1-Preparando_datos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Challenge 1\n",
    "\n",
    "El objetivo es  construir un modelo que prediga si un ítem que se publica en el marketplace tiene condición de \"nuevo\" o \"usado\" eligiendo una métrica adecuada para evaluarlo y fundamentando dicha elección. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Laura\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import jsonlines\n",
    "from pandas.io.json import json_normalize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data set cleaning <a class=\"anchor\" id=\"first-bullet\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_data = pd.read_json(\"MLA_100k.jsonlines\",lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(initial_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>seller_address</th>\n",
       "      <th>warranty</th>\n",
       "      <th>sub_status</th>\n",
       "      <th>condition</th>\n",
       "      <th>seller_contact</th>\n",
       "      <th>deal_ids</th>\n",
       "      <th>base_price</th>\n",
       "      <th>shipping</th>\n",
       "      <th>non_mercado_pago_payment_methods</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>...</th>\n",
       "      <th>status</th>\n",
       "      <th>video_id</th>\n",
       "      <th>catalog_product_id</th>\n",
       "      <th>subtitle</th>\n",
       "      <th>initial_quantity</th>\n",
       "      <th>start_time</th>\n",
       "      <th>permalink</th>\n",
       "      <th>geolocation</th>\n",
       "      <th>sold_quantity</th>\n",
       "      <th>available_quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'comment': '', 'longitude': -58.3986709, 'id'...</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>new</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>80.0</td>\n",
       "      <td>{'local_pick_up': True, 'methods': [], 'tags':...</td>\n",
       "      <td>[{'description': 'Transferencia bancaria', 'id...</td>\n",
       "      <td>74952096</td>\n",
       "      <td>...</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-09-05 20:42:53+00:00</td>\n",
       "      <td>http://articulo.mercadolibre.com.ar/MLA-578052...</td>\n",
       "      <td>{'latitude': -34.6280698, 'longitude': -58.398...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'comment': '', 'longitude': -58.5059173, 'id'...</td>\n",
       "      <td>NUESTRA REPUTACION</td>\n",
       "      <td>[]</td>\n",
       "      <td>used</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>{'local_pick_up': True, 'methods': [], 'tags':...</td>\n",
       "      <td>[{'description': 'Transferencia bancaria', 'id...</td>\n",
       "      <td>42093335</td>\n",
       "      <td>...</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-09-26 18:08:30+00:00</td>\n",
       "      <td>http://articulo.mercadolibre.com.ar/MLA-581565...</td>\n",
       "      <td>{'latitude': -34.5935524, 'longitude': -58.505...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'comment': '', 'longitude': -58.4143948, 'id'...</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>used</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>60.0</td>\n",
       "      <td>{'local_pick_up': True, 'methods': [], 'tags':...</td>\n",
       "      <td>[{'description': 'Transferencia bancaria', 'id...</td>\n",
       "      <td>133384258</td>\n",
       "      <td>...</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-09-09 23:57:07+00:00</td>\n",
       "      <td>http://articulo.mercadolibre.com.ar/MLA-578780...</td>\n",
       "      <td>{'latitude': -34.6233907, 'longitude': -58.414...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'comment': '', 'longitude': -58.4929208, 'id'...</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>new</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>580.0</td>\n",
       "      <td>{'local_pick_up': True, 'methods': [], 'tags':...</td>\n",
       "      <td>[{'description': 'Transferencia bancaria', 'id...</td>\n",
       "      <td>143001605</td>\n",
       "      <td>...</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-09-28 18:47:56+00:00</td>\n",
       "      <td>http://articulo.mercadolibre.com.ar/MLA-581877...</td>\n",
       "      <td>{'latitude': -34.6281894, 'longitude': -58.492...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'comment': '', 'longitude': -58.5495042, 'id'...</td>\n",
       "      <td>MI REPUTACION.</td>\n",
       "      <td>[]</td>\n",
       "      <td>used</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>30.0</td>\n",
       "      <td>{'local_pick_up': True, 'methods': [], 'tags':...</td>\n",
       "      <td>[{'description': 'Transferencia bancaria', 'id...</td>\n",
       "      <td>96873449</td>\n",
       "      <td>...</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-08-24 22:07:20+00:00</td>\n",
       "      <td>http://articulo.mercadolibre.com.ar/MLA-576112...</td>\n",
       "      <td>{'latitude': -34.6346547, 'longitude': -58.549...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      seller_address            warranty  \\\n",
       "0  {'comment': '', 'longitude': -58.3986709, 'id'...                None   \n",
       "1  {'comment': '', 'longitude': -58.5059173, 'id'...  NUESTRA REPUTACION   \n",
       "2  {'comment': '', 'longitude': -58.4143948, 'id'...                None   \n",
       "3  {'comment': '', 'longitude': -58.4929208, 'id'...                None   \n",
       "4  {'comment': '', 'longitude': -58.5495042, 'id'...      MI REPUTACION.   \n",
       "\n",
       "  sub_status condition seller_contact deal_ids  base_price  \\\n",
       "0         []       new           None       []        80.0   \n",
       "1         []      used           None       []      2650.0   \n",
       "2         []      used           None       []        60.0   \n",
       "3         []       new           None       []       580.0   \n",
       "4         []      used           None       []        30.0   \n",
