Hito 2.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Exploración Juegos de Steam\n",
    "### Grupo 10: Franco Becerra, Nicolás Becerra, Simón Sanfeliú y Antonio Vitalic\n",
    "### Noviembre 2022\n",
    "\n",
    "## Índice:\n",
    "* [Introducción y motivación](#intro)\n",
    "* [Exploración de datos](#exploracion)\n",
    "*   * [Juegos con más horas de juego](#most_playtime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducción y motivación <a class=\"anchor\" id=\"intro\"></a>\n",
    "\n",
    "El presente proyecto tiene como enfoque el estudio de la base de datos de juegos de Steam, un servicio de distribución digital de videojuegos establecido para juegos de computadora. La plataforma Steam es de los servicios más grandes en cuanto a la compra y venta de videojuegos en el mundo, teniendo un total de [132.000.000 de jugadores por mes (en 2021).](https://steamcommunity.com/groups/steamworks/announcements/detail/3133946090937137591) Dada la gran cantidad de dinero que fluye gracias a la industria de videojuegos [(T13)](https://steamcommunity.com/groups/steamworks/announcements/detail/3133946090937137591), es relevante estudiar cómo funciona una de las empresas más grandes en este rubro, viendo los juegos más demandados, los géneros más jugados, los precios de los juegos, los ratings de cada juego, entre otros. Luego, con toda esta información es posible hacerse una idea de cómo se está desarrollando esta industria y poder brindar un análisis a éste.\n",
    "\n",
    "La base de datos obtenida, la cual cuenta con información recopilada por voluntarios sobre los juegos dentro de Steam hasta el año 2019, tiene distintos atributos relevantes para el estudio de esta industria, como es el nombre del juego, su distribuidor, su género, el tiempo promedio de juego por quienes lo han comprado, cuántas personas lo tienen, cuántas calificaciones negativas y positivas tiene, su precio, entre otras. Las mencionadas anteriormente serán las más importantes para este proyecto, pues fueron las usadas para poder generar un análisis más desarrollado de ésta. Por ejemplo, es posible ver cuáles juegos son los más jugados de forma simple y así buscar su información correspondiente, o ver qué empresas (distribuidoras) son las que ganan más dinero a partir de los juegos que publican, o ver también qué juegos gratuitos resultan ser los más populares dentro de la plataforma. Todas estas características permiten adentrarse más en los datos dados y así encontrar patrones de comportamiento útiles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploración de datos <a class=\"anchor\" id=\"exploracion\"></a>\n",
    "\n",
    "La base de datos a utilizar fue obtenida en este [link](https://www.kaggle.com/datasets/nikdavis/steam-store-games) y muestra datos sobre los juegos dentro de Steam. Para cada uno de estos, muestra el nombre del juego, la fecha de lanzamiento, si se encuentra en inglés, el nombre del desarrollador y publicador, las plataformas a las que está disponible, la edad mínima requerida para poder jugar, las categorías del juego, su género, los tags de búsqueda, la cantidad de logros disponibles, la cantidad de calificaciones positivas y negativas, la mediana y el promedio del tiempo de juego, la cantidad de usuarios que poseen el juego y el valor monetario del juego."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>appid</th>\n",
       "      <th>english</th>\n",
       "      <th>required_age</th>\n",
       "      <th>achievements</th>\n",
       "      <th>positive_ratings</th>\n",
       "      <th>negative_ratings</th>\n",
       "      <th>average_playtime</th>\n",
       "      <th>median_playtime</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.707500e+04</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>2.707500e+04</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.00000</td>\n",
       "      <td>27075.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.962035e+05</td>\n",
       "      <td>0.981127</td>\n",
       "      <td>0.354903</td>\n",
       "      <td>45.248864</td>\n",
       "      <td>1.000559e+03</td>\n",
       "      <td>211.027147</td>\n",
       "      <td>149.804949</td>\n",
       "      <td>146.05603</td>\n",
       "      <td>6.078193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.508942e+05</td>\n",
       "      <td>0.136081</td>\n",
       "      <td>2.406044</td>\n",
       "      <td>352.670281</td>\n",
       "      <td>1.898872e+04</td>\n",
       "      <td>4284.938531</td>\n",
       "      <td>1827.038141</td>\n",
       "      <td>2353.88008</td>\n",
       "      <td>7.874922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.012300e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.990700e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.987600e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.260000e+02</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.069460e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9821.000000</td>\n",
       "      <td>2.644404e+06</td>\n",
       "      <td>487076.000000</td>\n",
       "      <td>190625.000000</td>\n",
       "      <td>190625.00000</td>\n",
       "      <td>421.990000</td>\n",
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      "text/plain": [
       "              appid       english  required_age  achievements  \\\n",
       "count  2.707500e+04  27075.000000  27075.000000  27075.000000   \n",
       "mean   5.962035e+05      0.981127      0.354903     45.248864   \n",
       "std    2.508942e+05      0.136081      2.406044    352.670281   \n",
       "min    1.000000e+01      0.000000      0.000000      0.000000   \n",
       "25%    4.012300e+05      1.000000      0.000000      0.000000   \n",
       "50%    5.990700e+05      1.000000      0.000000      7.000000   \n",
       "75%    7.987600e+05      1.000000      0.000000     23.000000   \n",
       "max    1.069460e+06      1.000000     18.000000   9821.000000   \n",
       "\n",
       "       positive_ratings  negative_ratings  average_playtime  median_playtime  \\\n",
       "count      2.707500e+04      27075.000000      27075.000000      27075.00000   \n",
       "mean       1.000559e+03        211.027147        149.804949        146.05603   \n",
       "std        1.898872e+04       4284.938531       1827.038141       2353.88008   \n",
       "min        0.000000e+00          0.000000          0.000000          0.00000   \n",
       "25%        6.000000e+00          2.000000          0.000000          0.00000   \n",
       "50%        2.400000e+01          9.000000          0.000000          0.00000   \n",
