sprint_6_proyecto_integrado.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "**Tabla de Contenidos**<a id='toc0_'></a>    \n",
    "- 1. [Proyecto Integrado](#toc1_)    \n",
    "  - 1.1. [Preparación de los datos](#toc1_1_)    \n",
    "    - 1.1.1. [Corregir errores](#toc1_1_1_)    \n",
    "  - 1.2. [Análisis de los datos](#toc1_2_)    \n",
    "  - 1.3. [Usuarios por región](#toc1_3_)    \n",
    "  - 1.4. [Pruebas de hipótesis](#toc1_4_)    \n",
    "    - 1.4.1. [Las calificaciones promedio de los usuarios para las plataformas Xbox One y PC son las mismas.](#toc1_4_1_)    \n",
    "    - 1.4.2. [Las calificaciones promedio de los usuarios para los géneros de Acción y Deportes son diferentes.](#toc1_4_2_)    \n",
    "  - 1.5. [Conclusión general](#toc1_5_)    \n",
    "\n",
    "<!-- vscode-jupyter-toc-config\n",
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. <a id='toc1_'></a>[Proyecto Integrado](#toc0_)\n",
    "\n",
    "En este proyecto se realizará el análisis de los datos de venta de videojuegos, en el cuál podremos observar distintos datos de información sobre cada uno de ellos. Se hará la limpieza de los datos, se organizaran y finalmente se tendrán en cuenta para realizar las pruebas de algunas hipótesis planteadas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1. <a id='toc1_1_'></a>[Preparación de los datos](#toc0_)\n",
    "\n",
    "Primero comenzaremos cargando las librerías que se van a utilizar y leyendo el DataFrame, posterior a esto se hará el análisis para encontrar posibles errores en los tipos de datos, o en la información de las columnas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar las librerías\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import stats as sts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th>Genre</th>\n",
       "      <th>NA_sales</th>\n",
       "      <th>EU_sales</th>\n",
       "      <th>JP_sales</th>\n",
       "      <th>Other_sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>41.36</td>\n",
       "      <td>28.96</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.45</td>\n",
       "      <td>76.0</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>15.68</td>\n",
       "      <td>12.76</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.29</td>\n",
       "      <td>82.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>15.61</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16711</th>\n",
       "      <td>LMA Manager 2007</td>\n",
       "      <td>X360</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16712</th>\n",
       "      <td>Haitaka no Psychedelica</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16713</th>\n",
       "      <td>Spirits &amp; Spells</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16715 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Name Platform  Year_of_Release         Genre  \\\n",
       "0                         Wii Sports      Wii           2006.0        Sports   \n",
       "1                  Super Mario Bros.      NES           1985.0      Platform   \n",
       "2                     Mario Kart Wii      Wii           2008.0        Racing   \n",
       "3                  Wii Sports Resort      Wii           2009.0        Sports   \n",
       "4           Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing   \n",
       "...                              ...      ...              ...           ...   \n",
       "16710  Samurai Warriors: Sanada Maru      PS3           2016.0        Action   \n",
       "16711               LMA Manager 2007     X360           2006.0        Sports   \n",
       "16712        Haitaka no Psychedelica      PSV           2016.0     Adventure   \n",
       "16713               Spirits & Spells      GBA           2003.0      Platform   \n",
       "16714            Winning Post 8 2016      PSV           2016.0    Simulation   \n",
       "\n",
       "       NA_sales  EU_sales  JP_sales  Other_sales  Critic_Score User_Score  \\\n",
       "0         41.36     28.96      3.77         8.45          76.0          8   \n",
       "1         29.08      3.58      6.81         0.77           NaN        NaN   \n",
       "2         15.68     12.76      3.79         3.29          82.0        8.3   \n",
       "3         15.61     10.93      3.28         2.95          80.0          8   \n",
       "4         11.27      8.89     10.22         1.00           NaN        NaN   \n",
       "...         ...       ...       ...          ...           ...        ...   \n",
       "16710      0.00      0.00      0.01         0.00           NaN        NaN   \n",
       "16711      0.00      0.01      0.00         0.00           NaN        NaN   \n",
       "16712      0.00      0.00      0.01         0.00           NaN        NaN   \n",
       "16713      0.01      0.00      0.00         0.00           NaN        NaN   \n",
       "16714      0.00      0.00      0.01         0.00           NaN        NaN   \n",
       "\n",
       "      Rating  \n",
       "0          E  \n",
       "1        NaN  \n",
       "2          E  \n",
       "3          E  \n",
       "4        NaN  \n",
       "...      ...  \n",
       "16710    NaN  \n",
       "16711    NaN  \n",
       "16712    NaN  \n",
       "16713    NaN  \n",
       "16714    NaN  \n",
       "\n",
       "[16715 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"datasets/games.csv\")\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.1. <a id='toc1_1_1_'></a>[Corregir errores](#toc0_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      "None\n",
      "\n",
      "Cantidad de Duplicados: 0\n"
     ]
    }
   ],
   "source": [
    "# Ver la información del DataFrame\n",
    "print(df.info())\n",
    "# Comprobar duplicados\n",
    "print()\n",
    "print(\"Cantidad de Duplicados:\",df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['name', 'platform', 'year_of_release', 'genre', 'na_sales', 'eu_sales',\n",
      "       'jp_sales', 'other_sales', 'critic_score', 'user_score', 'rating'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Cambiar los nombres de las columnas\n",
    "dic = {}\n",
    "for col in df.columns:\n",
    "    dic[col] = col.lower()\n",
    "df = df.rename(columns=dic)\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name                  2\n",
      "platform              0\n",
      "year_of_release     269\n",
      "genre                 2\n",
      "na_sales              0\n",
      "eu_sales              0\n",
      "jp_sales              0\n",
      "other_sales           0\n",
      "critic_score       8578\n",
      "user_score         6701\n",
      "rating             6766\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Ver cantidad de datos vacíos\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name               0\n",
      "platform           0\n",
      "year_of_release    0\n",
      "genre              0\n",
      "na_sales           0\n",
      "eu_sales           0\n",
      "jp_sales           0\n",
      "other_sales        0\n",
      "critic_score       0\n",
      "user_score         0\n",
      "rating             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Rellenar valores nulos\n",
    "df['name'].fillna('Unkwnown', inplace=True)\n",
    "df['genre'].fillna('Unkwnown', inplace=True)\n",
    "df['rating'].fillna('Unkwnown', inplace=True)\n",
    "df['year_of_release'].fillna(0, inplace=True)\n",
    "df['critic_score'].fillna(0, inplace=True)\n",
    "df['user_score'].fillna('0', inplace=True) # Se va a cambiar el tipo de dato después\n",
    "# Ver cantidad de datos vacíos\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "He decidido conservar los datos que poseen valores nulos, pues aunque no parecieran tener un impacto muy significativo dentro de los análisis próximos, puede que ayuden a observar con mejor precisión el comportamiento de la información. En el caso de las columnas `year_of_release`, `critic_score` y `user_score` las coloqué en 0, dado que no me parecío que rellenar los valores haciendo la mediana o promedio acercaría el valor a uno real, ya que cada juego puede tener valor completamente distinto e independiente; aunque no estoy muy seguro de que este enfoque sea correcto. En las otras columnas coloqué el valor `Unknown`, dado que la información también es desconocida."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reemplazar valores TBD en columna user_score\n",
    "df.loc[df['user_score']=='tbd', 'user_score'] = df['critic_score']/10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al revisar la columna `user_score` encontré que contenía el valor tbd (To Be Determinated), por ello la columna tenía un tipo de dato incorrecto. Para resolverlo, creí que la mejor manera era poniendo el mismo valor que tenían las filas en la columna `critic_score`, así ambos tienen el mismo valor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cambiar tipos de datos\n",
    "df['year_of_release'] = df['year_of_release'].astype('int')\n",
