-null  float16\n",
      " 7   other_sales      14738 non-null  float16\n",
      " 8   critic_score     7991 non-null   float64\n",
      " 9   user_score       7458 non-null   float16\n",
      " 10  rating_age       9825 non-null   object \n",
      "dtypes: float16(5), float64(2), object(4)\n",
      "memory usage: 834.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.reset_index(drop=True, inplace=True)\n",
    "duplicated_names.reset_index(drop=True, inplace=True)\n",
    "df = pd.concat([duplicated_names, df], axis=0, ignore_index=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "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_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>ds</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.109985</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>pc</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.300781</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>ps3</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.429932</td>\n",
       "      <td>0.509766</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.189941</td>\n",
       "      <td>65.0</td>\n",
       "      <td>6.601562</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.290039</td>\n",
       "      <td>0.280029</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>54.0</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>x360</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.819824</td>\n",
       "      <td>0.509766</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.140015</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7.101562</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14733</th>\n",
       "      <td>15 days</td>\n",
       "      <td>pc</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>5.800781</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14734</th>\n",
       "      <td>aiyoku no eustia</td>\n",
       "      <td>psv</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14735</th>\n",
       "      <td>woody woodpecker in crazy castle 5</td>\n",
       "      <td>gba</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14736</th>\n",
       "      <td>lma manager 2007</td>\n",
       "      <td>x360</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14737</th>\n",
       "      <td>haitaka no psychedelica</td>\n",
       "      <td>psv</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14738 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     name platform  year_of_release  \\\n",
       "0                  007: quantum of solace       ds           2008.0   \n",
       "1                  007: quantum of solace       pc           2008.0   \n",
       "2                  007: quantum of solace      ps3           2008.0   \n",
       "3                  007: quantum of solace      wii           2008.0   \n",
       "4                  007: quantum of solace     x360           2008.0   \n",
       "...                                   ...      ...              ...   \n",
       "14733                             15 days       pc           2009.0   \n",
       "14734                    aiyoku no eustia      psv           2014.0   \n",
       "14735  woody woodpecker in crazy castle 5      gba           2002.0   \n",
       "14736                    lma manager 2007     x360           2006.0   \n",
       "14737             haitaka no psychedelica      psv           2016.0   \n",
       "\n",
       "           genre  na_sales  eu_sales  jp_sales  other_sales  critic_score  \\\n",
       "0         Action  0.109985  0.010002  0.000000     0.010002          65.0   \n",
       "1         Action  0.010002  0.010002  0.000000     0.000000          70.0   \n",
       "2         Action  0.429932  0.509766  0.020004     0.189941          65.0   \n",
       "3         Action  0.290039  0.280029  0.010002     0.070007          54.0   \n",
       "4         Action  0.819824  0.509766  0.010002     0.140015          65.0   \n",
       "...          ...       ...       ...       ...          ...           ...   \n",
       "14733  Adventure  0.000000  0.010002  0.000000     0.000000          63.0   \n",
       "14734       Misc  0.000000  0.000000  0.010002     0.000000           NaN   \n",
       "14735   Platform  0.010002  0.000000  0.000000     0.000000           NaN   \n",
       "14736     Sports  0.000000  0.010002  0.000000     0.000000           NaN   \n",
       "14737  Adventure  0.000000  0.000000  0.010002     0.000000           NaN   \n",
       "\n",
       "       user_score rating_age  \n",
       "0             NaN          T  \n",
       "1        6.300781          T  \n",
       "2        6.601562          T  \n",
       "3        7.500000          T  \n",
       "4        7.101562          T  \n",
       "...           ...        ...  \n",
       "14733    5.800781        NaN  \n",
       "14734         NaN        NaN  \n",
       "14735         NaN        NaN  \n",
       "14736         NaN        NaN  \n",
       "14737         NaN        NaN  \n",
       "\n",
       "[14738 rows x 11 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Таким образом, мы получили таблицу, в которой нет дупликатов по именам и типам платформы.**\n",
    "\n",
    "Пропуски в столбцах user_score, critic_score и rating_age заменять не стоит, так как они несут смысловую нагрузку. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14738 entries, 0 to 14737\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   name             14738 non-null  object \n",
      " 1   platform         14738 non-null  object \n",
      " 2   year_of_release  14615 non-null  float64\n",
      " 3   genre            14738 non-null  object \n",
      " 4   na_sales         14738 non-null  float16\n",
      " 5   eu_sales         14738 non-null  float16\n",
      " 6   jp_sales         14738 non-null  float16\n",
      " 7   other_sales      14738 non-null  float16\n",
      " 8   critic_score     7991 non-null   float64\n",
      " 9   user_score       7458 non-null   float16\n",
      " 10  rating_age       9825 non-null   object \n",
      "dtypes: float16(5), float64(2), object(4)\n",
      "memory usage: 834.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['critic_score'] = df['critic_score'].astype('float16')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Некоторые игры имеют разные годы выпуска для разных платформ, поэтому мы не можем заменить пропуски в столбце с датой выхода.\n",
    "\n",
    "В завершение, посчитаем суммарные продажи во всех регионах:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
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       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>ds</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.109985</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>pc</td>\n",
       "      <td>2008.0</td>\n",
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       "      <td>0.010002</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.300781</td>\n",
       "      <td>T</td>\n",
       "      <td>0.020004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>ps3</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.429932</td>\n",
       "      <td>0.509766</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.189941</td>\n",
       "      <td>65.0</td>\n",
       "      <td>6.601562</td>\n",
       "      <td>T</td>\n",
       "      <td>1.149414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.290039</td>\n",
       "      <td>0.280029</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>54.0</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>T</td>\n",
       "      <td>0.649902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>x360</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.819824</td>\n",
       "      <td>0.509766</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.140015</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7.101562</td>\n",
       "      <td>T</td>\n",
       "      <td>1.479492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14733</th>\n",
       "      <td>15 days</td>\n",
       "      <td>pc</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>5.800781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14734</th>\n",
       "      <td>aiyoku no eustia</td>\n",
       "      <td>psv</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14735</th>\n",
       "      <td>woody woodpecker in crazy castle 5</td>\n",
       "      <td>gba</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14736</th>\n",
       "      <td>lma manager 2007</td>\n",
       "      <td>x360</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14737</th>\n",
       "      <td>haitaka no psychedelica</td>\n",
       "      <td>psv</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14738 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     name platform  year_of_release  \\\n",
       "0                  007: quantum of solace       ds           2008.0   \n",
       "1                  007: quantum of solace       pc           2008.0   \n",
       "2                  007: quantum of solace      ps3           2008.0   \n",
       "3                  007: quantum of solace      wii           2008.0   \n",
       "4                  007: quantum of solace     x360           2008.0   \n",
       "...                                   ...      ...              ...   \n",
       "14733                             15 days       pc           2009.0   \n",
       "14734                    aiyoku no eustia      psv           2014.0   \n",
       "14735  woody woodpecker in crazy castle 5      gba           2002.0   \n",
       "14736                    lma manager 2007     x360           2006.0   \n",
       "14737             haitaka no psychedelica      psv           2016.0   \n",
       "\n",
       "           genre  na_sales  eu_sales  jp_sales  other_sales  critic_score  \\\n",
       "0         Action  0.109985  0.010002  0.000000     0.010002          65.0   \n",
       "1         Action  0.010002  0.010002  0.000000     0.000000          70.0   \n",
       "2         Action  0.429932  0.509766  0.020004     0.189941          65.0   \n",
       "3         Action  0.290039  0.280029  0.010002     0.070007          54.0   \n",
       "4         Action  0.819824  0.509766  0.010002     0.140015          65.0   \n",
       "...          ...       ...       ...       ...          ...           ...   \n",
       "14733  Adventure  0.000000  0.010002  0.000000     0.000000          63.0   \n",
       "14734       Misc  0.000000  0.000000  0.010002     0.000000           NaN   \n",
       "14735   Platform  0.010002  0.000000  0.000000     0.000000           NaN   \n",
       "14736     Sports  0.000000  0.010002  0.000000     0.000000           NaN   \n",
       "14737  Adventure  0.000000  0.000000  0.010002     0.000000           NaN   \n",
       "\n",
       "       user_score rating_age  total_sales  \n",
       "0             NaN          T     0.130005  \n",
       "1        6.300781          T     0.020004  \n",
       "2        6.601562          T     1.149414  \n",
       "3        7.500000          T     0.649902  \n",
       "4        7.101562          T     1.479492  \n",
       "...           ...        ...          ...  \n",
       "14733    5.800781        NaN     0.010002  \n",
       "14734         NaN        NaN     0.010002  \n",
       "14735         NaN        NaN     0.010002  \n",
       "14736         NaN        NaN     0.010002  \n",
       "14737         NaN        NaN     0.010002  \n",
       "\n",
       "[14738 rows x 12 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['total_sales'] = df['na_sales'] + df['eu_sales'] + df['jp_sales'] + df['other_sales']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Исследовательский анализ данных<a id='analysisid'></a>\n",
    "Посмотрим, сколько игр выпускалось в разные годы и важны ли данные а все периоды. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>year_of_release</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988.0</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984.0</th>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>1989.0</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985.0</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983.0</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986.0</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991.0</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981.0</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993.0</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994.0</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995.0</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996.0</th>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999.0</th>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000.0</th>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998.0</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016.0</th>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013.0</th>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014.0</th>\n",
       "      <td>453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001.0</th>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015.0</th>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003.0</th>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004.0</th>\n",
       "      <td>723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005.0</th>\n",
       "      <td>872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006.0</th>\n",
       "      <td>952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011.0</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010.0</th>\n",
       "      <td>1133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008.0</th>\n",
       "      <td>1312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009.0</th>\n",
       "      <td>1324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name\n",
       "year_of_release      \n",
       "1980.0              4\n",
       "1988.0              9\n",
       "1984.0             10\n",
       "1989.0             10\n",
       "1985.0             11\n",
       "1990.0             13\n",
       "1983.0             13\n",
       "1987.0             14\n",
       "1986.0             18\n",
       "1982.0             30\n",
       "1992.0             31\n",
       "1991.0             33\n",
       "1981.0             42\n",
       "1993.0             55\n",
       "1994.0             94\n",
       "1995.0            189\n",
       "1996.0            228\n",
       "1997.0            243\n",
       "1999.0            268\n",
       "2000.0            304\n",
       "1998.0            309\n",
       "2016.0            394\n",
       "2013.0            438\n",
       "2014.0            453\n",
       "2001.0            462\n",
       "2015.0            467\n",
       "2012.0            534\n",
       "2003.0            710\n",
       "2004.0            723\n",
       "2002.0            783\n",
       "2005.0            872\n",
       "2006.0            952\n",
       "2011.0           1000\n",
       "2007.0           1130\n",
       "2010.0           1133\n",
       "2008.0           1312\n",
       "2009.0           1324"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_years = df.pivot_table(index = 'year_of_release', values = 'name', aggfunc = 'count')\n",
    "df_years.sort_values(by = 'name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_release</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981.0</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983.0</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984.0</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985.0</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986.0</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988.0</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989.0</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991.0</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993.0</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994.0</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995.0</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996.0</th>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998.0</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999.0</th>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000.0</th>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001.0</th>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003.0</th>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004.0</th>\n",
       "      <td>723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005.0</th>\n",
       "      <td>872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006.0</th>\n",
       "      <td>952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008.0</th>\n",
       "      <td>1312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009.0</th>\n",
       "      <td>1324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010.0</th>\n",
       "      <td>1133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011.0</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013.0</th>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014.0</th>\n",
       "      <td>453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015.0</th>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016.0</th>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name\n",
       "year_of_release      \n",
       "1980.0              4\n",
       "1981.0             42\n",
       "1982.0             30\n",
       "1983.0             13\n",
       "1984.0             10\n",
       "1985.0             11\n",
       "1986.0             18\n",
       "1987.0             14\n",
       "1988.0              9\n",
       "1989.0             10\n",
       "1990.0             13\n",
       "1991.0             33\n",
       "1992.0             31\n",
       "1993.0             55\n",
       "1994.0             94\n",
       "1995.0            189\n",
       "1996.0            228\n",
       "1997.0            243\n",
       "1998.0            309\n",
       "1999.0            268\n",
       "2000.0            304\n",
