Notebooks/ETL/ETL_user_items.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b04b5f8",
   "metadata": {},
   "source": [
    "## Informe de Extracción, Transformación y Carga de Datos: ETL_user_items"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c1f484",
   "metadata": {},
   "source": [
    "En esta sección, nos sumergiremos en el proceso de Extracción, Transformación y Carga (ETL) del set de datos australian_user_items donde se registran los juegos en la plataforma Steam por usuario. El objetivo principal es preparar estos datos para su análisis, asegurándonos de que sean aptos y consistentes.\n",
    "\n",
    "Comenzaremos importando las bibliotecas esenciales. Asegúrese de tener estas bibliotecas instaladas previamente para garantizar una ejecución sin contratiempos.\n",
    "\n",
    "A lo largo del informe, nos enfocaremos en abordar problemas potenciales en los datos, aplicar técnicas de limpieza y preprocesamiento, y finalmente, almacenar los datos transformados para futuras exploraciones y análisis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04bceaa5",
   "metadata": {},
   "source": [
    "### Importar Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad605394",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importamos pandas para el análisis de datos tabulares\n",
    "import pandas as pd\n",
    "from pandas import json_normalize\n",
    "\n",
    "# NumPy proporciona soporte para arreglos y matrices multidimensionales\n",
    "import numpy as np\n",
    "\n",
    "# El módulo os permite interactuar con el sistema operativo\n",
    "import os\n",
    "\n",
    "# Gdown facilita la descarga de archivos desde Google Drive utilizando su ID\n",
    "import gdown\n",
    "\n",
    "# JSON es un formato común para el intercambio de datos, y Python tiene soporte incorporado para trabajar con JSON\n",
    "import json\n",
    "\n",
    "import ast\n",
    "\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6197b3",
   "metadata": {},
   "source": [
    "### 1. Cargar el conjunto de datos original"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c5a804",
   "metadata": {},
   "source": [
    "Fuente de datos: **australian_user_items.json**\n",
    "\n",
    "Este archivo está disponible en Google Drive con acceso compartido. Puede descargarlo en el siguiente enlace.\n",
    "Datasets: https://bit.ly/3On6yYB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "941664cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_json_lines(file_path):\n",
    "\n",
    "    data = []\n",
    "    with open(file_path, 'r', encoding='utf-8') as file:\n",
    "        for line in file:\n",
    "            data.append(ast.literal_eval(line))\n",
    "    return pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90dda9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Carga y muestra el archivo\n",
    "df_item = load_json_lines('australian_users_items.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6253aa99",
   "metadata": {},
   "source": [
    "### 2. Explorar y entender el conjunto de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a5f8e2c",
   "metadata": {},
   "source": [
    "Exploramos y entendemos la estructura del conjunto de datos, revisando las primeras filas, información general y estadísticas descriptivas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5f7b553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primeras filas del DataFrame:\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>items_count</th>\n",
       "      <th>steam_id</th>\n",
       "      <th>user_url</th>\n",
       "      <th>items</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>277</td>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>http://steamcommunity.com/profiles/76561197970...</td>\n",
       "      <td>[{'item_id': '10', 'item_name': 'Counter-Strik...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>js41637</td>\n",
       "      <td>888</td>\n",
       "      <td>76561198035864385</td>\n",
       "      <td>http://steamcommunity.com/id/js41637</td>\n",
       "      <td>[{'item_id': '10', 'item_name': 'Counter-Strik...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>evcentric</td>\n",
       "      <td>137</td>\n",
       "      <td>76561198007712555</td>\n",
       "      <td>http://steamcommunity.com/id/evcentric</td>\n",
       "      <td>[{'item_id': '1200', 'item_name': 'Red Orchest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Riot-Punch</td>\n",
       "      <td>328</td>\n",
       "      <td>76561197963445855</td>\n",
       "      <td>http://steamcommunity.com/id/Riot-Punch</td>\n",
       "      <td>[{'item_id': '10', 'item_name': 'Counter-Strik...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>doctr</td>\n",
       "      <td>541</td>\n",
       "      <td>76561198002099482</td>\n",
       "      <td>http://steamcommunity.com/id/doctr</td>\n",