       "75%        1.260000e+02         42.000000          0.000000          0.00000   \n",
       "max        2.644404e+06     487076.000000     190625.000000     190625.00000   \n",
       "\n",
       "              price  \n",
       "count  27075.000000  \n",
       "mean       6.078193  \n",
       "std        7.874922  \n",
       "min        0.000000  \n",
       "25%        1.690000  \n",
       "50%        3.990000  \n",
       "75%        7.190000  \n",
       "max      421.990000  "
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set(style=\"ticks\", color_codes=True)\n",
    "import numpy as np\n",
    "\n",
    "steam = pd.DataFrame(pd.read_csv('steam.csv'))\n",
    "steam.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27075 entries, 0 to 27074\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   appid             27075 non-null  int64  \n",
      " 1   name              27075 non-null  object \n",
      " 2   release_date      27075 non-null  object \n",
      " 3   english           27075 non-null  int64  \n",
      " 4   developer         27075 non-null  object \n",
      " 5   publisher         27075 non-null  object \n",
      " 6   platforms         27075 non-null  object \n",
      " 7   required_age      27075 non-null  int64  \n",
      " 8   categories        27075 non-null  object \n",
      " 9   genres            27075 non-null  object \n",
      " 10  steamspy_tags     27075 non-null  object \n",
      " 11  achievements      27075 non-null  int64  \n",
      " 12  positive_ratings  27075 non-null  int64  \n",
      " 13  negative_ratings  27075 non-null  int64  \n",
      " 14  average_playtime  27075 non-null  int64  \n",
      " 15  median_playtime   27075 non-null  int64  \n",
      " 16  owners            27075 non-null  object \n",
      " 17  price             27075 non-null  float64\n",
      "dtypes: float64(1), int64(8), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "steam.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para simplicidad del cálculo en análisis, se asumirá que la gente que posee el juego corresponde a la marca de clase del rango que aparece en la columnas “owners”."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27075 entries, 0 to 27074\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   appid             27075 non-null  int64  \n",
      " 1   name              27075 non-null  object \n",
      " 2   release_date      27075 non-null  object \n",
      " 3   english           27075 non-null  int64  \n",
      " 4   developer         27075 non-null  object \n",
      " 5   publisher         27075 non-null  object \n",
      " 6   platforms         27075 non-null  object \n",
      " 7   required_age      27075 non-null  int64  \n",
      " 8   categories        27075 non-null  object \n",
      " 9   genres            27075 non-null  object \n",
      " 10  steamspy_tags     27075 non-null  object \n",
      " 11  achievements      27075 non-null  int64  \n",
      " 12  positive_ratings  27075 non-null  int64  \n",
      " 13  negative_ratings  27075 non-null  int64  \n",
      " 14  average_playtime  27075 non-null  int64  \n",
      " 15  median_playtime   27075 non-null  int64  \n",
      " 16  owners            27075 non-null  object \n",
      " 17  price             27075 non-null  float64\n",
      "dtypes: float64(1), int64(8), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "steam['owners'] = steam['owners'].str.replace('20000-50000', '35000')\n",
    "steam['owners'] = steam['owners'].str.replace('50000-100000', '75000')\n",
    "steam['owners'] = steam['owners'].str.replace('100000-200000', '150000')\n",
    "steam['owners'] = steam['owners'].str.replace('200000-500000', '350000')\n",
    "steam['owners'] = steam['owners'].str.replace('500000-1000000', '750000')\n",
    "steam['owners'] = steam['owners'].str.replace('1000000-2000000', '1500000')\n",
    "steam['owners'] = steam['owners'].str.replace('2000000-5000000', '3500000')\n",
    "steam['owners'] = steam['owners'].str.replace('5000000-10000000', '7500000')\n",
    "steam['owners'] = steam['owners'].str.replace('10000000-20000000', '15000000')\n",
    "steam['owners'] = steam['owners'].str.replace('20000000-50000000', '35000000')\n",
    "steam['owners'] = steam['owners'].str.replace('50000000-100000000', '75000000')\n",
    "steam['owners'] = steam['owners'].str.replace('100000000-200000000', '150000000')\n",
    "steam['owners'] = steam['owners'].str.replace('0-20000', '10000')\n",
    "steam.info()\n",
    "steam['owners'] = steam['owners'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Juegos con más horas de juego <a class=\"anchor\" id=\"most_playtime\"></a>\n",
    "\n",
    "Una característica de interés en la industria de los videojuegos corresponde a la de los juegos más jugados. Para esto, se determinarán aquellos juegos que registren la mayor cantidad de tiempo jugado, lo cual fue calculado como el tiempo promedio jugado (average_playtime) por la cantidad de personas que lo jugaron. Además, se relacionará con el precio del juego. Según el gráfico, incluso los 20 juegos más jugados presentan grandes diferencias entre sí. Notando los colores de cada barra, la mayoría de los juegos son gratuitos o de menos de 20 USD, incluso hay 4 juegos gratis dentro de los 5 más jugados. Esto puede explicarse a que todos tienen acceso a los juegos gratuitos y el carácter competitivo de juegos online multijugador, como lo son los primeros ejemplares, aportan a que mucha gente sume horas de juego simultaneamente a la estadística."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>publisher</th>\n",
       "      <th>owners</th>\n",
       "      <th>average_playtime</th>\n",
       "      <th>price</th>\n",
       "      <th>count</th>\n",
       "      <th>playtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6595</th>\n",
       "      <td>Dota 2</td>\n",
       "      <td>Valve</td>\n",
       "      <td>150000000</td>\n",
       "      <td>23944</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3591600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15999</th>\n",
       "      <td>PLAYERUNKNOWN'S BATTLEGROUNDS</td>\n",
       "      <td>PUBG Corporation</td>\n",
       "      <td>75000000</td>\n",
       "      <td>22938</td>\n",
       "      <td>26.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1720350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4813</th>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>Valve</td>\n",
       "      <td>75000000</td>\n",