       "2001.0            462\n",
       "2002.0            783\n",
       "2003.0            710\n",
       "2004.0            723\n",
       "2005.0            872\n",
       "2006.0            952\n",
       "2007.0           1130\n",
       "2008.0           1312\n",
       "2009.0           1324\n",
       "2010.0           1133\n",
       "2011.0           1000\n",
       "2012.0            534\n",
       "2013.0            438\n",
       "2014.0            453\n",
       "2015.0            467\n",
       "2016.0            394"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_years.sort_values(by = 'year_of_release')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что до 1993 выпускалось в год менее 50 игр. Такой размер выборки не даст нам надежной информации, поэтому возможно, стоит убрать из рассчета данную информацию. Посмотрим, сколько % от общей таблицы составляют данные за период до 1993. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Процент игр выпущенных до 1993 года: 1.61%\n"
     ]
    }
   ],
   "source": [
    "print('Процент игр выпущенных до 1993 года: {:.2%}'.format(len(df[df['year_of_release'] < 1993])/len(df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таких данных очень мало, удалим строки с информацией об играх, выпущенных до 1993 года."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.query('year_of_release >= 1993')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Далее посмотрим, менялись ли продажи по платформам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>12</th>\n",
       "      <td>pcfx</td>\n",
       "      <td>0.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3do</td>\n",
       "      <td>0.099976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>tg16</td>\n",
       "      <td>0.160034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ws</td>\n",
       "      <td>0.660156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ng</td>\n",
       "      <td>1.110352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>scd</td>\n",
       "      <td>1.860352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nes</td>\n",
       "      <td>3.720703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gen</td>\n",
       "      <td>4.671875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dc</td>\n",
       "      <td>13.328125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sat</td>\n",
       "      <td>25.796875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>psv</td>\n",
       "      <td>41.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>wiiu</td>\n",
       "      <td>69.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>snes</td>\n",
       "      <td>91.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>xone</td>\n",
       "      <td>134.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gb</td>\n",
       "      <td>134.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>n64</td>\n",
       "      <td>150.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gc</td>\n",
       "      <td>185.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pc</td>\n",
       "      <td>226.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3ds</td>\n",
       "      <td>228.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>xb</td>\n",
       "      <td>238.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ps4</td>\n",
       "      <td>249.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>psp</td>\n",
       "      <td>269.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gba</td>\n",
       "      <td>288.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ps</td>\n",
       "      <td>609.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ds</td>\n",
       "      <td>753.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>wii</td>\n",
       "      <td>834.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ps3</td>\n",
       "      <td>863.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>x360</td>\n",
       "      <td>910.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ps2</td>\n",
       "      <td>1163.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   platform  total_sales\n",
       "12     pcfx     0.029999\n",
       "0       3do     0.099976\n",
       "22     tg16     0.160034\n",
       "25       ws     0.660156\n",
       "10       ng     1.110352\n",
       "20      scd     1.860352\n",
       "9       nes     3.720703\n",
       "7       gen     4.671875\n",
       "2        dc    13.328125\n",
       "19      sat    25.796875\n",
       "18      psv    41.656250\n",
       "24     wiiu    69.437500\n",
       "21     snes    91.125000\n",
       "28     xone   134.375000\n",
       "4        gb   134.500000\n",
       "8       n64   150.875000\n",
       "6        gc   185.250000\n",
       "11       pc   226.875000\n",
       "1       3ds   228.750000\n",
       "27       xb   238.500000\n",
       "16      ps4   249.375000\n",
       "17      psp   269.000000\n",
       "5       gba   288.250000\n",
       "13       ps   609.000000\n",
       "3        ds   753.500000\n",
       "23      wii   834.000000\n",
       "15      ps3   863.500000\n",
       "26     x360   910.000000\n",
       "14      ps2  1163.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_platforms_sales = df.pivot_table(index = 'platform', values = ['total_sales'], aggfunc = 'sum').reset_index()\n",
    "df_platforms_sales.sort_values(by = 'total_sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Больше всего продавалось игр на таких платформах как:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0      ps2\n",
       "1     x360\n",
       "2      ps3\n",
       "3      wii\n",
       "4       ds\n",
       "5       ps\n",
       "6      gba\n",
       "7      psp\n",
       "8      ps4\n",
       "9       xb\n",
       "10     3ds\n",
       "11      pc\n",
       "12      gc\n",
       "13     n64\n",
       "14      gb\n",
       "15    xone\n",
       "Name: platform, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Больше всего продавалось игр на таких платформах как:') \n",
    "df_platforms_sales.sort_values(by = 'total_sales', ascending = False).query('total_sales > 100')['platform'].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проанализируем данные по платформам и годам, чтобы посмотреть, есть ли связь между годами выпуска и колличеством продаж игр на разных платформах. Для этого возьмем платформы с суммарных колличеством продаж более 300. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pl = df_platforms_sales.query('total_sales > 100')['platform'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_platform_year = df.query('platform in @pl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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_age</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>ds</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.109985</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>0.130005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>pc</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.300781</td>\n",
       "      <td>T</td>\n",
       "      <td>0.020004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>ps3</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.429932</td>\n",
       "      <td>0.509766</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.189941</td>\n",
       "      <td>65.0</td>\n",
       "      <td>6.601562</td>\n",
       "      <td>T</td>\n",
       "      <td>1.149414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.290039</td>\n",
       "      <td>0.280029</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>54.0</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>T</td>\n",
       "      <td>0.649902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>007: quantum of solace</td>\n",
       "      <td>x360</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.819824</td>\n",
       "      <td>0.509766</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.140015</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7.101562</td>\n",
       "      <td>T</td>\n",
       "      <td>1.479492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14730</th>\n",
       "      <td>storm: frontline nation</td>\n",
       "      <td>pc</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Strategy</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.199219</td>\n",
       "      <td>E10+</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14732</th>\n",
       "      <td>plushees</td>\n",
       "      <td>ds</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14733</th>\n",
       "      <td>15 days</td>\n",
       "      <td>pc</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>5.800781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14735</th>\n",
       "      <td>woody woodpecker in crazy castle 5</td>\n",
       "      <td>gba</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14736</th>\n",
       "      <td>lma manager 2007</td>\n",
       "      <td>x360</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13546 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     name platform  year_of_release  \\\n",
       "0                  007: quantum of solace       ds           2008.0   \n",
       "1                  007: quantum of solace       pc           2008.0   \n",
       "2                  007: quantum of solace      ps3           2008.0   \n",
       "3                  007: quantum of solace      wii           2008.0   \n",
       "4                  007: quantum of solace     x360           2008.0   \n",
       "...                                   ...      ...              ...   \n",
       "14730             storm: frontline nation       pc           2011.0   \n",
       "14732                            plushees       ds           2008.0   \n",
       "14733                             15 days       pc           2009.0   \n",
       "14735  woody woodpecker in crazy castle 5      gba           2002.0   \n",
       "14736                    lma manager 2007     x360           2006.0   \n",
       "\n",
       "            genre  na_sales  eu_sales  jp_sales  other_sales  critic_score  \\\n",
       "0          Action  0.109985  0.010002  0.000000     0.010002          65.0   \n",
       "1          Action  0.010002  0.010002  0.000000     0.000000          70.0   \n",
       "2          Action  0.429932  0.509766  0.020004     0.189941          65.0   \n",
       "3          Action  0.290039  0.280029  0.010002     0.070007          54.0   \n",
       "4          Action  0.819824  0.509766  0.010002     0.140015          65.0   \n",
       "...           ...       ...       ...       ...          ...           ...   \n",
       "14730    Strategy  0.000000  0.010002  0.000000     0.000000          60.0   \n",
       "14732  Simulation  0.010002  0.000000  0.000000     0.000000           NaN   \n",
       "14733   Adventure  0.000000  0.010002  0.000000     0.000000          63.0   \n",
       "14735    Platform  0.010002  0.000000  0.000000     0.000000           NaN   \n",
       "14736      Sports  0.000000  0.010002  0.000000     0.000000           NaN   \n",
       "\n",
       "       user_score rating_age  total_sales  \n",
       "0             NaN          T     0.130005  \n",
       "1        6.300781          T     0.020004  \n",
       "2        6.601562          T     1.149414  \n",
       "3        7.500000          T     0.649902  \n",
       "4        7.101562          T     1.479492  \n",
       "...           ...        ...          ...  \n",
       "14730    7.199219       E10+     0.010002  \n",
       "14732         NaN          E     0.010002  \n",
       "14733    5.800781        NaN     0.010002  \n",
       "14735         NaN        NaN     0.010002  \n",
       "14736         NaN        NaN     0.010002  \n",
       "\n",
       "[13546 rows x 12 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_platform_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "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>year_of_release</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3ds</td>\n",
       "      <td>2011</td>\n",
       "      <td>54.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3ds</td>\n",
       "      <td>2012</td>\n",
       "      <td>48.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3ds</td>\n",
       "      <td>2013</td>\n",
       "      <td>50.156250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3ds</td>\n",
       "      <td>2014</td>\n",
       "      <td>34.406250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3ds</td>\n",
       "      <td>2015</td>\n",
       "      <td>26.921875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>xb</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.180054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>xone</td>\n",
       "      <td>2013</td>\n",
       "      <td>18.593750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>xone</td>\n",
       "      <td>2014</td>\n",
       "      <td>47.093750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>xone</td>\n",
       "      <td>2015</td>\n",
       "      <td>44.906250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>xone</td>\n",
       "      <td>2016</td>\n",
       "      <td>23.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    platform  year_of_release  total_sales\n",
       "0        3ds             2011    54.750000\n",
       "1        3ds             2012    48.375000\n",
       "2        3ds             2013    50.156250\n",
       "3        3ds             2014    34.406250\n",
       "4        3ds             2015    26.921875\n",
       "..       ...              ...          ...\n",
       "148       xb             2008     0.180054\n",
       "149     xone             2013    18.593750\n",
       "150     xone             2014    47.093750\n",
       "151     xone             2015    44.906250\n",
       "152     xone             2016    23.750000\n",
       "\n",
       "[153 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_platform_gr = df_platform_year.pivot_table(index = ['platform', 'year_of_release'], values = 'total_sales', \n",
    "                                                aggfunc = 'sum').reset_index()\n",
    "df_platform_gr['year_of_release'] = df_platform_gr['year_of_release'].astype('int16')\n",
    "df_platform_gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#В цикле сделаем срез по каждому названию и построим отдельные графики:\n",
    "a = 4  # number of rows\n",
    "b = 4  # number of columns\n",
    "c = 1  # initialize plot counter\n",
    "fig = plt.figure(figsize=(20,15))\n",
    "fig.subplots_adjust(hspace=0.8, wspace=0.4)\n",
    "\n",
    "\n",
    "for name in df_platform_gr['platform'].unique():\n",
    "    plt.subplot(a, b, c)\n",
    "    plt.title(name)\n",
    "    ax = sns.barplot(x=\"year_of_release\", y=\"total_sales\",\n",
    "                        data=df_platform_gr[df_platform_gr['platform'] == name])\n",
    "    ax.set_xlabel('Год')\n",
    "    ax.set_ylabel('Mлн. копий')\n",
    "    plt.xticks(rotation=90)\n",
    "    c = c + 1\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year_of_release    9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_games = (df_platform_gr.pivot_table(index = 'platform', values = 'year_of_release', aggfunc = 'max')\n",
    "             .reset_index().query('year_of_release < 2016')['platform'].unique()\n",
    "            )\n",
    "games = (df_platform_gr.query('platform in @old_games')\n",
    "         .pivot_table(index = 'platform', values = 'year_of_release', aggfunc = 'count')\n",
    "        )\n",
    "games.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Исходя из графика и своной таблицы, мы видим, что в среднем новые платформы появляются и исчезают в течение около 9 лет. Пик наступает по-разному в зависимости от платформы, и скорее всего в зависимости от рекламной кампании. Пик продаж наступает через 1-5 лет, поэтому нам интересен период за последние 5 лет, за который мы проанализируем поведение популярности и продаваемости игр по платформам и определим наиболее перспективные.\n",
    "\n",
    "Возьмем срез данных начиная с 2010 года."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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_age</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2010 fifa world cup south africa</td>\n",
       "      <td>ps3</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>0.640137</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>0.219971</td>\n",
       "      <td>82.0</td>\n",
       "      <td>7.800781</td>\n",
       "      <td>E</td>\n",
       "      <td>1.230469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2010 fifa world cup south africa</td>\n",
       "      <td>psp</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.090027</td>\n",
       "      <td>0.219971</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.119995</td>\n",
       "      <td>69.0</td>\n",
       "      <td>7.398438</td>\n",
       "      <td>E</td>\n",
       "      <td>0.459961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2010 fifa world cup south africa</td>\n",
       "      <td>wii</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.229980</td>\n",
       "      <td>0.150024</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.040009</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.699219</td>\n",
       "      <td>E</td>\n",
       "      <td>0.429932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2010 fifa world cup south africa</td>\n",
       "      <td>x360</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.320068</td>\n",
       "      <td>0.419922</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.090027</td>\n",
       "      <td>83.0</td>\n",
       "      <td>8.101562</td>\n",
       "      <td>E</td>\n",
       "      <td>0.850098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014 fifa world cup brazil</td>\n",
       "      <td>ps3</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.150024</td>\n",