    "df['user_score'] = df['user_score'].astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "He cambiado el tipo de datos de dos columnas: `year_of_release` dado que el tipo original era `float` y parece mejor que el tipo de dato sea `int` para describir el año de lanzamiento del juego. La otra columna `user_score` tenía tipo `str` dado que había filas con un valor a determinar, para cambiar esto tomé el valor de la columna `critic_score` y lo reemplacé para cambiar el tipo de dato a `float`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora agregaré una columna extra al DataFrame con el cálculo de las ventas totales, es decir, la suma de las ventas en todas las regiones para cada juego."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>platform</th>\n",
       "      <th>year_of_release</th>\n",
       "      <th>genre</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>other_sales</th>\n",
       "      <th>critic_score</th>\n",
       "      <th>user_score</th>\n",
       "      <th>rating</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006</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>8.0</td>\n",
       "      <td>E</td>\n",
       "      <td>82.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Unkwnown</td>\n",
       "      <td>40.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008</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",
       "      <td>35.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009</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>8.0</td>\n",
       "      <td>E</td>\n",
       "      <td>32.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Unkwnown</td>\n",
       "      <td>31.38</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>16710</th>\n",
       "      <td>Samurai Warriors: Sanada Maru</td>\n",
       "      <td>PS3</td>\n",
       "      <td>2016</td>\n",
       "      <td>Action</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Unkwnown</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16711</th>\n",
       "      <td>LMA Manager 2007</td>\n",
       "      <td>X360</td>\n",
       "      <td>2006</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Unkwnown</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16712</th>\n",
       "      <td>Haitaka no Psychedelica</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Unkwnown</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16713</th>\n",
       "      <td>Spirits &amp; Spells</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2003</td>\n",
       "      <td>Platform</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Unkwnown</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16714</th>\n",
       "      <td>Winning Post 8 2016</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Unkwnown</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16715 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name platform  year_of_release         genre  \\\n",
       "0                         Wii Sports      Wii             2006        Sports   \n",
       "1                  Super Mario Bros.      NES             1985      Platform   \n",
       "2                     Mario Kart Wii      Wii             2008        Racing   \n",
       "3                  Wii Sports Resort      Wii             2009        Sports   \n",
       "4           Pokemon Red/Pokemon Blue       GB             1996  Role-Playing   \n",
       "...                              ...      ...              ...           ...   \n",
       "16710  Samurai Warriors: Sanada Maru      PS3             2016        Action   \n",
       "16711               LMA Manager 2007     X360             2006        Sports   \n",
       "16712        Haitaka no Psychedelica      PSV             2016     Adventure   \n",
       "16713               Spirits & Spells      GBA             2003      Platform   \n",
       "16714            Winning Post 8 2016      PSV             2016    Simulation   \n",
       "\n",
       "       na_sales  eu_sales  jp_sales  other_sales  critic_score  user_score  \\\n",
       "0         41.36     28.96      3.77         8.45          76.0         8.0   \n",
       "1         29.08      3.58      6.81         0.77           0.0         0.0   \n",
       "2         15.68     12.76      3.79         3.29          82.0         8.3   \n",
       "3         15.61     10.93      3.28         2.95          80.0         8.0   \n",
       "4         11.27      8.89     10.22         1.00           0.0         0.0   \n",
       "...         ...       ...       ...          ...           ...         ...   \n",
       "16710      0.00      0.00      0.01         0.00           0.0         0.0   \n",
       "16711      0.00      0.01      0.00         0.00           0.0         0.0   \n",
       "16712      0.00      0.00      0.01         0.00           0.0         0.0   \n",
       "16713      0.01      0.00      0.00         0.00           0.0         0.0   \n",
       "16714      0.00      0.00      0.01         0.00           0.0         0.0   \n",
       "\n",
       "         rating  total_sales  \n",
       "0             E        82.54  \n",
       "1      Unkwnown        40.24  \n",
       "2             E        35.52  \n",
       "3             E        32.77  \n",
       "4      Unkwnown        31.38  \n",
       "...         ...          ...  \n",
       "16710  Unkwnown         0.01  \n",
       "16711  Unkwnown         0.01  \n",
       "16712  Unkwnown         0.01  \n",
       "16713  Unkwnown         0.01  \n",
       "16714  Unkwnown         0.01  \n",
       "\n",
       "[16715 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['total_sales'] = df['na_sales']+df['eu_sales']+df['jp_sales']+df['other_sales']\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. <a id='toc1_2_'></a>[Análisis de los datos](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Mira cuántos juegos fueron lanzados en diferentes años. ¿Son significativos los datos de cada período?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mZs2hu3P2t5DuoCdgIumxswLGku6CN6nRyZmZmVnfVWzGj4hJEbEVcDXwoYjYKCLGAx8EruyvBM3MzKxvqjlnv2tEXNY1ERF/AN7duJTMzMysnqq59O5hSccB/52nPwU83LiUzMzMrJ6qObI/EJgAXJiHt+QyMzMzGwSquYPeU8CR/ZCLmZmZNUCPlb2kCcDXgO2AUV3lEbF7A/MyMzOzOqmmGf9s4C7SpXbfBpYB8xuYk5mZmdVRNZX9+IiYC7waEddHxCGAj+rNzMwGiWp647+a/z4iaV9ST/xu765nZmbWbNqPvbSwfNkJ+/ZzJv2vmsr+eEljgK8ApwIbAEc1NCszMzOrm2p64/8+jz4LvLex6ZiZmVm9dfcgnFNJ98YvFBFHNCQjMzMzq6vuOugtID0MZxQwBbg3DzsCa/VlpZLGSjpf0l2SlkiaJmlDSVdJujf/HZdjJekUSUsl/UXSlL6s28zMbKjp7kE4Z0bEmcAOwIyIODUiTgX2IFX4ffEj4PKI2AaYDCwBjgWuiYitgWvyNMA+wNZ5OAz4aR/XbWZmNqRU00FvHKlTXtfz60fnsl7Jnf3eA8wCiIhXgFckzQRm5LAzgXnAMcBM4KyICOCm3CqwaUQ80tsczMzMBlJ/XxlQTWV/AnCrpOtIz7N/D/CtPqxzErACOF3SZNKpgiOBjUsq8EeBjfP45sBDJa/vzGWrVfaSDiMd+TNx4sQ+pGdmZtZaerypTkScDkwlPQTnt8C03LzfWyNIfQB+GhE7AS/yZpN91zqDbjoHVshzTkR0RETHhAkT+pCemZlZa6lY2UvaJv+dAmxGOrp+CNisj53kOoHOiPhznj6fVPk/JmnTvM5Ngcfz/OXAFiWvb8tlZmZmVoXumvGPJjWL/0fBvKCXt8yNiEclPSTpHRFxN6nD3515OIh02uAg4OL8kkuAL0k6l9TC8KzP15uZWbMpOg/fLHfnq1jZR8RheXSfiHi5dJ6kUQUvqcU/A2dLWgu4DziY1MrwG0mHAg8An8ixlwEfAJYCK3OsmZmZVamaDnp/JDWz91RWtYhYBHQUzNqjIDaAw3u7LjMzs6GuuzvobULq9b6OpJ1IPfEhXYa3bj/kZmZmZnXQ3ZH9XqRr4duAE0vKnwe+0cCczMzMrI66O2d/JnCmpP0j4oJ+zMnMzGzAtdIjcas5Z/97Sf8AtJfGR8R3GpWUmZmZ1U81lf3FpMfb3gL8rbHpmJmZWb1VU9m3RcTeDc/EzMzMGqLH2+UCf5S0fcMzMTMzs4ao5sh+N2CWpPtJzfgiXf6+Q0MzMzMzs7qoprLfp+FZmJmZWUV9vTKgx8o+Ih4AkPQWoK+3yTUzM7N+1uM5e0n7SboXuB+4HlgG/KHBeZmZmVmdVNOM/11gV+DqiNhJ0nuBTzc2LTOz+mqlG6SY1aqa3vivRsSTwDBJwyLiOoofYmNmZmZNqJoj+2ckjQZuID2W9nHgxcamZWZmZvVSzZH9TNJz5I8CLgf+CnyokUmZmZlZ/VSs7CW9TdL0iHgxIl6PiFX54TgLgbH9lqGZmZn1SXdH9icDzxWUP5vnmZmZ2SDQXWW/cUTcXl6Yy9oblpGZmZnVVXeV/dhu5q1T5zzMzMysQbqr7BdI+nx5oaTPkR53a2ZmZoNAd5fefRm4UNKneLNy7wDWAj7S1xVLGg4sAJZHxAclTQLOBcbn9X0mIl6RtDZwFrAz8CTwyYhY1tf1m5mZDRUVj+wj4rGIeDfwbdItcpcB346IaRHxaB3WfSSwpGT6B8BJEfE24Gng0Fx+KPB0Lj8px5mZmVmVerzOPiKui4hT83BtPVYqqQ3YF/hFnhawO3B+DjkT+HAen5mnyfP3yPFmZmZWhWpuqtMIJwNfA17P0+OBZyJiVZ7uBDbP45sDDwHk+c/m+NVIOkzSAkkLVqxY0cDUzczMBpd+r+wlfRB4PCLq2skvIuZEREdEdEyYMKGeizYzMxvUqrk3fr1NB/aT9AFgFLAB8CNgrKQR+ei9DVie45cDWwCdkkYAY0gd9czMzKwK/V7ZR8TXga8DSJoBfDUiPiXpPOBjpB75BwEX55dckqf/lOdfGxHRz2mbWS8UPVbWj5Q1638Ddc6+yDHA0ZKWks7Jz83lc4Hxufxo4NgBys/MzGxQGohm/DdExDxgXh6/D9ilIOZl4OP9mpiZmVkLaaYjezMzM2sAV/ZmZmYtbkCb8c3Mequo8x+4A6BZEVf2ZtYUXHmbNY6b8c3MzFqcK3szM7MW58rezMysxbmyNzMza3Gu7M3MzFqcK3szM7MW50vvzKxqvjzObHDykb2ZmVmLc2VvZmbW4lq+Gd/NjmZmNtS1fGVvZpV5Z9hsaHAzvpmZWYtzZW9mZtbiXNmbmZm1OFf2ZmZmLc6VvZmZWYvr98pe0haSrpN0p6TFko7M5RtKukrSvfnvuFwuSadIWirpL5Km9HfOZmZmg9lAXHq3CvhKRCyUtD5wi6SrgFnANRFxgqRjgWOBY4B9gK3zMBX4af5rZmV8KZ2ZFen3I/uIeCQiFubx54ElwObATODMHHYm8OE8PhM4K5KbgLGSNu3frM3MzAavAT1nL6kd2An4M7BxRDySZz0KbJzHNwceKnlZZy4rX9ZhkhZIWrBixYrGJW1mZjbIDFhlL2k0cAHw5Yh4rnReRAQQtSwvIuZEREdEdEyYMKGOmZqZmQ1uA1LZSxpJqujPjojf5uLHuprn89/Hc/lyYIuSl7flMjMzM6vCQPTGFzAXWBIRJ5bMugQ4KI8fBFxcUv7Z3Ct/V+DZkuZ+MzMz68FA9MafDnwGuF3Solz2DeAE4DeSDgUeAD6R510GfABYCqwEDu7XbM3MzAa5fq/sI+J/AVWYvUdBfACHNzQpMzOzFuY76JmZmbU4V/ZmZmYtzpW9mZlZi3Nlb2Zm1uJc2ZuZmbU4V/ZmZmYtbiCuszezGhQ9yc5PsTOzWvjI3szMrMW5sjczM2txruzNzMxanCt7MzOzFufK3szMrMW5sjczM2txruzNzMxanK+zN+tnRdfNg6+dN7PG8ZG9mZlZi3Nlb2Zm1uLcjN9HbpJtPbVuU38GzKzZubLvZ64YzMysv7kZ38zMrMUNmiN7SXsDPwKGA7+IiBMGOKUhx09fMzMbnAZFZS9pOPBj4P1AJzBf0iURcefAZtZYjT533MhTCs2Wu3dUzGwoGxSVPbALsDQi7gOQdC4wE6h7Ze9z6mZm1moUEQOdQ48kfQzYOyI+l6c/A0yNiC+VxBwGHJYn3wHcXbCojYAnalh1M8U3Uy61xjdTLrXGN1MutcY3Uy61xjdTLrXGN1MutcY7l/rED1QuW0bEhMJXRETTD8DHSOfpu6Y/A/xnL5azYLDGN1Muzn1wxDdTLs59cMQ7l9bMPSIGTW/85cAWJdNtuczMzMx6MFgq+/nA1pImSVoLOAC4ZIBzMjMzGxQGRQe9iFgl6UvAFaRL706LiMW9WNScQRzfTLnUGt9MudQa30y51BrfTLnUGt9MudQa30y51BrvXOoT30y5AIOkg56ZmZn13mBpxjczM7NecmVvZmbW4lzZm5mZtbhB0UGvtyRtQ7rT3ua5aDlwSUQsGbiszMzM+lfLHtlLOgY4FxBwcx4EnCPp2IHMzcz6RtIYSZ+UdHQePilpbC+W8/6Csg0kvbWgfIcKy9hE0iZ5fIKkj0raroYcvl9D7KS8/G0K5k2UNCqPS9LBkk6V9EVJaxzYSdqvK76G9b9H0jvy+HRJX5VUeC9xSaMlfUzSUZKOkLS3pIp1jqRtJB0j6ZQ8HCNp21ryy8s5uJvl7yFpdFn53gWxu0h6Vx5/Z/6MfaCGHM6qIXa3vPw9K8yfKmmDPL6OpG9L+p2kH0gaU/V6WrU3vqR7gO0i4tWy8rWAxRGxdcFr6tISIOngiDi9wvI3B/4cES+UlO8dEZeXxe4CRETMl/ROYG/groi4rMoczoqIz1YZuxvp+QN3RMSVBfOnAksi4jlJ6wDHAlNIzyb4fkQ8WxJ7BHBhRDxU5bq77pvwcERcLekfgHcDS4A55dsvv2Yr4KOkGy29BtwD/Coinquwjr2AD7P6dr24/D3vIc//FxHfqbDsNuCaiFhWUn5IRJxWFivg40AA5wO7kz5vdwE/i4jXq8jj2ojYvaB8o4h4omT60+RtCvw8yr7okj4CXB8RT0maAPwHsBNpm34lIjrL4k8ELoiIG3vKMcdvCHwJeBiYC3wDmEbart+PiKfL4t8L7M/q2/QXEbG0YNmfBWYDV/LmzbXaSA/K+nZE1PJD+2BETCyZ/gRwMvA4MBKYFRHz87yFETGl7PVfIH0fBPwAmEV6z3cD/i0i5pbFn1KeAumOoGcBRMQRZfEXRcSH8/jMnNs80nfkXyPijJLYO4BdImKlpB8AbwUuIn3OiIhDypb9EvAi8AfgHOCKiHitm/fqZNJnagTpMug98mv/Hrg1Iv6lJPYTwFeBvwDvBf5IOrjcHvhURNxetuxjgANJB2hdn7020m/DuVHDU07Lt2kuOwI4nPT52xE4MiIuzvNW266SZgP75P/zKmAqcB3p83VFRHyvbNnl93xR/p+vBYiI/crib46IXfL453NeFwJ7Ar8r/18lLQYm50vQ5wArSb8fe+Tyj1b1xtR6y73BMpB+QLcsKN8SuLug/BhgEemL++k8HNtVVuO6HywoO4J0v/6LgGXAzJJ5C8tiZwM3AQuAf80fmv8L3AB8s2DZl5QNvwNe6JouiL+5ZPzz+X+cDdxY9L8Ci4EReXwO6Qdnt/ya35bFPkv6gf8f4J+ACT28V2cDv845/5L0of8McAZwZoX38UrgONIPyI+B75EqqRkF8ScDl5F+NHbLwwG57Ed93Kbfz9vkZOCvwD9X2qa57CekL+klwH8D5+X/9dyiXEg/lKXD7cDfuqbLYheWjB9H+jE+KK/jpIJl31ky/mvgKNKP6yzgqoL4Ffnz+ADwb8BOPbxfl5Eqv5+SKqdTgf8DfIe0o1Ua+6/A6aTv3PnAv+fP5a3AxwuWfTcwtqB8HHBPFd+P0u/Ji2Wxi4BN8/gupN+Rj+TpWwuWfTuwLjCe9J3bpCSXRQXxD+Vt/9m8fQ7K7+1BwEEF8beWjP8RmJTHNwJu62ab3gIMK5m+rWjZOc/PA9cAjwE/A/6+wjZdTKrI1gWeBtbN5SNJBwrln911S3K9Io/vAPyxYNn3ACMLytcC7q3iu7Had6TCdhqdx9vzZ/nIou2aY4fn//M5YINcvg5l37uu717epjNIOz4zgEfy+BrvZdk2nU/+jQTWA24viF9S9D3v+rx29z1cLbbawME2kI6El5L2POfk4fJctrc/bI35sJF+QIaR9lLnkn7ILif9mK1f9D7mvyNIPzbD87Qq/K+3l8SsC8zL4xPL38eu7Vrh86Hy7Zrf66LheWBVhVy6doLGkiq4k4q2aVd8/jsSeBJYq+R/L/pfu3YKtiHtpLaTKostKduRLdumC4H1StZVtE3vLhm/pbttWrp84O2kHc/FpIpwNvD2gvhFJe/z8h4+M7eXjI8Abszj4yirRLq2KTCmoHxM+TbN5U8D+5K/DyXDDOCxSrnk6U1JFecRFO/Ale5klVe+RZ+B9Uk7h78CNstl9xV9RguWf3N3yyft4O2exy/o+oyQdkSKKvvy7/Im+f/8E/BQQfwd+e+o/J6uk6eHU7KjUfLd6Go5Xqfs81m0TWs9OHuMdIS+ZdnQTmolLI9fXDY9mvS7dGLB5/HWovFuvhvDSDvLVwE7VrFNb8uf7fGU3eO+wmfmPODgPH460BFvfhfnV1pP+dCyHfQi4nJJbyftnZc2386P4qaq14HNSEcupTbN88ptDOxF+tCXEmkPvNywyE33EbFM0gzgfElb5teUWpVzXCnpr5GbpyPiJUlFuXQARwLfBP4lIhZJeikiri+IBRgmaRzpQ6qIWJGX/6KkVQXxd5ScmrhNUkdELMjvb3kze0Rqjr4SuFLSSFKT2IHAD4HyJzINy03565Eq7zHAU8DapIqqyAhSU+/apC8tEfFgXle5lyW9K3JTbIl3AS+XlT0DvCsiHitfiKSi0xIjImJVXv8zkj4EzJF0HmknsVxX7KuS5kfEK3l6VdF2jYj9cnP7HOCHEXGJpFcjovwzCrCOpJ1I23R4RLxYsq6iz/s8Sd8hHVXPk/SRiLgwN6c/WxAfeXn3AN8Fvqt0DvtA0k7O28riuz5j6wOjJbXnz/34gvfmdUkbRsRTpO/g8Lyup/Opj3LfAxZKupK08wNpZ+/9ObdyNwEri74Pksqfjvm8pLdGxF9zDo/k7+pFQNF5+JA0MtLppjfOXSudC1/j/HREPA98WdLOwNmSLi2KKzFZ0nOk34i1JW2ac1qL/D6V+BxwlqRvkbbhIkmLSDuiRxcse7X3NiIeBU4BTsm/S+UulfQ/pMr+F8BvJN1E2nG6oSz2MuBySTeQDrzOgzdO7xRt0y8D10i6l9W36dtIp4PK/Z508LRojX9KmlcQ/5ikHbviI+IFSR8ETiOdWij1iqR1I2IlsHPJcsdQUBfk37uT8vf+JEmP0X3n9zGkHUiRPj9d23Q0xe/N54AfSTqO9KS7P+Xfo4fyvOpUu1fQ6gO1twTMBXarsKxfFZRdS97rKykbQTpX91pZ+Z95swmstCluDAVHFyXz20hfqv+koNm5JG4ZcB9wf/7b1Ww5muI91zGkZvW/5txeza+7nnTOqDT21m7Wu25B2VF5WQ+QjiquAX5OOjKYXRB/JKkF5eeko4GuPd4JwA0F8VNyzneSd0BI5+1uAnYuiz2edM6zKPcfFJT9nuKWk+OB1wvK/0Bu3Skr34Syo7ay+euRjkAuBjorxFxXNnRt0zWOHnL5SOBbwIN5eJ3UgvErYGJBfMXtWiGfA0lHX4+RzsVfTTryWQ4cVhb7ybz9r8q57FuyTdf4LuV540inY76ShwOAcbXkWGG5k4GtK7xfnyoon0hxi+DmwPt6WJdI52v/uxd5jgWmVZi3LakvyP6k883DKsTN6MV6pwG75vG3ks7Lf6JoHcAH8vz3l5QNA9ausOxhwK457/3z+PC+btO87DbyKZaCedPLpivltxGwfRXr2pfUL6XWHNcln6apMH+D/PncGdi41uW3bAe93lDqKVptS0Cty24jHbE/WjBvepR0fJK0dkT8rSBuI9KP+O3l88ri9iV9gL9RY47rkj5E91eYvwEwibST0hnFR8Bvj3T0V8t6NwOIiIeVelS/j7SzcnOF+O1IP2h3RMRdVa5jE0q2a9F2qFXurEhEvFQwb/OIqOrJjJLWIzW7P95D3GTSD/zPashxOOnHa2U3MWNIrRRPdhMzOko6ldawbkVquRhBanZdHhGPFMRuCGwFLI2IZ6pc/sasvk3X+Dz2Nr6Ryx5Kude67ArLqOmz18j4Zsql1nhX9iUkTQSei9Qk205qHl8SFR66UyH+roi4o6/xjVz2UMs9v6aDkp7e3e0k1BLbbPHNlEujcpe0I6kj2RhSz22RjtyeAf4pIhaWxe9E6ig4htV7768RX7bs8tgvRsSt3eTS1/hac19t+b1YdsPia112d1TQu36g4pspl1rjXdlnStfef4HU2/mHpOanG0lNSXMj4sT+im+mXFog978nXVb2DKn560ZSE/CrwGei5BLBWmKbLb6ZcumH3BcBX4iIP5eV7wr8V0RM7m18I5c9lHLvxbKL+hRA2pH7ZkRs2F/xzZRLb+IrijqcD2mFgdS7eB3SOc7nWb2HelHv0YbFN1MuLZD7rSUxk0j3AIDUmevK3sY2W3wz5dIPua/R475k3tK+xDdy2UMp914s+2VS58rZBcMz/RnfTLn0Jr7S0LK98XvhtUi93V8BXiJdGkWkHur9Hd9MuQz23IdHvtqA1Plryxx/ldJNQnob22zxzZRLo3P/g1Iv9rN4s+f2FqRr14tulFRLfCOXPZRyr3XZC4GLIuKW8hmSinqcNzK+mXLpTXwhN+Nnks4gXRK0HukORatIH8rdSdeHf6K/4psplxbI/TTSZWPXAvuROgkdrdQZcWFEbNOb2GaLb6ZcGp17fs0+FN/tsvAOk7XEN3LZQyn3GmPfATxVstNXOm/jKOvY18j4ZsqlN/GVuLLPlHoLl97KdCrp8qEHgR9Hvm65P+KbKZcWyH0k6Q5h7yTdzOK0iHhNqSf9W6LkmvVaYpstvplyaXTuZtYLUWV7vwcPHjw0w0Dq4X0C6X4JT5FO5SzJZWP7Et/IZQ+l3Puw7LsGOr6ZculNfKWhuzs3DSlKT2j6jqTFkp6VtELSTZIO6u/4ZsqlhXK/oyx+Vl9imy2+mXJpdO7Ab0h3rnxvRGwYEeOB95J68/+mj/GNXPZQyr23y55RFv/0AMQ3Uy69iS/W6L3wwTKQ7k42i3Qt6NGk+39vDZxJwd2QGhnfTLk4d+fehLmvca/07ubVEt/IZQ+l3Jspl6GUe3dDVUFDYWDNh1jMz3+HkW7Y0m/xzZSLc3fuTZj7lcDXKLllKOlZFccAV/clvpHLHkq5N1MuQyn37gY347/pRaXnuiNpP9K5ESI95KDomq5GxjdTLs7duTdb7p8k3WfheklPS3qK9BjdDUn3ae9LfCOXPZRyb6ZchlLulVW7V9DqA+k5yzeTzoP8L/mxnaSHcRzRn/HNlItzd+7Nlnuetw3pGQqjy8rXeGhVrfGNXPZQyr2ZchlKuVcaqgoa6gP5yWrNEN9MuTj3wRHfTLnUI3fS0xHvJj12dhkws2Re0TPnq45v5LKHUu7NlMtQyr3b71K1gUN5oJvHxfZ3fDPl4twHR3wz5VKP3EmPPx6dx9uBBcCRefrWvsQ3ctlDKfdmymUo5d7d4NvlZpL+UmkWqTNEv8U3Uy61xjdTLrXGN1MutcY3Uy61xte6bNKz018AiIhlkmYA50vakuJz/LXEN3LZQyn3ZsplKOVeWbV7Ba0+AI+Rnre9ZdnQDjzcn/HNlItzd+5NmPu1wI5lZSNI92F/rS/xjVz2UMq9mXIZSrl3N1QVNBQGYC6wW4V5v+rP+GbKxbk79ybMvQ3YpEL89L7EN3LZQyn3ZsplKOXe3eB745uZmbU4X2dvZmbW4lzZm5mZtThX9mZWE0kflhSSSp9JP13SHyVdLOkLA5mfma3J5+zNrCaSfg1sBlwbEbMHOh8z65mP7M2sapJGA7sBhwIH5LIZkuZJOl/SXZLOlqQ8bw9Jt0q6XdJpktYewPTNhixX9mZWi5nA5RFxD/CkpJ1z+U7Al4F3AlsB0yWNAs4APhkR25OuDf5iv2dsZq7szawmBwLn5vFz8zTAzRHRGelJdYtIN8R5B3B/3jGA9Hz69/RfqmbWxbfLNbOqSNoQ2B3YXlIAw4EALgX+VhL6Gv5tMWsqPrI3s2p9DPhlRGwZEe0RsQVwP/B/KsTfDbRLelue/gxwfT/kaWZlXNmbWbUOBC4sK7uAN5vyVxMRLwMHA+dJuh14HfhZQzM0s0K+9M7MzKzF+cjezMysxbmyNzMza3Gu7M3MzFqcK3szM7MW58rezMysxbmyNzMza3Gu7M3MzFqcK3szM7MW9/8BIZmtYJi5jOwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_year = df.groupby('year_of_release', as_index=False).count()[['year_of_release', 'name']]\n",
    "group_year.plot(x='year_of_release', y='name',ylabel='Cantidad de juegos', xlabel='Año', kind='bar', figsize=(8, 4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso, podemos observar que la cantidad de juegos lanzados por año ha ido en aumento hasta los puntoas más altos que son 2008 y 2009, también se puede ver que la cantidad disminuye considerablemente posterior al año 2011. Así como se puede ver una cantidad de más de 200 juegos de los que desconocemos el año de lanzamiento (año 0)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Observa cómo varían las ventas de una plataforma a otra. Elige las plataformas con las mayores ventas totales y construye una distribución basada en los datos de cada año. Busca las plataformas que solían ser populares pero que ahora no tienen ventas. ¿Cuánto tardan generalmente las nuevas plataformas en aparecer y las antiguas en desaparecer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zz+ctb3kLANOmTevSfBvD0yMlSZIk9VkHHHAAd9xxBwD33XcfEyZMYOutt+a5557jz3/+M4sWLWLBggWcdtppAJx99tl8+ctf7tGMzrRJkiRJ6rN23nlnBgwYwG9/+1vmzJnDpEmTePzxx7njjjsYOnQoLS0tbLHFFk3NaGmTJEmS1KcdcMABzJkzhzlz5nDGGWfw+OOPM2fOHIYOHcqBBx7Y7HiWNqlUo6ffsM7Y4vPe3YQkkiRJvduBBx7InDlzWLhwIRMmTGDEiBF85StfYZtttuHEE0/k2WefbWo+r2mTJEmS1KcdcMABXH/99Wy33Xb079+f7bbbjueff5477riDAw44oNnxLG2SJEmS+raWlhaefvrp1StErhobOnQow4YNa2KyBk+PlCRJklSMjpbo7w79+/dfY5l/gFmzZq1+fMIJJ3DCCScAjdUje5ozbZIkSZJUMEubJEmSJBXM0iZJkiRJBbO0SZIkSVLBLG2SJEmSVDBLmyRJkiQVzNImSZIkqU/r378/ra2t7Lnnnuy999585StfYeXKlQC89NJLHHPMMbS0tDBhwgTe+ta3smzZsh7N533aJEmSJBXjwmm3dOn7O3XGwR0es+WWWzJ//nwAnnrqKT784Q/z4osvcs455/C1r32NHXfckYULFwLw4IMPMnDgwC7N2BFn2iRJkiSpssMOOzBz5ky+8Y1vkJk88cQT7LLLLqv3jx07lkGDBvVoJkubJEmSJLXx+te/nldffZWnnnqKj370o3zhC19g0qRJ/NM//RMPP/xwj+extEmSJEnSerS2tvKrX/2Ks846i2effZY3velNLFq0qEczdHhNW0RcCrwHeCozJ1Rj3wXGVoe8Fng+M1sjYjSwCHiw2ndnZk7r6tCSJEmS1F1+9atf0b9/f3bYYQcAhgwZwhFHHMERRxxBv379uPHGGxk/fnyP5dmYmbZZwGFtBzLz6MxszcxW4Frge212P7pqn4VNkiRJUp0sXbqUadOmcdpppxER/PznP+e5554D4OWXX+b+++9n1KhRPZqpw5m2zPxpNYO2jogI4ANAx0uySJIkSVKBli9fTmtrK6+88goDBgzg2GOP5YwzzgDg0Ucf5eSTTyYzWblyJe9+97v567/+6x7N19kl/w8CnszMtlfjjYmIe4AXgX/KzJ+198SImApMBRg5cmQnY0iSJEnqDTZmif6u9uqrr65333HHHcdxxx3Xg2nW1dmFSD4EXNFm+wlgZGbuA5wBfCcitmnviZk5MzMnZubE4cOHdzKGJEmSJPVOm13aImIAcATw3VVjmfnnzHymenw38CiwR2dDSpIkSVJf1ZmZtv8NPJCZS1YNRMTwiOhfPX49sDvwq85FlCRJkqS+q8PSFhFXAHcAYyNiSUScVO36IGueGgnwNmBBRMwHrgGmZeazXZhXkiRJUi+Tmc2O0GM252PdmNUjP7Se8RPaGbuWxi0AJEmSJKlDgwcP5plnnmH77bensTh975WZPPPMMwwePHiTntfZ1SMlSZIkabPtuuuuLFmyhKVLlzY7So8YPHgwu+666yY9x9ImSVIPWTRu/Dpj4x9Y1IQkklSOgQMHMmbMmGbHKFpnl/yXJEmSJHUjS5skSZIkFczTIyVJUj2dPbSdsRd6PockdTNn2iRJkiSpYJY2SZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWCWNkmSJEkqmEv+a6OMnn7DOmOLz3t3E5JIkiRJfYszbZIkSZJUMEubJEmSJBXM0iZJkiRJBbO0SZIkSVLBLG2SJEmSVDBLmyRJkiQVzNImSZIkSQWztEmSJElSwSxtkiRJklQwS5skSZIkFWxAswNIUilGT79hnbHF5727CUkkSZL+wpk2SZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWCWNkmSJEkqmKVNkiRJkgpmaZMkSZKkgnmfNkmSpCZrmd2yztjC4xc2IYmkEjnTJkmSJEkFs7RJkiRJUsEsbZIkSZJUMEubJEmSJBXM0iZJkiRJBbO0SZIkSVLBLG2SJEmSVDDv0yZJqp1F48avMzb+gUVNSCJJUvdzpk2SJEmSCtZhaYuISyPiqYi4t83Y2RHxeETMr97e1WbfZyLikYh4MCLe0V3BJUmSJKkv2JiZtlnAYe2Mn5+ZrdXbjQAR8Ubgg8Ce1XP+LSL6d1VYSZIkSeprOixtmflT4NmNfH/vA67MzD9n5q+BR4D9O5FPkiRJkvq0zlzTdlpELKhOn9y2GtsFeKzNMUuqsXVExNSImBsRc5cuXdqJGJIkSZLUe21uafsm8AagFXgC+MqmvoPMnJmZEzNz4vDhwzczhiRJkiT1bptV2jLzycx8NTNXAhfxl1MgHwdGtDl012pMkiRJkrQZNqu0RcRObTbfD6xaWfI64IMRMSgixgC7A//TuYiSJEmS1Hd