       "      <td>[{'item_id': '300', 'item_name': 'Day of Defea...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id  items_count           steam_id  \\\n",
       "0  76561197970982479          277  76561197970982479   \n",
       "1            js41637          888  76561198035864385   \n",
       "2          evcentric          137  76561198007712555   \n",
       "3         Riot-Punch          328  76561197963445855   \n",
       "4              doctr          541  76561198002099482   \n",
       "\n",
       "                                            user_url  \\\n",
       "0  http://steamcommunity.com/profiles/76561197970...   \n",
       "1               http://steamcommunity.com/id/js41637   \n",
       "2             http://steamcommunity.com/id/evcentric   \n",
       "3            http://steamcommunity.com/id/Riot-Punch   \n",
       "4                 http://steamcommunity.com/id/doctr   \n",
       "\n",
       "                                               items  \n",
       "0  [{'item_id': '10', 'item_name': 'Counter-Strik...  \n",
       "1  [{'item_id': '10', 'item_name': 'Counter-Strik...  \n",
       "2  [{'item_id': '1200', 'item_name': 'Red Orchest...  \n",
       "3  [{'item_id': '10', 'item_name': 'Counter-Strik...  \n",
       "4  [{'item_id': '300', 'item_name': 'Day of Defea...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostrar las primeras filas del DataFrame\n",
    "print(\"Primeras filas del DataFrame:\")\n",
    "df_item.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ee1efc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Información general del DataFrame:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 88310 entries, 0 to 88309\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   user_id      88310 non-null  object\n",
      " 1   items_count  88310 non-null  int64 \n",
      " 2   steam_id     88310 non-null  object\n",
      " 3   user_url     88310 non-null  object\n",
      " 4   items        88310 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 3.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Obtener información general del DataFrame\n",
    "print(\"\\nInformación general del DataFrame:\")\n",
    "df_item.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92d05d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        0.0\n",
       "items_count    0.0\n",
       "steam_id       0.0\n",
       "user_url       0.0\n",
       "items          0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Muestra el porcentajes de datos nulos por columnas\n",
    "porcentajes_nulos = (df_item.isnull().mean() * 100).round(2)\n",
    "porcentajes_nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1284295b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of                        user_id  items_count           steam_id  \\\n",
       "0            76561197970982479          277  76561197970982479   \n",
       "1                      js41637          888  76561198035864385   \n",
       "2                    evcentric          137  76561198007712555   \n",
       "3                   Riot-Punch          328  76561197963445855   \n",
       "4                        doctr          541  76561198002099482   \n",
       "...                        ...          ...                ...   \n",
       "88305        76561198323066619           22  76561198323066619   \n",
       "88306        76561198326700687          177  76561198326700687   \n",
       "88307  XxLaughingJackClown77xX            0  76561198328759259   \n",
       "88308        76561198329548331            7  76561198329548331   \n",
       "88309        edward_tremethick            0  76561198331598578   \n",
       "\n",
       "                                                user_url  \\\n",
       "0      http://steamcommunity.com/profiles/76561197970...   \n",
       "1                   http://steamcommunity.com/id/js41637   \n",
       "2                 http://steamcommunity.com/id/evcentric   \n",
       "3                http://steamcommunity.com/id/Riot-Punch   \n",
       "4                     http://steamcommunity.com/id/doctr   \n",
       "...                                                  ...   \n",
       "88305  http://steamcommunity.com/profiles/76561198323...   \n",
       "88306  http://steamcommunity.com/profiles/76561198326...   \n",
       "88307  http://steamcommunity.com/id/XxLaughingJackClo...   \n",
       "88308  http://steamcommunity.com/profiles/76561198329...   \n",
       "88309     http://steamcommunity.com/id/edward_tremethick   \n",
       "\n",
       "                                                   items  \n",
       "0      [{'item_id': '10', 'item_name': 'Counter-Strik...  \n",
       "1      [{'item_id': '10', 'item_name': 'Counter-Strik...  \n",
       "2      [{'item_id': '1200', 'item_name': 'Red Orchest...  \n",
       "3      [{'item_id': '10', 'item_name': 'Counter-Strik...  \n",
       "4      [{'item_id': '300', 'item_name': 'Day of Defea...  \n",
