02. EDA/EDA_items.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA Analysis\n",
    "**El propósito final de este análisis es definir, para el modelo de recomendación solicitado y las diferentes funciones, para cada conjunto de datos y función, las columnas y datos correspondientes que serán necesarios**.\n",
    "\n",
    "### Durante el proceso ETL ya nos ocupamos de:\n",
    "\n",
    "[ETL_items](01.%20ETL/ETL_items.ipynb)<br>\n",
    "-   valores faltantes y limpieza de datos\n",
    "-   eliminación de columnas de información irrelevante\n",
    "-   eliminación de filas con información no existente o no válida\n",
    "-   eliminación de valores duplicados\n",
    "-   corrección de todos los tipos de datos para cada columna y dato\n",
    "\n",
    "### Entonces en el **Análisis EDA** nos enfocaremos principalmente en:\n",
    "-   búsqueda de valores atípicos\n",
    "-   estadísticas y descripción de datos\n",
    "-   transformación de datos\n",
    "-   Variables:\n",
    "    - user_id: Este user_id es el identificador único para cada uno de los usuarios en la columna de la plataforma\n",
    "    - item_id: identificador único del juego en la plataforma\n",
    "    - item_name: nombre del juego o aplicación\n",
    "    - playtime_forever: El número total de minutos jugados \"registrados\"\n",
    "    - playtime_2weeks: El número total de minutos jugados en las últimas 2 semanas\n",
    "-   Análisis multivariable:\n",
    "\n",
    "### Un resumen de lo que encontrarás en el cuaderno:\n",
    "\n",
    "#### User_id\n",
    "Este user_id es el identificador único para cada uno de los usuarios en la plataforma.\n",
    "**Hallazgos**\n",
    "- Cuántos usuarios están registrados en la plataforma\n",
    "\n",
    "#### item_id\n",
    "- Cuántos juegos son elementos en las cuentas de los usuarios\n",
    "- Porcentaje del total de juegos\n",
    "- Promedio de cantidad de juegos por usuario\n",
    "- Los 10 elementos más vendidos (más usuarios lo tienen)\n",
    "- Los 10 juegos más jugados en las últimas 2 semanas\n",
    "\n",
    "#### Atípicos para item_id por usuario\n",
    "\n",
    "**Hallazgos**\n",
    "* La cantidad promedio de juegos por usuario es de 71 juegos\n",
    "* Más del 50% de los usuarios poseen más de 40 juegos cada uno.\n",
    "* Solo hay 39 usuarios con más de 2000 juegos cada uno, esto se considerará como atípicos pero representan menos del 0.056%\n",
    "* La **cantidad máxima** de juegos propiedad de un usuario es de *7762 juegos*\n",
    "- Los 10 elementos más vendidos (más usuarios lo tienen)\n",
    "\n",
    "#### item_name\n",
    "Este es el nombre del juego.\n",
    "\n",
    "### playtime_forever\n",
    "El número total de minutos jugados \"registrados\"\n",
    "- \tTop 5 usuarios con más minutos jugados en total\n",
    "-\tTop 5 juegos con más minutos jugados\n",
    "-\tPromedio de minutos jugados por usuario\n",
    "-\tPromedio de minutos por juego\n",
    "\n",
    "**Hallazgos**\n",
    "* Los 10 mejores juegos representan aproximadamente el 41% del tiempo total de juego.\n",
    "* El tiempo de juego promedio por usuario es de 1497 minutos\n",
    "* El 50% de todos los usuarios juegan más de 887 minutos.\n",
    "* El promedio de minutos por juego es de 277 minutos\n",
    "* El 50% de los juegos se juegan durante más de 90 minutos en promedio\n",
    "\n",
    "### playtime_2weeks\n",
    "El número total de minutos jugados \"registrados\" en las últimas 2 semanas\n",
    "- \tTop 5 usuarios con más minutos jugados en total\n",
    "-\tTop 5 juegos con más minutos jugados\n",
    "-\tPromedio de minutos jugados por usuario\n",
    "-\tPromedio de minutos por juego\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import pandas as pd\n",
    "import gzip\n",
    "import ast\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from datetime import datetime\n",
    "import seaborn as sns  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comenzamos por cargar el dataframe de parquet a pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definimos el path al .parquet file\n",
    "items = 'data/items.parquet'\n",
    "\n",
    "# Leemos el .parquet file a un DataFrame\n",
    "df_items = pd.read_parquet(items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.3 Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_name</th>\n",
       "      <th>playtime_forever</th>\n",
       "      <th>playtime_2weeks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>20</td>\n",