1eHPtiLgCmAwMi4glwOeAyRHRCiSwGPgYQGbeFxFXAfcDK4BTM/PVbkkuSZIkSX1Ah6UtMz/UzvAlGzj+XODczoSSJEmSJDV0WNokSaqDC6fdssb2qTMOblISSZK6VmeW/JckSZIkdTNLmyRJkiQVzNMjJUlqorVP6wRP7ZQkrcmZNkmSJEkqmKVNkiRJkgrm6ZGSJEk97eyha26PGdmcHJJqwZk2SZIkSSqYpU2SJEmSCubpkZIkqddomd2yxvbC4xc2KYkkdR1n2iRJkiSpYJY2SZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWCWNkmSJEkqmKVNkiRJkgpmaZMkSZKkglnaJEmSJKlgA5odQJLUHIvGjV9nbPwDi5qQRJIkbYgzbZIkSZJUMEubJEmSJBXM0iZJkiRJBfOaNkmSusLZQ9sZe6Hnc0iSeh1n2iRJkiSpYJY2SZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWCWNkmSJEkqmKVNkiRJkgrmfdokSdIaWma3rDN2VRNySJIanGmTJEmSpII50yZJ3eDCabessX3qjIOblESSJNWdM22SJEmSVDBLmyRJkiQVzNImSZIkSQXzmjZJktRrLRo3fp2x8Q8sakISSdp8zrRJkiRJUsEsbZIkSZJUsA5LW0RcGhFPRcS9bca+FBEPRMSCiPh+RLy2Gh8dEcsjYn71NqMbs0uSJElSr7cxM22zgMPWGvsxMCEz9wIeAj7TZt+jmdlavU3rmpiSJEmS1Dd1WNoy86fAs2uN/Xdmrqg27wR27YZskiRJktTndcU1bR8FbmqzPSYi7omI2yLioPU9KSKmRsTciJi7dOnSLoghSZIkSb1Pp0pbRPwjsAK4vBp6AhiZmfsAZwDfiYht2ntuZs7MzImZOXH48OGdiSFJkiRJvdZml7aIOAF4D3BMZiZAZv45M5+pHt8NPArs0QU5JUmSJKlP2qzSFhGHAX8PHJ6ZL7UZHx4R/avHrwd2B37VFUElSZIkqS8a0NEBEXEFMBkYFhFLgM/RWC1yEPDjiAC4s1op8m3A/4mIV4CVwLTMfLbddyxJkiRJ6lCHpS0zP9TO8CXrOfZa4NrOhpIkSZIkNXTF6pGSJEmSpG5iaZMkSZKkglnaJEmSJKlgHV7TJkmSermzh665PWZkc3JIktplaZMkrXbhtFvWGTt1xsFNSCJJklaxtEmSpM2ydsm34EtS9/CaNkmSJEkqmDNtkiR1k5bZLWtsX9WkHJKkenOmTZIkSZIKZmmTJEmSpIJZ2iRJkiSpYJY2SZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWCWNkmSJEkqmKVNkiRJkgo2oNkBJEmSpJ7WMrtlnbGFxy9sQhKpY860SZIkSVLBLG2SJEmSVDBLmyRJkiQVzNImSZIkSQWztEmSJElSwSxtkiRJklQwS5skSZIkFczSJkmSJEkFs7RJkiRJUsEsbZIkSZJUsAHNDiBJkqT6apndssb2wuMXNimJ1Hs50yZJkiRJBbO0SZIkSVLBLG2SJEmSVDBLmyRJkiQVzNImSZIkSQWztEmSJElSwSxtkiRJklQw79MmSTU3evoN64wtPu/dTUgiSZK6gzNtkiRJklQwS5skSZIkFczSJkmSJEkF26jSFhGXRsRTEXFvm7HtIuLHEfFw9e+21XhExNcj4pGIWBAR+3ZXeEmSJEnq7TZ2pm0WcNhaY9OBmzNzd+DmahvgncDu1dtU4JudjylJkiRJfdNGlbbM/Cnw7FrD7wNmV49nA3/VZvxb2XAn8NqI2KkLskqSJElSn9OZJf93zMwnqse/B3asHu8CPNbmuCXV2BNtxoiIqTRm4hg5cmQnYkiSJKlHnD103bEx/h4ndbcuWYgkMxPITXzOzMycmJkThw8f3hUxJEmSJKnX6Uxpe3LVaY/Vv09V448DI9oct2s1JkmSJEnaRJ0pbdcBx1ePjwd+2Gb8uGoVybcAL7Q5jVKSJEmStAk26pq2iLgCmAwMi4glwOeA84CrIuIk4DfAB6rDbwTeBTwCvASc2MWZJUmSJKnP2KjSlpkfWs+ut7dzbAKndiaUJEmSJKmhSxYikSRJkiR1D0ubJEmSJBXM0iZJkiRJBevMzbUlSep6a9+89+wXmpNDkqRCONMmSZIkSQWztEmSJElSwSxtkiRJklQwS5skSZIkFczSJkmSJEkFs7RJkiRJUsEsbZIkSZJUMEubJEmSJBXM0iZJkiRJBbO0SZIkSVLBLG2SJEmSVDBLmyRJkiQVbECzA0iSJEnd7uyha26PGdmcHNJmcKZNkiRJkgpmaZMkSZKkglnaJEmSJKlgljZJkiRJKpilTZIkSZIKZmmTJEmSpIK55L8kSepTLpx2yzpjp844uAlJJGnjONMmSZIkSQWztEmSJElSwSxtkiRJklQwS5skSZIkFczSJkmSJEkFs7RJkiRJUsEsbZIkSZJUMEubJEmSJBXM0iZJkiRJBbO0SZIkSVLBLG2SJEmSVDBLmyRJkiQVzNImSZIkSQWztEmSJElSwSxtkiRJklSwAZv7xIgYC3y3zdDrgc8CrwX+Flhajf9DZt64ua8jSZIkSX3ZZpe2zHwQaAWIiP7A48D3gROB8zPzy10RUJIkSZL6ss0ubWt5O/BoZv4mIrroXUqServR029YZ2zx4CYEkSSpYF11TdsHgSvabJ8WEQsi4tKI2LaLXkOSJEmS+pxOl7aI2AI4HLi6Gvom8AYap04+AXxlPc+bGhFzI2Lu0qVL2ztEkiRJkvq8rphpeycwLzOfBMjMJzPz1cxcCVwE7N/ekzJzZmZOzMyJw4cP74IYkiRJktT7dEVp+xBtTo2MiJ3a7Hs/cG8XvIYkSZIk9UmdWogkIrYCDgE+1mb4ixHRCiSweK19kiRtkpbZLeuMXdWEHJIkNUunSltm/hHYfq2xYzuVSJIkSZK0WletHilJkiRJ6gaWNkmSJEkqmKVNkiRJkgpmaZMkSZKkglnaJEmSJKlgljZJkiRJKpilTZIkSZIKZmmTJEmSpIJZ2iRJkiSpYJY2SZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWCWNkmSJEkqmKVNkiRJkgpmaZMkSZKkglnaJEmSJKlgljZJkiRJKpilTZIkSZIKZmmTJEmSpIJZ2iRJkiSpYJY2SZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWADmh1AkiRJ61o0bvw6Y+MfWNSEJJKazZk2SZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWCWNkmSJEkqmKVNkiRJkgpmaZMkSZKkglnaJEmSJKlgljZJkiRJKpilTZIkSZIKNqDZASRJqpvR029YZ2zx4CYEkST1Cc60SZIkSVLBLG2SJEmSVDBLmyRJkiQVrNPXtEXEYuAPwKvAisycGBHbAd8FRgOLgQ9k5nOdfS1J0kY6e2g7Yy/0fA5JktRpXTXTNiUzWzNzYrU9Hbg5M3cHbq62JUmSJEmbqLtOj3wfMLt6PBv4q256HUmSJEnq1bqitCXw3xFxd0RMrcZ2zMwnqse/B3bsgteRJEmSpD6nK+7T9tbMfDwidgB+HBEPtN2ZmRkRufaTqoI3FWDkyJFdEEOSJEmSep9Oz7Rl5uPVv08B3wf2B56MiJ0Aqn+faud5MzNzYmZOHD58eGdjSJIkSVKv1KnSFhFbRcTWqx4DhwL3AtcBx1eHHQ/8sDOvI0mSJEl9VWdPj9wR+H5ErHpf38nM/4qIu4CrIuIk4DfABzr5OpIkSZLUJ3WqtGXmr4C92xl/Bnh7Z963JEmSpHW1zG5ZY3vh8QublEQ9pbuW/JckSZIkdQFLmyRJkiQVzNImSZIkSQWztEmSJElSwSxtkiRJklQwS5skSZIkFczSJkmSJEkFs7RJkiRJUsEsbZIkSZJUMEubJEmSJBXM0iZJkiRJBbO0SZIkSVLBLG2SJEmSVDBLmyRJkiQVzNImSZIkSQWztEmSJElSwQY0O0BfMnr6DeuMLT7v3U1IIkmSJKkunGmTJEmSpIJZ2iRJkiSpYJY2SZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWCWNkmSJEkqmPdpkyRJkkp19tB1x8aM7Pkcaipn2iRJkiSpYJY2SZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWCWNkmSJEkqmKVNkiRJkgpmaZMkSZKkglnaJEmSJKlgljZJkiRJKpilTZIkSZIKZmmTJEmSpIJZ2iRJkiSpYJY2SZIkSSqYpU2SJEmSCrbZpS0iRkTErRFxf0TcFxGfrMbPjojHI2J+9faurosrSZIkSX3LgE48dwXwd5k5LyK2Bu6OiB9X+87PzC93Pp4kSepKo6ffsM7Y4sFNCKLNcuG0W9bYPnXGwU1KIqknbXZpy8wngCeqx3+IiEXALl0VTJIkSZLURde0RcRoYB/gF9XQaRGxICIujYhtu+I1JEmSJKkv6nRpi4ghwLXA6Zn5IvBN4A1AK42ZuK+s53lTI2JuRMxdunRpZ2NIkiRJUq/UqdIWEQNpFLbLM/N7AJn5ZGa+mpkrgYuA/dt7bmbOzMyJmTlx+PDhnYkhSZIkSb1WZ1aPDOASYFFmfrXN+E5tDns/cO/mx5MkSZKkvq0zq0ceCBwLLIyI+dXYPwAfiohWIIHFwMc68RqSJEmS1Kd1ZvXI24FoZ9eNmx9HkiRJktRWl6weKUmSJEnqHp05PVKSJElaw6Jx49cZG//AoiYkkXoPS5sk9REts1vW2L6qSTkkSdKm8fRISZIkSSqYpU2SJEmSCmZpkyRJkqSCWdokSZIkqWCWNkmSJEkqmKVNkiRJkgpmaZMkSZKkgnmfNknFunDaLeuMnTrj4CYkkSRJah5LmyRJkqRu1zK7ZZ2xhccvbEKS+vH0SEmSJEkqmKVNkiRJkgpmaZMkSZKkglnaJEmSJKlgljZJkiRJKpilTZIkSZIKZmmTJEmSpIJZ2iRJkiSpYN5cW5IkSVLXO3vomttjRjYnRy/gTJskSZIkFcyZNkmSJHWrC6fdss7YqTMObkISqZ6caZMkSZKkglnaJEmSJKlgljZJkiRJKpilTZIkSZIK5kIkkrQhay9XfPYLzckhSep2i8aNX2ds/AOLmpBEWpMzbZIkSZJUMEubJEmSJBXM0iZJkiRJBbO0SZIkSVLBXIhEkiQVb/T0G9YZWzy4CUEkqQmcaZMkSZKkglnaJEmSJKlgljZJkiRJKpjXtEmSJEk15k3Bu1/L7JY1thcev7BHX9+ZNkmSJEkqmKVNkiRJkgpmaZMkSZKkgnXbNW0RcRjwNaA/cHFmntddryVJkiR1hwun3bLG9qkzDm5SEvWYs4euOzZmZM/naKNbSltE9AcuBA4BlgB3RcR1mXl/d7yeJEmSpPpxEZWN012nR+4PPJKZv8rMl4Ergfd102tJkiRJUq8Vmdn17zTiSOCwzPybavtY4M2ZeVqbY6YCU6vNscCDXR7kL4YBT3fj++8udc0N9c1e19xQ3+x1zQ31zV7X3FDf7HXNDfXNXtfcUN/sdc0N9c1e19xQ3+zdmXtUZg5vb0fT7tOWmTOBmT3xWhExNzMn9sRrdaW65ob6Zq9rbqhv9rrmhvpmr2tuqG/2uuaG+mava26ob/a65ob6Zq9rbqhv9mbl7q7TIx8HRrTZ3rUakyRJkiRtgu4qbXcBu0fEmIjYAvggcF03vZYkSZIk9VrdcnpkZq6IiNOAH9FY8v/SzLyvO15rI/XIaZjdoK65ob7Z65ob6pu9rrmhvtnrmhvqm72uuaG+2euaG+qbva65ob7Z65ob6pu9Kbm7ZSESSZIkSVLX6K7TIyVJkiRJXcDSJkmSJEkFs7RJkiRJUsEsbZIkSZJUMEtbQSJiaEQcHRFnVG9HR8Rrm51rc0XEIc3O0JGI2CYi3tDO+F7NyLOxIuJ1EfG66vHwiDgiIvZsdq7NERH/0uwMm6q6nckRETGu2Vk6EhEjI2Jw9Tgi4sSI+L8RcXJEdMsKwl0hIg5flbuOIuJtETG2enxgRJwZEe9udq6ORMSQiDgyIj4VEZ+IiMMioha/K0TEuIj4dER8vXr7dESMb3auzRURJzY7Q0eqz/nbI2LIWuOHNSvTxoiI/SPiTdXjN1a/c72r2bk2R0R8q9kZNlVEvLX6nB/a7CwdiYg3R8Q21eMtI+KciPjPiPhCRAzt0SyuHlmGiDgO+Bzw3/zlRuS7AocA52RmHf+n/G1mjmx2jvWJiA8AFwBPAQOBEzLzrmrfvMzct4nx1isiPgZMBwL4AnACcC/wVuCLmXlJ89JtWER8fe0h4FjgWwCZ+YkeD7URIuIHmflX1eP30fi6+QlwAPCvmTmrWdk6EhH3Avtn5ksR8QXgDcAPgIMBMvOjTYy3XhGxHPgjcBNwBfCjzHy1uak2TkRcAOxP47Y6PwLeTuPj+F/APZl5VvPSrV/1PfFMYAEwBZhD44+7LcAxmbmwifE2KCI+DXwIuBJYUg3vSuM+sVdm5nnNyra5avAz9BPAqcAioBX4ZGb+sNpX8s/QzwHvpPH/54+BNwO30vh960eZeW4T421QRKx9z+Og8f/qLQCZeXiPh9oIEfE/mbl/9fhvaXzdfB84FPjPkv//jIj7gL2r25nNBF4CrqHxfX3vzDyix7L0xtIWEVOAvwZGAK8CDwEXZ+YjTQ22ARHxIPDmzHx+rfFtgV9k5h5NCdaBdr6BrN4FHJyZW/Vknk0REfOBd2bmExGxP43i8JnM/H5E3JOZ+zQ3YfsiYiGNHzJbAr8BdsvM31dfK7dmZmsz821IRDwG3EbjjxNRDX+Zxi+KZObsJkXboLZfDxExh8YvsL+OiGHAzZm5d3MTrl9E3J+Zb6we3w28KTNXVtu/LDV7RNxDo1geSeMX7wk0fshfkZm3NTNbR6of8hNo/D/6OLBLVZoH0ihtE5oacD0iYgHwlirrMODyzHxHdebBjMw8oMkR1ysiHgL2zMxX1hrfArgvM3dvTrINqz7n7e4C9sjMQT2ZZ1NUP4smZeayiBhN4xfZb2fm12rwM7QVGAT8Htg1M1+MiC1p/L5V7Jk2ETEPuB+4GEgaXydX0PgeSanfG9f6GXoX8K7MXBoRWwF3ZmZLcxOuX0Qsyszx1eM1/hgREfN78neuYk+N2VwR8a/A64Cbq39/DTwKXB0R/5KZVzcz3wYEjf8B17aSv/xyW6KDgI8Ay9YaDxp/aS5Z/8x8AiAz/6cq+9dHxAja/29Rilcy8yXgpYh4NDN/D5CZz0VEybkB3gh8HjgMODMzfxcRnyu1rLXR9vM6IDN/DZCZT0fEyiZl2liPRcTBmXkLsJjGH7N+ExHbNzdWhzIznwMuAi6KxunAHwDOi4hdM3NEc+NtUGZmtvnaWPX1s5KyL0sIYHn1+I/ADgCZuWDV6UEFWwnsTOMPWW3tVO0r1Y7AO4Dn1hoPGjOdJeuXmcsAMnNxREwGromIUZT9e8uKatZ+1c/QFwEyc3kNvp9PBD4J/CNwVmbOj4jlpZa1NvpVf1juR2PCaClAZv4xIlY0N1qH7o2IEzPzMuCXETExM+dGxB7AKx09uSv1utIGvGdVY4+IK4HbMvOsiLgG+BlQamk7F5gXEf8NPFaNjaQxXf/5pqXq2J3AS+19w6hmD0v2h4h4Q2Y+ClDNuE2mcepYydeHZUQMrP6ivPoamWhc/1PyL4Rk5h+A0yNiP+DyiLiBwjNX9o6IF2n8IjIoInaqvl62APo3OVtH/gb4VkScDbwAzK9mmV8LnNG8WB1a45e+6o8TXwe+Xv1SWLIbIuJnwGAafxG/KiLupHF65E+bmmzDbgT+KyJ+SuMPK1cDRMR2lP1LOMDpwM0R8TBr/gzdDTitWaE2wvXAkMycv/aOiPhJj6fZNE9GROuq7NWM23uAS2mcUluqlyPiNdUfP/dbNVhdn1R0aavOkjg/Iq6u/n2SevwuPxS4m2qCos3P0CGU/73lb4CvRcQ/AU8Dd1RnDT1W7esxve70yIj4JTAlM5+NiJHAVZn5lmrffZlZ7C/j1V8h3gHsUg09TuP86rX/AqcuEBF70yicD681PhD4QGZe3pxkG1Z9XT/RzmlAuwDjM/P/NSfZpomIAE6hcXrNR5qdZ3NEY6Gg8Zl5R7OzdCQaCzLsQeMH/BLgrlWnSZYoIiZn5k+anWNzRcQkGjNud0ZjsaP3A78Frin88/4uGjPiv8zMH1dj/YCBmfnnpobrQJVzf9b8GXpXXa6FrJuI2JXGrNXv29l3YGb+vAmxOhQRg9r7Wq5OCd6p5Gs31xaNxY0OzMx/aHaWzRERrwF2XHX2Ssmqsw3GUP0MzcwnezxDLyxtRwNfpHEd21jg5My8ISKGA1/LzA83NWAHImJH2vzAacYXxeaoa26ob/a65ob6Zq9rbqhv9rrmhvpmr2vu9YmIIatO46uTuuaG+mava26ob/a65oaez97rShusPpXj9cAjudbCHqWKiFZgBo0p5CU0pot3BZ4HTsnMeU0LtwERsQ/wTRq52656+TwF54Z1PudrZz85M+9pTrINq2tu6DB7sV8vHXyd+znvBnXNDfXNXtfcHYnCV2Fcn7rmhvpmr2tuqG/2uuaGns9eh/NgN1l1auTrgSkR8SrwUGY+0OxcHZgFfCwzf9F2MCLeAlwGFLnCG41sdcwNG/6cz6Lc7LOoZ27onV/nsyg3N9T3cz6LeuaG+mafRT1zExHruz4zgCHr2dd0dc0N9c1e19xQ3+x1zQ1lZa/DAgCbJCL+V0TMBc6jcTHsVOCSiPhJNFYFLNVWa/+gBMjMO4Fil82nvrmhvtnrmhvqm72uuaG+2euaG+qbva65Af4F2BbYeq23IZT9u05dc0N9s9c1N9Q3e11zQ0HZe+NM2wXAodm4/8MY4KuZeWBEHAJcQuNGfiW6qVpJ71v8ZeWrEcBxwH81LVXH6pob6pu9rrmhvtnrmhvqm72uuaG+2euaG2Ae8IPMvHvtHRHRoyu8baK65ob6Zq9rbqhv9rrmhoKy97pr2iJiQVY3RoyI/jRWjtq32i599ch3Au9jzZWvrsvMG5uXqmN1zQ31zV7X3FDf7HXNDfXNXtfcUN/sNc49Fng2q/s/rbVvx1IXU6lrbqhv9rrmhvpmr2tuKCt7byxtl9K4kektwOE0Vr46o1pWdF5mjmtqQEmSJEnaBKWfR7o5PkbjBn6TgP8HnFWNJ417oBUpIoZGxHkRsSgino2IZ6rH50XjXlBFqmtuqG/2uuaG+mava26ob/a65ob6