       "...                                                  ...  \n",
       "88305  [{'item_id': '413850', 'item_name': 'CS:GO Pla...  \n",
       "88306  [{'item_id': '11020', 'item_name': 'TrackMania...  \n",
       "88307                                                 []  \n",
       "88308  [{'item_id': '304930', 'item_name': 'Unturned'...  \n",
       "88309                                                 []  \n",
       "\n",
       "[88310 rows x 5 columns]>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explora las columnas del DataFrame\n",
    "df_item.head   # Se observa que la columna items es una columna anidada"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6477b37",
   "metadata": {},
   "source": [
    "Es importante destacar que no se observan valores nulos en ninguna de las columnas, lo que sugiere que todos los usuarios tienen información completa en estas columnas.\n",
    "\n",
    "Para obtener una comprensión más profunda de estos datos se estará examinando algunos registros específicos para comprender mejor la naturaleza de los elementos.La columna 'items' es una lista, así que se explora la misma para conocer su estructura."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d92e2a3",
   "metadata": {},
   "source": [
    "### 3. Limpiar y preprocesar el conjunto de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bee99f",
   "metadata": {},
   "source": [
    "- Manejar valores nulos o duplicados.\n",
    "- Identificar y eliminar columnas irrelevantes (si es necesario).\n",
    "- Normalizar columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99d95c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'item_id': '10',\n",
       "  'item_name': 'Counter-Strike',\n",
       "  'playtime_forever': 6,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '20',\n",
       "  'item_name': 'Team Fortress Classic',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '30',\n",
       "  'item_name': 'Day of Defeat',\n",
       "  'playtime_forever': 7,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '40',\n",
       "  'item_name': 'Deathmatch Classic',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '50',\n",
       "  'item_name': 'Half-Life: Opposing Force',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '60',\n",
       "  'item_name': 'Ricochet',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '70',\n",
       "  'item_name': 'Half-Life',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '130',\n",
       "  'item_name': 'Half-Life: Blue Shift',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '300',\n",
       "  'item_name': 'Day of Defeat: Source',\n",
       "  'playtime_forever': 4733,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '240',\n",
       "  'item_name': 'Counter-Strike: Source',\n",
       "  'playtime_forever': 1853,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '3830',\n",
       "  'item_name': 'Psychonauts',\n",
       "  'playtime_forever': 333,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '2630',\n",
       "  'item_name': 'Call of Duty 2',\n",
       "  'playtime_forever': 75,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '3900',\n",
       "  'item_name': \"Sid Meier's Civilization IV\",\n",
       "  'playtime_forever': 338,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '34440',\n",
       "  'item_name': \"Sid Meier's Civilization IV\",\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '3920',\n",
       "  'item_name': \"Sid Meier's Pirates!\",\n",
       "  'playtime_forever': 2,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '6400',\n",
       "  'item_name': 'Joint Task Force',\n",
       "  'playtime_forever': 286,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '6910',\n",
       "  'item_name': 'Deus Ex: Game of the Year Edition',\n",
       "  'playtime_forever': 2685,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '7670',\n",
       "  'item_name': 'BioShock',\n",
       "  'playtime_forever': 633,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '409710',\n",
       "  'item_name': 'BioShock Remastered',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '220',\n",
       "  'item_name': 'Half-Life 2',\n",
       "  'playtime_forever': 696,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '320',\n",
       "  'item_name': 'Half-Life 2: Deathmatch',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '340',\n",
       "  'item_name': 'Half-Life 2: Lost Coast',\n",
       "  'playtime_forever': 37,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '360',\n",
       "  'item_name': 'Half-Life Deathmatch: Source',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '380',\n",
       "  'item_name': 'Half-Life 2: Episode One',\n",
       "  'playtime_forever': 168,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '400',\n",
       "  'item_name': 'Portal',\n",