       "      <td>0.360107</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.090027</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4.300781</td>\n",
       "      <td>E</td>\n",
       "      <td>0.609863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14727</th>\n",
       "      <td>the longest 5 minutes</td>\n",
       "      <td>psv</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14730</th>\n",
       "      <td>storm: frontline nation</td>\n",
       "      <td>pc</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Strategy</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.199219</td>\n",
       "      <td>E10+</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14731</th>\n",
       "      <td>strawberry nauts</td>\n",
       "      <td>psv</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14734</th>\n",
       "      <td>aiyoku no eustia</td>\n",
       "      <td>psv</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14737</th>\n",
       "      <td>haitaka no psychedelica</td>\n",
       "      <td>psv</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4419 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name platform  year_of_release      genre  \\\n",
       "13     2010 fifa world cup south africa      ps3           2010.0     Sports   \n",
       "14     2010 fifa world cup south africa      psp           2010.0     Sports   \n",
       "15     2010 fifa world cup south africa      wii           2010.0     Sports   \n",
       "16     2010 fifa world cup south africa     x360           2010.0     Sports   \n",
       "17           2014 fifa world cup brazil      ps3           2014.0     Sports   \n",
       "...                                 ...      ...              ...        ...   \n",
       "14727             the longest 5 minutes      psv           2016.0     Action   \n",
       "14730           storm: frontline nation       pc           2011.0   Strategy   \n",
       "14731                  strawberry nauts      psv           2016.0  Adventure   \n",
       "14734                  aiyoku no eustia      psv           2014.0       Misc   \n",
       "14737           haitaka no psychedelica      psv           2016.0  Adventure   \n",
       "\n",
       "       na_sales  eu_sales  jp_sales  other_sales  critic_score  user_score  \\\n",
       "13     0.300049  0.640137  0.070007     0.219971          82.0    7.800781   \n",
       "14     0.090027  0.219971  0.029999     0.119995          69.0    7.398438   \n",
       "15     0.229980  0.150024  0.010002     0.040009          70.0    6.699219   \n",
       "16     0.320068  0.419922  0.020004     0.090027          83.0    8.101562   \n",
       "17     0.150024  0.360107  0.010002     0.090027          73.0    4.300781   \n",
       "...         ...       ...       ...          ...           ...         ...   \n",
       "14727  0.000000  0.000000  0.010002     0.000000           NaN         NaN   \n",
       "14730  0.000000  0.010002  0.000000     0.000000          60.0    7.199219   \n",
       "14731  0.000000  0.000000  0.010002     0.000000           NaN         NaN   \n",
       "14734  0.000000  0.000000  0.010002     0.000000           NaN         NaN   \n",
       "14737  0.000000  0.000000  0.010002     0.000000           NaN         NaN   \n",
       "\n",
       "      rating_age  total_sales  \n",
       "13             E     1.230469  \n",
       "14             E     0.459961  \n",
       "15             E     0.429932  \n",
       "16             E     0.850098  \n",
       "17             E     0.609863  \n",
       "...          ...          ...  \n",
       "14727        NaN     0.010002  \n",
       "14730       E10+     0.010002  \n",
       "14731        NaN     0.010002  \n",
       "14734        NaN     0.010002  \n",
       "14737        NaN     0.010002  \n",
       "\n",
       "[4419 rows x 12 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual = df.query('year_of_release >= 2010')\n",
    "df_actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "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>year_of_release</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3ds</td>\n",
       "      <td>2011</td>\n",
       "      <td>54.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3ds</td>\n",
       "      <td>2012</td>\n",
       "      <td>48.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3ds</td>\n",
       "      <td>2013</td>\n",
       "      <td>50.156250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3ds</td>\n",
       "      <td>2014</td>\n",
       "      <td>34.406250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3ds</td>\n",
       "      <td>2015</td>\n",
       "      <td>26.921875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>x360</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.490234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>xone</td>\n",
       "      <td>2013</td>\n",
       "      <td>18.593750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>xone</td>\n",
       "      <td>2014</td>\n",
       "      <td>47.093750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>xone</td>\n",
       "      <td>2015</td>\n",
       "      <td>44.906250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>xone</td>\n",
       "      <td>2016</td>\n",
       "      <td>23.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   platform  year_of_release  total_sales\n",
       "0       3ds             2011    54.750000\n",
       "1       3ds             2012    48.375000\n",
       "2       3ds             2013    50.156250\n",
       "3       3ds             2014    34.406250\n",
       "4       3ds             2015    26.921875\n",
       "..      ...              ...          ...\n",
       "60     x360             2016     1.490234\n",
       "61     xone             2013    18.593750\n",
       "62     xone             2014    47.093750\n",
       "63     xone             2015    44.906250\n",
       "64     xone             2016    23.750000\n",
       "\n",
       "[65 rows x 3 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual_gr = df_actual.pivot_table(index = ['platform', 'year_of_release'], values = 'total_sales', aggfunc = 'sum').reset_index()\n",
    "df_actual_gr['year_of_release'] = df_actual_gr['year_of_release'].astype('int16')\n",
    "df_actual_gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_actual_gr['platform'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ps3', 'psp', 'wii', 'x360', 'ps4', 'xone', 'pc', '3ds', 'psv',\n",
       "       'ds', 'wiiu', 'ps2'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actual['platform'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на поведение параметра продаж по отдельным платформам:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#В цикле сделаем срез по каждому названию и построим отдельные графики:\n",
    "a = 4  # number of rows\n",
    "b = 3  # number of columns\n",
    "c = 1  # initialize plot counter\n",
    "fig = plt.figure(figsize=(20,15))\n",
    "fig.subplots_adjust(hspace=0.6, wspace=0.4)\n",
    "\n",
    "\n",
    "for name in df_actual_gr['platform'].unique():\n",
    "    plt.subplot(a, b, c)\n",
    "    plt.title(name)\n",
    "    ax = sns.barplot(x=\"year_of_release\", y=\"total_sales\",\n",
    "                        data=df_actual_gr[df_actual_gr['platform'] == name])\n",
    "    ax.set_xlabel('Год')\n",
    "    ax.set_ylabel('Сумма продаж, млн. копий')\n",
    "    c = c + 1\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Суммарные продажи на платформах за 2016 год."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>platform</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ps4</td>\n",
       "      <td>60.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>xone</td>\n",
       "      <td>23.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3ds</td>\n",
       "      <td>14.164062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pc</td>\n",
       "      <td>4.890625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>wiiu</td>\n",
       "      <td>3.589844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>psv</td>\n",
       "      <td>2.720703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ps3</td>\n",
       "      <td>2.109375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>x360</td>\n",
       "      <td>1.490234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>wii</td>\n",
       "      <td>0.179932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  platform  total_sales\n",
       "3      ps4    60.250000\n",
       "8     xone    23.750000\n",
       "0      3ds    14.164062\n",
       "1       pc     4.890625\n",
       "6     wiiu     3.589844\n",
       "4      psv     2.720703\n",
       "2      ps3     2.109375\n",
       "7     x360     1.490234\n",
       "5      wii     0.179932"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('year_of_release == 2016').pivot_table(index = 'platform', values = 'total_sales', aggfunc = 'sum').reset_index().sort_values(by = 'total_sales', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Исходя из графиков, мы видим, что среди платформ нет таких, которые имели бы пик продаж в 2016 году. Поэтому, следует взять такие, у которых в 2016 году уровень продаж находится на достаточно высоком уровне, а также не наблюдалось сильного спада.\n",
    "\n",
    "Из 12 топовых по продажам платформ с данными начиная с 2012 года, выделим ps4, xone, 3ds, psv и wiiu как потециально прибыльные, платформа pc хоть и показала высокие продажи за 2016 год, для нее наблюдается сильный спад продаж."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "five = ['3ds', 'ps4', 'psv', 'wiiu', 'xone']\n",
    "df_five = df_actual.query('platform in @five')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <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_age</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7 days to die</td>\n",
       "      <td>ps4</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.040009</td>\n",
       "      <td>0.080017</td>\n",
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       "      <td>45.0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7 days to die</td>\n",
       "      <td>xone</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.199219</td>\n",
       "      <td>M</td>\n",
       "      <td>0.049988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>adventure time: the secret of the nameless kin...</td>\n",
       "      <td>3ds</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.601562</td>\n",
       "      <td>E10+</td>\n",
       "      <td>0.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>aegis of earth: protonovus assault</td>\n",
       "      <td>ps4</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7.800781</td>\n",
       "      <td>T</td>\n",
       "      <td>0.020004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>aegis of earth: protonovus assault</td>\n",
       "      <td>psv</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.296875</td>\n",
       "      <td>T</td>\n",
       "      <td>0.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14719</th>\n",
       "      <td>outdoors unleashed: africa 3d</td>\n",
       "      <td>3ds</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14727</th>\n",
       "      <td>the longest 5 minutes</td>\n",
       "      <td>psv</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14731</th>\n",
       "      <td>strawberry nauts</td>\n",
       "      <td>psv</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14734</th>\n",
       "      <td>aiyoku no eustia</td>\n",
       "      <td>psv</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14737</th>\n",
       "      <td>haitaka no psychedelica</td>\n",
       "      <td>psv</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1365 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    name platform  \\\n",
       "28                                         7 days to die      ps4   \n",
       "29                                         7 days to die     xone   \n",
       "45     adventure time: the secret of the nameless kin...      3ds   \n",
       "48                    aegis of earth: protonovus assault      ps4   \n",
       "49                    aegis of earth: protonovus assault      psv   \n",
       "...                                                  ...      ...   \n",
       "14719                      outdoors unleashed: africa 3d      3ds   \n",
       "14727                              the longest 5 minutes      psv   \n",
       "14731                                   strawberry nauts      psv   \n",
       "14734                                   aiyoku no eustia      psv   \n",
       "14737                            haitaka no psychedelica      psv   \n",
       "\n",
       "       year_of_release      genre  na_sales  eu_sales  jp_sales  other_sales  \\\n",
       "28              2016.0     Action  0.040009  0.080017  0.000000     0.020004   \n",
       "29              2016.0     Action  0.029999  0.020004  0.000000     0.000000   \n",
       "45              2014.0     Action  0.000000  0.070007  0.000000     0.000000   \n",
       "48              2016.0     Action  0.020004  0.000000  0.000000     0.000000   \n",
       "49              2016.0     Action  0.010002  0.010002  0.000000     0.010002   \n",
       "...                ...        ...       ...       ...       ...          ...   \n",
       "14719           2011.0     Sports  0.010002  0.000000  0.000000     0.000000   \n",
       "14727           2016.0     Action  0.000000  0.000000  0.010002     0.000000   \n",
       "14731           2016.0  Adventure  0.000000  0.000000  0.010002     0.000000   \n",
       "14734           2014.0       Misc  0.000000  0.000000  0.010002     0.000000   \n",
       "14737           2016.0  Adventure  0.000000  0.000000  0.010002     0.000000   \n",
       "\n",
       "       critic_score  user_score rating_age  total_sales  \n",
       "28             45.0    5.898438          M     0.140015  \n",
       "29             35.0    5.199219          M     0.049988  \n",
       "45             65.0    5.601562       E10+     0.070007  \n",
       "48             57.0    7.800781          T     0.020004  \n",
       "49              NaN    8.296875          T     0.029999  \n",
       "...             ...         ...        ...          ...  \n",
       "14719           NaN         NaN          T     0.010002  \n",
       "14727           NaN         NaN        NaN     0.010002  \n",
       "14731           NaN         NaN        NaN     0.010002  \n",
       "14734           NaN         NaN        NaN     0.010002  \n",
       "14737           NaN         NaN        NaN     0.010002  \n",
       "\n",
       "[1365 rows x 12 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_five"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим ящики с усами по данным:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Платформа')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "ax = sns.boxplot(x = 'platform', y=\"total_sales\", \n",
    "               data=df_five, palette = 'pastel')\n",
    "ax.tick_params(labelsize = 15)\n",
    "plt.ylabel('Млн.копий', fontsize=15)\n",
    "plt.xlabel('Платформа', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из графиков видно, что для всех платформ есть выбросы: отдельные игры, которые имели очень большое количество продаж, по сравнению с остальными. Эти выбросы могли оказать негативное влияние и на восприятие графиков с общим количеством проданных копий, на основе которых мы выявили потенциально прибыльные платформы.\n",
    "\n",
    "Удалять данные выбросы некорректно, так как они отражают отдельные популярные игры, которые скорее всего останутся на рынке и , следовательно, будут влиять на успешность отдельных платформ.\n",
    "\n",
    "Также, можно заключить, что на платформах 3ds и ps4 больше отдельных игр, имеющих очень высокие показатели продаж, что делает эти платформы более прибыльными. \n",
    "\n",
    "Разница между платформами формируется под влиянием многих факторов, например, некоторые платформы дешевле других. Или же, если платформа является продолжением аналогичного предшественника того же брэнда, покупатели будут склонны к покупке новой версии, для которой у них уже есть игры. Также, на разницу могут влиять определенные уникальные характеристики платформы и рекламная кампания. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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_age</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7 days to die</td>\n",
       "      <td>ps4</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.040009</td>\n",
       "      <td>0.080017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.898438</td>\n",
       "      <td>M</td>\n",
       "      <td>0.140015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7 days to die</td>\n",
       "      <td>xone</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.199219</td>\n",
       "      <td>M</td>\n",
       "      <td>0.049988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>adventure time: the secret of the nameless kin...</td>\n",
       "      <td>3ds</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.601562</td>\n",
       "      <td>E10+</td>\n",
       "      <td>0.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>aegis of earth: protonovus assault</td>\n",
       "      <td>ps4</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7.800781</td>\n",
       "      <td>T</td>\n",
       "      <td>0.020004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>aegis of earth: protonovus assault</td>\n",
       "      <td>psv</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.296875</td>\n",
       "      <td>T</td>\n",
       "      <td>0.029999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 name platform  \\\n",
       "28                                      7 days to die      ps4   \n",
       "29                                      7 days to die     xone   \n",
       "45  adventure time: the secret of the nameless kin...      3ds   \n",
       "48                 aegis of earth: protonovus assault      ps4   \n",