Zq9rblgj+wN1yl7X3FDf7HXNDfXNXtfcUFb2XlfaMvOVzPy3zDwtMy9adVPNzFyemb9pdr4NuAp4DpiSmdtl5vbAFBpLLV/VzGAdqGtuqG/2uuaG+mava26ob/a65ob6Zq9rbvhL9slrZX+OsrPXNTfUN3tdc0N9s9c1NxSUvTeeHjkE+HvgCBoXUL8MPArMyMxZTYy2QRHxYGaO3dR9zVbX3FDf7HXNDfXNXtfcUN/sdc0N9c1e19xQ3+x1zQ31zV7X3FDf7HXNDWVl73UzbcDlwK+Aw4BzgK8DxwJTIuJfmhmsA7+JiL+PiB1XDUTEjhHxaf6yileJ6pob6pu9rrmhvtnrmhvqm72uuaG+2euaG+qbva65ob7Z65ob6pu9rrmhoOy9sbSNzsxZmbkkM78KHJ6ZDwMn0ph9K9XRwPbAbRHxXEQ8C/wE2A74QDODdaCuuaG+2euaG+qbva65ob7Z65ob6pu9rrmhvtnrmhvqm72uuaG+2euaGwrK3htPj5wD/H1m3h4RhwOnZuY7qn2lT8GOA3YF7szMZW3GD8vMYu+RU9fcUN/sdc0N9c1e19xQ3+x1zQ31zV7X3FDf7HXNDfXNXtfcUN/sdc0NBWXPzF71BuwF/A+NCwRvB/aoxocDn2h2vg3k/gTwIPADYDHwvjb75jU7X2/LXefsdc1d5+x1zV3n7HXNXefsdc1d5+x1zV3n7HXNXefsdc1dWvamfzJ6+BN/YrMzbCDbQmBI9Xg0MBf4ZLV9T7Pz9bbcdc5e19x1zl7X3HXOXtfcdc5e19x1zl7X3HXOXtfcdc5e19ylZR9A33IOcFmzQ6xHv6ymXDNzcURMBq6JiFFANDNYB+qaG+qbva65ob7Z65ob6pu9rrmhvtnrmhvqm72uuaG+2euaG+qbva65oaDsvW4hkohYsJ63hcCOHb6D5nkyIlpXbVRfIO8BhgEtzQq1EeqaG+qbva65ob7Z65ob6pu9rrmhvtnrmhvqm72uuaG+2euaG+qbva65oaDsvXEhkieBd9C4pm2NXcCczNy551N1LCJ2BVZk5u/b2XdgZv68CbE6VNfcUN/sdc0N9c1e19xQ3+x1zQ31zV7X3FDf7HXNDfXNXtfcUN/sdc0NZWXvjaXtEuCyzLy9nX3fycwPNyGWJEmSJG2WXlfaJEmSJKk36XXXtEmSJElSb2JpkyRJkqSCWdokSX1KRBwUEfdFxPyI2LIT72d0RNzbldkkSWqPpU2SVGsR0X8Tn3IM8K+Z2ZqZyzt4333tfqaSpAJZ2iRJPSYi/k9EnN5m+9yI+GREnBURd1X31Tynzf4fRMTd1czY1DbjyyLiKxHxS2DSel7r7RFxT0QsjIhLI2JQRPwN8AHg8xFx+XqeNzkifhYR1wH3R0T/iPhSm3wfa+c57R4TEUMi4uaImFfleF81vlVE3BARv4yIeyPi6Gp8v4i4rfqYfxQRO23Gp1mS1MtY2iRJPelS4DiAiOgHfBD4PbA7sD/QCuwXEW+rjv9oZu4HTAQ+ERHbV+NbAb/IzL3Xc4uXwcAs4OjMbAEGACdn5sXAdcBZmXnMBnLuC3wyM/cATgJeyMw3AW8C/jYixqx1/PqO+RPw/szcF5gCfCUiAjgM+F2VfwLwXxExEPi/wJHVx3wpcO4GP5uSpD7B0z4kST0mMxdHxDMRsQ+wI3APjZJzaPUYYAiNEvdTGkXt/dX4iGr8GeBV4NoNvNRY4NeZ+VC1PRs4FbhgI6P+T2b+unp8KLBXRBxZbQ+tcjzU5vj1HbME+JeqhK4Edqk+7oU0CtwXgOsz82cRMQGYAPy40evoDzyxkXklSb2YpU2S1NMuBk4AXkdjNuntNK4x+/e2B0XEZOB/A5My86WI+AkwuNr9p8x8tRsz/rFtFODjmfmjtfKN3ohjTgCGA/tl5isRsRgYnJkPRcS+wLuA/y8ibga+D9yXme2e7ilJ6rs8PVKS1NO+T+P0wDcBP6rePhoRQwAiYpeI2IHGbNVzVWEbB7xlE17jQWB0ROxWbR8L3LaZeX8EnFydvkhE7BERW23kMUOBp6rCNgUYVe3fGXgpM/8D+BKN0zEfBIZHxKTqmIERsedmZpYk9SLOtEmSelRmvhwRtwLPV7Nl/x0R44E7qtMClwEfAf4LmBYRi2gUmjs34TX+FBEnAldXK0DeBczYzMgXA6OBedX1aEuBv9rIYy4H/jMiFgJzgQeq41uAL0XESuAVGtfbvVydXvn1iBhK42f0BcB9m5lbktRLRGY2O4MkqQ+pFiCZBxyVmQ83O48kSaXz9EhJUo+JiDcCjwA3W9gkSdo4zrRJkmotIr4PrL0E/6fXXhSknee1AN9ea/jPmfnmrswnSVJnWdokSZIkqWCeHilJkiRJBbO0SZIkSVLBLG2SJEmSVDBLmyRJkiQVzNImSZIkSQX7/wET0LDQsMqc1gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupar por plataformas y ordenar por ventas totales:\n",
    "platforms_by_sales = df.groupby('platform', as_index=False).sum().sort_values('total_sales', ascending=False).reset_index(drop=True)\n",
    "\n",
    "# Obtener las 5 plataformas con mayor numero de ventas totales:\n",
    "platforms = platforms_by_sales['platform'].head().unique()\n",
    "\n",
    "# Tabla dinámica de las ventas por plataforma en cada año\n",
    "sales_by_year_p = df.pivot_table(index='year_of_release', columns='platform', values='total_sales', aggfunc='sum')#.fillna(0)\n",
    "\n",
    "# Mostrar solamente las 5 plataformas con mayor número de ventas\n",
    "sales_by_year_pt = sales_by_year_p.loc[1999:, platforms]\n",
    "sales_by_year_pt.plot(kind='bar', figsize=(15,8))\n",
    "plt.show()\n",
    "# display(platforms_by_sales)#sort_values(by='total_sales', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos observar que el mayor número de ventas comenzó a partir del año 2000 en la plataforma PS2, y nuevas plataformas emergen a partir del 2004, con ello podemos pensar que es apartir de 5 años que surgen nuevas plataformas en las que las personas empiezan a centrar su compra de juegos. También podemos ver que las diversas plataformas cuentan con un número significativo de diferencia entre sus ventas, por lo que las ventas variarán en cuestión de la plataforma."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Determina para qué período debes tomar datos. Para hacerlo mira tus respuestas a las preguntas anteriores. Los datos deberían permitirte construir un modelo para 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Observar los datos para juegos lanzados desde el 2010\n",
    "plat = df[df['year_of_release']>=2010]['platform'].unique()\n",
    "# Acortar la grafica sólo para mostrar datos de las plataformas con juegos después del 2010\n",
    "sales_by_year_t = sales_by_year_p.loc[2010:, plat]\n",
    "sales_by_year_t.plot(kind='line', figsize=(15,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por los datos comprendidos entre 2015 a 2016, podemos pensar que las nuevas plataformas y las que tienen mayor número de ventas para el año 2017 pueden ser PS4, XOne y 3DS, pues el resto parece estar volviendose de menor consumo, por lo tanto, nos enfocaremos en este periodo para hacer el análisis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ¿Qué plataformas son líderes en ventas? ¿Cuáles crecen y cuáles se reducen? Elige varias plataformas potencialmente rentables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_of_release</th>\n",
       "      <th>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>total_sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platform</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PS4</th>\n",
       "      <td>606679</td>\n",
       "      <td>63.86</td>\n",
       "      <td>83.54</td>\n",
       "      <td>12.05</td>\n",
       "      <td>28.70</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>1315.9</td>\n",
       "      <td>188.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOne</th>\n",
       "      <td>336592</td>\n",
       "      <td>50.30</td>\n",
       "      <td>28.10</td>\n",
       "      <td>0.18</td>\n",
       "      <td>7.71</td>\n",
       "      <td>8244.0</td>\n",
       "      <td>809.6</td>\n",
       "      <td>86.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3DS</th>\n",
       "      <td>266026</td>\n",
       "      <td>10.31</td>\n",
       "      <td>7.39</td>\n",
       "      <td>23.55</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2070.0</td>\n",
       "      <td>247.6</td>\n",
       "      <td>42.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WiiU</th>\n",
       "      <td>84644</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.49</td>\n",
       "      <td>4.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>211.9</td>\n",
       "      <td>20.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PS3</th>\n",
       "      <td>223703</td>\n",
       "      <td>5.66</td>\n",
       "      <td>7.30</td>\n",
       "      <td>4.97</td>\n",
       "      <td>2.49</td>\n",
       "      <td>63.0</td>\n",
       "      <td>194.3</td>\n",
       "      <td>20.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC</th>\n",
       "      <td>209614</td>\n",
       "      <td>3.72</td>\n",
       "      <td>9.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.95</td>\n",
       "      <td>5945.0</td>\n",
       "      <td>528.9</td>\n",
       "      <td>13.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X360</th>\n",
       "      <td>96733</td>\n",
       "      <td>8.05</td>\n",
       "      <td>4.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>167.1</td>\n",
       "      <td>13.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSV</th>\n",
       "      <td>393010</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.08</td>\n",
       "      <td>8.41</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1746.0</td>\n",
       "      <td>288.1</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wii</th>\n",
       "      <td>10076</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSP</th>\n",
       "      <td>6045</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year_of_release  na_sales  eu_sales  jp_sales  other_sales  \\\n",
       "platform                                                               \n",
       "PS4                606679     63.86     83.54     12.05        28.70   \n",
       "XOne               336592     50.30     28.10      0.18         7.71   \n",
       "3DS                266026     10.31      7.39     23.55         1.67   \n",
       "WiiU                84644      8.86      6.49      4.12         1.48   \n",
       "PS3                223703      5.66      7.30      4.97         2.49   \n",
       "PC                 209614      3.72      9.10      0.00         0.95   \n",
       "X360                96733      8.05      4.21      0.00         1.22   \n",
       "PSV                393010      0.54      1.08      8.41         0.47   \n",
       "Wii                 10076      0.49      0.73      0.00         0.10   \n",
       "PSP                  6045      0.00      0.00      0.12         0.00   \n",
       "\n",
       "          critic_score  user_score  total_sales  \n",
       "platform                                         \n",
       "PS4            13300.0      1315.9       188.15  \n",
       "XOne            8244.0       809.6        86.29  \n",
       "3DS             2070.0       247.6        42.92  \n",
       "WiiU            2000.0       211.9        20.95  \n",
       "PS3               63.0       194.3        20.42  \n",
       "PC              5945.0       528.9        13.77  \n",
       "X360               0.0       167.1        13.48  \n",
       "PSV             1746.0       288.1        10.50  \n",