       "  'playtime_forever': 173,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '420',\n",
       "  'item_name': 'Half-Life 2: Episode Two',\n",
       "  'playtime_forever': 323,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '9340',\n",
       "  'item_name': 'Company of Heroes: Opposing Fronts',\n",
       "  'playtime_forever': 692,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '228200',\n",
       "  'item_name': 'Company of Heroes (New Steam Version)',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '11450',\n",
       "  'item_name': 'Overlord',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '7940',\n",
       "  'item_name': 'Call of Duty 4: Modern Warfare',\n",
       "  'playtime_forever': 1185,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '4700',\n",
       "  'item_name': 'Medieval II: Total War',\n",
       "  'playtime_forever': 477,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '12900',\n",
       "  'item_name': 'Audiosurf',\n",
       "  'playtime_forever': 115,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '13250',\n",
       "  'item_name': 'Unreal Gold',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '16100',\n",
       "  'item_name': 'Virtual Villagers: A New Home',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '15700',\n",
       "  'item_name': \"Oddworld: Abe's Oddysee\",\n",
       "  'playtime_forever': 28,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '15710',\n",
       "  'item_name': \"Oddworld: Abe's Exoddus\",\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '17330',\n",
       "  'item_name': 'Crysis Warhead',\n",
       "  'playtime_forever': 31,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '17340',\n",
       "  'item_name': 'Crysis Wars',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '22000',\n",
       "  'item_name': 'World of Goo',\n",
       "  'playtime_forever': 195,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '500',\n",
       "  'item_name': 'Left 4 Dead',\n",
       "  'playtime_forever': 513,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '4560',\n",
       "  'item_name': 'Company of Heroes',\n",
       "  'playtime_forever': 1061,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '17460',\n",
       "  'item_name': 'Mass Effect',\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 500,\n",
       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '8870',\n",
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       "  'playtime_forever': 2084,\n",
       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 3478,\n",
       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       " {'item_id': '236390',\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
       " {'item_id': '222730',\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 272,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '239350',\n",
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       "  'playtime_forever': 458,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '218620',\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '236850',\n",
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       "  'playtime_forever': 89,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '47790',\n",
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       "  'playtime_forever': 230,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '47830',\n",
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       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
       " {'item_id': '250900',\n",
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       "  'playtime_forever': 329,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '204450',\n",
       "  'item_name': 'Call of Juarez Gunslinger',\n",
       "  'playtime_forever': 241,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '233270',\n",
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       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '251990',\n",
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       "  'playtime_2weeks': 0},\n",
       " {'item_id': '253980',\n",
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       "  'playtime_2weeks': 0},\n",
       " {'item_id': '236450',\n",
       "  'item_name': 'PAC-MAN Championship Edition DX+',\n",
       "  'playtime_forever': 154,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '242050',\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '213670',\n",
       "  'item_name': 'South Park™: The Stick of Truth™',\n",
       "  'playtime_forever': 121,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '238010',\n",
       "  'item_name': \"Deus Ex: Human Revolution - Director's Cut\",\n",
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       "  'playtime_2weeks': 0},\n",
       " {'item_id': '245470',\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'item_name': 'Valiant Hearts: The Great War™ / Soldats Inconnus : Mémoires de la Grande Guerre™',\n",