       "\n",
       "                                            shipping  \\\n",
       "0  {'local_pick_up': True, 'methods': [], 'tags':...   \n",
       "1  {'local_pick_up': True, 'methods': [], 'tags':...   \n",
       "2  {'local_pick_up': True, 'methods': [], 'tags':...   \n",
       "3  {'local_pick_up': True, 'methods': [], 'tags':...   \n",
       "4  {'local_pick_up': True, 'methods': [], 'tags':...   \n",
       "\n",
       "                    non_mercado_pago_payment_methods  seller_id  ...  status  \\\n",
       "0  [{'description': 'Transferencia bancaria', 'id...   74952096  ...  active   \n",
       "1  [{'description': 'Transferencia bancaria', 'id...   42093335  ...  active   \n",
       "2  [{'description': 'Transferencia bancaria', 'id...  133384258  ...  active   \n",
       "3  [{'description': 'Transferencia bancaria', 'id...  143001605  ...  active   \n",
       "4  [{'description': 'Transferencia bancaria', 'id...   96873449  ...  active   \n",
       "\n",
       "  video_id catalog_product_id subtitle  initial_quantity  \\\n",
       "0     None                NaN      NaN                 1   \n",
       "1     None                NaN      NaN                 1   \n",
       "2     None                NaN      NaN                 1   \n",
       "3     None                NaN      NaN                 1   \n",
       "4     None                NaN      NaN                 1   \n",
       "\n",
       "                 start_time  \\\n",
       "0 2015-09-05 20:42:53+00:00   \n",
       "1 2015-09-26 18:08:30+00:00   \n",
       "2 2015-09-09 23:57:07+00:00   \n",
       "3 2015-09-28 18:47:56+00:00   \n",
       "4 2015-08-24 22:07:20+00:00   \n",
       "\n",
       "                                           permalink  \\\n",
       "0  http://articulo.mercadolibre.com.ar/MLA-578052...   \n",
       "1  http://articulo.mercadolibre.com.ar/MLA-581565...   \n",
       "2  http://articulo.mercadolibre.com.ar/MLA-578780...   \n",
       "3  http://articulo.mercadolibre.com.ar/MLA-581877...   \n",
       "4  http://articulo.mercadolibre.com.ar/MLA-576112...   \n",
       "\n",
       "                                         geolocation sold_quantity  \\\n",
       "0  {'latitude': -34.6280698, 'longitude': -58.398...             0   \n",
       "1  {'latitude': -34.5935524, 'longitude': -58.505...             0   \n",
       "2  {'latitude': -34.6233907, 'longitude': -58.414...             0   \n",
       "3  {'latitude': -34.6281894, 'longitude': -58.492...             0   \n",
       "4  {'latitude': -34.6346547, 'longitude': -58.549...             0   \n",
       "\n",
       "  available_quantity  \n",
       "0                  1  \n",
       "1                  1  \n",
       "2                  1  \n",
       "3                  1  \n",
       "4                  1  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000\n"
     ]
    }
   ],
   "source": [
    "file = open(\"MLA_100k.jsonlines\", 'r', encoding='utf-8')\n",
    "data = []\n",
    "for line in file.readlines():\n",
    "    dic = json.loads(line)\n",
    "    data.append(dic)\n",
    "  \n",
    "print(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Laura\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df = pd.io.json.json_normalize(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>warranty</th>\n",
       "      <th>sub_status</th>\n",
       "      <th>condition</th>\n",
       "      <th>seller_contact</th>\n",
       "      <th>deal_ids</th>\n",
       "      <th>base_price</th>\n",
       "      <th>non_mercado_pago_payment_methods</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>variations</th>\n",
       "      <th>site_id</th>\n",
       "      <th>...</th>\n",
       "      <th>location.longitude</th>\n",
       "      <th>location.country.name</th>\n",
       "      <th>location.country.id</th>\n",
       "      <th>location.address_line</th>\n",
       "      <th>location.latitude</th>\n",
       "      <th>location.zip_code</th>\n",
       "      <th>location.city.name</th>\n",
       "      <th>location.city.id</th>\n",
       "      <th>location.state.name</th>\n",
       "      <th>location.state.id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>new</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>80.0</td>\n",
       "      <td>[{'description': 'Transferencia bancaria', 'id...</td>\n",
       "      <td>74952096</td>\n",
       "      <td>[]</td>\n",
       "      <td>MLA</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NUESTRA REPUTACION</td>\n",
       "      <td>[]</td>\n",
       "      <td>used</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>[{'description': 'Transferencia bancaria', 'id...</td>\n",
       "      <td>42093335</td>\n",
       "      <td>[]</td>\n",
       "      <td>MLA</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>used</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>60.0</td>\n",
       "      <td>[{'description': 'Transferencia bancaria', 'id...</td>\n",
       "      <td>133384258</td>\n",
       "      <td>[]</td>\n",
       "      <td>MLA</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "      <td>new</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>580.0</td>\n",
       "      <td>[{'description': 'Transferencia bancaria', 'id...</td>\n",
       "      <td>143001605</td>\n",
       "      <td>[]</td>\n",
       "      <td>MLA</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MI REPUTACION.</td>\n",
       "      <td>[]</td>\n",
       "      <td>used</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>30.0</td>\n",
       "      <td>[{'description': 'Transferencia bancaria', 'id...</td>\n",
       "      <td>96873449</td>\n",
       "      <td>[]</td>\n",
       "      <td>MLA</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             warranty sub_status condition  seller_contact deal_ids  \\\n",
       "0                None         []       new             NaN       []   \n",