       "      <td>22494</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1687050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21938</th>\n",
       "      <td>Team Fortress 2</td>\n",
       "      <td>Valve</td>\n",
       "      <td>35000000</td>\n",
       "      <td>8495</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>297325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4810</th>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Valve</td>\n",
       "      <td>15000000</td>\n",
       "      <td>17612</td>\n",
       "      <td>7.19</td>\n",
       "      <td>1</td>\n",
       "      <td>264180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25358</th>\n",
       "      <td>Warframe</td>\n",
       "      <td>Digital Extremes</td>\n",
       "      <td>35000000</td>\n",
       "      <td>5845</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>204575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9188</th>\n",
       "      <td>Garry's Mod</td>\n",
       "      <td>Valve</td>\n",
       "      <td>15000000</td>\n",
       "      <td>12422</td>\n",
       "      <td>6.99</td>\n",
       "      <td>1</td>\n",
       "      <td>186330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9631</th>\n",
       "      <td>Grand Theft Auto V</td>\n",
       "      <td>Rockstar Games</td>\n",
       "      <td>15000000</td>\n",
       "      <td>9837</td>\n",
       "      <td>24.99</td>\n",
       "      <td>1</td>\n",
       "      <td>147555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24595</th>\n",
       "      <td>Unturned</td>\n",
       "      <td>Smartly Dressed Games</td>\n",
       "      <td>35000000</td>\n",
       "      <td>3248</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>113680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22429</th>\n",
       "      <td>The Elder Scrolls V: Skyrim</td>\n",
       "      <td>Bethesda Softworks</td>\n",
       "      <td>15000000</td>\n",
       "      <td>7089</td>\n",
       "      <td>9.99</td>\n",
       "      <td>1</td>\n",
       "      <td>106335.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name              publisher     owners  \\\n",
       "6595                             Dota 2                  Valve  150000000   \n",
       "15999     PLAYERUNKNOWN'S BATTLEGROUNDS       PUBG Corporation   75000000   \n",
       "4813   Counter-Strike: Global Offensive                  Valve   75000000   \n",
       "21938                   Team Fortress 2                  Valve   35000000   \n",
       "4810                     Counter-Strike                  Valve   15000000   \n",
       "25358                          Warframe       Digital Extremes   35000000   \n",
       "9188                        Garry's Mod                  Valve   15000000   \n",
       "9631                 Grand Theft Auto V         Rockstar Games   15000000   \n",
       "24595                          Unturned  Smartly Dressed Games   35000000   \n",
       "22429       The Elder Scrolls V: Skyrim     Bethesda Softworks   15000000   \n",
       "\n",
       "       average_playtime  price  count   playtime  \n",
       "6595              23944   0.00      1  3591600.0  \n",
       "15999             22938  26.99      1  1720350.0  \n",
       "4813              22494   0.00      1  1687050.0  \n",
       "21938              8495   0.00      1   297325.0  \n",
       "4810              17612   7.19      1   264180.0  \n",
       "25358              5845   0.00      1   204575.0  \n",
       "9188              12422   6.99      1   186330.0  \n",
       "9631               9837  24.99      1   147555.0  \n",
       "24595              3248   0.00      1   113680.0  \n",
       "22429              7089   9.99      1   106335.0  "
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_played = steam.groupby(['name','publisher','owners','average_playtime','price'])\n",
    "most_played = most_played.size().reset_index(name='count')\n",
    "most_played['playtime'] = most_played['average_playtime'] * most_played['owners'] /1000000\n",
    "most_played = most_played.sort_values(by=['playtime'], ascending=False)\n",
    "most_played.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ePZoVK1Ywf/58+vbtC8BPP/2Eg4MDK1euNHyRNnToUNq3b8+6dev48ssvsbOzM6p/9erVOXbsGEePHqVu3bov1BciIiIi8uZ6+PAhffv2NQRhH1WiRAnDu1nlypWN9lWsWDHHmWSeRFMfi4iIiIiIiLwm4uLimDFjxhP/4uLijPKnpaWxZs0aihQpwpgxYwxBMQAHBwcCAgIAWLp0abZztWjRwmjb0tKSyZMnM378eKNAI0CdOnUASE1NNaRl/Zr88OHDRnlHjBjBnj17aNWqlYmtN82pU6eIiYmhQYMGRiMu8+bNy6hRowxByCyOjo5MnDiRQYMGZSvr8fbdv3+fDRs2ULx4cT777DOjvIMHD+b99983SjN336WmpnLp0iUqVqxoCGw+rkKFCoYgbda2jY0NAJ999plR/7i4uACZ01G/iBe5Hv38/IzakvWZPlqnJUuW8PDhQ4YOHWoI0gLkyZOHESNGYGFhwbJlywzpGRkZXLt2jfj4eEOalZUV8+bN49ChQ9mCtIBhumytUysiIiLybqtUqRIJCQmULVvW8JeUlMTkyZO5c+cOVatWxdramuPHjxsdd+bMGaMfY/4VjagVEREREREReU20b9+ef/3rX0/cP3LkSH755RfD9rlz57h79y7u7u45TpXr6uoKkG390aJFi2ab4tbGxgZPT09DufHx8SQmJhIfH8+RI0cASE9PN+Tv3Lkz27ZtY9SoUcyYMYMGDRpQv3593N3dDdMHv0onTpwAoGbNmtn2OTg4ULJkSX777TdDWrly5ShXrhz3798nNjaWc+fOkZiYyJkzZwzrzma178KFC9y5cwdXV1fy5s1rVLalpSXOzs7s2LHDkGbuvrt69SoAxYoVe2KecuXKZUuztrbm+vXrODg4GKVnBZf//PPPvzz30zzv9WhjY2MUeAUoUqQIgNFaslnB0/3792crAzLbd+7cOe7cuYO1tTWffvopM2fOpF27djg5ORn62NXV1TDC9nFZfZrVxyIiIiLyburXrx8BAQHMmDEDT09PkpKSGD16NPb29pQoUQKAvn37EhISgp2dHc7Ozqxfv559+/Yxf/78Zz6PArUiIiIiIiIib6hbt24BULhw4Rz3Z40Y/OOPP4zSn7T+6c6dOwkKCuL06dNA5ujUChUqULVqVc6ePWsYCQrQoEEDwsPDCQsLY+/evSxdupSlS5eSL18+2rRpw5gxY3jvvfdeuI1Pcu3aNQDDlySPs7OzMwrUZmRkEBYWxty5c0lJSQEyA5QffvghTk5OXLlyxdC+GzduAOS4Fi1gGJn6KHP2XVb9Hh+9+6inHZ8/f/4n7nsRz3s95lSfrNG1j/bbzZs3AVi4cOFT63Hz5k2sra0ZPHgw5cqVY+nSpURHRxMbG8ucOXOwsbGhX79+himSH5XVb1l9LCIiIiLvpn/84x8EBwcTGhrKrFmzsLGxoVGjRnzxxReGPH5+fhQsWJDg4GCSk5OpUKEC06dPN8yq8ywUqBURERERERF5Q2UFEpOTk3PcnxXYyimw+LiTJ0/i5+dH4cKFGTduHLVq1aJs2bJYWVkRHx/P6tWrsx3j4uKCi4sLaWlpnDhxgr1797Jq1SpWrFiBhYUF48ePN6k9OQXnsjwe3LO2tgb+Fxx83J07d4y2f/zxRyZNmoSTkxOBgYFUqVKF0qVLY2FhweLFi9m9e7chb1Z/ZfXf4+7evWu0be6+ywq0P6l+ueVlXo85yfrM9+3bR/HixZ/pmLZt29K2bVtu3brFoUOH2LVrF2vWrOHbb7/lgw8+yLYmb1aA9lUFs0VERETk9ZTTzEYtWrTItmTM43r16kWvXr2e+7xao1ZERERERETkDVW+fHkKFizI6dOncxwBGBUVBUDlypX/sqy1a9eSnp7OV199hbe3N5UqVcLKygqAs2fPAv8LoD58+JDZs2cTHBwMZK776erqSkBAAIsWLQLg0KFDJrcnazrax4OsAOfPnzfarl69OgDHjh3LlvfGjRskJCQYpa1atQqA2bNn06RJE+zt7Q3rtGa1L0uZMmWwsbHh5MmTRlPvQmYfPL5+qbn7rnTp0sD/RhW/Ll7m9ZgTJycngGzrgEHmtM0TJ04kLCyMjIwMkpOT+f7771m5ciWQOcq3UaNGBAYG8s033wA593NWn2b1sYiIiIjIq6RArYiIiIiIiMgbKmuq3Dt37jBx4kQePHhg2JeYmGgIBrZv3/4vy8oapfnodMEAv//+O0FBQQCG8i0sLIiIiCA0NJTDhw8b5b948SIA9vb2JrenQoUKQOaIyUcDpMeOHWPv3r1GeWvWrEnlypXZuXOn0WjY9PR0Jk2axP3795+pfVFRUSxduhTAcIylpSXt27fn+vXrBAUFGY3wnTt3brYyzN13tra22NnZkZCQkK2duellXo856dixIwCTJ0/m8uXLRvumTp3Kjz/+SHR0NHny5MHa2pq5c+cSHBxMamqqUd6sfn58rV6AuLg4AD788MPnqqOIiIiIiCk09bGIiIiIiIjIG2z48OFER0ezatUqYmJiqFu3Ljdv3iQiIoJbt24xcOBA3Nzc/rIcT09PwsLCmDp1KjExMZQpU4bffvuNiIgILC0tyZcvH9evXzfk/+KLL/D19cXX15emTZtib29PUlISW7ZsoUCBAgwePNjktjg5OVGrVi2io6Pp0KEDHh4ehjJdXFw4ePCgUf7x48fTo0cP+vfvT5MmTShdujRRUVGcP3+e/PnzGwUxvby8iI6Opl+/frRo0YIiRYoQFxfH/v37KVasGCkpKUbt8/f3JzIykrCwMI4ePYqLiwtxcXFERUXh4OBgCPblVt81aNCAlStXEhMTQ82aNU3u61flZV2POXFxccHPz4+ZM2fi6elJo0aNeP/99zly5AjHjh3jb3/7G6NGjQIyp2H29/dnypQpeHp60rRpU4oUKcLp06fZs2cPZcuWxcfHJ9s5jhw5wnvvvUft2rVfqB9ERERERJ6FRtSKiIiIiIiIvMEKFy7Mzz//zIABA0hPT2fJkiXs2rWLWrVqMWfOHPz9/Z+pHEdHR8LCwnB1deXAgQMsXLiQU6dO0aZNG9auXYurqysXL14kPj4eAFdXVxYtWsTHH3/MsWPHCAsLY//+/TRu3Jjly5fj7Oz8XO2ZOXMmPj4+pKamsmDBAv773/8yYcIEevbsmS1vjRo1WLx4MR4eHkRGRvLzzz9ja2vL4sWLKViwIAULFjTk9fb2ZsKECZQuXZq1a9eybNkyUlNTGThwIBs3bqRgwYLs3r2b9PR0IHM91PDwcPr06UNKSgrh4eFcu3aN6dOnZwuM5kbftWvXDsBoNPHr4GVdj08yePBgfvjhB6pVq8b27dtZuHAhN27coHfv3ixdupRSpUoZ8vbr14/g4GDKly/Ptm3bmD9/PgkJCfTs2ZMlS5ZQtGhRo7IvXbpEfHw8zZs3N7p2RERERERelTwZj87fIyIiIiIiIiJiBhcvXqRJkya4ubmxcOFCk49PS0sjOTmZ0qVLkzdvXqN99+7dw8XFhb///e+sX7/+ZVX5tePj40NSUhIRERHZ+kBMFxwczOzZs1m7di0VK1bM7eqIiIiIyDtAI2pFRERERERExOySk5MBso1qfFZ//PEHzZo1o2PHjkZroQLMmzeP9PR06tWr98L1fJ0NHDiQpKQkduzYkdtVeeOlpaWxYsUKWrRooSCtiIiIiJiN1qgVEREREREREbO5cOECM2bM4Pjx40DmNMDPo2jRorRu3ZrVq1fTpk0b3N3dyZs3L7/++isHDx7E3t6egQMHvsyqv3YaNmxIs2bNmDp1Kp988olG1b6ABQsWcO/ePYYPH57bVRERERGRd4imPhYRERERERERs9m+fTsjR44kPT2d5s2bExgYiJWV1XOV9eDBA5YtW8bKlSu5cOEC9+7do2TJkjRu3Jj+/fs/92jdN8m1a9do3bo1AwYMoEuXLrldnTdSamoqzZo146uvvqJt27a5XR0REREReYcoUCsiIiIiIiIiIiIiIiIiYmZao1ZERERERERERERERERExMwUqBURERERERERERERERERMTMFakVEREREREREREREREREzEyBWhERERERERERERERERERM1OgVkRERERERERERERERETEzBSoFRERERERERERERERERExMwVqRURERERERERERERERETMTIFaEREREREREREREREREREzU6BWRERERERERERERERERMTM/h8hnjPheo4jeAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(y='name', x='playtime', data=most_played.head(20), palette='Dark2', hue='price',dodge=False)\n",
    "plt.title('Top 20 juegos más jugados', fontsize=20)\n",
    "plt.xlabel('Horas jugadas (millones)', fontsize=15)\n",
    "plt.ylabel('Juego', fontsize=15)\n",
    "plt.legend(fontsize=15, title='Precio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cantidad de juegos por empresa\n",
    "\n",
    "Dado que ya se ha empezado a adentrarse en el flujo de dinero generado por esta industria, es relevante ver cuántos juegos tienen publicados distintos distribuidores. Esto permite ver cuánta oferta se está dando por empresa, además de ver cuáles juegos aportan más dinero a las empresas más allá de su compra inicial, con expansiones de contenido o ediciones especiales, por ejemplo. Esto se comenta puesto que es posible que se encuentren empresas con muy pocos juegos, pero que a la vez ganen mucho (un gran ejemplo de esto es Minecraft, el juego más vendido de todos los tiempos, que por mucho tiempo fue el único juego de la compañía Mojang).\n",
    "\n",
    "Teniendo esto en consideración, es posible ver que la empresa con más juegos en la plataforma es Big Fish Games, la cual cuenta con 212 juegos en Steam. Luego de ésta, le siguen otras distribuidoras más conocidas en el top 20, como Ubisoft, Square Enix y SEGA. ¿A qué se debe que esta compañía y otras menos conocidas tengan tantos juegos? El caso de la empresa con más juegos es bastante particular, ya que es una empresa especializada en juegos de casino o máquinas de azar, juegos que usualmente vienen en packs, requieren pocas especificaciones en el computador, suelen ser gratis o a bajo costo y ganan más al ser muy variados. Luego, se podría considerar este dato como un outlier, puesto que el género de juegos que aborda no resulta ser de los más jugados en la plataforma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "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>publisher</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>Big Fish Games</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11243</th>\n",
       "      <td>Strategy First</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12414</th>\n",
       "      <td>Ubisoft</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11530</th>\n",
       "      <td>THQ Nordic</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11058</th>\n",
       "      <td>Square Enix</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10465</th>\n",
       "      <td>Sekai Project</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>Choice of Games</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2844</th>\n",
       "      <td>Dagestan Technology</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1C Entertainment</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10196</th>\n",
       "      <td>SEGA</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 publisher  count\n",
       "1440        Big Fish Games    212\n",
       "11243       Strategy First    136\n",
       "12414              Ubisoft    111\n",
       "11530           THQ Nordic     98\n",
       "11058          Square Enix     97\n",
       "10465        Sekai Project     96\n",
       "2267       Choice of Games     94\n",
       "2844   Dagestan Technology     88\n",
       "99        1C Entertainment     88\n",
       "10196                 SEGA     78"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_publisher = steam.groupby(['publisher'])\n",
    "games_publisher = games_publisher.size().reset_index(name='count')\n",
    "games_publisher = games_publisher.sort_values(by=['count'], ascending=False)\n",
    "games_publisher.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(y='publisher', x='count', data=games_publisher.head(20), color='teal', dodge=False)\n",
    "plt.title('Top 20 empresas con más juegos', fontsize=20)\n",
    "plt.xlabel('Número de juegos', fontsize=15)\n",
    "plt.ylabel('Empresas', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dinero vendido por juego según cada publicador\n",
    "\n",