       "      <td>Team Fortress Classic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>30</td>\n",
       "      <td>Day of Defeat</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id  item_id              item_name  playtime_forever  \\\n",
       "0  76561197970982479       10         Counter-Strike                 6   \n",
       "1  76561197970982479       20  Team Fortress Classic                 0   \n",
       "2  76561197970982479       30          Day of Defeat                 7   \n",
       "\n",
       "   playtime_2weeks  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_items.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5094082 entries, 0 to 5094081\n",
      "Data columns (total 5 columns):\n",
      " #   Column            Dtype \n",
      "---  ------            ----- \n",
      " 0   user_id           object\n",
      " 1   item_id           int64 \n",
      " 2   item_name         object\n",
      " 3   playtime_forever  int64 \n",
      " 4   playtime_2weeks   int64 \n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 194.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_items.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User_id\n",
    "#### Cuantos users hay registrados en la Plataforma Steam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70912"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_items['user_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item_id\n",
    "- ¿Cuántos juegos del total están registrados en las cuentas de los usuarios?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total amount of games sold 10978\n",
      "This amount represents 49.0 % of the total amount of games available on the platform\n"
     ]
    }
   ],
   "source": [
    "amount_of_games_sold = df_items['item_id'].nunique()\n",
    "\n",
    "percentage_items = round((df_items['item_id'].nunique()/22530),2)*100\n",
    "\n",
    "print('Total amount of games sold', amount_of_games_sold)\n",
    "print('This amount represents', percentage_items,'% of the total amount of games available on the platform')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Cantidad promedio de juegos por usuario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average items per user: 71.83667080324909\n"
     ]
    }
   ],
   "source": [
    "# Group the DataFrame by 'user_id' and calculate the count of items per user\n",
    "items_per_user = df_items.groupby('user_id')['item_id'].count()\n",
    "\n",
    "# Calculate the average count of items per user\n",
    "average_items_per_user = items_per_user.mean()\n",
    "\n",
    "# Display the average\n",
    "print(\"Average items per user:\", average_items_per_user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    70912.000000\n",
       "mean        71.836671\n",
       "std        132.366763\n",
       "min          1.000000\n",
       "25%         14.000000\n",
       "50%         40.000000\n",
       "75%         87.000000\n",
       "max       7762.000000\n",
       "Name: item_id, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_per_user.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* El promedio de juegos por usuario es de 71 juegos.\n",
    "* Más del 50% de los usuarios poseen más de 40 juegos cada uno.\n",
    "* Solo hay 39 usuarios con más de 2000 juegos cada uno, estos serán considerados valores atípicos pero representan menos del 0.056%.\n",
    "* La **cantidad máxima** de juegos propiedad de un usuario es de *7762 juegos*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outliers para items_id por user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupar el DataFrame por 'user_id' y calcular el conteo de elementos por usuario\n",
    "elementos_por_usuario = df_elementos.groupby('user_id')['item_id'].count()\n",
    "\n",
    "# Z-Score\n",
    "z_scores = (elementos_por_usuario - elementos_por_usuario.mean()) / elementos_por_usuario.std()\n",
    "valores_atípicos_zscore = elementos_por_usuario[abs(z_scores) > 3]\n",
    "\n",
    "# Crear subtramas con dos filas y una sola columna\n",
    "fig, axs = plt.subplots(2, 1, figsize=(16, 4))\n",
    "\n",
    "# Diagrama de caja\n",
    "axs[0].boxplot(elementos_por_usuario, vert=False)\n",
    "axs[0].set_title('Diagrama de Caja de Elementos por Usuario')\n",
    "axs[0].set_xlabel('Elementos por Usuario')\n",
    "axs[0].set_ylabel('Elementos')\n",
    "\n",
    "# Gráfico de puntos\n",
    "sns.stripplot(x=elementos_por_usuario, ax=axs[1], color='blue', alpha=0.5)\n",
    "axs[1].set_title('Gráfico de Puntos de Elementos por Usuario')\n",
