Limpieza_y_Preparacion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6ofoSnKd9OwN"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "#%matplotlib notebook\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VEzQATNn9Owd"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"properatti.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploración inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Unnamed: 0</th>\n",
       "      <th>operation</th>\n",
       "      <th>property_type</th>\n",
       "      <th>place_name</th>\n",
       "      <th>place_with_parent_names</th>\n",
       "      <th>country_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>geonames_id</th>\n",
       "      <th>lat-lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>...</th>\n",
       "      <th>surface_covered_in_m2</th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "      <th>price_per_m2</th>\n",
       "      <th>floor</th>\n",
       "      <th>rooms</th>\n",
       "      <th>expenses</th>\n",
       "      <th>properati_url</th>\n",
       "      <th>description</th>\n",
       "      <th>title</th>\n",
       "      <th>image_thumbnail</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>sell</td>\n",
       "      <td>PH</td>\n",
       "      <td>Mataderos</td>\n",
       "      <td>|Argentina|Capital Federal|Mataderos|</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3430787.000</td>\n",
       "      <td>-34.6618237,-58.5088387</td>\n",
       "      <td>-34.662</td>\n",
       "      <td>...</td>\n",
       "      <td>40.000</td>\n",
       "      <td>1127.273</td>\n",
       "      <td>1550.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>http://www.properati.com.ar/15bo8_venta_ph_mat...</td>\n",
       "      <td>2 AMBIENTES TIPO CASA PLANTA BAJA POR PASILLO,...</td>\n",
       "      <td>2 AMB TIPO CASA SIN EXPENSAS EN PB</td>\n",
       "      <td>https://thumbs4.properati.com/8/BluUYiHJLhgIIK...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>La Plata</td>\n",
       "      <td>|Argentina|Bs.As. G.B.A. Zona Sur|La Plata|</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Bs.As. G.B.A. Zona Sur</td>\n",
       "      <td>3432039.000</td>\n",
       "      <td>-34.9038831,-57.9643295</td>\n",
       "      <td>-34.904</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>http://www.properati.com.ar/15bob_venta_depart...</td>\n",
       "      <td>Venta de departamento en décimo piso al frente...</td>\n",
       "      <td>VENTA Depto 2 dorm. a estrenar 7 e/ 36 y 37   ...</td>\n",
       "      <td>https://thumbs4.properati.com/7/ikpVBu2ztHA7jv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>Mataderos</td>\n",
       "      <td>|Argentina|Capital Federal|Mataderos|</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3430787.000</td>\n",
       "      <td>-34.6522615,-58.5229825</td>\n",
       "      <td>-34.652</td>\n",
       "      <td>...</td>\n",
       "      <td>55.000</td>\n",
       "      <td>1309.091</td>\n",
       "      <td>1309.091</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>http://www.properati.com.ar/15bod_venta_depart...</td>\n",
       "      <td>2 AMBIENTES 3ER PISO LATERAL LIVING COMEDOR AM...</td>\n",
       "      <td>2 AMB 3ER PISO CON ASCENSOR APTO CREDITO</td>\n",
       "      <td>https://thumbs4.properati.com/5/SXKr34F_IwG3W_...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 operation property_type place_name  \\\n",
       "0           0      sell            PH  Mataderos   \n",
       "1           1      sell     apartment   La Plata   \n",
       "2           2      sell     apartment  Mataderos   \n",
       "\n",
       "                       place_with_parent_names country_name  \\\n",
       "0        |Argentina|Capital Federal|Mataderos|    Argentina   \n",
       "1  |Argentina|Bs.As. G.B.A. Zona Sur|La Plata|    Argentina   \n",
       "2        |Argentina|Capital Federal|Mataderos|    Argentina   \n",
       "\n",
       "               state_name  geonames_id                  lat-lon     lat  \\\n",
       "0         Capital Federal  3430787.000  -34.6618237,-58.5088387 -34.662   \n",
       "1  Bs.As. G.B.A. Zona Sur  3432039.000  -34.9038831,-57.9643295 -34.904   \n",
       "2         Capital Federal  3430787.000  -34.6522615,-58.5229825 -34.652   \n",
       "\n",
       "                         ...                          surface_covered_in_m2  \\\n",
       "0                        ...                                         40.000   \n",
       "1                        ...                                            nan   \n",
       "2                        ...                                         55.000   \n",
       "\n",
       "   price_usd_per_m2 price_per_m2  floor  rooms  expenses  \\\n",
       "0          1127.273     1550.000    nan    nan       nan   \n",
       "1               nan          nan    nan    nan       nan   \n",
       "2          1309.091     1309.091    nan    nan       nan   \n",
       "\n",