       "1  NUESTRA REPUTACION         []      used             NaN       []   \n",
       "2                None         []      used             NaN       []   \n",
       "3                None         []       new             NaN       []   \n",
       "4      MI REPUTACION.         []      used             NaN       []   \n",
       "\n",
       "   base_price                   non_mercado_pago_payment_methods  seller_id  \\\n",
       "0        80.0  [{'description': 'Transferencia bancaria', 'id...   74952096   \n",
       "1      2650.0  [{'description': 'Transferencia bancaria', 'id...   42093335   \n",
       "2        60.0  [{'description': 'Transferencia bancaria', 'id...  133384258   \n",
       "3       580.0  [{'description': 'Transferencia bancaria', 'id...  143001605   \n",
       "4        30.0  [{'description': 'Transferencia bancaria', 'id...   96873449   \n",
       "\n",
       "  variations site_id  ... location.longitude  location.country.name  \\\n",
       "0         []     MLA  ...                NaN                    NaN   \n",
       "1         []     MLA  ...                NaN                    NaN   \n",
       "2         []     MLA  ...                NaN                    NaN   \n",
       "3         []     MLA  ...                NaN                    NaN   \n",
       "4         []     MLA  ...                NaN                    NaN   \n",
       "\n",
       "  location.country.id location.address_line location.latitude  \\\n",
       "0                 NaN                   NaN               NaN   \n",
       "1                 NaN                   NaN               NaN   \n",
       "2                 NaN                   NaN               NaN   \n",
       "3                 NaN                   NaN               NaN   \n",
       "4                 NaN                   NaN               NaN   \n",
       "\n",
       "  location.zip_code location.city.name location.city.id location.state.name  \\\n",
       "0               NaN                NaN              NaN                 NaN   \n",
       "1               NaN                NaN              NaN                 NaN   \n",
       "2               NaN                NaN              NaN                 NaN   \n",
       "3               NaN                NaN              NaN                 NaN   \n",
       "4               NaN                NaN              NaN                 NaN   \n",
       "\n",
       "  location.state.id  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['warranty', 'sub_status', 'condition', 'seller_contact', 'deal_ids',\n",
       "       'base_price', 'non_mercado_pago_payment_methods', 'seller_id',\n",
       "       'variations', 'site_id', 'listing_type_id', 'price', 'attributes',\n",
       "       'buying_mode', 'tags', 'listing_source', 'parent_item_id',\n",
       "       'coverage_areas', 'category_id', 'descriptions', 'last_updated',\n",
       "       'international_delivery_mode', 'pictures', 'id', 'official_store_id',\n",
       "       'differential_pricing', 'accepts_mercadopago', 'original_price',\n",
       "       'currency_id', 'thumbnail', 'title', 'automatic_relist', 'date_created',\n",
       "       'secure_thumbnail', 'stop_time', 'status', 'video_id',\n",
       "       'catalog_product_id', 'subtitle', 'initial_quantity', 'start_time',\n",
       "       'permalink', 'sold_quantity', 'available_quantity',\n",
       "       'seller_address.comment', 'seller_address.longitude',\n",
       "       'seller_address.id', 'seller_address.country.name',\n",
       "       'seller_address.country.id', 'seller_address.address_line',\n",
       "       'seller_address.latitude',\n",
       "       'seller_address.search_location.neighborhood.name',\n",
       "       'seller_address.search_location.neighborhood.id',\n",
       "       'seller_address.search_location.state.name',\n",
       "       'seller_address.search_location.state.id',\n",
       "       'seller_address.search_location.city.name',\n",
       "       'seller_address.search_location.city.id', 'seller_address.zip_code',\n",
       "       'seller_address.city.name', 'seller_address.city.id',\n",
       "       'seller_address.state.name', 'seller_address.state.id',\n",
       "       'shipping.local_pick_up', 'shipping.methods', 'shipping.tags',\n",
       "       'shipping.free_shipping', 'shipping.mode', 'shipping.dimensions',\n",
       "       'geolocation.latitude', 'geolocation.longitude',\n",
       "       'shipping.free_methods', 'seller_contact.area_code2',\n",
       "       'seller_contact.phone2', 'seller_contact.webpage',\n",
       "       'seller_contact.email', 'seller_contact.contact',\n",
       "       'seller_contact.area_code', 'seller_contact.other_info',\n",
       "       'seller_contact.phone', 'location.open_hours',\n",
       "       'location.neighborhood.name', 'location.neighborhood.id',\n",
       "       'location.longitude', 'location.country.name', 'location.country.id',\n",
       "       'location.address_line', 'location.latitude', 'location.zip_code',\n",
       "       'location.city.name', 'location.city.id', 'location.state.name',\n",
       "       'location.state.id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 92)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['condition']=df['condition'].map({\"new\":1,\"used\":0})\n",
    "df['condition'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tipo de datos <a class=\"anchor\" id=\"first-bullet\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 92 columns):\n",
      " #   Column                                            Non-Null Count   Dtype  \n",
      "---  ------                                            --------------   -----  \n",
      " 0   warranty                                          39104 non-null   object \n",
      " 1   sub_status                                        100000 non-null  object \n",
      " 2   condition                                         100000 non-null  int64  \n",