    "axs[1].set_xlabel('Elementos por Usuario')\n",
    "axs[1].set_ylabel('Elementos')\n",
    "\n",
    "# Ajustar el diseño\n",
    "plt.tight_layout()\n",
    "\n",
    "# Mostrar gráficos\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " amount of users with more than 2000 games 39\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65868</th>\n",
       "      <td>phrostb</td>\n",
       "      <td>7762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69170</th>\n",
       "      <td>thugnificent</td>\n",
       "      <td>6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57814</th>\n",
       "      <td>chidvd</td>\n",
       "      <td>6410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65886</th>\n",
       "      <td>piepai</td>\n",
       "      <td>6132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64104</th>\n",
       "      <td>mayshowganmore</td>\n",
       "      <td>5027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              user_id  item_id\n",
       "65868         phrostb     7762\n",
       "69170    thugnificent     6700\n",
       "57814          chidvd     6410\n",
       "65886          piepai     6132\n",
       "64104  mayshowganmore     5027"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupar el DataFrame original por 'user_id' y contar el número de elementos por usuario\n",
    "items_per_user = df_items.groupby('user_id')['item_id'].count().reset_index()\n",
    "users_with_more_than_2000_games = items_per_user[items_per_user['item_id'] > 2000]\n",
    "amount_users_morethan2000games = users_with_more_than_2000_games['item_id'].count()\n",
    "print(' amount of users with more than 2000 games', amount_users_morethan2000games)\n",
    "users_with_more_than_2000_games.sort_values(by='item_id', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 items mas vendidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_name\n",
       "Dota 2 Test                         49136\n",
       "Counter-Strike: Global Offensive    43331\n",
       "Garry's Mod                         42849\n",
       "Unturned                            38278\n",
       "Left 4 Dead 2 Beta                  36661\n",
       "Left 4 Dead 2                       36661\n",
       "Terraria                            28934\n",
       "Warframe                            25516\n",
       "Portal 2                            24206\n",
       "Counter-Strike: Source              23952\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_items['item_name'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the top 10 most common item names\n",
    "top_items = df_items['item_name'].value_counts().head(10)\n",
    "\n",
    "# Plot the bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "top_items.sort_values().plot(kind='barh', color='skyblue')\n",
    "plt.title('Top 10 Item Names')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Item Name')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### playtime_forever\n",
    "El número total de minutos jugados \"en registro\"\n",
    "#### \tPrincipales 5 usuarios con más minutos jugados en total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5045723942"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_items['playtime_forever'].sum() # Cantidad total de tiempo jugada en la plataforma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>user_id</th>\n",
       "      <th>item_name</th>\n",
       "      <th>playtime_forever</th>\n",
       "      <th>playtime_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>--000--</td>\n",
       "      <td>ACE - Arena: Cyber Evolution</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>--000--</td>\n",
       "      <td>APB Reloaded</td>\n",
       "      <td>2763</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>--000--</td>\n",
       "      <td>AdVenture Capitalist</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>--000--</td>\n",
       "      <td>Apotheon Arena</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>--000--</td>\n",
       "      <td>BLOCKADE 3D</td>\n",
       "      <td>445</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5073721</th>\n",
       "      <td>zzzmidmiss</td>\n",
       "      <td>Wallace &amp; Gromit Ep 2: The Last Resort</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5073722</th>\n",
       "      <td>zzzmidmiss</td>\n",
       "      <td>Wallace &amp; Gromit Ep 3: Muzzled!</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5073723</th>\n",