       "49                 aegis of earth: protonovus assault      psv   \n",
       "\n",
       "    year_of_release   genre  na_sales  eu_sales  jp_sales  other_sales  \\\n",
       "28           2016.0  Action  0.040009  0.080017       0.0     0.020004   \n",
       "29           2016.0  Action  0.029999  0.020004       0.0     0.000000   \n",
       "45           2014.0  Action  0.000000  0.070007       0.0     0.000000   \n",
       "48           2016.0  Action  0.020004  0.000000       0.0     0.000000   \n",
       "49           2016.0  Action  0.010002  0.010002       0.0     0.010002   \n",
       "\n",
       "    critic_score  user_score rating_age  total_sales  \n",
       "28          45.0    5.898438          M     0.140015  \n",
       "29          35.0    5.199219          M     0.049988  \n",
       "45          65.0    5.601562       E10+     0.070007  \n",
       "48          57.0    7.800781          T     0.020004  \n",
       "49           NaN    8.296875          T     0.029999  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_five.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Теперь посмотрим, как влияют на продажи внутри одной популярной платформы отзывы пользователей и критиков.**\n",
    "Для этого построим диаграммы рассеяния внутри платформ для данных из столбцов critic_score и user_score и столбца с данными о продажах. \n",
    "\n",
    "1. Влияние отзывов критиков на продажи игр."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = 3  # number of rows\n",
    "b = 2  # number of columns\n",
    "c = 1  # initialize plot counter\n",
    "fig = plt.figure(figsize=(20,15))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.1)\n",
    "\n",
    "for name in df_five['platform'].unique():\n",
    "    df_five.sort_values(by = 'critic_score')\n",
    "    plt.subplot(a, b, c)\n",
    "    plt.title(name, fontsize=15)\n",
    "    ax = sns.scatterplot(x=\"critic_score\", y=\"total_sales\",\n",
    "               data=df_five[df_five['platform'] == name])\n",
    "    ax.tick_params(labelsize = 15)\n",
    "    plt.xlabel('Critic score', fontsize=15)\n",
    "    plt.ylabel('Total_sales', fontsize=15)\n",
    "    c = c + 1\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Влияние отзывов пользователей на продажи игр."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = 3  # number of rows\n",
    "b = 2  # number of columns\n",
    "c = 1  # initialize plot counter\n",
    "fig = plt.figure(figsize=(20,15))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.1)\n",
    "\n",
    "for name in df_five['platform'].unique():\n",
    "    df_five.sort_values(by = 'user_score')\n",
    "    plt.subplot(a, b, c)\n",
    "    plt.title(name, fontsize=15)\n",
    "    ax = sns.scatterplot(x=\"user_score\", y=\"total_sales\",\n",
    "               data=df_five[df_five['platform'] == name], color = 'red')\n",
    "    ax.tick_params(labelsize = 15)\n",
    "    plt.xlabel('User score', fontsize=15)\n",
    "    plt.ylabel('Total_sales', fontsize=15)\n",
    "    c = c + 1\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что как отзывы критиков, так и отзывы пользователей окаывают слабое положительное влияние на продажи игр на некоторых платформах. Посмотрим на значения корреляции для этих показателей и сделаем выводы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ps4 :\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>critic_score</th>\n",
       "      <th>user_score</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>critic_score</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.403808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.562865</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_sales</th>\n",
       "      <td>0.403808</td>\n",
       "      <td>-0.027458</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "              critic_score  user_score  total_sales\n",
       "critic_score      1.000000    0.562865     0.403808\n",
       "user_score        0.562865    1.000000    -0.027458\n",
       "total_sales       0.403808   -0.027458     1.000000"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xone :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>critic_score</th>\n",
       "      <th>user_score</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>critic_score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491174</td>\n",
       "      <td>0.412046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_score</th>\n",
       "      <td>0.491174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.053510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_sales</th>\n",
       "      <td>0.412046</td>\n",
       "      <td>-0.053510</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              critic_score  user_score  total_sales\n",
       "critic_score      1.000000    0.491174     0.412046\n",
       "user_score        0.491174    1.000000    -0.053510\n",
       "total_sales       0.412046   -0.053510     1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3ds :\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>user_score</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>critic_score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.745261</td>\n",
       "      <td>0.348744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_score</th>\n",
       "      <td>0.745261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.222129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_sales</th>\n",
       "      <td>0.348744</td>\n",
       "      <td>0.222129</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              critic_score  user_score  total_sales\n",
       "critic_score      1.000000    0.745261     0.348744\n",
       "user_score        0.745261    1.000000     0.222129\n",
       "total_sales       0.348744    0.222129     1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "psv :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>critic_score</th>\n",
       "      <th>user_score</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>critic_score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.734347</td>\n",
       "      <td>0.131781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_score</th>\n",
       "      <td>0.734347</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_sales</th>\n",
       "      <td>0.131781</td>\n",
       "      <td>0.013569</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "              critic_score  user_score  total_sales\n",
       "critic_score      1.000000    0.734347     0.131781\n",
       "user_score        0.734347    1.000000     0.013569\n",
       "total_sales       0.131781    0.013569     1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wiiu :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>critic_score</th>\n",
       "      <th>user_score</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>critic_score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.770158</td>\n",
       "      <td>0.348330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_score</th>\n",
       "      <td>0.770158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_sales</th>\n",
       "      <td>0.348330</td>\n",
       "      <td>0.400276</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              critic_score  user_score  total_sales\n",
       "critic_score      1.000000    0.770158     0.348330\n",
       "user_score        0.770158    1.000000     0.400276\n",
       "total_sales       0.348330    0.400276     1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_df_five = df_five[['platform', 'critic_score', 'user_score', 'total_sales']]\n",
    "for name in corr_df_five['platform'].unique():\n",
    "    print(name, ':')\n",
    "    display(corr_df_five[corr_df_five['platform'] == name].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на общее количество игр для разных платформ, чтобы лучше понять полученные результаты."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platform</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3ds</th>\n",
       "      <td>453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ps4</th>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>psv</th>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wiiu</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xone</th>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name\n",
       "platform      \n",
       "3ds        453\n",
       "ps4        283\n",
       "psv        324\n",
       "wiiu       115\n",
       "xone       190"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_five.pivot_table(index = 'platform', values = 'name', aggfunc = 'count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так, исходя из таблиц корреляции видно, что для всех платформ, кроме wiiu, отзывы критиков имеют больший эффект на продажи игр, по сравнению с отзывами пользователей. Отзывы критиков положительно коррелируют с продажами игр на всех платформах, однако связь слабая (или очень слабая в случае с платформой psv). Отзывы пользователей не оказывают никакого влияния на продаваемость игр для платформ PS Vita, PS4 и Xbox One, очень слабо и слабо влияют на данный показатель для платформы 3DS и положительно но слабо коррелируют с продажами игр на платформе Wii U, соответственно.\n",
    "\n",
    "Можно предположить, что популярность платформы в целом влияет на корреляцию продаж и отзывов пользователей. Так, Wii U не самая популярная платформа, ассортимент игр на ней наименьший, и она имеет самое высокое значение корреляции с млн. проданных копий. Наоборот, платформы Xbox One и PS4 довольно популярны, и корреляции между отзывами пользователей и продажами не наблюдается. Однако, на данный показатель могут оказывать влияние и другие факторы, например, год выпуска или успешность предшественника и т.д. \n",
    "\n",
    "\n",
    "**Далее посмотрим на общее распределение игр по жанрам.** \n",
    "\n",
    "Что можно сказать о самых прибыльных жанрах? Выделяются ли жанры с высокими и низкими продажами?\n",
    "\n",
    "Сгруппируем данные таблицы df_five по жанру:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "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>genre</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>189.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shooter</td>\n",
       "      <td>122.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>117.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sports</td>\n",
       "      <td>92.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Platform</td>\n",
       "      <td>61.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Racing</td>\n",
       "      <td>40.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Misc</td>\n",
       "      <td>31.843750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Simulation</td>\n",
       "      <td>28.078125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fighting</td>\n",
       "      <td>15.593750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>12.742188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Puzzle</td>\n",
       "      <td>6.781250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Strategy</td>\n",
       "      <td>4.332031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           genre  total_sales\n",
       "0         Action   189.500000\n",
       "1        Shooter   122.937500\n",
       "2   Role-Playing   117.687500\n",
       "3         Sports    92.437500\n",
       "4       Platform    61.250000\n",
       "5         Racing    40.500000\n",
       "6           Misc    31.843750\n",
       "7     Simulation    28.078125\n",
       "8       Fighting    15.593750\n",
       "9      Adventure    12.742188\n",
       "10        Puzzle     6.781250\n",
       "11      Strategy     4.332031"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_five_genre = df_five.pivot_table(index = 'genre', values = 'total_sales', aggfunc = 'sum').reset_index()\n",
    "df_five_genre.sort_values(by = 'total_sales', ascending = False).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.261963\n",
       "Name: total_sales, dtype: float16"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_five_genre[df_five_genre['genre'] == 'Action']['total_sales'])/ df_five_genre['total_sales'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8    0.169922\n",
       "Name: total_sales, dtype: float16"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_five_genre[df_five_genre['genre'] == 'Shooter']['total_sales'])/ df_five_genre['total_sales'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    0.16272\n",
       "Name: total_sales, dtype: float16"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_five_genre[df_five_genre['genre'] == 'Role-Playing']['total_sales'])/ df_five_genre['total_sales'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    0.127808\n",
       "Name: total_sales, dtype: float16"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_five_genre[df_five_genre['genre'] == 'Sports']['total_sales'])/ df_five_genre['total_sales'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0.084656\n",
       "Name: total_sales, dtype: float16"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_five_genre[df_five_genre['genre'] == 'Platform']['total_sales'])/ df_five_genre['total_sales'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наиболее популярный жанр - Action. Продажи игр с данным жанром примерно на 35% выше второго по списку жанра - Shooter. В целом, экшн(26.19%), стрелялки(16.99%), ролевые(16.27%) и спортивные игры(12.78%) наиболее популярны и имеют больше всего продаж. \n",
    "\n",
    "Из наименее популярных, выделим игры относящиеся к стратегиям, паззлам, приключениям и дракам. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Портрет пользователя<a id='customerid'></a>\n",
    "### Популярные платформы<a id='platformsid'></a>\n",
    "Создадим сводную таблицу по продажам игр разных платформ в зависимости от региона:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3do</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3ds</td>\n",
       "      <td>54.093750</td>\n",
       "      <td>90.437500</td>\n",
       "      <td>72.625000</td>\n",
       "      <td>11.640625</td>\n",
       "      <td>228.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dc</td>\n",
       "      <td>1.179688</td>\n",
       "      <td>7.628906</td>\n",
       "      <td>4.328125</td>\n",
       "      <td>0.190063</td>\n",
       "      <td>13.328125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ds</td>\n",
       "      <td>172.250000</td>\n",
       "      <td>170.250000</td>\n",
       "      <td>356.250000</td>\n",
       "      <td>54.687500</td>\n",
       "      <td>753.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gb</td>\n",
       "      <td>30.046875</td>\n",
       "      <td>51.843750</td>\n",
       "      <td>47.906250</td>\n",
       "      <td>4.710938</td>\n",
       "      <td>134.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gba</td>\n",
       "      <td>68.875000</td>\n",
       "      <td>44.125000</td>\n",
       "      <td>168.250000</td>\n",
       "      <td>6.890625</td>\n",
       "      <td>288.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gc</td>\n",
       "      <td>36.062500</td>\n",
       "      <td>19.843750</td>\n",
       "      <td>124.437500</td>\n",
       "      <td>4.871094</td>\n",
       "      <td>185.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gen</td>\n",
       "      <td>0.899902</td>\n",
       "      <td>1.320312</td>\n",
       "      <td>2.259766</td>\n",
       "      <td>0.189941</td>\n",
       "      <td>4.671875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>n64</td>\n",
       "      <td>27.687500</td>\n",
       "      <td>27.937500</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>3.269531</td>\n",
       "      <td>150.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nes</td>\n",
       "      <td>0.439941</td>\n",
       "      <td>1.279297</td>\n",
       "      <td>1.940430</td>\n",
       "      <td>0.059998</td>\n",
       "      <td>3.720703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ng</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.110352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.110352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pc</td>\n",
       "      <td>123.687500</td>\n",
       "      <td>0.170044</td>\n",
       "      <td>82.812500</td>\n",
       "      <td>20.203125</td>\n",
       "      <td>226.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>pcfx</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ps</td>\n",
       "      <td>180.375000</td>\n",
       "      <td>122.125000</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>34.562500</td>\n",
       "      <td>609.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ps2</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>126.250000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>176.500000</td>\n",
       "      <td>1163.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ps3</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>66.625000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>125.125000</td>\n",
       "      <td>863.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ps4</td>\n",
       "      <td>114.437500</td>\n",
       "      <td>10.367188</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>38.593750</td>\n",
       "      <td>249.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>psp</td>\n",
       "      <td>61.250000</td>\n",
       "      <td>66.875000</td>\n",
       "      <td>102.437500</td>\n",
       "      <td>38.312500</td>\n",
       "      <td>269.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>psv</td>\n",
       "      <td>10.828125</td>\n",
       "      <td>14.617188</td>\n",
       "      <td>10.843750</td>\n",
       "      <td>5.371094</td>\n",
       "      <td>41.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sat</td>\n",
       "      <td>0.540039</td>\n",
       "      <td>24.453125</td>\n",
       "      <td>0.720215</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>25.796875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>scd</td>\n",