      " 3   seller_contact                                    0 non-null       float64\n",
      " 4   deal_ids                                          100000 non-null  object \n",
      " 5   base_price                                        100000 non-null  float64\n",
      " 6   non_mercado_pago_payment_methods                  100000 non-null  object \n",
      " 7   seller_id                                         100000 non-null  int64  \n",
      " 8   variations                                        100000 non-null  object \n",
      " 9   site_id                                           100000 non-null  object \n",
      " 10  listing_type_id                                   100000 non-null  object \n",
      " 11  price                                             100000 non-null  float64\n",
      " 12  attributes                                        100000 non-null  object \n",
      " 13  buying_mode                                       100000 non-null  object \n",
      " 14  tags                                              100000 non-null  object \n",
      " 15  listing_source                                    100000 non-null  object \n",
      " 16  parent_item_id                                    76989 non-null   object \n",
      " 17  coverage_areas                                    100000 non-null  object \n",
      " 18  category_id                                       100000 non-null  object \n",
      " 19  descriptions                                      100000 non-null  object \n",
      " 20  last_updated                                      100000 non-null  object \n",
      " 21  international_delivery_mode                       100000 non-null  object \n",
      " 22  pictures                                          100000 non-null  object \n",
      " 23  id                                                100000 non-null  object \n",
      " 24  official_store_id                                 818 non-null     float64\n",
      " 25  differential_pricing                              0 non-null       object \n",
      " 26  accepts_mercadopago                               100000 non-null  bool   \n",
      " 27  original_price                                    143 non-null     float64\n",
      " 28  currency_id                                       100000 non-null  object \n",
      " 29  thumbnail                                         100000 non-null  object \n",
      " 30  title                                             100000 non-null  object \n",
      " 31  automatic_relist                                  100000 non-null  bool   \n",
      " 32  date_created                                      100000 non-null  object \n",
      " 33  secure_thumbnail                                  100000 non-null  object \n",
      " 34  stop_time                                         100000 non-null  object \n",
      " 35  status                                            100000 non-null  object \n",
      " 36  video_id                                          2985 non-null    object \n",
      " 37  catalog_product_id                                11 non-null      float64\n",
      " 38  subtitle                                          0 non-null       object \n",
      " 39  initial_quantity                                  100000 non-null  int64  \n",
      " 40  start_time                                        100000 non-null  object \n",
      " 41  permalink                                         100000 non-null  object \n",
      " 42  sold_quantity                                     100000 non-null  int64  \n",
      " 43  available_quantity                                100000 non-null  int64  \n",
      " 44  seller_address.comment                            100000 non-null  object \n",
      " 45  seller_address.longitude                          100000 non-null  object \n",
      " 46  seller_address.id                                 100000 non-null  object \n",
      " 47  seller_address.country.name                       100000 non-null  object \n",
      " 48  seller_address.country.id                         100000 non-null  object \n",
      " 49  seller_address.address_line                       100000 non-null  object \n",
      " 50  seller_address.latitude                           100000 non-null  object \n",
      " 51  seller_address.search_location.neighborhood.name  100000 non-null  object \n",
      " 52  seller_address.search_location.neighborhood.id    100000 non-null  object \n",
      " 53  seller_address.search_location.state.name         100000 non-null  object \n",
      " 54  seller_address.search_location.state.id           100000 non-null  object \n",
      " 55  seller_address.search_location.city.name          100000 non-null  object \n",
      " 56  seller_address.search_location.city.id            100000 non-null  object \n",
      " 57  seller_address.zip_code                           100000 non-null  object \n",
      " 58  seller_address.city.name                          100000 non-null  object \n",
      " 59  seller_address.city.id                            100000 non-null  object \n",
      " 60  seller_address.state.name                         100000 non-null  object \n",
      " 61  seller_address.state.id                           100000 non-null  object \n",
      " 62  shipping.local_pick_up                            100000 non-null  bool   \n",
      " 63  shipping.methods                                  96992 non-null   object \n",
      " 64  shipping.tags                                     100000 non-null  object \n",
      " 65  shipping.free_shipping                            100000 non-null  bool   \n",
      " 66  shipping.mode                                     100000 non-null  object \n",
      " 67  shipping.dimensions                               23 non-null      object \n",
      " 68  geolocation.latitude                              100000 non-null  object \n",