       "      <td>zzzmidmiss</td>\n",
       "      <td>Wallace &amp; Gromit Ep 4: The Bogey Man</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5073724</th>\n",
       "      <td>zzzmidmiss</td>\n",
       "      <td>Warframe</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5073725</th>\n",
       "      <td>zzzmidmiss</td>\n",
       "      <td>theHunter</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5073726 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id                               item_name  playtime_forever  \\\n",
       "0           --000--            ACE - Arena: Cyber Evolution                 0   \n",
       "1           --000--                            APB Reloaded              2763   \n",
       "2           --000--                    AdVenture Capitalist                 0   \n",
       "3           --000--                          Apotheon Arena                74   \n",
       "4           --000--                             BLOCKADE 3D               445   \n",
       "...             ...                                     ...               ...   \n",
       "5073721  zzzmidmiss  Wallace & Gromit Ep 2: The Last Resort                 0   \n",
       "5073722  zzzmidmiss         Wallace & Gromit Ep 3: Muzzled!                 0   \n",
       "5073723  zzzmidmiss    Wallace & Gromit Ep 4: The Bogey Man                 0   \n",
       "5073724  zzzmidmiss                                Warframe                10   \n",
       "5073725  zzzmidmiss                               theHunter                16   \n",
       "\n",
       "         playtime_percentage  \n",
       "0                        0.0  \n",
       "1                        0.0  \n",
       "2                        0.0  \n",
       "3                        0.0  \n",
       "4                        0.0  \n",
       "...                      ...  \n",
       "5073721                  0.0  \n",
       "5073722                  0.0  \n",
       "5073723                  0.0  \n",
       "5073724                  0.0  \n",
       "5073725                  0.0  \n",
       "\n",
       "[5073726 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupar el DataFrame por 'user_id' y sumar el 'playtime_forever'\n",
    "tiempo_total_por_usuario = df_elementos.groupby(['user_id', 'item_name'])['playtime_forever'].sum().reset_index()\n",
    "\n",
    "# Calcular el tiempo total de juego\n",
    "tiempo_total = df_items['playtime_forever'].sum()\n",
    "\n",
    "# Agregar una columna con el porcentaje de tiempo de juego para cada elemento\n",
    "tiempo_total_por_usuario['porcentaje_tiempo_juego'] = round((tiempo_total_por_usuario['playtime_forever'] / tiempo_total) * 100, 2)\n",
    "\n",
    "# Mostrar el DataFrame resultante\n",
    "tiempo_total_por_usuario\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####\tTop 10 games with the most minutes played"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>item_id</th>\n",
       "      <th>item_name</th>\n",
       "      <th>playtime_forever</th>\n",
       "      <th>playtime_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>730</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>775784836</td>\n",
       "      <td>15.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>4000</td>\n",
       "      <td>Garry's Mod</td>\n",
       "      <td>441871026</td>\n",
       "      <td>8.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1673</th>\n",
       "      <td>105600</td>\n",
       "      <td>Terraria</td>\n",
       "      <td>152997644</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>72850</td>\n",
       "      <td>The Elder Scrolls V: Skyrim</td>\n",
       "      <td>134851450</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403</th>\n",
       "      <td>230410</td>\n",
       "      <td>Warframe</td>\n",
       "      <td>122726853</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>240</td>\n",
       "      <td>Counter-Strike: Source</td>\n",
       "      <td>111314706</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>550</td>\n",
       "      <td>Left 4 Dead 2</td>\n",
       "      <td>101191416</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2162</th>\n",
       "      <td>218620</td>\n",
       "      <td>PAYDAY 2</td>\n",
       "      <td>98405781</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>8930</td>\n",
       "      <td>Sid Meier's Civilization V</td>\n",
       "      <td>81187409</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>252490</td>\n",