       "                                       properati_url  \\\n",
       "0  http://www.properati.com.ar/15bo8_venta_ph_mat...   \n",
       "1  http://www.properati.com.ar/15bob_venta_depart...   \n",
       "2  http://www.properati.com.ar/15bod_venta_depart...   \n",
       "\n",
       "                                         description  \\\n",
       "0  2 AMBIENTES TIPO CASA PLANTA BAJA POR PASILLO,...   \n",
       "1  Venta de departamento en décimo piso al frente...   \n",
       "2  2 AMBIENTES 3ER PISO LATERAL LIVING COMEDOR AM...   \n",
       "\n",
       "                                               title  \\\n",
       "0                 2 AMB TIPO CASA SIN EXPENSAS EN PB   \n",
       "1  VENTA Depto 2 dorm. a estrenar 7 e/ 36 y 37   ...   \n",
       "2           2 AMB 3ER PISO CON ASCENSOR APTO CREDITO   \n",
       "\n",
       "                                     image_thumbnail  \n",
       "0  https://thumbs4.properati.com/8/BluUYiHJLhgIIK...  \n",
       "1  https://thumbs4.properati.com/7/ikpVBu2ztHA7jv...  \n",
       "2  https://thumbs4.properati.com/5/SXKr34F_IwG3W_...  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121220, 26)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Unnamed: 0</th>\n",
       "      <th>geonames_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>price</th>\n",
       "      <th>price_aprox_local_currency</th>\n",
       "      <th>price_aprox_usd</th>\n",
       "      <th>surface_total_in_m2</th>\n",
       "      <th>surface_covered_in_m2</th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "      <th>price_per_m2</th>\n",
       "      <th>floor</th>\n",
       "      <th>rooms</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>121220.000</td>\n",
       "      <td>102503.000</td>\n",
       "      <td>69670.000</td>\n",
       "      <td>69670.000</td>\n",
       "      <td>100810.000</td>\n",
       "      <td>100810.000</td>\n",
       "      <td>100810.000</td>\n",
       "      <td>81892.000</td>\n",
       "      <td>101313.000</td>\n",
       "      <td>68617.000</td>\n",
       "      <td>87658.000</td>\n",
       "      <td>7899.000</td>\n",
       "      <td>47390.000</td>\n",
       "      <td>14262.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60609.500</td>\n",
       "      <td>3574442.318</td>\n",
       "      <td>-34.626</td>\n",
       "      <td>-59.266</td>\n",
       "      <td>468525.925</td>\n",
       "      <td>4229396.829</td>\n",
       "      <td>239700.577</td>\n",
       "      <td>233.795</td>\n",
       "      <td>133.050</td>\n",
       "      <td>2160.087</td>\n",
       "      <td>6912.216</td>\n",
       "      <td>17.452</td>\n",
       "      <td>3.081</td>\n",
       "      <td>5009.234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34993.344</td>\n",
       "      <td>354130.624</td>\n",
       "      <td>1.981</td>\n",
       "      <td>2.300</td>\n",
       "      <td>2260100.589</td>\n",
       "      <td>6904714.126</td>\n",
       "      <td>391323.876</td>\n",
       "      <td>1782.222</td>\n",
       "      <td>724.351</td>\n",
       "      <td>2759.289</td>\n",
       "      <td>28378.645</td>\n",
       "      <td>120.244</td>\n",
       "      <td>1.861</td>\n",
       "      <td>120440.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>3427208.000</td>\n",
       "      <td>-54.824</td>\n",
       "      <td>-75.679</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.600</td>\n",
       "      <td>1.510</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30304.750</td>\n",
       "      <td>3430234.000</td>\n",
       "      <td>-34.669</td>\n",
       "      <td>-58.727</td>\n",
       "      <td>110000.000</td>\n",
       "      <td>1583309.440</td>\n",
       "      <td>89733.880</td>\n",
       "      <td>50.000</td>\n",
       "      <td>45.000</td>\n",
       "      <td>1218.182</td>\n",
       "      <td>1550.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60609.500</td>\n",
       "      <td>3433910.000</td>\n",
       "      <td>-34.598</td>\n",
       "      <td>-58.480</td>\n",
       "      <td>185000.000</td>\n",
       "      <td>2558452.500</td>\n",
       "      <td>145000.000</td>\n",
       "      <td>84.000</td>\n",
       "      <td>75.000</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>2213.115</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>2000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90914.250</td>\n",
       "      <td>3836668.000</td>\n",
       "      <td>-34.441</td>\n",
       "      <td>-58.396</td>\n",
       "      <td>420000.000</td>\n",
       "      <td>4675792.500</td>\n",
       "      <td>265000.000</td>\n",
       "      <td>200.000</td>\n",
       "      <td>150.000</td>\n",
       "      <td>2486.412</td>\n",
       "      <td>3355.549</td>\n",
       "      <td>6.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>4000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>121219.000</td>\n",
       "      <td>6948895.000</td>\n",
       "      <td>4.546</td>\n",