      " 69  geolocation.longitude                             100000 non-null  object \n",
      " 70  shipping.free_methods                             3006 non-null    object \n",
      " 71  seller_contact.area_code2                         2219 non-null    object \n",
      " 72  seller_contact.phone2                             2219 non-null    object \n",
      " 73  seller_contact.webpage                            2219 non-null    object \n",
      " 74  seller_contact.email                              2219 non-null    object \n",
      " 75  seller_contact.contact                            2219 non-null    object \n",
      " 76  seller_contact.area_code                          2219 non-null    object \n",
      " 77  seller_contact.other_info                         2219 non-null    object \n",
      " 78  seller_contact.phone                              2219 non-null    object \n",
      " 79  location.open_hours                               2219 non-null    object \n",
      " 80  location.neighborhood.name                        2219 non-null    object \n",
      " 81  location.neighborhood.id                          2219 non-null    object \n",
      " 82  location.longitude                                2219 non-null    object \n",
      " 83  location.country.name                             2219 non-null    object \n",
      " 84  location.country.id                               2219 non-null    object \n",
      " 85  location.address_line                             2219 non-null    object \n",
      " 86  location.latitude                                 2219 non-null    object \n",
      " 87  location.zip_code                                 2219 non-null    object \n",
      " 88  location.city.name                                2219 non-null    object \n",
      " 89  location.city.id                                  2219 non-null    object \n",
      " 90  location.state.name                               2219 non-null    object \n",
      " 91  location.state.id                                 2219 non-null    object \n",
      "dtypes: bool(4), float64(6), int64(5), object(77)\n",
      "memory usage: 67.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"shipping.local_pick_up\"] = df[\"shipping.local_pick_up\"].astype(int)\n",
    "df['shipping.local_pick_up'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"shipping.free_shipping\"] = df[\"shipping.free_shipping\"].astype(int)\n",
    "df['shipping.free_shipping'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"accepts_mercadopago\"] = df[\"accepts_mercadopago\"].astype(int)\n",
    "df['accepts_mercadopago'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"automatic_relist\"] = df[\"automatic_relist\"].astype(int)\n",
    "df['automatic_relist'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "warranty               False\n",
       "sub_status             False\n",
       "condition               True\n",
       "seller_contact          True\n",
       "deal_ids               False\n",
       "                       ...  \n",
       "location.zip_code      False\n",
       "location.city.name     False\n",
       "location.city.id       False\n",
       "location.state.name    False\n",
       "location.state.id      False\n",
       "Length: 92, dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = (df.dtypes == 'float64') | (df.dtypes == 'int32')  | (df.dtypes == 'int64')\n",
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['warranty', 'sub_status', 'condition', 'seller_contact', 'deal_ids',\n",
       "       'base_price', 'non_mercado_pago_payment_methods', 'seller_id',\n",
       "       'variations', 'site_id', 'listing_type_id', 'price', 'attributes',\n",
       "       'buying_mode', 'tags', 'listing_source', 'parent_item_id',\n",
       "       'coverage_areas', 'category_id', 'descriptions', 'last_updated',\n",
       "       'international_delivery_mode', 'pictures', 'id', 'official_store_id',\n",
       "       'differential_pricing', 'accepts_mercadopago', 'original_price',\n",
       "       'currency_id', 'thumbnail', 'title', 'automatic_relist', 'date_created',\n",
       "       'secure_thumbnail', 'stop_time', 'status', 'video_id',\n",
       "       'catalog_product_id', 'subtitle', 'initial_quantity', 'start_time',\n",
       "       'permalink', 'sold_quantity', 'available_quantity',\n",
       "       'seller_address.comment', 'seller_address.longitude',\n",
       "       'seller_address.id', 'seller_address.country.name',\n",
       "       'seller_address.country.id', 'seller_address.address_line',\n",
       "       'seller_address.latitude',\n",
       "       'seller_address.search_location.neighborhood.name',\n",
       "       'seller_address.search_location.neighborhood.id',\n",
       "       'seller_address.search_location.state.name',\n",
       "       'seller_address.search_location.state.id',\n",
       "       'seller_address.search_location.city.name',\n",
       "       'seller_address.search_location.city.id', 'seller_address.zip_code',\n",
       "       'seller_address.city.name', 'seller_address.city.id',\n",
       "       'seller_address.state.name', 'seller_address.state.id',\n",
       "       'shipping.local_pick_up', 'shipping.methods', 'shipping.tags',\n",
       "       'shipping.free_shipping', 'shipping.mode', 'shipping.dimensions',\n",
       "       'geolocation.latitude', 'geolocation.longitude',\n",
       "       'shipping.free_methods', 'seller_contact.area_code2',\n",
       "       'seller_contact.phone2', 'seller_contact.webpage',\n",
       "       'seller_contact.email', 'seller_contact.contact',\n",
       "       'seller_contact.area_code', 'seller_contact.other_info',\n",
       "       'seller_contact.phone', 'location.open_hours',\n",
       "       'location.neighborhood.name', 'location.neighborhood.id',\n",
       "       'location.longitude', 'location.country.name', 'location.country.id',\n",