       "      <td>0.360107</td>\n",
       "      <td>0.449951</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049988</td>\n",
       "      <td>1.860352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>snes</td>\n",
       "      <td>5.531250</td>\n",
       "      <td>66.875000</td>\n",
       "      <td>17.843750</td>\n",
       "      <td>0.870117</td>\n",
       "      <td>91.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>tg16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>wii</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>457.000000</td>\n",
       "      <td>72.562500</td>\n",
       "      <td>834.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>wiiu</td>\n",
       "      <td>21.453125</td>\n",
       "      <td>11.023438</td>\n",
       "      <td>31.953125</td>\n",
       "      <td>5.019531</td>\n",
       "      <td>69.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ws</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.660156</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.660156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>x360</td>\n",
       "      <td>249.875000</td>\n",
       "      <td>11.351562</td>\n",
       "      <td>568.500000</td>\n",
       "      <td>80.250000</td>\n",
       "      <td>910.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>xb</td>\n",
       "      <td>56.281250</td>\n",
       "      <td>1.370117</td>\n",
       "      <td>172.875000</td>\n",
       "      <td>7.992188</td>\n",
       "      <td>238.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>xone</td>\n",
       "      <td>43.375000</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>78.687500</td>\n",
       "      <td>12.023438</td>\n",
       "      <td>134.375000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   platform    eu_sales    jp_sales    na_sales  other_sales  total_sales\n",
       "0       3do    0.000000    0.099976    0.000000     0.000000     0.099976\n",
       "1       3ds   54.093750   90.437500   72.625000    11.640625   228.750000\n",
       "2        dc    1.179688    7.628906    4.328125     0.190063    13.328125\n",
       "3        ds  172.250000  170.250000  356.250000    54.687500   753.500000\n",
       "4        gb   30.046875   51.843750   47.906250     4.710938   134.500000\n",
       "5       gba   68.875000   44.125000  168.250000     6.890625   288.250000\n",
       "6        gc   36.062500   19.843750  124.437500     4.871094   185.250000\n",
       "7       gen    0.899902    1.320312    2.259766     0.189941     4.671875\n",
       "8       n64   27.687500   27.937500   92.000000     3.269531   150.875000\n",
       "9       nes    0.439941    1.279297    1.940430     0.059998     3.720703\n",
       "10       ng    0.000000    1.110352    0.000000     0.000000     1.110352\n",
       "11       pc  123.687500    0.170044   82.812500    20.203125   226.875000\n",
       "12     pcfx    0.000000    0.029999    0.000000     0.000000     0.029999\n",
       "13       ps  180.375000  122.125000  272.000000    34.562500   609.000000\n",
       "14      ps2  312.000000  126.250000  548.000000   176.500000  1163.000000\n",
       "15      ps3  301.000000   66.625000  371.000000   125.125000   863.500000\n",
       "16      ps4  114.437500   10.367188   86.000000    38.593750   249.375000\n",
       "17      psp   61.250000   66.875000  102.437500    38.312500   269.000000\n",
       "18      psv   10.828125   14.617188   10.843750     5.371094    41.656250\n",
       "19      sat    0.540039   24.453125    0.720215     0.070007    25.796875\n",
       "20      scd    0.360107    0.449951    1.000000     0.049988     1.860352\n",
       "21     snes    5.531250   66.875000   17.843750     0.870117    91.125000\n",
       "22     tg16    0.000000    0.160034    0.000000     0.000000     0.160034\n",
       "23      wii  240.000000   64.500000  457.000000    72.562500   834.000000\n",
       "24     wiiu   21.453125   11.023438   31.953125     5.019531    69.437500\n",
       "25       ws    0.000000    0.660156    0.000000     0.000000     0.660156\n",
       "26     x360  249.875000   11.351562  568.500000    80.250000   910.000000\n",
       "27       xb   56.281250    1.370117  172.875000     7.992188   238.500000\n",
       "28     xone   43.375000    0.300049   78.687500    12.023438   134.375000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales_regions = df.pivot_table(index = 'platform', \n",
    "                                       values = ['na_sales', 'eu_sales', 'jp_sales', 'other_sales', 'total_sales'], \n",
    "                                       aggfunc = 'sum').reset_index()\n",
    "df_sales_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['platform', 'eu_sales', 'jp_sales', 'na_sales', 'other_sales',\n",
       "       'total_sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales_regions.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавим столбцы с процентом от общих продаж:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>platform</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>eu_per</th>\n",
       "      <th>jp_per</th>\n",
       "      <th>na_per</th>\n",
       "      <th>other_per</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ps2</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>126.250000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>176.500000</td>\n",
       "      <td>1163.000000</td>\n",
       "      <td>26.83</td>\n",
       "      <td>10.86</td>\n",
       "      <td>47.12</td>\n",
       "      <td>15.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x360</td>\n",
       "      <td>249.875000</td>\n",
       "      <td>11.351562</td>\n",
       "      <td>568.500000</td>\n",
       "      <td>80.250000</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>27.46</td>\n",
       "      <td>1.25</td>\n",
       "      <td>62.47</td>\n",
       "      <td>8.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ps3</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>66.625000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>125.125000</td>\n",
       "      <td>863.500000</td>\n",
       "      <td>34.86</td>\n",
       "      <td>7.72</td>\n",
       "      <td>42.96</td>\n",
       "      <td>14.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wii</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>457.000000</td>\n",
       "      <td>72.562500</td>\n",
       "      <td>834.000000</td>\n",
       "      <td>28.78</td>\n",
       "      <td>7.73</td>\n",
       "      <td>54.80</td>\n",
       "      <td>8.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ds</td>\n",
       "      <td>172.250000</td>\n",
       "      <td>170.250000</td>\n",
       "      <td>356.250000</td>\n",
       "      <td>54.687500</td>\n",
       "      <td>753.500000</td>\n",
       "      <td>22.86</td>\n",
       "      <td>22.59</td>\n",
       "      <td>47.28</td>\n",
       "      <td>7.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ps</td>\n",
       "      <td>180.375000</td>\n",
       "      <td>122.125000</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>34.562500</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>29.62</td>\n",
       "      <td>20.05</td>\n",
       "      <td>44.66</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gba</td>\n",
       "      <td>68.875000</td>\n",
       "      <td>44.125000</td>\n",
       "      <td>168.250000</td>\n",
       "      <td>6.890625</td>\n",
       "      <td>288.250000</td>\n",
       "      <td>23.89</td>\n",
       "      <td>15.31</td>\n",
       "      <td>58.37</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>psp</td>\n",
       "      <td>61.250000</td>\n",
       "      <td>66.875000</td>\n",
       "      <td>102.437500</td>\n",
       "      <td>38.312500</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>22.77</td>\n",
       "      <td>24.86</td>\n",
       "      <td>38.08</td>\n",
       "      <td>14.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ps4</td>\n",
       "      <td>114.437500</td>\n",
       "      <td>10.367188</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>38.593750</td>\n",
       "      <td>249.375000</td>\n",
       "      <td>45.89</td>\n",
       "      <td>4.16</td>\n",
       "      <td>34.49</td>\n",
       "      <td>15.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>xb</td>\n",
       "      <td>56.281250</td>\n",
       "      <td>1.370117</td>\n",
       "      <td>172.875000</td>\n",
       "      <td>7.992188</td>\n",
       "      <td>238.500000</td>\n",
       "      <td>23.60</td>\n",
       "      <td>0.57</td>\n",
       "      <td>72.48</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3ds</td>\n",
       "      <td>54.093750</td>\n",
       "      <td>90.437500</td>\n",
       "      <td>72.625000</td>\n",
       "      <td>11.640625</td>\n",
       "      <td>228.750000</td>\n",
       "      <td>23.65</td>\n",
       "      <td>39.54</td>\n",
       "      <td>31.75</td>\n",
       "      <td>5.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pc</td>\n",
       "      <td>123.687500</td>\n",
       "      <td>0.170044</td>\n",
       "      <td>82.812500</td>\n",
       "      <td>20.203125</td>\n",
       "      <td>226.875000</td>\n",
       "      <td>54.52</td>\n",
       "      <td>0.07</td>\n",
       "      <td>36.50</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gc</td>\n",
       "      <td>36.062500</td>\n",
       "      <td>19.843750</td>\n",
       "      <td>124.437500</td>\n",
       "      <td>4.871094</td>\n",
       "      <td>185.250000</td>\n",
       "      <td>19.47</td>\n",
       "      <td>10.71</td>\n",
       "      <td>67.17</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>n64</td>\n",
       "      <td>27.687500</td>\n",
       "      <td>27.937500</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>3.269531</td>\n",
       "      <td>150.875000</td>\n",
       "      <td>18.35</td>\n",
       "      <td>18.52</td>\n",
       "      <td>60.98</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gb</td>\n",
       "      <td>30.046875</td>\n",
       "      <td>51.843750</td>\n",
       "      <td>47.906250</td>\n",
       "      <td>4.710938</td>\n",
       "      <td>134.500000</td>\n",
       "      <td>22.34</td>\n",
       "      <td>38.55</td>\n",
       "      <td>35.62</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>xone</td>\n",
       "      <td>43.375000</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>78.687500</td>\n",
       "      <td>12.023438</td>\n",
       "      <td>134.375000</td>\n",
       "      <td>32.28</td>\n",
       "      <td>0.22</td>\n",
       "      <td>58.56</td>\n",
       "      <td>8.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>snes</td>\n",
       "      <td>5.531250</td>\n",
       "      <td>66.875000</td>\n",
       "      <td>17.843750</td>\n",
       "      <td>0.870117</td>\n",
       "      <td>91.125000</td>\n",
       "      <td>6.07</td>\n",
       "      <td>73.39</td>\n",
       "      <td>19.58</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>wiiu</td>\n",
       "      <td>21.453125</td>\n",
       "      <td>11.023438</td>\n",
       "      <td>31.953125</td>\n",
       "      <td>5.019531</td>\n",
       "      <td>69.437500</td>\n",
       "      <td>30.90</td>\n",
       "      <td>15.88</td>\n",
       "      <td>46.02</td>\n",
       "      <td>7.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>psv</td>\n",
       "      <td>10.828125</td>\n",
       "      <td>14.617188</td>\n",
       "      <td>10.843750</td>\n",
       "      <td>5.371094</td>\n",
       "      <td>41.656250</td>\n",
       "      <td>25.99</td>\n",
       "      <td>35.09</td>\n",
       "      <td>26.03</td>\n",
       "      <td>12.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sat</td>\n",
       "      <td>0.540039</td>\n",
       "      <td>24.453125</td>\n",
       "      <td>0.720215</td>\n",
       "      <td>0.070007</td>\n",
       "      <td>25.796875</td>\n",
       "      <td>2.09</td>\n",
       "      <td>94.79</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>dc</td>\n",
       "      <td>1.179688</td>\n",
       "      <td>7.628906</td>\n",
       "      <td>4.328125</td>\n",
       "      <td>0.190063</td>\n",
       "      <td>13.328125</td>\n",
       "      <td>8.85</td>\n",
       "      <td>57.24</td>\n",
       "      <td>32.47</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>gen</td>\n",
       "      <td>0.899902</td>\n",
       "      <td>1.320312</td>\n",
       "      <td>2.259766</td>\n",
       "      <td>0.189941</td>\n",
       "      <td>4.671875</td>\n",
       "      <td>19.26</td>\n",
       "      <td>28.26</td>\n",
       "      <td>48.37</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>nes</td>\n",
       "      <td>0.439941</td>\n",
       "      <td>1.279297</td>\n",
       "      <td>1.940430</td>\n",
       "      <td>0.059998</td>\n",
       "      <td>3.720703</td>\n",
       "      <td>11.82</td>\n",
       "      <td>34.38</td>\n",
       "      <td>52.15</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>scd</td>\n",
       "      <td>0.360107</td>\n",
       "      <td>0.449951</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049988</td>\n",
       "      <td>1.860352</td>\n",
       "      <td>19.36</td>\n",
       "      <td>24.19</td>\n",
       "      <td>53.75</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ng</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.110352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.110352</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ws</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.660156</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.660156</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>tg16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160034</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3do</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>pcfx</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   platform    eu_sales    jp_sales    na_sales  other_sales  total_sales  \\\n",
       "0       ps2  312.000000  126.250000  548.000000   176.500000  1163.000000   \n",
       "1      x360  249.875000   11.351562  568.500000    80.250000   910.000000   \n",
       "2       ps3  301.000000   66.625000  371.000000   125.125000   863.500000   \n",
       "3       wii  240.000000   64.500000  457.000000    72.562500   834.000000   \n",
       "4        ds  172.250000  170.250000  356.250000    54.687500   753.500000   \n",
       "5        ps  180.375000  122.125000  272.000000    34.562500   609.000000   \n",
       "6       gba   68.875000   44.125000  168.250000     6.890625   288.250000   \n",
       "7       psp   61.250000   66.875000  102.437500    38.312500   269.000000   \n",
       "8       ps4  114.437500   10.367188   86.000000    38.593750   249.375000   \n",
       "9        xb   56.281250    1.370117  172.875000     7.992188   238.500000   \n",
       "10      3ds   54.093750   90.437500   72.625000    11.640625   228.750000   \n",
       "11       pc  123.687500    0.170044   82.812500    20.203125   226.875000   \n",
       "12       gc   36.062500   19.843750  124.437500     4.871094   185.250000   \n",
       "13      n64   27.687500   27.937500   92.000000     3.269531   150.875000   \n",
       "14       gb   30.046875   51.843750   47.906250     4.710938   134.500000   \n",
       "15     xone   43.375000    0.300049   78.687500    12.023438   134.375000   \n",
       "16     snes    5.531250   66.875000   17.843750     0.870117    91.125000   \n",
       "17     wiiu   21.453125   11.023438   31.953125     5.019531    69.437500   \n",
       "18      psv   10.828125   14.617188   10.843750     5.371094    41.656250   \n",
       "19      sat    0.540039   24.453125    0.720215     0.070007    25.796875   \n",
       "20       dc    1.179688    7.628906    4.328125     0.190063    13.328125   \n",
       "21      gen    0.899902    1.320312    2.259766     0.189941     4.671875   \n",
       "22      nes    0.439941    1.279297    1.940430     0.059998     3.720703   \n",
       "23      scd    0.360107    0.449951    1.000000     0.049988     1.860352   \n",
       "24       ng    0.000000    1.110352    0.000000     0.000000     1.110352   \n",
       "25       ws    0.000000    0.660156    0.000000     0.000000     0.660156   \n",
       "26     tg16    0.000000    0.160034    0.000000     0.000000     0.160034   \n",
       "27      3do    0.000000    0.099976    0.000000     0.000000     0.099976   \n",
       "28     pcfx    0.000000    0.029999    0.000000     0.000000     0.029999   \n",
       "\n",
       "   eu_per  jp_per na_per other_per  \n",
       "0   26.83   10.86  47.12     15.18  \n",
       "1   27.46    1.25  62.47      8.82  \n",
       "2   34.86    7.72  42.96     14.49  \n",
       "3   28.78    7.73  54.80      8.70  \n",
       "4   22.86   22.59  47.28      7.26  \n",
       "5   29.62   20.05  44.66      5.68  \n",
       "6   23.89   15.31  58.37      2.39  \n",
       "7   22.77   24.86  38.08     14.24  \n",
       "8   45.89    4.16  34.49     15.48  \n",
       "9   23.60    0.57  72.48      3.35  \n",
       "10  23.65   39.54  31.75      5.09  \n",
       "11  54.52    0.07  36.50      8.90  \n",
       "12  19.47   10.71  67.17      2.63  \n",
       "13  18.35   18.52  60.98      2.17  \n",
       "14  22.34   38.55  35.62      3.50  \n",
       "15  32.28    0.22  58.56      8.95  \n",
       "16   6.07   73.39  19.58      0.95  \n",
       "17  30.90   15.88  46.02      7.23  \n",
       "18  25.99   35.09  26.03     12.89  \n",
       "19   2.09   94.79   2.79      0.27  \n",
       "20   8.85   57.24  32.47      1.43  \n",
       "21  19.26   28.26  48.37      4.07  \n",
       "22  11.82   34.38  52.15      1.61  \n",
       "23  19.36   24.19  53.75      2.69  \n",
       "24   0.00  100.00   0.00      0.00  \n",
       "25   0.00  100.00   0.00      0.00  \n",
       "26   0.00  100.00   0.00      0.00  \n",