       "Wii                0.0         0.0         1.32  \n",
       "PSP                0.0         0.0         0.12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Buscar los lideres en ventas para generaciones posteriores,a partir del 2015\n",
    "display( df[df['year_of_release']>=2015].groupby('platform').sum().sort_values('total_sales', ascending=False) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por la información obtenida por medio de los gráficos y de la tabla mostrada, podemos pensar que son las plataformas PS4, XOne y 3DS las que serían potencialmente más rentables, sin embargo, se puede observar una notable reducción en las ventas del resto de consolas con respecto a los años pasados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Crea un diagrama de caja para las ventas globales de todos los juegos, desglosados por plataforma. ¿Son significativas las diferencias en las ventas? ¿Qué sucede con las ventas promedio en varias plataformas? Describe tus hallazgos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupar las ventas por plataforma\n",
    "box_df = df[['platform', 'total_sales']].groupby('platform', as_index=False).sum().sort_values('total_sales', ascending=False).head(15)\n",
    "#sns.boxplot(data=box_df, orient='h' )\n",
    "#display(box_df)\n",
    "sns.boxplot(  y=box_df['total_sales'], x=box_df['platform'] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "He realizado un diagrama de caja nos muestra que hay una media de ventas aproximada a 200, con valores minimos y máximos muy cercanos a los cuartiles. También podemos observar varios valores atípicos, muy por encima del promedio de la muestra. Pero se puede concluir que las ventas por plataforma suelen generar cerca de $200 en ventas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Mira cómo las reseñas de usuarios y profesionales afectan las ventas de una plataforma popular (tu elección). Crea un gráfico de dispersión y calcula la correlación entre las reseñas y las ventas. Saca conclusiones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico de dispersion para la plataforma PS4 respecto a las reseñas\n",
    "df_plat = df[df['platform']=='PS4']\n",
    "df_plat.plot(x='user_score', y='total_sales', kind='scatter', alpha=0.7)\n",
    "df_plat.plot(x='critic_score', y='total_sales', kind='scatter', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gracias a los gráficos de dispersión se puede observar que existe una muy ligera correlación positiva entre las reseñas, tanto de usuarios como de críticos, aunque no es una relación extremadamente fuerte, si es positiva. La relación es más fuerte en el aspecto de las reseñas de la crítica especializada, más que de los usuarios. Elegí la plataforma PS4 para hacer el análisis, pues es la que más ventas obtuvo en los últimos años."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Teniendo en cuenta tus conclusiones compara las ventas de los mismos juegos en otras plataformas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obtener los juegos que se analizaron en la plataforma PS4\n",
    "games_ps4 = df_plat['name'].unique()\n",
    "# Buscar los mismos juegos en otras plataformas\n",
    "games_total = df[df['name'].isin(games_ps4)].groupby('platform', as_index=False).sum()\n",
    "# Visualizar el número de ventas de los juegos en otras plataformas\n",
    "games_total.plot(x='platform', y='total_sales', kind='bar')\n",
    "plt.show()\n",
    "#display(games_total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede observar que los juegos que más se vendieron en la plataforma PS4 también fueron mayormente vendidos en la plataforma PS3, seguidos por las plataformas X360 y XOne. En el resto de plataformas las ventas de los mismos juegos es mucho menor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Echa un vistazo a la distribución general de los juegos por género. ¿Qué se puede decir de los géneros más rentables? ¿Puedes generalizar acerca de los géneros con ventas altas y bajas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <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>1744.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sports</td>\n",
       "      <td>1331.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shooter</td>\n",
       "      <td>1052.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>934.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Platform</td>\n",
       "      <td>827.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Misc</td>\n",
       "      <td>802.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Racing</td>\n",
       "      <td>728.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fighting</td>\n",
       "      <td>447.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Simulation</td>\n",
       "      <td>389.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Puzzle</td>\n",
       "      <td>242.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>237.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Strategy</td>\n",
       "      <td>174.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Unkwnown</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           genre  total_sales\n",
       "0         Action      1744.17\n",
       "1         Sports      1331.27\n",
       "2        Shooter      1052.45\n",
       "3   Role-Playing       934.56\n",
       "4       Platform       827.77\n",
       "5           Misc       802.51\n",
       "6         Racing       728.67\n",
       "7       Fighting       447.53\n",
       "8     Simulation       389.97\n",
       "9         Puzzle       242.57\n",
       "10     Adventure       237.59\n",
       "11      Strategy       174.23\n",
       "12      Unkwnown         2.42"
      ]
     },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_of_release</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>1427</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>1426</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>1255</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007</td>\n",
       "      <td>1197</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>1136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>1006</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2005</td>\n",
       "      <td>939</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2002</td>\n",
       "      <td>829</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2003</td>\n",
       "      <td>775</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2004</td>\n",
       "      <td>762</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    year_of_release  name\n",
       "0              2008  1427\n",
       "1              2009  1426\n",
       "2              2010  1255\n",
       "3              2007  1197\n",
       "4              2011  1136\n",
       "5              2006  1006\n",
       "6              2005   939\n",
       "7              2002   829\n",
       "8              2003   775\n",
       "9              2004   762\n",
       "10             2012   653\n",
       "11             2015   606\n",
       "12             2014   581"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obtener los generos que generaron más ventas totales\n",
    "genre_games = df.groupby('genre', as_index=False).sum().sort_values('total_sales', ascending=False).reset_index(drop=True)\n",
    "display(genre_games[['genre','total_sales']])\n",
    "# Los años de lanzamiento de los generos más rentables\n",
    "year_best_genres = df[df['genre'].isin(genre_games['genre'])].groupby('year_of_release', as_index=False).count().sort_values('total_sales', ascending=False).reset_index(drop=True)\n",
    "display(year_best_genres[['year_of_release','name']].head(13))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya se han obtenido los generos que han generado más ganancias y se observa que la mayoría de ellos fueron lanzados después del 2005 y en su mayoría antes del 2011. Por los análisis anteiroes se puede decir que estos fueron los años en los que hubo mayor número de ventas en distintas plataformas. Por lo que puede llegarse a pensar que son los generos de Acción y Deportes los que más se pueden vender para las consolas PS4 o XOne en los años posteriores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3. <a id='toc1_3_'></a>[Usuarios por región](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para cada región (NA, UE, JP) determina:\n",
    "\n",
    "- Las cinco plataformas principales. Describe las variaciones en sus cuotas de mercado de una región a otra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NA SALES\n"
     ]
    },
    {
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       "      <th>year_of_release</th>\n",
       "      <th>na_sales</th>\n",
       "      <th>eu_sales</th>\n",
       "      <th>jp_sales</th>\n",
       "      <th>other_sales</th>\n",
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       "      <th>user_score</th>\n",
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       "      <th>platform</th>\n",
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       "    <tr>\n",
       "      <th>X360</th>\n",
       "      <td>2476173</td>\n",
       "      <td>602.47</td>\n",
       "      <td>270.76</td>\n",
       "      <td>12.43</td>\n",
       "      <td>85.76</td>\n",