       "       'location.address_line', 'location.latitude', 'location.zip_code',\n",
       "       'location.city.name', 'location.city.id', 'location.state.name',\n",
       "       'location.state.id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warranty\n",
      "sub_status\n",
      "condition\n",
      "seller_contact\n",
      "deal_ids\n",
      "base_price\n",
      "non_mercado_pago_payment_methods\n",
      "seller_id\n",
      "variations\n",
      "site_id\n",
      "listing_type_id\n",
      "price\n",
      "attributes\n",
      "buying_mode\n",
      "tags\n",
      "listing_source\n",
      "parent_item_id\n",
      "coverage_areas\n",
      "category_id\n",
      "descriptions\n",
      "last_updated\n",
      "international_delivery_mode\n",
      "pictures\n",
      "id\n",
      "official_store_id\n",
      "differential_pricing\n",
      "accepts_mercadopago\n",
      "original_price\n",
      "currency_id\n",
      "thumbnail\n",
      "title\n",
      "automatic_relist\n",
      "date_created\n",
      "secure_thumbnail\n",
      "stop_time\n",
      "status\n",
      "video_id\n",
      "catalog_product_id\n",
      "subtitle\n",
      "initial_quantity\n",
      "start_time\n",
      "permalink\n",
      "sold_quantity\n",
      "available_quantity\n",
      "seller_address.comment\n",
      "seller_address.longitude\n",
      "seller_address.id\n",
      "seller_address.country.name\n",
      "seller_address.country.id\n",
      "seller_address.address_line\n",
      "seller_address.latitude\n",
      "seller_address.search_location.neighborhood.name\n",
      "seller_address.search_location.neighborhood.id\n",
      "seller_address.search_location.state.name\n",
      "seller_address.search_location.state.id\n",
      "seller_address.search_location.city.name\n",
      "seller_address.search_location.city.id\n",
      "seller_address.zip_code\n",
      "seller_address.city.name\n",
      "seller_address.city.id\n",
      "seller_address.state.name\n",
      "seller_address.state.id\n",
      "shipping.local_pick_up\n",
      "shipping.methods\n",
      "shipping.tags\n",
      "shipping.free_shipping\n",
      "shipping.mode\n",
      "shipping.dimensions\n",
      "geolocation.latitude\n",
      "geolocation.longitude\n",
      "shipping.free_methods\n",
      "seller_contact.area_code2\n",
      "seller_contact.phone2\n",
      "seller_contact.webpage\n",
      "seller_contact.email\n",
      "seller_contact.contact\n",
      "seller_contact.area_code\n",
      "seller_contact.other_info\n",
      "seller_contact.phone\n",
      "location.open_hours\n",
      "location.neighborhood.name\n",
      "location.neighborhood.id\n",
      "location.longitude\n",
      "location.country.name\n",
      "location.country.id\n",
      "location.address_line\n",
      "location.latitude\n",
      "location.zip_code\n",
      "location.city.name\n",
      "location.city.id\n",
      "location.state.name\n",
      "location.state.id\n"
     ]
    }
   ],
   "source": [
    "for el in num.index:\n",
    "    print(el)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = [c for c in num.index if num[c]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['condition',\n",
       " 'seller_contact',\n",
       " 'base_price',\n",
       " 'seller_id',\n",
       " 'price',\n",
       " 'official_store_id',\n",
       " 'accepts_mercadopago',\n",
       " 'original_price',\n",
       " 'automatic_relist',\n",
       " 'catalog_product_id',\n",
       " 'initial_quantity',\n",
       " 'sold_quantity',\n",
       " 'available_quantity',\n",
       " 'shipping.local_pick_up',\n",
       " 'shipping.free_shipping']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['warranty',\n",
       " 'sub_status',\n",
       " 'deal_ids',\n",
       " 'non_mercado_pago_payment_methods',\n",
       " 'variations',\n",
       " 'site_id',\n",
       " 'listing_type_id',\n",
       " 'attributes',\n",
       " 'buying_mode',\n",
       " 'tags',\n",
       " 'listing_source',\n",
       " 'parent_item_id',\n",
       " 'coverage_areas',\n",
       " 'category_id',\n",
       " 'descriptions',\n",
       " 'last_updated',\n",
       " 'international_delivery_mode',\n",
       " 'pictures',\n",
       " 'id',\n",
       " 'differential_pricing',\n",
       " 'currency_id',\n",
       " 'thumbnail',\n",
       " 'title',\n",
       " 'date_created',\n",
       " 'secure_thumbnail',\n",
       " 'stop_time',\n",
       " 'status',\n",
       " 'video_id',\n",
       " 'subtitle',\n",
       " 'start_time',\n",
       " 'permalink',\n",
       " 'seller_address.comment',\n",
       " 'seller_address.longitude',\n",
       " 'seller_address.id',\n",
       " 'seller_address.country.name',\n",
       " 'seller_address.country.id',\n",
       " 'seller_address.address_line',\n",
       " 'seller_address.latitude',\n",
       " 'seller_address.search_location.neighborhood.name',\n",
       " 'seller_address.search_location.neighborhood.id',\n",
       " 'seller_address.search_location.state.name',\n",
       " 'seller_address.search_location.state.id',\n",
       " 'seller_address.search_location.city.name',\n",
       " 'seller_address.search_location.city.id',\n",
       " 'seller_address.zip_code',\n",
       " 'seller_address.city.name',\n",
       " 'seller_address.city.id',\n",
       " 'seller_address.state.name',\n",
       " 'seller_address.state.id',\n",
       " 'shipping.methods',\n",
       " 'shipping.tags',\n",
       " 'shipping.mode',\n",
       " 'shipping.dimensions',\n",
       " 'geolocation.latitude',\n",
       " 'geolocation.longitude',\n",
       " 'shipping.free_methods',\n",
       " 'seller_contact.area_code2',\n",
       " 'seller_contact.phone2',\n",
       " 'seller_contact.webpage',\n",
       " 'seller_contact.email',\n",
       " 'seller_contact.contact',\n",
       " 'seller_contact.area_code',\n",
       " 'seller_contact.other_info',\n",
       " 'seller_contact.phone',\n",