    "En el mercado de los videojuegos existen muchos publicadores famosos y conocidos por generar muchos ingresos. Sin embargo, no todos estos ingresos provienen de exclusivamente de la compra de su videojuego, pues en muchos casos obtienen ingresos por compras dentro del mismo juego o de mercancía relacionada con el juego. Sin embargo, se consideró que resultaría interesante ver que publicadores generaron más dinero con solo la compra de sus videojuegos, pues puede servir para esclarecer ideas sobre el ingreso de estos publicadores y sobre características de sus videojuegos, por ejemplo que no necesariamente se tiene que tener un juego a un precio elevado para poder obtener grandes cantidades de ingresos, o por ejemplo que puede ser mejor expandirse (Ubisoft Paris está bajo Ubisoft entertaiment, el cual es conocido de ser uno de las empresas que más grande de videojuegos) que no hacerlo (PUBG Corporation no genera más que Ubisoft a pesar de tener más ingreso por compra). El income calculado se obtuvo multiplicando el precio del juego por la cantidad promediada de compradores, eso para cada juego y luego se grupo por publisher, sumando todos los income de cada juego de cada publisher. Finalmente se ordenaron de menor a mayor y se graficaron los primeros 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "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>publisher</th>\n",
       "      <th>owners</th>\n",
       "      <th>price</th>\n",
       "      <th>count</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13409</th>\n",
       "      <td>PUBG Corporation</td>\n",
       "      <td>75000000</td>\n",
       "      <td>26.99</td>\n",
       "      <td>1</td>\n",
       "      <td>2024.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3153</th>\n",
       "      <td>CAPCOM Co., Ltd.</td>\n",
       "      <td>7500000</td>\n",
       "      <td>49.99</td>\n",
       "      <td>1</td>\n",
       "      <td>374.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15300</th>\n",
       "      <td>Rockstar Games</td>\n",
       "      <td>15000000</td>\n",
       "      <td>24.99</td>\n",
       "      <td>1</td>\n",
       "      <td>374.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17419</th>\n",
       "      <td>Studio Wildcard</td>\n",
       "      <td>7500000</td>\n",
       "      <td>44.99</td>\n",
       "      <td>1</td>\n",
       "      <td>337.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19830</th>\n",
       "      <td>Warner Bros Interactive Entertainment</td>\n",
       "      <td>7500000</td>\n",
       "      <td>44.99</td>\n",
       "      <td>1</td>\n",
       "      <td>337.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6259</th>\n",
       "      <td>Facepunch Studios</td>\n",
       "      <td>7500000</td>\n",
       "      <td>27.79</td>\n",
       "      <td>1</td>\n",
       "      <td>208.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3172</th>\n",
       "      <td>CD PROJEKT RED</td>\n",
       "      <td>7500000</td>\n",
       "      <td>24.99</td>\n",
       "      <td>1</td>\n",
       "      <td>187.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>2K;Aspyr (Mac);Aspyr (Linux)</td>\n",
       "      <td>3500000</td>\n",
       "      <td>49.99</td>\n",
       "      <td>1</td>\n",
       "      <td>174.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>Activision (Excluding Japan and Asia);FromSoft...</td>\n",
       "      <td>3500000</td>\n",
       "      <td>49.90</td>\n",
       "      <td>1</td>\n",
       "      <td>174.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13520</th>\n",
       "      <td>Paradox Interactive</td>\n",
       "      <td>7500000</td>\n",
       "      <td>22.99</td>\n",
       "      <td>1</td>\n",
       "      <td>172.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>2K;Aspyr (Mac);Aspyr (Linux)</td>\n",
       "      <td>7500000</td>\n",
       "      <td>19.99</td>\n",
       "      <td>2</td>\n",
       "      <td>149.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>Bethesda Softworks</td>\n",
       "      <td>7500000</td>\n",
       "      <td>19.99</td>\n",
       "      <td>1</td>\n",
       "      <td>149.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>Activision;Aspyr (Mac)</td>\n",
       "      <td>7500000</td>\n",
       "      <td>19.99</td>\n",
       "      <td>1</td>\n",
       "      <td>149.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>Bethesda Softworks</td>\n",
       "      <td>15000000</td>\n",
       "      <td>9.99</td>\n",
       "      <td>1</td>\n",
       "      <td>149.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19032</th>\n",
       "      <td>Ubisoft</td>\n",
       "      <td>3500000</td>\n",
       "      <td>41.99</td>\n",
       "      <td>1</td>\n",
       "      <td>146.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18650</th>\n",
       "      <td>Torn Banner Studios</td>\n",
       "      <td>7500000</td>\n",
       "      <td>18.99</td>\n",
       "      <td>1</td>\n",
       "      <td>142.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17010</th>\n",
       "      <td>Square Enix;Feral Interactive (Mac);Feral Inte...</td>\n",
       "      <td>3500000</td>\n",
       "      <td>39.99</td>\n",
       "      <td>1</td>\n",
       "      <td>139.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6917</th>\n",
       "      <td>FromSoftware, Inc.;BANDAI NAMCO Entertainment</td>\n",
       "      <td>3500000</td>\n",
       "      <td>39.99</td>\n",
       "      <td>1</td>\n",
       "      <td>139.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>Activision</td>\n",
       "      <td>3500000</td>\n",
       "      <td>39.99</td>\n",
       "      <td>1</td>\n",
       "      <td>139.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19033</th>\n",
       "      <td>Ubisoft</td>\n",
       "      <td>7500000</td>\n",
       "      <td>16.99</td>\n",
       "      <td>1</td>\n",
       "      <td>127.425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               publisher    owners  price  \\\n",
       "13409                                   PUBG Corporation  75000000  26.99   \n",
       "3153                                    CAPCOM Co., Ltd.   7500000  49.99   \n",
       "15300                                     Rockstar Games  15000000  24.99   \n",
       "17419                                    Studio Wildcard   7500000  44.99   \n",
       "19830              Warner Bros Interactive Entertainment   7500000  44.99   \n",
       "6259                                   Facepunch Studios   7500000  27.79   \n",
       "3172                                      CD PROJEKT RED   7500000  24.99   \n",
       "282                         2K;Aspyr (Mac);Aspyr (Linux)   3500000  49.99   \n",
       "787    Activision (Excluding Japan and Asia);FromSoft...   3500000  49.90   \n",
       "13520                                Paradox Interactive   7500000  22.99   \n",
       "283                         2K;Aspyr (Mac);Aspyr (Linux)   7500000  19.99   \n",
       "2327                                  Bethesda Softworks   7500000  19.99   \n",
       "792                               Activision;Aspyr (Mac)   7500000  19.99   \n",