       "      <td>Rust</td>\n",
       "      <td>80110135</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      item_id                         item_name  playtime_forever  \\\n",
       "23        730  Counter-Strike: Global Offensive         775784836   \n",
       "163      4000                       Garry's Mod         441871026   \n",
       "1673   105600                          Terraria         152997644   \n",
       "1543    72850       The Elder Scrolls V: Skyrim         134851450   \n",
       "2403   230410                          Warframe         122726853   \n",
       "11        240            Counter-Strike: Source         111314706   \n",
       "21        550                     Left 4 Dead 2         101191416   \n",
       "2162   218620                          PAYDAY 2          98405781   \n",
       "326      8930        Sid Meier's Civilization V          81187409   \n",
       "2909   252490                              Rust          80110135   \n",
       "\n",
       "      playtime_percentage  \n",
       "23                  15.38  \n",
       "163                  8.76  \n",
       "1673                 3.03  \n",
       "1543                 2.67  \n",
       "2403                 2.43  \n",
       "11                   2.21  \n",
       "21                   2.01  \n",
       "2162                 1.95  \n",
       "326                  1.61  \n",
       "2909                 1.59  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupar el DataFrame por 'item_id' y 'item_name' y sumar el 'playtime_forever'\n",
    "tiempo_total_por_elemento = df_items.groupby(['item_id', 'item_name'])['playtime_forever'].sum().reset_index()\n",
    "\n",
    "# Calcular el tiempo total de juego\n",
    "tiempo_total = df_items['playtime_forever'].sum()\n",
    "\n",
    "# Agregar una columna con el porcentaje de tiempo de juego para cada elemento\n",
    "tiempo_total_por_elemento['porcentaje_tiempo_juego'] = round((tiempo_total_por_elemento['playtime_forever'] / tiempo_total) * 100, 2)\n",
    "\n",
    "# Mostrar el DataFrame resultante\n",
    "tiempo_total_por_elemento.sort_values(by='playtime_forever', ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort the DataFrame by total playtime in descending order and select the top 10 items\n",
    "top_items = tiempo_total_por_elemento.sort_values(by='playtime_forever', ascending=False).head(10)\n",
    "\n",
    "# Create the horizontal bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(top_items['item_name'], top_items['playtime_forever'], color='skyblue')\n",
    "plt.xlabel('Total Playtime')\n",
    "plt.ylabel('Item Name')\n",
    "plt.title('Top 10 Items by Total Playtime')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to display the highest value at the top\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-\tAverage number of minutes played by user\n",
    "-\tAverage number of minutes per game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10978.000000\n",
       "mean       277.565159\n",
       "std        994.862408\n",
       "min          0.000000\n",
       "25%         13.583924\n",
       "50%         89.256609\n",
       "75%        204.886803\n",
       "max      38283.000000\n",
       "Name: playtime_forever, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Promedoio para playtime por game\n",
    "avg_playtime_by_game = df_items.groupby('item_id')['playtime_forever'].mean()\n",
    "avg_playtime_by_game.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     70912.000000\n",
       "mean       1497.533975\n",
       "std        2823.894221\n",
       "min           0.000000\n",
       "25%         412.638020\n",
       "50%         887.333333\n",
       "75%        1728.892857\n",
       "max      216412.000000\n",
       "Name: playtime_forever, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Promedio del  playtime por  user\n",
    "avg_playtime_by_user = df_items.groupby('user_id')['playtime_forever'].mean()\n",
    "avg_playtime_by_user.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Los 10 juegos principales representan aproximadamente el 41% del tiempo total jugado.\n",
    "* El tiempo promedio de juego por usuario es de 1497 minutos.\n",
    "* El 50% de todos los usuarios juegan más de 887 minutos.\n",
    "* El promedio de minutos por juego es de 277 minutos.\n",
    "* El 50% de los juegos se juegan durante más de 90 minutos en promedio."
   ]
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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