       "  'playtime_forever': 467,\n",
       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
       " {'item_id': '230230',\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 154,\n",
       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
       " {'item_id': '306660',\n",
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       "  'playtime_forever': 1245,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '316790',\n",
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       " {'item_id': '318430',\n",
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       "  'playtime_2weeks': 0},\n",
       " {'item_id': '282070',\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '319630',\n",
       "  'item_name': 'Life Is Strange™',\n",
       "  'playtime_forever': 37,\n",
       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 6494,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '232150',\n",
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       "  'playtime_2weeks': 0},\n",
       " {'item_id': '340170',\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 395,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '396660',\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 1159,\n",
       "  'playtime_2weeks': 0},\n",
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       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
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       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '289130',\n",
       "  'item_name': 'Endless Legend',\n",
       "  'playtime_forever': 593,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '268500',\n",
       "  'item_name': 'XCOM 2',\n",
       "  'playtime_forever': 1495,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '461640',\n",
       "  'item_name': 'Sins Of The Demon RPG',\n",
       "  'playtime_forever': 0,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '379720',\n",
       "  'item_name': 'DOOM',\n",
       "  'playtime_forever': 793,\n",
       "  'playtime_2weeks': 0},\n",
       " {'item_id': '273350',\n",
       "  'item_name': 'Evolve Stage 2',\n",
       "  'playtime_forever': 58,\n",
       "  'playtime_2weeks': 0}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_item['items'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a936874",
   "metadata": {},
   "source": [
    "Efectivamente la columna 'items' se encuentra anidada, es decir, es una lista de diccionarios. A continuación, se normaliza la columna de manera de obtener una columna por cada clave de ese diccionario, manteniendo la trazabilidad de las columnas 'steam_id','items_count','user_id' y 'user_url'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f532e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eliminar_duplicados(df_user_item):\n",
    "    # Aplana la columna 'reviews' utilizando json_normalize\n",
    "    flattened_reviews = json_normalize(df_user_item['items'])\n",
    "\n",
    "    # Agrega 'user_id' al principio de las columnas aplanadas\n",
    "    flattened_reviews = pd.concat([df_user_item['user_id'], flattened_reviews], axis=1)\n",
    "\n",
    "    # Elimina duplicados basados en 'user_id'\n",
    "    user_reviews = flattened_reviews[~flattened_reviews.duplicated(subset='user_id')]\n",
    "\n",
    "    return user_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f1fdcd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def desanidar(df_user_item):\n",
    "    # Aplana la columna 'items' utilizando json_normalize\n",
    "    flattened_items = json_normalize(df_user_item['items'])\n",
    "\n",
    "    df_user_item_sin_duplicados = flattened_items[~flattened_items.duplicated(subset='user_id')]\n",
    "\n",
    "    return df_user_item_sin_duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0a45de51",
   "metadata": {},
   "outputs": [],
   "source": [
    "def desanidar(df_user_item):\n",
    "    # Aplana la columna 'items' utilizando json_normalize\n",
    "    flattened_items = json_normalize(df_user_item['items'])\n",
    "\n",
    "    # Agrega las columnas 'steam_id', 'items_count', 'user_id', 'user_url' al principio de las columnas aplanadas\n",
    "    flattened_items = pd.concat([df_user_item[['steam_id', 'items_count', 'user_id', 'user_url']], flattened_items], axis=1)\n",
    "\n",
    "    # Elimina duplicados basados en 'user_id'\n",
    "    df_user_item_sin_duplicados = flattened_items[~flattened_items.duplicated(subset='user_id')]\n",
    "\n",
    "    return df_user_item_sin_duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "59eeaa97",
   "metadata": {},
   "outputs": [],
   "source": [
    "def desanidar_y_concatenar(df_item):\n",
    "    # Aplana la columna 'items' utilizando json_normalize\n",
    "    items_aplanados = json_normalize(df_item['items'])\n",
    "\n",
    "    # Concatena las columnas 'steam_id', 'items_count', 'user_id', 'user_url' al principio de las columnas aplanadas\n",