       "2328                                  Bethesda Softworks  15000000   9.99   \n",
       "19032                                            Ubisoft   3500000  41.99   \n",
       "18650                                Torn Banner Studios   7500000  18.99   \n",
       "17010  Square Enix;Feral Interactive (Mac);Feral Inte...   3500000  39.99   \n",
       "6917       FromSoftware, Inc.;BANDAI NAMCO Entertainment   3500000  39.99   \n",
       "786                                           Activision   3500000  39.99   \n",
       "19033                                            Ubisoft   7500000  16.99   \n",
       "\n",
       "       count   revenue  \n",
       "13409      1  2024.250  \n",
       "3153       1   374.925  \n",
       "15300      1   374.850  \n",
       "17419      1   337.425  \n",
       "19830      1   337.425  \n",
       "6259       1   208.425  \n",
       "3172       1   187.425  \n",
       "282        1   174.965  \n",
       "787        1   174.650  \n",
       "13520      1   172.425  \n",
       "283        2   149.925  \n",
       "2327       1   149.925  \n",
       "792        1   149.925  \n",
       "2328       1   149.850  \n",
       "19032      1   146.965  \n",
       "18650      1   142.425  \n",
       "17010      1   139.965  \n",
       "6917       1   139.965  \n",
       "786        1   139.965  \n",
       "19033      1   127.425  "
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenue = steam.groupby(['publisher','owners','price'])\n",
    "revenue = revenue.size().reset_index(name='count')\n",
    "revenue['revenue'] = revenue['owners'] * revenue['price']/1000000\n",
    "revenue = revenue.sort_values(by=['revenue'], ascending=False)\n",
    "revenue.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nicol\\Downloads\\virtual\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 26041 (\\N{CJK UNIFIED IDEOGRAPH-65B9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\nicol\\Downloads\\virtual\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 22359 (\\N{CJK UNIFIED IDEOGRAPH-5757}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\nicol\\Downloads\\virtual\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\nicol\\Downloads\\virtual\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(y='publisher', x='revenue', data=revenue.head(20), color='teal', dodge=False)\n",
    "plt.title('Top 20 empresas con más ingresos', fontsize=20)\n",
    "plt.xlabel('Ingresos (millones)', fontsize=15)\n",
    "plt.ylabel('Empresas', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Juegos gratis más populares\n",
    "\n",
    "Los juegos free-to-play siempre han tenido una gran recepción y en muchos casos son relevantes a nivel competitivo global, es por eso por lo que puede resultar útil o interesante el ver que juegos gratuitos son los más valorados de acuerdo con el público consumidor, además de la cantidad de jugadores de aquellos juegos valorados muy positivamente. Además, se pueden ver las características que comparten los juegos mejor valorados como estudio de lo que le gusta al público consumidor (por ejemplo, que sea multijugador o que sea competitivo). El rating promedio se obtuvo como sumando la cantidad de rating positivos y negativos (estos quedan restando) y dividiéndolo por 2 (con el fin de aquellos que tengan muchos ratings positivos y que sean más que los negativos serán los que tienen un mejor rating en general), luego se ordenaron de acuerdo con el rating general de mayor a menor y se graficó el top 20.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>owners</th>\n",
       "      <th>positive_ratings</th>\n",
       "      <th>negative_ratings</th>\n",
       "      <th>price</th>\n",
       "      <th>count</th>\n",
       "      <th>average rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4813</th>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>75000000</td>\n",
       "      <td>2644404</td>\n",
       "      <td>402313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1523358.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6595</th>\n",
       "      <td>Dota 2</td>\n",
       "      <td>150000000</td>\n",
       "      <td>863507</td>\n",
       "      <td>142079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>502793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21938</th>\n",
       "      <td>Team Fortress 2</td>\n",
       "      <td>35000000</td>\n",
       "      <td>515879</td>\n",
       "      <td>34036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>274957.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24595</th>\n",
       "      <td>Unturned</td>\n",
       "      <td>35000000</td>\n",
       "      <td>292574</td>\n",
       "      <td>31482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>162028.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25358</th>\n",
       "      <td>Warframe</td>\n",
       "      <td>35000000</td>\n",
       "      <td>226541</td>\n",
       "      <td>20268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>123404.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26224</th>\n",
       "      <td>Z1 Battle Royale</td>\n",
       "      <td>15000000</td>\n",
       "      <td>111434</td>\n",
       "      <td>91664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>101549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16094</th>\n",
       "      <td>Paladins®</td>\n",
       "      <td>15000000</td>\n",
       "      <td>169580</td>\n",
       "      <td>32407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>100993.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12707</th>\n",
       "      <td>Life is Strange - Episode 1</td>\n",
       "      <td>7500000</td>\n",
       "      <td>118634</td>\n",
       "      <td>4496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>61565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6478</th>\n",
       "      <td>Doki Doki Literature Club!</td>\n",
       "      <td>3500000</td>\n",
       "      <td>108556</td>\n",
       "      <td>3588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>56072.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286</th>\n",
       "      <td>Robocraft</td>\n",
       "      <td>15000000</td>\n",
       "      <td>80360</td>\n",
       "      <td>30987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>55673.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name     owners  positive_ratings  \\\n",
       "4813   Counter-Strike: Global Offensive   75000000           2644404   \n",
       "6595                             Dota 2  150000000            863507   \n",