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       "      <td>6628.0</td>\n",
       "      <td>971.42</td>\n",
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       "    <tr>\n",
       "      <th>PS2</th>\n",
       "      <td>4263750</td>\n",
       "      <td>583.84</td>\n",
       "      <td>339.29</td>\n",
       "      <td>139.20</td>\n",
       "      <td>193.44</td>\n",
       "      <td>89208.0</td>\n",
       "      <td>10315.3</td>\n",
       "      <td>1255.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wii</th>\n",
       "      <td>2583531</td>\n",
       "      <td>496.90</td>\n",
       "      <td>262.21</td>\n",
       "      <td>69.33</td>\n",
       "      <td>79.07</td>\n",
       "      <td>36752.0</td>\n",
       "      <td>4268.1</td>\n",
       "      <td>907.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PS3</th>\n",
       "      <td>2626158</td>\n",
       "      <td>393.49</td>\n",
       "      <td>330.29</td>\n",
       "      <td>80.19</td>\n",
       "      <td>135.68</td>\n",
       "      <td>57714.0</td>\n",
       "      <td>6069.6</td>\n",
       "      <td>939.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS</th>\n",
       "      <td>4259361</td>\n",
       "      <td>382.40</td>\n",
       "      <td>188.89</td>\n",
       "      <td>175.57</td>\n",
       "      <td>59.26</td>\n",
       "      <td>45717.0</td>\n",
       "      <td>5047.5</td>\n",
       "      <td>806.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "          year_of_release  na_sales  eu_sales  jp_sales  other_sales  \\\n",
       "platform                                                               \n",
       "X360              2476173    602.47    270.76     12.43        85.76   \n",
       "PS2               4263750    583.84    339.29    139.20       193.44   \n",
       "Wii               2583531    496.90    262.21     69.33        79.07   \n",
       "PS3               2626158    393.49    330.29     80.19       135.68   \n",
       "DS                4259361    382.40    188.89    175.57        59.26   \n",
       "\n",
       "          critic_score  user_score  total_sales  \n",
       "platform                                         \n",
       "X360           62853.0      6628.0       971.42  \n",
       "PS2            89208.0     10315.3      1255.77  \n",
       "Wii            36752.0      4268.1       907.51  \n",
       "PS3            57714.0      6069.6       939.65  \n",
       "DS             45717.0      5047.5       806.12  "
      ]
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      "EU SALES\n"
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       "      <td>939.65</td>\n",
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       "      <td>496.90</td>\n",
       "      <td>262.21</td>\n",
       "      <td>69.33</td>\n",
       "      <td>79.07</td>\n",
       "      <td>36752.0</td>\n",
       "      <td>4268.1</td>\n",
       "      <td>907.51</td>\n",
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       "      <th>PS</th>\n",
       "      <td>2377627</td>\n",
       "      <td>336.52</td>\n",
       "      <td>213.61</td>\n",
       "      <td>139.82</td>\n",
       "      <td>40.91</td>\n",
       "      <td>14303.0</td>\n",
       "      <td>1501.2</td>\n",
       "      <td>730.86</td>\n",
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       "          year_of_release  na_sales  eu_sales  jp_sales  other_sales  \\\n",
       "platform                                                               \n",
       "PS2               4263750    583.84    339.29    139.20       193.44   \n",
       "PS3               2626158    393.49    330.29     80.19       135.68   \n",
       "X360              2476173    602.47    270.76     12.43        85.76   \n",
       "Wii               2583531    496.90    262.21     69.33        79.07   \n",
       "PS                2377627    336.52    213.61    139.82        40.91   \n",
       "\n",
       "          critic_score  user_score  total_sales  \n",
       "platform                                         \n",
       "PS2            89208.0     10315.3      1255.77  \n",
       "PS3            57714.0      6069.6       939.65  \n",
       "X360           62853.0      6628.0       971.42  \n",
       "Wii            36752.0      4268.1       907.51  \n",
       "PS             14303.0      1501.2       730.86  "
      ]
     },
     "metadata": {},
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    },
    {
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      "JP SALES\n"
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       "      <th>DS</th>\n",
       "      <td>4259361</td>\n",
       "      <td>382.40</td>\n",
       "      <td>188.89</td>\n",
       "      <td>175.57</td>\n",
       "      <td>59.26</td>\n",
       "      <td>45717.0</td>\n",
       "      <td>5047.5</td>\n",
       "      <td>806.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PS</th>\n",
       "      <td>2377627</td>\n",
       "      <td>336.52</td>\n",
       "      <td>213.61</td>\n",
       "      <td>139.82</td>\n",
       "      <td>40.91</td>\n",
       "      <td>14303.0</td>\n",
       "      <td>1501.2</td>\n",
       "      <td>730.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PS2</th>\n",
       "      <td>4263750</td>\n",
       "      <td>583.84</td>\n",
       "      <td>339.29</td>\n",
       "      <td>139.20</td>\n",
       "      <td>193.44</td>\n",
       "      <td>89208.0</td>\n",
       "      <td>10315.3</td>\n",
       "      <td>1255.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNES</th>\n",
       "      <td>476529</td>\n",
       "      <td>61.23</td>\n",
       "      <td>19.04</td>\n",
       "      <td>116.55</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3DS</th>\n",
       "      <td>1030721</td>\n",
       "      <td>83.49</td>\n",
       "      <td>61.48</td>\n",
       "      <td>100.67</td>\n",
       "      <td>13.36</td>\n",
       "      <td>11273.0</td>\n",
       "      <td>1243.9</td>\n",
       "      <td>259.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year_of_release  na_sales  eu_sales  jp_sales  other_sales  \\\n",
       "platform                                                               \n",
       "DS                4259361    382.40    188.89    175.57        59.26   \n",
       "PS                2377627    336.52    213.61    139.82        40.91   \n",
       "PS2               4263750    583.84    339.29    139.20       193.44   \n",
       "SNES               476529     61.23     19.04    116.55         3.22   \n",
       "3DS               1030721     83.49     61.48    100.67        13.36   \n",
       "\n",
       "          critic_score  user_score  total_sales  \n",
       "platform                                         \n",
       "DS             45717.0      5047.5       806.12  \n",
       "PS             14303.0      1501.2       730.86  \n",
       "PS2            89208.0     10315.3      1255.77  \n",
       "SNES               0.0         0.0       200.04  \n",
       "3DS            11273.0      1243.9       259.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Buscar los lideres en ventas por region\n",
    "def sales_reg(region):\n",
    "    return df.groupby('platform').sum().sort_values(region, ascending=False)\n",
    "# Región NA\n",
    "na_sales = sales_reg('na_sales')\n",
    "print('NA SALES')\n",
    "display( na_sales.head() )\n",
    "# Región EU\n",
    "print('EU SALES')\n",
    "eu_sales = sales_reg('eu_sales')\n",
    "display( eu_sales.head() )\n",
    "# Región JP\n",
    "print('JP SALES')\n",
    "jp_sales = sales_reg('jp_sales')\n",
    "display( jp_sales.head() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se pueden observar las diferencias claras en cuanto a las plataformas que más han generado ingresos de acuerdo a cada región de las especificadas, pues en ningún caso coinciden en la mayoría de ganancias para algúna plataforma específica. También observamos que difieren bastante las ganancias obtenidas en cada una de estas regiones, siendo en la región NA la que tiene el mayor número de ventas en comparación a las otras."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Los cinco géneros principales. Explica la diferencia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NA SALES\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "genre\n",
       "Action      879.01\n",
       "Sports      684.43\n",
       "Shooter     592.24\n",
       "Platform    445.50\n",
       "Misc        407.27\n",
       "Name: na_sales, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EU SALES\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "genre\n",
       "Action     519.13\n",
       "Sports     376.79\n",
       "Shooter    317.34\n",
       "Racing     236.51\n",
       "Misc       212.74\n",
       "Name: eu_sales, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JP SALES\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "genre\n",
       "Role-Playing    355.41\n",
       "Action          161.43\n",
       "Sports          135.54\n",
       "Platform        130.83\n",
       "Misc            108.11\n",
       "Name: jp_sales, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Buscar los generos lideres en ventas por region\n",
    "def genre_reg(region):\n",