    "    df_desanidado = pd.concat([df_item[['steam_id', 'items_count', 'user_id', 'user_url']], items_aplanados], axis=1)\n",
    "\n",
    "    # Elimina duplicados basados en 'user_id'\n",
    "    df_sin_duplicados = df_desanidado[~df_desanidado.duplicated(subset='user_id')]\n",
    "\n",
    "    return df_sin_duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fad8116",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_items2 = pd.json_normalize(filas_items, record_path=['items'], meta=['steam_id','items_count','user_id', 'user_url'] )\n",
    "df_items2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a62e8b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user_item_2 = desanidar_y_concatenar(df_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3b43b24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inicializa una lista para almacenar los DataFrames normalizados\n",
    "lista_dataframes = []\n",
    "\n",
    "# Itera sobre los elementos de la columna 'items'\n",
    "for fila in df_item['items']:\n",
    "    # Normaliza la fila actual y agrega el DataFrame resultante a la lista\n",
    "    df_normalizado = pd.json_normalize(fila)\n",
    "    lista_dataframes.append(df_normalizado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ae12e2b8",
   "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_2weeks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>30</td>\n",
       "      <td>Day of Defeat</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>Deathmatch Classic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>Half-Life: Opposing Force</td>\n",
       "      <td>0</td>\n",
       "      <td>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>5153204</th>\n",
       "      <td>346330</td>\n",
       "      <td>BrainBread 2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5153205</th>\n",
       "      <td>373330</td>\n",
       "      <td>All Is Dust</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5153206</th>\n",
       "      <td>388490</td>\n",
       "      <td>One Way To Die: Steam Edition</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5153207</th>\n",
       "      <td>521570</td>\n",
       "      <td>You Have 10 Seconds 2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5153208</th>\n",
       "      <td>519140</td>\n",
       "      <td>Minds Eyes</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5153209 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_id                      item_name  playtime_forever  \\\n",
       "0            10                 Counter-Strike                 6   \n",
       "1            20          Team Fortress Classic                 0   \n",
       "2            30                  Day of Defeat                 7   \n",
       "3            40             Deathmatch Classic                 0   \n",
       "4            50      Half-Life: Opposing Force                 0   \n",
       "...         ...                            ...               ...   \n",
       "5153204  346330                   BrainBread 2                 0   \n",
       "5153205  373330                    All Is Dust                 0   \n",
       "5153206  388490  One Way To Die: Steam Edition                 3   \n",
       "5153207  521570          You Have 10 Seconds 2                 4   \n",
       "5153208  519140                     Minds Eyes                 3   \n",
       "\n",
       "         playtime_2weeks  \n",
       "0                      0  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      0  \n",
       "4                      0  \n",
       "...                  ...  \n",
       "5153204                0  \n",
       "5153205                0  \n",
       "5153206                3  \n",
       "5153207                4  \n",
       "5153208                3  \n",
       "\n",
       "[5153209 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatena todos los DataFrames en uno solo\n",
    "data_items_col_items = pd.concat(lista_dataframes, ignore_index=True)\n",
    "data_items_col_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd5d5b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convertir la columna 'playtime_forever' de minutos a horas\n",
    "data_items_col_items['hours_game'] = data_items_col_items['playtime_forever'] / 60\n",
    "\n",
    "# Redondear los valores a dos decimales (opcional)\n",
    "data_items_col_items['hours_game'] = data_items_col_items['hours_game'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3c5583a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 valores máximos:\n",
      "         hours_game\n",
      "587715     10712.88\n",
      "2499068    10588.25\n",
      "4075729    10540.87\n",
      "1495340    10223.52\n",
      "1836985    10001.13\n",
      "\n",
      "Top 5 valores mínimos:\n",
      "   hours_game\n",
      "1         0.0\n",
      "3         0.0\n",