       " 'location.open_hours',\n",
       " 'location.neighborhood.name',\n",
       " 'location.neighborhood.id',\n",
       " 'location.longitude',\n",
       " 'location.country.name',\n",
       " 'location.country.id',\n",
       " 'location.address_line',\n",
       " 'location.latitude',\n",
       " 'location.zip_code',\n",
       " 'location.city.name',\n",
       " 'location.city.id',\n",
       " 'location.state.name',\n",
       " 'location.state.id']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = (df.dtypes == object)\n",
    "obj_cols = [c for c in obj.index if obj[c]]\n",
    "obj_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>condition</th>\n",
       "      <th>seller_contact</th>\n",
       "      <th>base_price</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>price</th>\n",
       "      <th>official_store_id</th>\n",
       "      <th>accepts_mercadopago</th>\n",
       "      <th>original_price</th>\n",
       "      <th>automatic_relist</th>\n",
       "      <th>catalog_product_id</th>\n",
       "      <th>initial_quantity</th>\n",
       "      <th>sold_quantity</th>\n",
       "      <th>available_quantity</th>\n",
       "      <th>shipping.local_pick_up</th>\n",
       "      <th>shipping.free_shipping</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>818.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.537580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.252423e+04</td>\n",
       "      <td>8.425269e+07</td>\n",
       "      <td>5.252433e+04</td>\n",
       "      <td>206.443765</td>\n",
       "      <td>0.977810</td>\n",
       "      <td>1593.341958</td>\n",
       "      <td>0.046970</td>\n",
       "      <td>3.727643e+06</td>\n",
       "      <td>35.093370</td>\n",
       "      <td>2.396990</td>\n",
       "      <td>34.842380</td>\n",
       "      <td>0.795610</td>\n",
       "      <td>0.030160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.498588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.623127e+06</td>\n",
       "      <td>5.497257e+07</td>\n",
       "      <td>8.623127e+06</td>\n",
       "      <td>128.252953</td>\n",
       "      <td>0.147302</td>\n",
       "      <td>2245.798072</td>\n",
       "      <td>0.211576</td>\n",
       "      <td>1.884698e+06</td>\n",
       "      <td>421.076196</td>\n",
       "      <td>42.685077</td>\n",
       "      <td>420.808403</td>\n",
       "      <td>0.403257</td>\n",
       "      <td>0.171028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.400000e-01</td>\n",
       "      <td>1.304000e+03</td>\n",
       "      <td>8.400000e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.440400e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>3.953591e+07</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.050702e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>7.631063e+07</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>858.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.093232e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000e+02</td>\n",
       "      <td>1.325659e+08</td>\n",
       "      <td>8.000000e+02</td>\n",
       "      <td>312.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.103216e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.222222e+09</td>\n",
       "      <td>1.946906e+08</td>\n",
       "      <td>2.222222e+09</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.434513e+06</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>8676.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           condition  seller_contact    base_price     seller_id  \\\n",
       "count  100000.000000             0.0  1.000000e+05  1.000000e+05   \n",
       "mean        0.537580             NaN  5.252423e+04  8.425269e+07   \n",
       "std         0.498588             NaN  8.623127e+06  5.497257e+07   \n",
       "min         0.000000             NaN  8.400000e-01  1.304000e+03   \n",
       "25%         0.000000             NaN  9.000000e+01  3.953591e+07   \n",
       "50%         1.000000             NaN  2.500000e+02  7.631063e+07   \n",
       "75%         1.000000             NaN  8.000000e+02  1.325659e+08   \n",
       "max         1.000000             NaN  2.222222e+09  1.946906e+08   \n",
       "\n",
       "              price  official_store_id  accepts_mercadopago  original_price  \\\n",
       "count  1.000000e+05         818.000000        100000.000000      143.000000   \n",
       "mean   5.252433e+04         206.443765             0.977810     1593.341958   \n",
       "std    8.623127e+06         128.252953             0.147302     2245.798072   \n",
       "min    8.400000e-01           1.000000             0.000000      120.000000   \n",
       "25%    9.000000e+01          84.000000             1.000000      449.000000   \n",
       "50%    2.500000e+02         216.000000             1.000000      858.000000   \n",
       "75%    8.000000e+02         312.750000             1.000000     1500.000000   \n",
       "max    2.222222e+09         446.000000             1.000000    13999.000000   \n",
       "\n",
       "       automatic_relist  catalog_product_id  initial_quantity  sold_quantity  \\\n",
       "count     100000.000000        1.100000e+01     100000.000000  100000.000000   \n",
       "mean           0.046970        3.727643e+06         35.093370       2.396990   \n",
       "std            0.211576        1.884698e+06        421.076196      42.685077   \n",
       "min            0.000000        9.440400e+04          1.000000       0.000000   \n",
       "25%            0.000000        3.050702e+06          1.000000       0.000000   \n",
       "50%            0.000000        5.093232e+06          1.000000       0.000000   \n",
       "75%            0.000000        5.103216e+06          2.000000       0.000000   \n",
       "max            1.000000        5.434513e+06       9999.000000    8676.000000   \n",
       "\n",
       "       available_quantity  shipping.local_pick_up  shipping.free_shipping  \n",