       "21938                   Team Fortress 2   35000000            515879   \n",
       "24595                          Unturned   35000000            292574   \n",
       "25358                          Warframe   35000000            226541   \n",
       "26224                  Z1 Battle Royale   15000000            111434   \n",
       "16094                         Paladins®   15000000            169580   \n",
       "12707       Life is Strange - Episode 1    7500000            118634   \n",
       "6478         Doki Doki Literature Club!    3500000            108556   \n",
       "18286                         Robocraft   15000000             80360   \n",
       "\n",
       "       negative_ratings  price  count  average rating  \n",
       "4813             402313    0.0      1       1523358.5  \n",
       "6595             142079    0.0      1        502793.0  \n",
       "21938             34036    0.0      1        274957.5  \n",
       "24595             31482    0.0      1        162028.0  \n",
       "25358             20268    0.0      1        123404.5  \n",
       "26224             91664    0.0      1        101549.0  \n",
       "16094             32407    0.0      1        100993.5  \n",
       "12707              4496    0.0      1         61565.0  \n",
       "6478               3588    0.0      1         56072.0  \n",
       "18286             30987    0.0      1         55673.5  "
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular_games = steam.groupby(['name','owners','positive_ratings','negative_ratings','price'])\n",
    "popular_games = popular_games.size().reset_index(name='count')\n",
    "popular_games = popular_games[popular_games['price'] == 0]\n",
    "popular_games['average rating'] = (popular_games['positive_ratings'] + popular_games['negative_ratings'])/2\n",
    "popular_games = popular_games.sort_values(by=['average rating'], ascending=False)\n",
    "popular_games.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(y='name', x='average rating', data=popular_games.head(20), palette='Dark2', hue='owners',dodge=False)\n",
    "plt.title('Juegos Populares gratuitos en base a owners y rating', fontsize=20)\n",
    "plt.xlabel('Rating Promedio', fontsize=15)\n",
    "plt.ylabel('Juego', fontsize=15)\n",
    "plt.legend(fontsize=15, title='Compradores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Juegos Lanzados Por Año\n",
    "\n",
    "Viendo todo lo que se ha descrito anteriormente, es relevante ver cómo ha sido el crecimiento de juegos disponibles en la empresa a lo largo de los años. Como se mencionó al inicio, la industria de los juegos es una de las más importantes ahora mismo y esto implica que cada vez habrá más demanda por contenido similar. Luego, si se tiene cómo ha sido el crecimiento de estos, se podría generar un modelo capaz de predecir o estimar la cantidad de videojuegos disponibles que habrán en algunos años más.\n",
    "\n",
    "En consecuencia, se puede ver que en la última década ha habido un crecimiento exponencial de la cantidad de juegos ofrecidos en la plataforma. Se ve que el crecimiento fue paulatino y estable para los primeros años en que se iban publicando juegos, pero ya a partir del 2013 en adelante cada año crecía cada vez más este número. Cabe mencionar que los datos tienen juegos hasta mayo del 2019, por lo que esta barra debería estar mucho más alta que la de 2018 al final de ese periodo de tiempo. ¿Por qué ocurre este boom en la cantidad de juegos ofrecidos? El fenómeno puede explicarse por varias razones, siendo las principales el que en la última década se ha facilitado el desarrollo y distribución independiente de juegos, por lo que es posible que una sola persona haga un juego; además de que cada vez más persona comienzan a disfrutar de los videojuegos, siendo que la cultura “gamer” ha ido tomando espacio en la cultura actual. Junto con lo anterior, los avances tecnológicos han facilitado el aumento de especificaciones en los computadores con tal de que casi cualquier persona pueda tener los requerimientos mínimos para poder jugar, lo que aumenta su demanda y así la oferta para satisfacerla. Con todo esto, se esperaría que la cantidad de juegos en Steam siga aumentando en una tasa similar, sin embargo, tal como ocurrió al inicio del gráfico mostrado, se espera que en algún momento la cantidad ofrecida se estanque por un tiempo, tanto por factores externos globales como por factores respecto a la producción de hardware y software suficientemente novedoso como para mantener este crecimiento exponencial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "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>anio</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019</td>\n",
       "      <td>2213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018</td>\n",
       "      <td>8160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017</td>\n",
       "      <td>6357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016</td>\n",
       "      <td>4361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015</td>\n",
       "      <td>2597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014</td>\n",
       "      <td>1555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2010</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    anio  count\n",
       "22  2019   2213\n",
       "21  2018   8160\n",
       "20  2017   6357\n",
       "19  2016   4361\n",
       "18  2015   2597\n",
       "17  2014   1555\n",
       "16  2013    418\n",
       "15  2012    320\n",
       "14  2011    239\n",
       "13  2010    238"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anio=pd.DataFrame(steam['release_date'].str.split('-',expand=True))\n",
    "anio.columns=['anio','mes','dia']\n",
    "juegos_anio=steam.join(anio)\n",
    "juegos_anio=juegos_anio.groupby(['anio'])\n",
    "juegos_anio=juegos_anio.size().reset_index(name='count')\n",
    "juegos_anio=juegos_anio.sort_values(by=['anio'], ascending=False)\n",
    "juegos_anio.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(y='anio', x='count', data=juegos_anio.head(20), color='teal', dodge=False)\n",
    "plt.title('Lanzamiento De Juegos Por Año', fontsize=20)\n",
    "plt.xlabel('Cantidad De Juegos', fontsize=15)\n",
    "plt.ylabel('Año De Lanzamiento', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_26820\\3118378800.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  docs['genres'][i] = docs['genres'][i].lower().split(';')\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_26820\\3118378800.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  docs['genres'][i] = '; '.join(docs['genres'][i])\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_26820\\3118378800.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  docs['genres'][i] = uniques.index(docs['genres'][i])\n"