      "4         0.0\n",
      "5         0.0\n",
      "6         0.0\n"
     ]
    }
   ],
   "source": [
    "# Obtener los 5 valores máximos\n",
    "top_5_max = data_items_col_items.nlargest(5, 'hours_game')\n",
    "\n",
    "# Obtener los 5 valores mínimos\n",
    "top_5_min = data_items_col_items.nsmallest(5, 'hours_game')\n",
    "\n",
    "# Mostrar los resultados\n",
    "print(\"Top 5 valores máximos:\")\n",
    "print(top_5_max[['hours_game']])\n",
    "\n",
    "print(\"\\nTop 5 valores mínimos:\")\n",
    "print(top_5_min[['hours_game']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c4f831dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Existen 1847730 filas con valor 0 en la columna 'hours_game'.\n"
     ]
    }
   ],
   "source": [
    "# Contar las filas con valor 0 en la columna 'hours_game'\n",
    "filas_con_cero = (df_UserItems['hours_game'] == 0).sum()\n",
    "\n",
    "# Imprimir el resultado\n",
    "print(f\"Existen {filas_con_cero} filas con valor 0 en la columna 'hours_game'.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c67d7c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminar las filas con valor 0 en la columna 'hours_game'\n",
    "df_UserItems = df_UserItems[df_UserItems['hours_game'] != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "455f2b5e",
   "metadata": {},
   "source": [
    "Se identificaron 1,847,730 registros con un valor de 0 en la columna 'hours_game'. Dado que el análisis de horas jugadas es un requisito, se ha tomado la decisión de eliminar todos los registros con este valor nulo en la columna 'hours_game', ya que no contribuyen al estudio propuesto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "63ab66d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['item_id', 'user_id', 'hours_game'], dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Eliminamos columna no relevantes\n",
    "df_UserItems = df_UserItems.drop(['playtime_2weeks','user_url','items_count','item_name','steam_id','playtime_forever'], axis=1)\n",
    "df_UserItems.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7421524f",
   "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>user_id</th>\n",
       "      <th>hours_game</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>300</td>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>78.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>240</td>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>30.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3830</td>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5153202</th>\n",
       "      <td>304930</td>\n",
       "      <td>76561198329548331</td>\n",
       "      <td>11.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5153203</th>\n",
       "      <td>227940</td>\n",
       "      <td>76561198329548331</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5153206</th>\n",
       "      <td>388490</td>\n",
       "      <td>76561198329548331</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5153207</th>\n",
       "      <td>521570</td>\n",
       "      <td>76561198329548331</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5153208</th>\n",
       "      <td>519140</td>\n",
       "      <td>76561198329548331</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3246375 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_id            user_id  hours_game\n",
       "0            10  76561197970982479        0.10\n",
       "2            30  76561197970982479        0.12\n",
       "8           300  76561197970982479       78.88\n",
       "9           240  76561197970982479       30.88\n",
       "10         3830  76561197970982479        5.55\n",
       "...         ...                ...         ...\n",
       "5153202  304930  76561198329548331       11.28\n",
       "5153203  227940  76561198329548331        0.72\n",
       "5153206  388490  76561198329548331        0.05\n",
       "5153207  521570  76561198329548331        0.07\n",
       "5153208  519140  76561198329548331        0.05\n",
       "\n",
       "[3246375 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_UserItems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f584e1e",
   "metadata": {},
   "source": [
    "### 4. Guardar el conjunto de datos limpio\n",
    "Guardamos el conjunto de datos limpio en un nuevo archivo CSV/JSON/PARQUET para facilitar el acceso y la reutilización."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c4250ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Los archivos se almacenan en local \n",
    "df_UserItems.to_csv('user_items_cleaned.csv', index=False)\n",
    "df_UserItems.to_json('user_items_cleaned.json', orient='records', lines=True)\n",
    "df_UserItems.to_parquet('user_items_cleaned.parquet', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a056fa3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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