    "    return df.groupby('genre').sum().sort_values(region, ascending=False)\n",
    "# Región NA\n",
    "na_gens = genre_reg('na_sales')\n",
    "print('NA SALES')\n",
    "display( na_gens.head()['na_sales'] )\n",
    "# Región EU\n",
    "print('EU SALES')\n",
    "eu_gens = genre_reg('eu_sales')\n",
    "display( eu_gens.head()['eu_sales'] )\n",
    "# Región JP\n",
    "print('JP SALES')\n",
    "jp_gens = genre_reg('jp_sales')\n",
    "display( jp_gens.head()['jp_sales'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En esta ocasion sí se puede hallar una gran similitud entre los géneros preferidos por región, pues de los 5 géneros más vendidos, las regiones EU y NA comparten 4. Aunque en una preferencia distinta, algunos de estos generos también se pueden encontrar entre los más vendidos de la región JP. Por lo que se puede decir, que los generos: Acción, Deportes y Misc, serán los que mayor cantidad de ingresos pueden generar en las 3 regiones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Si las clasificaciones de ESRB afectan a las ventas en regiones individuales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NA SALES\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rating\n",
       "E           1292.99\n",
       "Unkwnown    1242.46\n",
       "T            759.75\n",
       "M            748.48\n",
       "E10+         353.32\n",
       "Name: na_sales, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EU SALES\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rating\n",
       "E           710.25\n",
       "Unkwnown    613.83\n",
       "M           483.97\n",
       "T           427.03\n",
       "E10+        188.52\n",
       "Name: eu_sales, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JP SALES\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rating\n",
       "Unkwnown    841.96\n",
       "E           198.11\n",
       "T           151.40\n",
       "M            64.24\n",
       "E10+         40.20\n",
       "Name: jp_sales, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def rating_sales(region):\n",
    "    return df.groupby('rating').sum().sort_values(region, ascending=False)\n",
    "# Región NA\n",
    "na_rate = rating_sales('na_sales')\n",
    "print('NA SALES')\n",
    "display( na_rate.head()['na_sales'] )\n",
    "# Región EU\n",
    "print('EU SALES')\n",
    "eu_rate = rating_sales('eu_sales')\n",
    "display( eu_rate.head()['eu_sales'] )\n",
    "# Región JP\n",
    "print('JP SALES')\n",
    "jp_rate = rating_sales('jp_sales')\n",
    "display( jp_rate.head()['jp_sales'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con este análisis por región ayuda a complementar el análisis general anterior y a hacer zoom a los resultados por cada una de las regiones. Se puede observar que las regiones NA y EU coinciden en los primeros 2 ratings con mayoría de ventas y teniendo el menor número en ventas los juegos clasificados como E10+."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4. <a id='toc1_4_'></a>[Pruebas de hipótesis](#toc0_)\n",
    "\n",
    "Establece tu mismo el valor de umbral alfa.\n",
    "\n",
    "Explica:\n",
    "\n",
    "- Cómo formulaste las hipótesis nula y alternativa.\n",
    "\n",
    "- Qué criterio utilizaste para probar las hipótesis y por qué."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.1. <a id='toc1_4_1_'></a>[Las calificaciones promedio de los usuarios para las plataformas Xbox One y PC son las mismas.](#toc0_)\n",
    "\n",
    "Hipótesis Nula: Las calificaciones promedio de los usuarios en las plataformas Xbox One y PC son las mismas.\n",
    "\n",
    "Hipótesis Alternativa: Las calificaciones promedio de los usuarios en las plataformas Xbox One y PC son diferentes.\n",
    "\n",
    "He elegido estas dos hipótesis para comprobar si las calificaciones en promedio son las mismas para dos plataformas específicas, la hipótesis alternativa se ha elegido en función en la hipótesis nula. Dado que se requiere hacer la comparación entre dos poblaciones diferentes, he optado por utilizar la prueba para valores independientes: `ttest`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Varianza plataforma XOne: 9.389805438541856 Varianza plataforma PC: 10.013672918889062\n",
      "LeveneResult(statistic=0.007879200458610286, pvalue=0.9292833651034925)\n",
      "valor-p: 0.0010728118414519362 \n",
      "\n",
      "Se rechaza la hipótesis nula\n"
     ]
    }
   ],
   "source": [
    "# Prueba la hipótesis:\n",
    "# Las calificaciones promedio de los usuarios para las plataformas Xbox One y PC son las mismas\n",
    "\n",
    "sample_1 = df[df['platform']=='XOne']['user_score']\n",
    "sample_2 = df[df['platform']=='PC']['user_score']\n",
    "alpha = 0.05 # el nivel de significancia estadística crítica, si el valor p es menor que alpha, rechazamos la hipótesis\n",
    "\n",
    "print('Varianza plataforma XOne:', np.var(sample_1), \"Varianza plataforma PC:\", np.var(sample_2))\n",
    "print(sts.levene(sample_1, sample_2))\n",
    "\n",
    "results = sts.ttest_ind(sample_1, sample_2, equal_var=False) \n",
    "print('valor-p:',results.pvalue,'\\n')\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print('Se rechaza la hipótesis nula')\n",
    "else:\n",
    "    print('No se puede rechazar la hipótesis nula')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "He realizado primero la prueba de Levene, que ayudará a indicar si las varianzas de las poblaciones son iguales, en este caso se ha obtenido un número mayor al umbral alpha, lo que indica que las varianzas pueden llegar a ser similares.\n",
    "\n",
    "Al realizar la prueba, hemos obtenido un valor p más bajo que el valor alpha. Esto nos indica que es baja la probabilidad de encontrar valores similares para las dos plataformas que estamos comparando y por lo tanto, rechazamos la hipótesis nula."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.2. <a id='toc1_4_2_'></a>[Las calificaciones promedio de los usuarios para los géneros de Acción y Deportes son diferentes.](#toc0_)\n",
    "\n",
    "Hipótesis Nula: Las calificaciones promedio de los usuarios para los géneros de Acción y Deportes son diferentes.\n",
    "\n",
    "Hipótesis Alternativa: Las calificaciones promedio de los usuarios para los géneros de Acción y Deportes son iguales.\n",
    "\n",
    "He elegido estas dos hipótesis que ayudan a comprobar si las calificaciones en promedio son las mismas para dos generos específicas, la hipótesis alternativa se ha elegido en función en la hipótesis nula. Dado que se requiere hacer la comparación entre dos poblaciones diferentes, he optado por utilizar la prueba para valores independientes: `ttest`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Varianza género Acción: 12.69097237122892 Varianza género Deportes: 12.932367138367063\n",
      "LeveneResult(statistic=11.111170834269762, pvalue=0.000863550661349195)\n",
      "valor-p: 0.0002613330924977687 \n",
      "\n",
      "Se rechaza la hipótesis nula\n"
     ]
    }
   ],
   "source": [
    "# Prueba la hipótesis:\n",
    "# Las calificaciones promedio de los usuarios para los géneros de Acción y Deportes son diferentes\n",
    "\n",
    "sample_1 = df[df['genre']=='Action']['user_score']\n",
    "sample_2 = df[df['genre']=='Sports']['user_score']\n",
    "alpha = 0.05 # el nivel de significancia estadística crítica, si el valor p es menor que alpha, rechazamos la hipótesis\n",
    "\n",
    "print('Varianza género Acción:', np.var(sample_1), \"Varianza género Deportes:\", np.var(sample_2))\n",
    "print(sts.levene(sample_1, sample_2))\n",
    "\n",
    "results = sts.ttest_ind(sample_1, sample_2, equal_var=True) \n",
    "print('valor-p:',results.pvalue,'\\n')\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print('Se rechaza la hipótesis nula')\n",
    "else:\n",
    "    print('No se puede rechazar la hipótesis nula')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta vez, al realizar la prueba de Levene para calcular las varianzas iguales, se obtiene un valor menor al del umbral alpha, indicando que las varianzas pueden tener una diferencia notable.\n",
    "\n",
    "Al realizar la prueba, el valor p es demasiado bajo, por lo que será más difícil hallar valores que se acerquen a lo que estamos buscando, por lo tanto se rechaza la hipótesis nula.\n",
    "\n",
    "Por estas dos hipótesis podemos pensar que no hay muchas similitudes entre los promedios de las críticas de los usuarios en cuanto a las plataformas o los géneros elegidos. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5. <a id='toc1_5_'></a>[Conclusión general](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En conclusión, si todos mis procedimientos fueron correcto, puedo decir que las plataformas en crecimiento son XOne y PS4, cada nueva plataforma suele durar aproximadamente 10 años. La región que produce más ingresos es la región de NA, en términos generales. No parece haber una relación sumamente fuerte entre las reseñas y las ventas, sin embargo, se deben tener en cuenta pues la relación que llegan a tener es positiva, así que puede verse afectada la venta de un juego según las reseñas. Finalmente, gracias a las hipótesis podemos decir, que es muy poco probable encontrar similitudes de reseñas de usuarios entre distintas plataformas o distintos generos."
   ]
  }
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