       "27   0.00  100.00   0.00      0.00  \n",
       "28   0.00  100.00   0.00      0.00  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambdafunc = lambda x: pd.Series([\"{:.2f}\".format(x['eu_sales']/x['total_sales']*100),\n",
    "                                  \"{:.2f}\".format(x['jp_sales']/x['total_sales']*100),\n",
    "                                  \"{:.2f}\".format(x['na_sales']/x['total_sales']*100),\n",
    "                                  \"{:.2f}\".format(x['other_sales']/x['total_sales']*100)])\n",
    "df_sales_regions[['eu_per', 'jp_per', 'na_per', 'other_per']] = df_sales_regions.apply(lambdafunc, axis=1)\n",
    "df_sales_regions = df_sales_regions.sort_values(by = 'total_sales', ascending = False).reset_index(drop = True)\n",
    "df_sales_regions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5 платформ (ng, ws, tg16, 3do, pcfx) существуют только в Японии."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь найдем 5 самых популярных игр для каждого из регионов (европа, Япония, Северная Америка):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>platform</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>eu_per</th>\n",
       "      <th>jp_per</th>\n",
       "      <th>na_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ps2</td>\n",
       "      <td>312.000</td>\n",
       "      <td>126.250000</td>\n",
       "      <td>548.0</td>\n",
       "      <td>26.83</td>\n",
       "      <td>10.86</td>\n",
       "      <td>47.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ps3</td>\n",
       "      <td>301.000</td>\n",
       "      <td>66.625000</td>\n",
       "      <td>371.0</td>\n",
       "      <td>34.86</td>\n",
       "      <td>7.72</td>\n",
       "      <td>42.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x360</td>\n",
       "      <td>249.875</td>\n",
       "      <td>11.351562</td>\n",
       "      <td>568.5</td>\n",
       "      <td>27.46</td>\n",
       "      <td>1.25</td>\n",
       "      <td>62.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wii</td>\n",
       "      <td>240.000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>457.0</td>\n",
       "      <td>28.78</td>\n",
       "      <td>7.73</td>\n",
       "      <td>54.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ps</td>\n",
       "      <td>180.375</td>\n",
       "      <td>122.125000</td>\n",
       "      <td>272.0</td>\n",
       "      <td>29.62</td>\n",
       "      <td>20.05</td>\n",
       "      <td>44.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  platform  eu_sales    jp_sales  na_sales eu_per jp_per na_per\n",
       "0      ps2   312.000  126.250000     548.0  26.83  10.86  47.12\n",
       "2      ps3   301.000   66.625000     371.0  34.86   7.72  42.96\n",
       "1     x360   249.875   11.351562     568.5  27.46   1.25  62.47\n",
       "3      wii   240.000   64.500000     457.0  28.78   7.73  54.80\n",
       "5       ps   180.375  122.125000     272.0  29.62  20.05  44.66"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>platform</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>eu_per</th>\n",
       "      <th>jp_per</th>\n",
       "      <th>na_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ds</td>\n",
       "      <td>172.25000</td>\n",
       "      <td>170.2500</td>\n",
       "      <td>356.25000</td>\n",
       "      <td>22.86</td>\n",
       "      <td>22.59</td>\n",
       "      <td>47.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ps2</td>\n",
       "      <td>312.00000</td>\n",
       "      <td>126.2500</td>\n",
       "      <td>548.00000</td>\n",
       "      <td>26.83</td>\n",
       "      <td>10.86</td>\n",
       "      <td>47.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ps</td>\n",
       "      <td>180.37500</td>\n",
       "      <td>122.1250</td>\n",
       "      <td>272.00000</td>\n",
       "      <td>29.62</td>\n",
       "      <td>20.05</td>\n",
       "      <td>44.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3ds</td>\n",
       "      <td>54.09375</td>\n",
       "      <td>90.4375</td>\n",
       "      <td>72.62500</td>\n",
       "      <td>23.65</td>\n",
       "      <td>39.54</td>\n",
       "      <td>31.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>snes</td>\n",
       "      <td>5.53125</td>\n",
       "      <td>66.8750</td>\n",
       "      <td>17.84375</td>\n",
       "      <td>6.07</td>\n",
       "      <td>73.39</td>\n",
       "      <td>19.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   platform   eu_sales  jp_sales   na_sales eu_per jp_per na_per\n",
       "4        ds  172.25000  170.2500  356.25000  22.86  22.59  47.28\n",
       "0       ps2  312.00000  126.2500  548.00000  26.83  10.86  47.12\n",
       "5        ps  180.37500  122.1250  272.00000  29.62  20.05  44.66\n",
       "10      3ds   54.09375   90.4375   72.62500  23.65  39.54  31.75\n",
       "16     snes    5.53125   66.8750   17.84375   6.07  73.39  19.58"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>platform</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>eu_per</th>\n",
       "      <th>jp_per</th>\n",
       "      <th>na_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x360</td>\n",
       "      <td>249.875</td>\n",
       "      <td>11.351562</td>\n",
       "      <td>568.50</td>\n",
       "      <td>27.46</td>\n",
       "      <td>1.25</td>\n",
       "      <td>62.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ps2</td>\n",
       "      <td>312.000</td>\n",
       "      <td>126.250000</td>\n",
       "      <td>548.00</td>\n",
       "      <td>26.83</td>\n",
       "      <td>10.86</td>\n",
       "      <td>47.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wii</td>\n",
       "      <td>240.000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>457.00</td>\n",
       "      <td>28.78</td>\n",
       "      <td>7.73</td>\n",
       "      <td>54.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ps3</td>\n",
       "      <td>301.000</td>\n",
       "      <td>66.625000</td>\n",
       "      <td>371.00</td>\n",
       "      <td>34.86</td>\n",
       "      <td>7.72</td>\n",
       "      <td>42.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ds</td>\n",
       "      <td>172.250</td>\n",
       "      <td>170.250000</td>\n",
       "      <td>356.25</td>\n",
       "      <td>22.86</td>\n",
       "      <td>22.59</td>\n",
       "      <td>47.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  platform  eu_sales    jp_sales  na_sales eu_per jp_per na_per\n",
       "1     x360   249.875   11.351562    568.50  27.46   1.25  62.47\n",
       "0      ps2   312.000  126.250000    548.00  26.83  10.86  47.12\n",
       "3      wii   240.000   64.500000    457.00  28.78   7.73  54.80\n",
       "2      ps3   301.000   66.625000    371.00  34.86   7.72  42.96\n",
       "4       ds   172.250  170.250000    356.25  22.86  22.59  47.28"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_eu = df_sales_regions.sort_values(by = 'eu_sales', ascending = False).head()\n",
    "top_na = df_sales_regions.sort_values(by = 'na_sales', ascending = False).head()\n",
    "top_jp = df_sales_regions.sort_values(by = 'jp_sales', ascending = False).head()\n",
    "display(top_eu[['platform', 'eu_sales', 'jp_sales', 'na_sales', 'eu_per', 'jp_per', 'na_per']], \n",
    "        top_jp[['platform', 'eu_sales', 'jp_sales', 'na_sales', 'eu_per', 'jp_per', 'na_per']], \n",
    "        top_na[['platform', 'eu_sales', 'jp_sales', 'na_sales', 'eu_per', 'jp_per', 'na_per']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В Европе наиболее популярные платформы: ps2, ps3, x360, wii, **ps**.\n",
    "\n",
    "В Северной Америке - x360, ps2, wii, ps3, **ds**.\n",
    "\n",
    "В Японии - ds, ps2, ps, 3ds, psp.\n",
    "\n",
    "Как мы видим, в Европе и Америке 4 наиболее популярные платформы схожи. Однако в Японии список популярных платформ отличается: наиболее популярна ds,которая занимает пятую по популярности позицию в Штатах и отсутствует в пяти топовых платформах Европы. Также, в Японии присутствуют две платформы: 3ds и psp, которых нет в списке топовых платформ Штатов и Европы. Платформу ps2 можно выделить как наиболее популярную в целом. \n",
    "\n",
    "Доли продаж показывают, что в целом в Штатах продажи игр гораздо выше, чем в Европе и Японии, и составляют около половины всех продаж по топовым платформам. В таблице с топовыми платформами Японии мы видим, что процент продаж платформ 3ds и psp в Штатах ниже 40%, что говорит о том, что данные платформы популярны в Японии гораздо больше, чем в США, учитывая размеры стран. Подобный вывод можно сделать о платформе ps2 в Европе. \n",
    "\n",
    "### Популярные жанры<a id='genresid'></a>\n",
    "Создадим сводную таблицу по жанрам для разных регионов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genres = df.pivot_table(index = 'genre', \n",
    "                           values = ['na_sales', 'eu_sales', 'jp_sales', 'other_sales', 'total_sales'], \n",
    "                           aggfunc = 'sum').reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавим столбцы с процентом от общих продаж:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>genre</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>eu_per</th>\n",
       "      <th>jp_per</th>\n",
       "      <th>na_per</th>\n",
       "      <th>other_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>447.50000</td>\n",
       "      <td>130.125000</td>\n",
       "      <td>722.50000</td>\n",
       "      <td>165.875000</td>\n",
       "      <td>1466.000</td>\n",
       "      <td>30.53</td>\n",
       "      <td>8.88</td>\n",
       "      <td>49.28</td>\n",
       "      <td>11.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sports</td>\n",
       "      <td>327.50000</td>\n",
       "      <td>94.562500</td>\n",
       "      <td>606.50000</td>\n",
       "      <td>119.125000</td>\n",
       "      <td>1148.000</td>\n",
       "      <td>28.53</td>\n",
       "      <td>8.24</td>\n",
       "      <td>52.83</td>\n",
       "      <td>10.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shooter</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>30.453125</td>\n",
       "      <td>497.50000</td>\n",
       "      <td>94.437500</td>\n",
       "      <td>907.500</td>\n",
       "      <td>31.40</td>\n",
       "      <td>3.36</td>\n",
       "      <td>54.82</td>\n",
       "      <td>10.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>180.00000</td>\n",
       "      <td>287.250000</td>\n",
       "      <td>316.75000</td>\n",
       "      <td>54.187500</td>\n",
       "      <td>838.000</td>\n",
       "      <td>21.48</td>\n",
       "      <td>34.28</td>\n",
       "      <td>37.80</td>\n",
       "      <td>6.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Misc</td>\n",
       "      <td>187.25000</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>357.00000</td>\n",
       "      <td>63.656250</td>\n",
       "      <td>704.500</td>\n",
       "      <td>26.58</td>\n",
       "      <td>13.70</td>\n",
       "      <td>50.67</td>\n",
       "      <td>9.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Racing</td>\n",
       "      <td>209.25000</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>315.75000</td>\n",
       "      <td>68.812500</td>\n",
       "      <td>639.500</td>\n",
       "      <td>32.72</td>\n",
       "      <td>7.15</td>\n",
       "      <td>49.37</td>\n",
       "      <td>10.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Platform</td>\n",
       "      <td>153.50000</td>\n",
       "      <td>75.875000</td>\n",
       "      <td>289.75000</td>\n",
       "      <td>42.093750</td>\n",
       "      <td>561.000</td>\n",
       "      <td>27.36</td>\n",
       "      <td>13.52</td>\n",
       "      <td>51.65</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Simulation</td>\n",
       "      <td>108.25000</td>\n",
       "      <td>58.718750</td>\n",
       "      <td>171.25000</td>\n",
       "      <td>29.468750</td>\n",
       "      <td>367.750</td>\n",
       "      <td>29.44</td>\n",
       "      <td>15.97</td>\n",
       "      <td>46.57</td>\n",
       "      <td>8.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fighting</td>\n",
       "      <td>81.81250</td>\n",
       "      <td>65.625000</td>\n",
       "      <td>177.12500</td>\n",
       "      <td>31.375000</td>\n",
       "      <td>356.000</td>\n",
       "      <td>22.98</td>\n",
       "      <td>18.43</td>\n",
       "      <td>49.75</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>40.375000</td>\n",
       "      <td>83.93750</td>\n",
       "      <td>14.171875</td>\n",
       "      <td>188.500</td>\n",
       "      <td>26.53</td>\n",
       "      <td>21.42</td>\n",
       "      <td>44.53</td>\n",
       "      <td>7.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Strategy</td>\n",
       "      <td>40.71875</td>\n",
       "      <td>43.437500</td>\n",
       "      <td>63.78125</td>\n",
       "      <td>10.171875</td>\n",
       "      <td>158.125</td>\n",
       "      <td>25.75</td>\n",
       "      <td>27.47</td>\n",
       "      <td>40.34</td>\n",
       "      <td>6.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Puzzle</td>\n",
       "      <td>41.78125</td>\n",
       "      <td>35.687500</td>\n",
       "      <td>69.87500</td>\n",
       "      <td>10.609375</td>\n",
       "      <td>158.000</td>\n",
       "      <td>26.44</td>\n",
       "      <td>22.59</td>\n",
       "      <td>44.22</td>\n",
       "      <td>6.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           genre   eu_sales    jp_sales   na_sales  other_sales  total_sales  \\\n",
       "0         Action  447.50000  130.125000  722.50000   165.875000     1466.000   \n",
       "1         Sports  327.50000   94.562500  606.50000   119.125000     1148.000   \n",
       "2        Shooter  285.00000   30.453125  497.50000    94.437500      907.500   \n",
       "3   Role-Playing  180.00000  287.250000  316.75000    54.187500      838.000   \n",
       "4           Misc  187.25000   96.500000  357.00000    63.656250      704.500   \n",
       "5         Racing  209.25000   45.750000  315.75000    68.812500      639.500   \n",
       "6       Platform  153.50000   75.875000  289.75000    42.093750      561.000   \n",
       "7     Simulation  108.25000   58.718750  171.25000    29.468750      367.750   \n",
       "8       Fighting   81.81250   65.625000  177.12500    31.375000      356.000   \n",
       "9      Adventure   50.00000   40.375000   83.93750    14.171875      188.500   \n",
       "10      Strategy   40.71875   43.437500   63.78125    10.171875      158.125   \n",
       "11        Puzzle   41.78125   35.687500   69.87500    10.609375      158.000   \n",
       "\n",
       "   eu_per jp_per na_per other_per  \n",
       "0   30.53   8.88  49.28     11.31  \n",
       "1   28.53   8.24  52.83     10.38  \n",
       "2   31.40   3.36  54.82     10.41  \n",
       "3   21.48  34.28  37.80      6.47  \n",
       "4   26.58  13.70  50.67      9.04  \n",
       "5   32.72   7.15  49.37     10.76  \n",
       "6   27.36  13.52  51.65      7.50  \n",
       "7   29.44  15.97  46.57      8.01  \n",
       "8   22.98  18.43  49.75      8.81  \n",
       "9   26.53  21.42  44.53      7.52  \n",
       "10  25.75  27.47  40.34      6.43  \n",
       "11  26.44  22.59  44.22      6.71  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambdafunc = lambda x: pd.Series([\"{:.2f}\".format(x['eu_sales']/x['total_sales']*100),\n",
    "                                  \"{:.2f}\".format(x['jp_sales']/x['total_sales']*100),\n",
    "                                  \"{:.2f}\".format(x['na_sales']/x['total_sales']*100),\n",
    "                                  \"{:.2f}\".format(x['other_sales']/x['total_sales']*100)])\n",
    "df_genres[['eu_per', 'jp_per', 'na_per', 'other_per']] = df_genres.apply(lambdafunc, axis=1)\n",
    "df_genres = df_genres.sort_values(by = 'total_sales', ascending = False).reset_index(drop = True)\n",
    "df_genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наблюдаем похожее процентное соотношение: в Северной америке продажи примерно в два раза больше. \n",
    "\n",
    "Посмотрим на 5 самых популярных жанра для каждого региона: Европы, Северной Америки, Японии."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>eu_per</th>\n",
       "      <th>jp_per</th>\n",
       "      <th>na_per</th>\n",
       "      <th>other_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>447.50</td>\n",
       "      <td>130.125000</td>\n",
       "      <td>722.50</td>\n",
       "      <td>165.87500</td>\n",
       "      <td>1466.0</td>\n",
       "      <td>30.53</td>\n",
       "      <td>8.88</td>\n",
       "      <td>49.28</td>\n",
       "      <td>11.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sports</td>\n",
       "      <td>327.50</td>\n",
       "      <td>94.562500</td>\n",
       "      <td>606.50</td>\n",
       "      <td>119.12500</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>28.53</td>\n",
       "      <td>8.24</td>\n",
       "      <td>52.83</td>\n",
       "      <td>10.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shooter</td>\n",
       "      <td>285.00</td>\n",
       "      <td>30.453125</td>\n",
       "      <td>497.50</td>\n",
       "      <td>94.43750</td>\n",
       "      <td>907.5</td>\n",
       "      <td>31.40</td>\n",
       "      <td>3.36</td>\n",
       "      <td>54.82</td>\n",
       "      <td>10.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Racing</td>\n",
       "      <td>209.25</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>315.75</td>\n",
       "      <td>68.81250</td>\n",
       "      <td>639.5</td>\n",
       "      <td>32.72</td>\n",
       "      <td>7.15</td>\n",
       "      <td>49.37</td>\n",
       "      <td>10.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Misc</td>\n",
       "      <td>187.25</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>357.00</td>\n",
       "      <td>63.65625</td>\n",
       "      <td>704.5</td>\n",