       "count       100000.000000           100000.000000           100000.000000  \n",
       "mean            34.842380                0.795610                0.030160  \n",
       "std            420.808403                0.403257                0.171028  \n",
       "min              1.000000                0.000000                0.000000  \n",
       "25%              1.000000                1.000000                0.000000  \n",
       "50%              1.000000                1.000000                0.000000  \n",
       "75%              2.000000                1.000000                0.000000  \n",
       "max           9999.000000                1.000000                1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num = df[num_cols]\n",
    "# Estadísticas de las columnas númericas\n",
    "df_num.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17a1f548b70>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['accepts_mercadopago'].hist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limpieza de datos <a class=\"anchor\" id=\"first-bullet\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>condition</th>\n",
       "      <th>seller_contact</th>\n",
       "      <th>base_price</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>price</th>\n",
       "      <th>official_store_id</th>\n",
       "      <th>accepts_mercadopago</th>\n",
       "      <th>original_price</th>\n",
       "      <th>automatic_relist</th>\n",
       "      <th>catalog_product_id</th>\n",
       "      <th>initial_quantity</th>\n",
       "      <th>sold_quantity</th>\n",
       "      <th>available_quantity</th>\n",
       "      <th>shipping.local_pick_up</th>\n",
       "      <th>shipping.free_shipping</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>818</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>143</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>11</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       condition  seller_contact  base_price  seller_id     price  \\\n",
       "False        NaN        100000.0         NaN        NaN       NaN   \n",
       "True    100000.0             NaN    100000.0   100000.0  100000.0   \n",
       "\n",
       "       official_store_id  accepts_mercadopago  original_price  \\\n",
       "False              99182                  NaN           99857   \n",
       "True                 818             100000.0             143   \n",
       "\n",
       "       automatic_relist  catalog_product_id  initial_quantity  sold_quantity  \\\n",
       "False               NaN               99989               NaN            NaN   \n",
       "True           100000.0                  11          100000.0       100000.0   \n",
       "\n",
       "       available_quantity  shipping.local_pick_up  shipping.free_shipping  \n",
       "False                 NaN                     NaN                     NaN  \n",
       "True             100000.0                100000.0                100000.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num.notnull().apply(pd.Series.value_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    100000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "available = df_num.notnull()\n",
    "available.all(axis=1).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_num = df_num.drop('official_store_id',axis=1)\n",
    "df_num = df_num.drop('original_price',axis=1)\n",
    "df_num = df_num.drop('catalog_product_id',axis=1)\n",
    "df_num = df_num.drop('seller_contact',axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>condition</th>\n",
       "      <th>base_price</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>price</th>\n",
       "      <th>accepts_mercadopago</th>\n",
       "      <th>automatic_relist</th>\n",
       "      <th>initial_quantity</th>\n",
       "      <th>sold_quantity</th>\n",
       "      <th>available_quantity</th>\n",
       "      <th>shipping.local_pick_up</th>\n",
       "      <th>shipping.free_shipping</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>74952096</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>42093335</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>133384258</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>580.0</td>\n",
       "      <td>143001605</td>\n",
       "      <td>580.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>96873449</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condition  base_price  seller_id   price  accepts_mercadopago  \\\n",
       "0          1        80.0   74952096    80.0                    1   \n",
       "1          0      2650.0   42093335  2650.0                    1   \n",
       "2          0        60.0  133384258    60.0                    1   \n",
       "3          1       580.0  143001605   580.0                    1   \n",
       "4          0        30.0   96873449    30.0                    1   \n",
       "\n",
       "   automatic_relist  initial_quantity  sold_quantity  available_quantity  \\\n",
       "0                 0                 1              0                   1   \n",
       "1                 0                 1              0                   1   \n",
       "2                 0                 1              0                   1   \n",
       "3                 0                 1              0                   1   \n",
       "4                 0                 1              0                   1   \n",
       "\n",
       "   shipping.local_pick_up  shipping.free_shipping  \n",
       "0                       1                       0  \n",
       "1                       1                       0  \n",
       "2                       1                       0  \n",
       "3                       1                       0  \n",
       "4                       1                       0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_num.to_csv('MLA_100k.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}