     ]
    }
   ],
   "source": [
    "docs = steam\n",
    "\n",
    "for i in range(len(docs['genres'])):\n",
    "    docs['genres'][i] = docs['genres'][i].lower().split(';')\n",
    "    docs['genres'][i].sort()\n",
    "    docs['genres'][i] = '; '.join(docs['genres'][i])\n",
    "\n",
    "uniques = docs[\"genres\"].unique().tolist()\n",
    "\n",
    "for i in range(len(docs['genres'])):\n",
    "    docs['genres'][i] = uniques.index(docs['genres'][i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Counter-Strike'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\nicol\\Documents\\GitHub\\grupo_10_data_mining\\Hito 2.ipynb Celda 25\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/nicol/Documents/GitHub/grupo_10_data_mining/Hito%202.ipynb#X52sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdecomposition\u001b[39;00m \u001b[39mimport\u001b[39;00m PCA\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/nicol/Documents/GitHub/grupo_10_data_mining/Hito%202.ipynb#X52sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m X_pca \u001b[39m=\u001b[39m PCA(n_components\u001b[39m=\u001b[39;49m\u001b[39m2\u001b[39;49m)\u001b[39m.\u001b[39;49mfit_transform(docs)\n",
      "File \u001b[1;32mc:\\Users\\nicol\\Downloads\\virtual\\lib\\site-packages\\sklearn\\decomposition\\_pca.py:433\u001b[0m, in \u001b[0;36mPCA.fit_transform\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    411\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit_transform\u001b[39m(\u001b[39mself\u001b[39m, X, y\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    412\u001b[0m     \u001b[39m\"\"\"Fit the model with X and apply the dimensionality reduction on X.\u001b[39;00m\n\u001b[0;32m    413\u001b[0m \n\u001b[0;32m    414\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    431\u001b[0m \u001b[39m    C-ordered array, use 'np.ascontiguousarray'.\u001b[39;00m\n\u001b[0;32m    432\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 433\u001b[0m     U, S, Vt \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fit(X)\n\u001b[0;32m    434\u001b[0m     U \u001b[39m=\u001b[39m U[:, : \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_components_]\n\u001b[0;32m    436\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwhiten:\n\u001b[0;32m    437\u001b[0m         \u001b[39m# X_new = X * V / S * sqrt(n_samples) = U * sqrt(n_samples)\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\nicol\\Downloads\\virtual\\lib\\site-packages\\sklearn\\decomposition\\_pca.py:456\u001b[0m, in \u001b[0;36mPCA._fit\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[39mif\u001b[39;00m issparse(X):\n\u001b[0;32m    451\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\n\u001b[0;32m    452\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mPCA does not support sparse input. See \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    453\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mTruncatedSVD for a possible alternative.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    454\u001b[0m     )\n\u001b[1;32m--> 456\u001b[0m X \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[0;32m    457\u001b[0m     X, dtype\u001b[39m=\u001b[39;49m[np\u001b[39m.\u001b[39;49mfloat64, np\u001b[39m.\u001b[39;49mfloat32], ensure_2d\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, copy\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcopy\n\u001b[0;32m    458\u001b[0m )\n\u001b[0;32m    460\u001b[0m \u001b[39m# Handle n_components==None\u001b[39;00m\n\u001b[0;32m    461\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_components \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\nicol\\Downloads\\virtual\\lib\\site-packages\\sklearn\\base.py:577\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    575\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mValidation should be done on X, y or both.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    576\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m no_val_y:\n\u001b[1;32m--> 577\u001b[0m     X \u001b[39m=\u001b[39m check_array(X, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mX\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_params)\n\u001b[0;32m    578\u001b[0m     out \u001b[39m=\u001b[39m X\n\u001b[0;32m    579\u001b[0m \u001b[39melif\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_y:\n",
      "File \u001b[1;32mc:\\Users\\nicol\\Downloads\\virtual\\lib\\site-packages\\sklearn\\utils\\validation.py:856\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    854\u001b[0m         array \u001b[39m=\u001b[39m array\u001b[39m.\u001b[39mastype(dtype, casting\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39munsafe\u001b[39m\u001b[39m\"\u001b[39m, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m    855\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 856\u001b[0m         array \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49masarray(array, order\u001b[39m=\u001b[39;49morder, dtype\u001b[39m=\u001b[39;49mdtype)\n\u001b[0;32m    857\u001b[0m \u001b[39mexcept\u001b[39;00m ComplexWarning \u001b[39mas\u001b[39;00m complex_warning:\n\u001b[0;32m    858\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    859\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mComplex data not supported\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{}\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(array)\n\u001b[0;32m    860\u001b[0m     ) \u001b[39mfrom\u001b[39;00m \u001b[39mcomplex_warning\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\nicol\\Downloads\\virtual\\lib\\site-packages\\pandas\\core\\generic.py:2064\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2063\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__array__\u001b[39m(\u001b[39mself\u001b[39m, dtype: npt\u001b[39m.\u001b[39mDTypeLike \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m np\u001b[39m.\u001b[39mndarray:\n\u001b[1;32m-> 2064\u001b[0m     \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39;49masarray(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_values, dtype\u001b[39m=\u001b[39;49mdtype)\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Counter-Strike'"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "X_pca = PCA(n_components=2).fit_transform(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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