       "      <td>26.58</td>\n",
       "      <td>13.70</td>\n",
       "      <td>50.67</td>\n",
       "      <td>9.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     genre  eu_sales    jp_sales  na_sales  other_sales  total_sales eu_per  \\\n",
       "0   Action    447.50  130.125000    722.50    165.87500       1466.0  30.53   \n",
       "1   Sports    327.50   94.562500    606.50    119.12500       1148.0  28.53   \n",
       "2  Shooter    285.00   30.453125    497.50     94.43750        907.5  31.40   \n",
       "3   Racing    209.25   45.750000    315.75     68.81250        639.5  32.72   \n",
       "4     Misc    187.25   96.500000    357.00     63.65625        704.5  26.58   \n",
       "\n",
       "  jp_per na_per other_per  \n",
       "0   8.88  49.28     11.31  \n",
       "1   8.24  52.83     10.38  \n",
       "2   3.36  54.82     10.41  \n",
       "3   7.15  49.37     10.76  \n",
       "4  13.70  50.67      9.04  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>total_sales</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>447.50</td>\n",
       "      <td>130.125000</td>\n",
       "      <td>722.50</td>\n",
       "      <td>165.87500</td>\n",
       "      <td>1466.0</td>\n",
       "      <td>30.53</td>\n",
       "      <td>8.88</td>\n",
       "      <td>49.28</td>\n",
       "      <td>11.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sports</td>\n",
       "      <td>327.50</td>\n",
       "      <td>94.562500</td>\n",
       "      <td>606.50</td>\n",
       "      <td>119.12500</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>28.53</td>\n",
       "      <td>8.24</td>\n",
       "      <td>52.83</td>\n",
       "      <td>10.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shooter</td>\n",
       "      <td>285.00</td>\n",
       "      <td>30.453125</td>\n",
       "      <td>497.50</td>\n",
       "      <td>94.43750</td>\n",
       "      <td>907.5</td>\n",
       "      <td>31.40</td>\n",
       "      <td>3.36</td>\n",
       "      <td>54.82</td>\n",
       "      <td>10.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Misc</td>\n",
       "      <td>187.25</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>357.00</td>\n",
       "      <td>63.65625</td>\n",
       "      <td>704.5</td>\n",
       "      <td>26.58</td>\n",
       "      <td>13.70</td>\n",
       "      <td>50.67</td>\n",
       "      <td>9.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>180.00</td>\n",
       "      <td>287.250000</td>\n",
       "      <td>316.75</td>\n",
       "      <td>54.18750</td>\n",
       "      <td>838.0</td>\n",
       "      <td>21.48</td>\n",
       "      <td>34.28</td>\n",
       "      <td>37.80</td>\n",
       "      <td>6.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          genre  eu_sales    jp_sales  na_sales  other_sales  total_sales  \\\n",
       "0        Action    447.50  130.125000    722.50    165.87500       1466.0   \n",
       "1        Sports    327.50   94.562500    606.50    119.12500       1148.0   \n",
       "2       Shooter    285.00   30.453125    497.50     94.43750        907.5   \n",
       "3          Misc    187.25   96.500000    357.00     63.65625        704.5   \n",
       "4  Role-Playing    180.00  287.250000    316.75     54.18750        838.0   \n",
       "\n",
       "  eu_per jp_per na_per other_per  \n",
       "0  30.53   8.88  49.28     11.31  \n",
       "1  28.53   8.24  52.83     10.38  \n",
       "2  31.40   3.36  54.82     10.41  \n",
       "3  26.58  13.70  50.67      9.04  \n",
       "4  21.48  34.28  37.80      6.47  "
      ]
     },
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    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>eu_per</th>\n",
       "      <th>jp_per</th>\n",
       "      <th>na_per</th>\n",
       "      <th>other_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>180.00</td>\n",
       "      <td>287.2500</td>\n",
       "      <td>316.75</td>\n",
       "      <td>54.18750</td>\n",
       "      <td>838.0</td>\n",
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       "      <td>34.28</td>\n",
       "      <td>37.80</td>\n",
       "      <td>6.47</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Action</td>\n",
       "      <td>447.50</td>\n",
       "      <td>130.1250</td>\n",
       "      <td>722.50</td>\n",
       "      <td>165.87500</td>\n",
       "      <td>1466.0</td>\n",
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       "      <td>8.88</td>\n",
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       "      <td>11.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Misc</td>\n",
       "      <td>187.25</td>\n",
       "      <td>96.5000</td>\n",
       "      <td>357.00</td>\n",
       "      <td>63.65625</td>\n",
       "      <td>704.5</td>\n",
       "      <td>26.58</td>\n",
       "      <td>13.70</td>\n",
       "      <td>50.67</td>\n",
       "      <td>9.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sports</td>\n",
       "      <td>327.50</td>\n",
       "      <td>94.5625</td>\n",
       "      <td>606.50</td>\n",
       "      <td>119.12500</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>28.53</td>\n",
       "      <td>8.24</td>\n",
       "      <td>52.83</td>\n",
       "      <td>10.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Platform</td>\n",
       "      <td>153.50</td>\n",
       "      <td>75.8750</td>\n",
       "      <td>289.75</td>\n",
       "      <td>42.09375</td>\n",
       "      <td>561.0</td>\n",
       "      <td>27.36</td>\n",
       "      <td>13.52</td>\n",
       "      <td>51.65</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          genre  eu_sales  jp_sales  na_sales  other_sales  total_sales  \\\n",
       "0  Role-Playing    180.00  287.2500    316.75     54.18750        838.0   \n",
       "1        Action    447.50  130.1250    722.50    165.87500       1466.0   \n",
       "2          Misc    187.25   96.5000    357.00     63.65625        704.5   \n",
       "3        Sports    327.50   94.5625    606.50    119.12500       1148.0   \n",
       "4      Platform    153.50   75.8750    289.75     42.09375        561.0   \n",
       "\n",
       "  eu_per jp_per na_per other_per  \n",
       "0  21.48  34.28  37.80      6.47  \n",
       "1  30.53   8.88  49.28     11.31  \n",
       "2  26.58  13.70  50.67      9.04  \n",
       "3  28.53   8.24  52.83     10.38  \n",
       "4  27.36  13.52  51.65      7.50  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topg_eu = df_genres.sort_values(by = 'eu_sales', ascending = False).head().reset_index(drop = True)\n",
    "topg_na = df_genres.sort_values(by = 'na_sales', ascending = False).head().reset_index(drop = True)\n",
    "topg_jp = df_genres.sort_values(by = 'jp_sales', ascending = False).head().reset_index(drop = True)\n",
    "display(topg_eu, topg_na, topg_jp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализируем данные:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>other_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>447.50000</td>\n",
       "      <td>722.50000</td>\n",
       "      <td>130.125000</td>\n",
       "      <td>165.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sports</td>\n",
       "      <td>327.50000</td>\n",
       "      <td>606.50000</td>\n",
       "      <td>94.562500</td>\n",
       "      <td>119.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shooter</td>\n",
       "      <td>285.00000</td>\n",
       "      <td>497.50000</td>\n",
       "      <td>30.453125</td>\n",
       "      <td>94.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>180.00000</td>\n",
       "      <td>316.75000</td>\n",
       "      <td>287.250000</td>\n",
       "      <td>54.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Misc</td>\n",
       "      <td>187.25000</td>\n",
       "      <td>357.00000</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>63.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Racing</td>\n",
       "      <td>209.25000</td>\n",
       "      <td>315.75000</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>68.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Platform</td>\n",
       "      <td>153.50000</td>\n",
       "      <td>289.75000</td>\n",
       "      <td>75.875000</td>\n",
       "      <td>42.093750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Simulation</td>\n",
       "      <td>108.25000</td>\n",
       "      <td>171.25000</td>\n",
       "      <td>58.718750</td>\n",
       "      <td>29.468750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fighting</td>\n",
       "      <td>81.81250</td>\n",
       "      <td>177.12500</td>\n",
       "      <td>65.625000</td>\n",
       "      <td>31.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>83.93750</td>\n",
       "      <td>40.375000</td>\n",
       "      <td>14.171875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Strategy</td>\n",
       "      <td>40.71875</td>\n",
       "      <td>63.78125</td>\n",
       "      <td>43.437500</td>\n",
       "      <td>10.171875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Puzzle</td>\n",
       "      <td>41.78125</td>\n",
       "      <td>69.87500</td>\n",
       "      <td>35.687500</td>\n",
       "      <td>10.609375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           genre   eu_sales   na_sales    jp_sales  other_sales\n",
       "0         Action  447.50000  722.50000  130.125000   165.875000\n",
       "1         Sports  327.50000  606.50000   94.562500   119.125000\n",
       "2        Shooter  285.00000  497.50000   30.453125    94.437500\n",
       "3   Role-Playing  180.00000  316.75000  287.250000    54.187500\n",
       "4           Misc  187.25000  357.00000   96.500000    63.656250\n",
       "5         Racing  209.25000  315.75000   45.750000    68.812500\n",
       "6       Platform  153.50000  289.75000   75.875000    42.093750\n",
       "7     Simulation  108.25000  171.25000   58.718750    29.468750\n",
       "8       Fighting   81.81250  177.12500   65.625000    31.375000\n",
       "9      Adventure   50.00000   83.93750   40.375000    14.171875\n",
       "10      Strategy   40.71875   63.78125   43.437500    10.171875\n",
       "11        Puzzle   41.78125   69.87500   35.687500    10.609375"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_vis = df_genres[['genre', 'eu_sales', 'na_sales', 'jp_sales', 'other_sales']]\n",
    "genre_vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Поменяем местами столбцы и строки\n",
    "df_t = genre_vis.T.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": false
   },
   "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>genre</th>\n",
       "      <th>Action</th>\n",
       "      <th>Sports</th>\n",
       "      <th>Shooter</th>\n",
       "      <th>Role-Playing</th>\n",
       "      <th>Misc</th>\n",
       "      <th>Racing</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Simulation</th>\n",
       "      <th>Fighting</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Strategy</th>\n",
       "      <th>Puzzle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eu_sales</td>\n",
       "      <td>447.5</td>\n",
       "      <td>327.5</td>\n",
       "      <td>285.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>187.25</td>\n",
       "      <td>209.25</td>\n",
       "      <td>153.5</td>\n",
       "      <td>108.25</td>\n",
       "      <td>81.8125</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.71875</td>\n",
       "      <td>41.78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>na_sales</td>\n",
       "      <td>722.5</td>\n",
       "      <td>606.5</td>\n",
       "      <td>497.5</td>\n",
       "      <td>316.75</td>\n",
       "      <td>357.0</td>\n",
       "      <td>315.75</td>\n",
       "      <td>289.75</td>\n",
       "      <td>171.25</td>\n",
       "      <td>177.125</td>\n",
       "      <td>83.9375</td>\n",
       "      <td>63.78125</td>\n",
       "      <td>69.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jp_sales</td>\n",
       "      <td>130.125</td>\n",
       "      <td>94.5625</td>\n",
       "      <td>30.453125</td>\n",
       "      <td>287.25</td>\n",
       "      <td>96.5</td>\n",
       "      <td>45.75</td>\n",
       "      <td>75.875</td>\n",
       "      <td>58.71875</td>\n",
       "      <td>65.625</td>\n",
       "      <td>40.375</td>\n",
       "      <td>43.4375</td>\n",
       "      <td>35.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>other_sales</td>\n",
       "      <td>165.875</td>\n",
       "      <td>119.125</td>\n",
       "      <td>94.4375</td>\n",
       "      <td>54.1875</td>\n",
       "      <td>63.65625</td>\n",
       "      <td>68.8125</td>\n",
       "      <td>42.09375</td>\n",
       "      <td>29.46875</td>\n",
       "      <td>31.375</td>\n",
       "      <td>14.171875</td>\n",
       "      <td>10.171875</td>\n",
       "      <td>10.609375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         genre   Action   Sports    Shooter Role-Playing      Misc   Racing  \\\n",
       "1     eu_sales    447.5    327.5      285.0        180.0    187.25   209.25   \n",
       "2     na_sales    722.5    606.5      497.5       316.75     357.0   315.75   \n",
       "3     jp_sales  130.125  94.5625  30.453125       287.25      96.5    45.75   \n",
       "4  other_sales  165.875  119.125    94.4375      54.1875  63.65625  68.8125   \n",
       "\n",
       "   Platform Simulation Fighting  Adventure   Strategy     Puzzle  \n",
       "1     153.5     108.25  81.8125       50.0   40.71875   41.78125  \n",
       "2    289.75     171.25  177.125    83.9375   63.78125     69.875  \n",
       "3    75.875   58.71875   65.625     40.375    43.4375    35.6875  \n",
       "4  42.09375   29.46875   31.375  14.171875  10.171875  10.609375  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'index':'genre', 0:'Action', 1:'Sports', 2:'Shooter', 3:'Role-Playing', \n",
    "    4:'Misc', 5:'Racing', 6:'Platform', 7:'Simulation',\n",
    "    8:'Fighting', 9:'Adventure', 10:'Strategy', 11:'Puzzle'}\n",
    "df_t.rename(columns = d, inplace = True)\n",
    "df_t = df_t.loc[1:]\n",
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#В цикле сделаем срез по каждому названию и построим отдельные графики:\n",
    "a = 2  # number of rows\n",
    "b = 2  # number of columns\n",
    "c = 1  # initialize plot counter\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "fig.subplots_adjust(hspace=0.6, wspace=0.2)\n",
    "\n",
    "\n",
    "for name in df_t['genre'].unique():\n",
    "    plt.subplot(a, b, c)\n",
    "    plt.title(name)\n",
    "    ax = sns.barplot(data=df_t[df_t['genre'] == name])\n",
    "    ax.set_xlabel('Млн. копий')\n",
    "    ax.set_ylabel('Жанр')\n",
    "    plt.xticks(rotation=90)\n",
    "    c = c + 1\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Самые популярные жанры:\n",
    "* **в Европе:** экшн, спорт, стрелялки, гонки, Misc.\n",
    "* **в Северной Америке:** экшн, спорт, стрелялки, misc, ролевые.\n",
    "* **в Японии:** ролевые, экшн, Misc, спорт, platform. \n",
    "\n",
    "\n",
    "В целом, в Штатах и Европе 4 из 5 топовых жанров одинаковы. В Штатах ролевые игры входят в 5 топ жанров, а в Европе - гонки. В целом, смотря на график сверху, можно отметить, что в Европе и США в целом распределение продаж по жанрам похоже, в то время как в Японии это распределение очень сильно отличается. \n",
    "\n",
    "В Японии наиболее популярный жанр - ролевые игры, которые занимают лишь пятое место в Штатах и отсутствуют в списке пяти топ-жанров Европы. Также присутствует жанр Platform, который занимает 7-е место в Северной Америке и Европе. \n",
    "\n",
    "Отличие Японии от США и Европы можно объяснить огромной разницей в культуре и менталитете.\n",
    "\n",
    "### Рейтинг ESRB<a id='ersbid'></a>\n",
    "**Посмотрим, влияет ли рейтинг ESRB на продажи в отдельном регионе.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заменим пропущенные значения в столбце рейтинга на 'unknown':\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\anaconda\\lib\\site-packages\\pandas\\core\\generic.py:6392: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return self._update_inplace(result)\n"
     ]
    }
   ],
   "source": [
    "df['rating_age'].fillna('unknown', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сгруппируем данные по рейтингу:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>rating_age</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E</td>\n",
       "      <td>704.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>1273.000000</td>\n",
       "      <td>231.375000</td>\n",
       "      <td>2406.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>unknown</td>\n",
       "      <td>323.500000</td>\n",
       "      <td>550.500000</td>\n",
       "      <td>557.500000</td>\n",
       "      <td>71.375000</td>\n",
       "      <td>1502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T</td>\n",
       "      <td>421.000000</td>\n",
       "      <td>150.750000</td>\n",
       "      <td>747.500000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>1472.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>63.593750</td>\n",
       "      <td>743.000000</td>\n",
       "      <td>176.125000</td>\n",
       "      <td>1462.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E10+</td>\n",
       "      <td>183.375000</td>\n",
       "      <td>40.187500</td>\n",
       "      <td>345.500000</td>\n",
       "      <td>71.937500</td>\n",
       "      <td>641.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K-A</td>\n",
       "      <td>0.270020</td>\n",
       "      <td>1.459961</td>\n",
       "      <td>2.560547</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>4.320312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AO</td>\n",
       "      <td>0.609863</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.259766</td>\n",
       "      <td>0.090027</td>\n",
       "      <td>1.958984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EC</td>\n",
       "      <td>0.110046</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.530273</td>\n",
       "      <td>0.109985</td>\n",
       "      <td>1.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RP</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.040009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  rating_age    eu_sales    jp_sales     na_sales  other_sales  total_sales\n",
       "1          E  704.000000  198.000000  1273.000000   231.375000  2406.000000\n",
       "8    unknown  323.500000  550.500000   557.500000    71.375000  1502.000000\n",
       "7          T  421.000000  150.750000   747.500000   153.000000  1472.000000\n",
       "5          M  480.000000   63.593750   743.000000   176.125000  1462.000000\n",
       "2       E10+  183.375000   40.187500   345.500000    71.937500   641.000000\n",
       "4        K-A    0.270020    1.459961     2.560547     0.029999     4.320312\n",
       "0         AO    0.609863    0.000000     1.259766     0.090027     1.958984\n",
       "3         EC    0.110046    0.000000     1.530273     0.109985     1.750000\n",
       "6         RP    0.029999    0.000000     0.000000     0.010002     0.040009"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ersb = df.pivot_table(index = 'rating_age', values = ['na_sales', 'eu_sales', 'jp_sales', 'other_sales', 'total_sales'], \n",
    "                      aggfunc = 'sum').reset_index()\n",
    "ersb = ersb.sort_values(by = 'total_sales',ascending = False)\n",
    "ersb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <th>8</th>\n",
       "      <th>7</th>\n",
       "      <th>5</th>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rating_age</td>\n",
       "      <td>E</td>\n",
       "      <td>unknown</td>\n",
       "      <td>T</td>\n",
       "      <td>M</td>\n",
       "      <td>E10+</td>\n",
       "      <td>K-A</td>\n",
       "      <td>AO</td>\n",
       "      <td>EC</td>\n",
       "      <td>RP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eu_sales</td>\n",
       "      <td>704.0</td>\n",
       "      <td>323.5</td>\n",
       "      <td>421.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>183.375</td>\n",
       "      <td>0.27002</td>\n",
       "      <td>0.609863</td>\n",
       "      <td>0.110046</td>\n",
       "      <td>0.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jp_sales</td>\n",
       "      <td>198.0</td>\n",
       "      <td>550.5</td>\n",
       "      <td>150.75</td>\n",
       "      <td>63.59375</td>\n",
       "      <td>40.1875</td>\n",
       "      <td>1.459961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>na_sales</td>\n",
       "      <td>1273.0</td>\n",
       "      <td>557.5</td>\n",
       "      <td>747.5</td>\n",
       "      <td>743.0</td>\n",
       "      <td>345.5</td>\n",
       "      <td>2.560547</td>\n",
       "      <td>1.259766</td>\n",
       "      <td>1.530273</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>other_sales</td>\n",
       "      <td>231.375</td>\n",
       "      <td>71.375</td>\n",
       "      <td>153.0</td>\n",
       "      <td>176.125</td>\n",
       "      <td>71.9375</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.090027</td>\n",
       "      <td>0.109985</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>total_sales</td>\n",
       "      <td>2406.0</td>\n",
       "      <td>1502.0</td>\n",
       "      <td>1472.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>4.320312</td>\n",
       "      <td>1.958984</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.040009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         index        1        8       7         5        2         4  \\\n",
       "0   rating_age        E  unknown       T         M     E10+       K-A   \n",
       "1     eu_sales    704.0    323.5   421.0     480.0  183.375   0.27002   \n",
       "2     jp_sales    198.0    550.5  150.75  63.59375  40.1875  1.459961   \n",
       "3     na_sales   1273.0    557.5   747.5     743.0    345.5  2.560547   \n",
       "4  other_sales  231.375   71.375   153.0   176.125  71.9375  0.029999   \n",
       "5  total_sales   2406.0   1502.0  1472.0    1462.0    641.0  4.320312   \n",
       "\n",
       "          0         3         6  \n",
       "0        AO        EC        RP  \n",
       "1  0.609863  0.110046  0.029999  \n",
       "2       0.0       0.0       0.0  \n",
       "3  1.259766  1.530273       0.0  \n",
       "4  0.090027  0.109985  0.010002  \n",
       "5  1.958984      1.75  0.040009  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ersb_t = ersb.T.reset_index()\n",
    "ersb_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ERSB</th>\n",
       "      <th>E</th>\n",
       "      <th>unknown</th>\n",
       "      <th>T</th>\n",
       "      <th>M</th>\n",
       "      <th>E10+</th>\n",
       "      <th>K-A</th>\n",
       "      <th>AO</th>\n",
       "      <th>EC</th>\n",
       "      <th>RP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eu_sales</td>\n",
       "      <td>704.0</td>\n",
       "      <td>323.5</td>\n",
       "      <td>421.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>183.375</td>\n",
       "      <td>0.27002</td>\n",
       "      <td>0.609863</td>\n",
       "      <td>0.110046</td>\n",
       "      <td>0.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jp_sales</td>\n",
       "      <td>198.0</td>\n",
       "      <td>550.5</td>\n",
       "      <td>150.75</td>\n",
       "      <td>63.59375</td>\n",
       "      <td>40.1875</td>\n",
       "      <td>1.459961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>na_sales</td>\n",
       "      <td>1273.0</td>\n",
       "      <td>557.5</td>\n",
       "      <td>747.5</td>\n",
       "      <td>743.0</td>\n",
       "      <td>345.5</td>\n",
       "      <td>2.560547</td>\n",
       "      <td>1.259766</td>\n",
       "      <td>1.530273</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>other_sales</td>\n",
       "      <td>231.375</td>\n",
       "      <td>71.375</td>\n",
       "      <td>153.0</td>\n",
       "      <td>176.125</td>\n",
       "      <td>71.9375</td>\n",
       "      <td>0.029999</td>\n",
       "      <td>0.090027</td>\n",
       "      <td>0.109985</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ERSB        E unknown       T         M     E10+       K-A  \\\n",
       "1     eu_sales    704.0   323.5   421.0     480.0  183.375   0.27002   \n",
       "2     jp_sales    198.0   550.5  150.75  63.59375  40.1875  1.459961   \n",
       "3     na_sales   1273.0   557.5   747.5     743.0    345.5  2.560547   \n",
       "4  other_sales  231.375  71.375   153.0   176.125  71.9375  0.029999   \n",
       "\n",
       "         AO        EC        RP  \n",
       "1  0.609863  0.110046  0.029999  \n",
       "2       0.0       0.0       0.0  \n",
       "3  1.259766  1.530273       0.0  \n",
       "4  0.090027  0.109985  0.010002  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'index':'ERSB', 0:'AO', 1:'E', 2:'E10+', 3:'EC', \n",
    "    4:'K-A', 5:'M', 6:'RP', 7:'T',\n",
    "    8:'unknown'}\n",
    "ersb_t.rename(columns = d, inplace = True)\n",
    "ersb_t = ersb_t.loc[1:4]\n",
    "ersb_t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на рейтинг ERSB разных регионов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = 2  # number of rows\n",
    "b = 2  # number of columns\n",
    "c = 1  # initialize plot counter\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "fig.subplots_adjust(hspace=0.6, wspace=0.2)\n",
    "\n",
    "\n",
    "for name in ersb_t['ERSB'].unique():\n",
    "    plt.subplot(a, b, c)\n",
    "    plt.title(name)\n",
    "    ax = sns.barplot(data=ersb_t[ersb_t['ERSB'] == name])\n",
    "    ax.set_xlabel('ERSB')\n",
    "    ax.set_ylabel('Млн. копий')\n",
    "    \n",
    "    c = c + 1\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выделяются 4 основных возрастных категории (E, T, M, E10+), внутри которых представлено наибольшее количество продаж. \n",
    "* «E» - («Everyone») — «Для всех»\n",
    "* «T» - («Teen») — «Подросткам»\n",
    "* «M» - («Mature») — «Для взрослых»\n",
    "* «E10+» -  («Everyone 10 and older») — «Для всех от 10 лет и старше»\n",
    "\n",
    "Остальные категории:\n",
    "* «AO» («Adults Only 18+») — «Только для взрослых»\n",
    "* «RP» («Rating Pending») — «Рейтинг ожидается»\n",
    "* «EC» («Early childhood») — «Для детей младшего возраста»\n",
    "* \"K-A\" (\"Kids to Adults\") - «От детей до взрослых», аналогичен «Е»\n",
    "\n",
    "Таким образом:\n",
    "* Во всех регионах наибольшее число продаж составляют игры для всех возрастных категорий.\n",
    "* В **Японии** игры для подростков более популярны, чем игры для взрослых и для людей старше 10 лет. \n",
    "* В **Европе** второе место занимают игры для взрослых, далее идет игры для подростков, и четвертое место занимают игры для всех старше 10 лет. \n",
    "* В **Северной Америке** игры для подростков и взрослых имеют одинаковую популярность, а игры категории Е+10 менее популярны. \n",
    "* Пропущенные значения в огомном количестве представлены в исходных данных для игр в Японии. (Для Штатов и Европы - примерно одинаковый процент). Это странно."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Провекра гипотез<a id='hypothesisid'></a>\n",
    "Статистически проверим:\n",
    "1. Одинаковы ли средние пользовательские рейтинги платформ Xbox One и PC.\n",
    "2. Различаются ли средние пользовательские рейтинги жанров Action и Sports.\n",
    "\n",
    "Для проведения t-теста Стьюдента воспользуемся методом **st.ttest_ind()**, т.к. наша цель установить разницу между двумя наборами данных (выборками). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>user_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pc</td>\n",
       "      <td>6.535156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>xone</td>\n",
       "      <td>6.480469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   platform  user_score\n",
       "2        pc    6.535156\n",
       "11     xone    6.480469"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_scores = df_actual.pivot_table(index = 'platform', values = 'user_score').reset_index()\n",
    "user_scores.query('platform == \"xone\" or  platform == \"pc\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для того, чтобы проверить одинаковы ли средние пользовательские рейтинги платформ Xbox One и PC, установим нулевую и альтернативную гипотезы:\n",
    "* H${0}$: средние пользовательские рейтинги двух платформ одинаковы.\n",
    "* H${A}$: средние пользовательские рейтинги двух платформ различаются.\n",
    "\n",
    "\n",
    "Уровень значимости зададим 95%.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение:  0.7113922013784785\n",
      "Не получилось отвергнуть нулевую гипотезу: средние пользовательские рейтинги платформ  Xbox One и PC одинаковы\n"
     ]
    }
   ],
   "source": [
    "pc = df_actual.loc[df['platform'] == 'pc']['user_score']\n",
    "xone = df_actual.loc[df['platform'] == 'xone']['user_score']\n",
    "result = st.ttest_ind(pc, xone, nan_policy = 'omit')\n",
    "alpha = 0.05\n",
    "print('p-значение: ', result.pvalue)\n",
    "if result.pvalue < alpha:\n",
    "    print(\"Отвергаем нулевую гипотезу: средние пользовательские рейтинги платформ  Xbox One и PC различаются\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу: средние пользовательские рейтинги платформ  Xbox One и PC одинаковы\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Результаты t-теста Стьюдента свидетельствуют об отсутствии статистически достоверной разницы между пользовательскими рейтингами платформ Xbox One и PС.\n",
    "\n",
    "Далее посмотрим на данные о жанрах Action и Sports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "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>genre</th>\n",
       "      <th>user_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>6.777344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sports</td>\n",
       "      <td>5.906250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     genre  user_score\n",
       "0   Action    6.777344\n",
       "10  Sports    5.906250"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_genres = df_actual.pivot_table(index = 'genre', values = 'user_score').reset_index()\n",
    "two_genres.query('genre == \"Action\" or  genre == \"Sports\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для того, чтобы проверить различаются ли средние пользовательские рейтинги жанров Action и Sports, установим нулевую и альтернативную гипотезы:\n",
    "* H${0}$: средние пользовательские рейтинги двух жанров одинаковы.\n",
    "* H${A}$: средние пользовательские рейтинги двух жанров различаются.\n",
    "\n",
    "Уровень значимости зададим 95%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение:  3.962736258173501e-18\n",
      "Отвергаем нулевую гипотезу: средние пользовательские рейтинги  жанров Action и Sports различаются\n"
     ]
    }
   ],
   "source": [
    "action = df_actual.loc[df['genre'] == 'Action']['user_score']\n",
    "sports = df_actual.loc[df['genre'] == 'Sports']['user_score']\n",
    "result = st.ttest_ind(action, sports, nan_policy = 'omit')\n",
    "alpha = 0.05\n",
    "print('p-значение: ', result.pvalue)\n",
    "if result.pvalue < alpha:\n",
    "    print(\"Отвергаем нулевую гипотезу: средние пользовательские рейтинги  жанров Action и Sports различаются\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу: средние пользовательские рейтинги  жанров Action и Sports одинаковы\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Результаты t-теста Стьюдента свидетельствуют о том, что **пользовательские рейтинги жанров Action и Sports различаются.** Т.е. рейтинг жанра Action выше жанра Sports. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Общий вывод.<a id='conclusionid'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. В ходе данной работы проанализировали данные о продажах и рейтингах компьютерных игр по всему миру с целью выявления потенциально популярных игр и планирования рекламной кампании на 2017 год.\n",
    "2. В период с 1994 года по настоящее время разные платформы имели различную популярность. В среднем новые платформы появляются и исчезают в течение периода около 10 лет. Пик продаж наступает через 1-6 лет, в зависимости от платформы и, вероятно, проведенных рекламных кампаний. \n",
    "3. Начиная с 2010 года, 12 платформ были представлены на мировом рынке. Из них, Play Station 4 (PS4), Xbox One, 3DS, PS Vita и Wii U к 2016 году имели наиболее высокие показатели по продажам за 2016 год, а также наблюдалось наименьшее снижение продаж: \n",
    "* PS4 - 60.25 млн. копий\n",
    "* Xbox One - 23.75 млн. копий\n",
    "* 3DS - 14.16 млн. копий\n",
    "* PS Vita - 2.72 млн. копий\n",
    "* Wii U - 3.58 млн. копий\n",
    "\n",
    "Также, на платформах PS Vita, 3DS и PS4 больше отдельных игр, имеющих очень высокие показатели продаж, что делает эти платформы более прибыльными. \n",
    "\n",
    "4. Как **отзывы критиков**, так и **отзывы пользователей** оказывают слабое положительное влияние на продажи игр на платформах PS4, Xbox One, 3DS, PS Vita и Wii U. При этом, для всех платформ, кроме Wii U, отзывы критиков имеют больший эффект на продажи игр, по сравнению с отзывами пользователей.\n",
    "\n",
    "\n",
    "5. Среди платформ PS4, Xbox One, 3DS, PS Vita и Wii U наиболее популярные жанры - Action(26%), Shooter(17%), Role-Playing(16%), Sports(13%) и Platform(8%). Наименее популярны Strategy и Puzzle.\n",
    "\n",
    "\n",
    "6. Был составлен **портрет пользователя для каждого региона**.\n",
    "\n",
    "\n",
    "* Наиболее популярные платформы:\n",
    "\n",
    "В **Европе**  PS2, PS2, Xbox 360, Wii, PS. \n",
    "\n",
    "В **Северной Америке** - Xbox360, PS2, Wii, PS3, DS. \n",
    "\n",
    "В **Японии** - DS, PS2, PS, 3DS, PSP. Платформу PS2 можно выделить как наиболее популярную в целом. \n",
    "* В целом в Штатах продажи игр гораздо выше, чем в Европе и Японии, и составляют около половины всех продаж по топовым платформам.\n",
    "* Самые популярные жанры: \n",
    "\n",
    "в **Европе:** Action, Sports, Shooter, Races, Misc.\n",
    "\n",
    "\n",
    "в **Северной Америке**: Action, Sports, Shooter, Misc, Role_Playing.\n",
    "\n",
    "\n",
    "в **Японии:** Role_Playing, Action, Misc, Sports, Platform.\n",
    "\n",
    "* Среди всех **категорий игр**, выделяются 4 наиболее популярные возрастные категории (E, T, M, E10+), внутри которых представлено наибольшее количество продаж.\n",
    "\n",
    "«E» - («Everyone») — «Для всех»\n",
    "\n",
    "\n",
    "«T» - («Teen») — «Подросткам»\n",
    "\n",
    "\n",
    "«M» - («Mature») — «Для взрослых»\n",
    "\n",
    "\n",
    "«E10+» - («Everyone 10 and older») — «Для всех от 10 лет и старше»\n",
    "\n",
    "Во всех регионах наибольшее число продаж составляют игры для всех возрастных категорий.\n",
    "\n",
    "\n",
    "В **Японии** игры для подростков более популярны, чем игры для взрослых и для людей старше 10 лет.\n",
    "\n",
    "\n",
    "В **Европе** второе место занимают игры для взрослых, далее идет игры для подростков, и четвертое место занимают игры для всех старше 10 лет.\n",
    "\n",
    "\n",
    "В **Северной Америке** игры для подростков и взрослых имеют одинаковую популярность, а игры категории Е+10 менее популярны.\n",
    "\n",
    "7. Был проведен **статистический анализ данных**:\n",
    "\n",
    "a) пользовательские рейтинги игр на платформах PC и Xbox One статистически не различаются.\n",
    "\n",
    "b) Пользовательские рейтинги жанров Action и Sports статистически различаются. Жанр Action (6.8) имеет более высокий рейтинг по сравнению с жанром Sports (5.8).\n",
    "\n",
    "**Общий вывод:** рекламные кампании следует планировать по-разному для каждого региона, опираясь на полученные результаты. На 2017 год следует сфокусироваться на продвижении таких платформ, как PS4, Xbox One, 3DS, PS Vita и Wii U. \n",
    "\n",
    "**Примечание:** В дополнение интересно было бы провести анализ выбросов на платформах PS4, Xbox One, 3DS, PS Vita и Wii U, чтобы найти наиболее популярные игры в разных регионах. "
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