       "      <td>-53.733</td>\n",
       "      <td>650000000.000</td>\n",
       "      <td>821271104.300</td>\n",
       "      <td>46545445.000</td>\n",
       "      <td>200000.000</td>\n",
       "      <td>187000.000</td>\n",
       "      <td>206333.333</td>\n",
       "      <td>3999999.960</td>\n",
       "      <td>3150.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>10001500.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  geonames_id       lat       lon         price  \\\n",
       "count  121220.000   102503.000 69670.000 69670.000    100810.000   \n",
       "mean    60609.500  3574442.318   -34.626   -59.266    468525.925   \n",
       "std     34993.344   354130.624     1.981     2.300   2260100.589   \n",
       "min         0.000  3427208.000   -54.824   -75.679         0.000   \n",
       "25%     30304.750  3430234.000   -34.669   -58.727    110000.000   \n",
       "50%     60609.500  3433910.000   -34.598   -58.480    185000.000   \n",
       "75%     90914.250  3836668.000   -34.441   -58.396    420000.000   \n",
       "max    121219.000  6948895.000     4.546   -53.733 650000000.000   \n",
       "\n",
       "       price_aprox_local_currency  price_aprox_usd  surface_total_in_m2  \\\n",
       "count                  100810.000       100810.000            81892.000   \n",
       "mean                  4229396.829       239700.577              233.795   \n",
       "std                   6904714.126       391323.876             1782.222   \n",
       "min                         0.000            0.000                0.000   \n",
       "25%                   1583309.440        89733.880               50.000   \n",
       "50%                   2558452.500       145000.000               84.000   \n",
       "75%                   4675792.500       265000.000              200.000   \n",
       "max                 821271104.300     46545445.000           200000.000   \n",
       "\n",
       "       surface_covered_in_m2  price_usd_per_m2  price_per_m2    floor  \\\n",
       "count             101313.000         68617.000     87658.000 7899.000   \n",
       "mean                 133.050          2160.087      6912.216   17.452   \n",
       "std                  724.351          2759.289     28378.645  120.244   \n",
       "min                    0.000             0.600         1.510    1.000   \n",
       "25%                   45.000          1218.182      1550.000    1.000   \n",
       "50%                   75.000          1800.000      2213.115    3.000   \n",
       "75%                  150.000          2486.412      3355.549    6.000   \n",
       "max               187000.000        206333.333   3999999.960 3150.000   \n",
       "\n",
       "          rooms     expenses  \n",
       "count 47390.000    14262.000  \n",
       "mean      3.081     5009.234  \n",
       "std       1.861   120440.256  \n",
       "min       1.000        1.000  \n",
       "25%       2.000     1000.000  \n",
       "50%       3.000     2000.000  \n",
       "75%       4.000     4000.000  \n",
       "max      32.000 10001500.000  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                         0\n",
       "operation                          0\n",
       "property_type                      0\n",
       "place_name                        23\n",
       "place_with_parent_names            0\n",
       "country_name                       0\n",
       "state_name                         0\n",
       "geonames_id                    18717\n",
       "lat-lon                        51550\n",
       "lat                            51550\n",
       "lon                            51550\n",
       "price                          20410\n",
       "currency                       20411\n",
       "price_aprox_local_currency     20410\n",
       "price_aprox_usd                20410\n",
       "surface_total_in_m2            39328\n",
       "surface_covered_in_m2          19907\n",
       "price_usd_per_m2               52603\n",
       "price_per_m2                   33562\n",
       "floor                         113321\n",
       "rooms                          73830\n",
       "expenses                      106958\n",
       "properati_url                      0\n",
       "description                        2\n",
       "title                              0\n",
       "image_thumbnail                 3112\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detectamos errores posibles en la carga de la moneda y los corregimos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Cantidad de valores en ARS: 10'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array(['Boedo', 'Ituzaingó', 'Rosario', 'Villa Ballester'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pricesUSD = df[(\n",
    "    ~np.isnan(df['price']) & #aca estoy sacando los NaN\n",
    "    [False if pd.isnull(x) else x=='USD' for x in df['currency']] #aca estoy filtrando los que tengan currency en USD\n",
    ")].sort_values(by='price_usd_per_m2', ascending=False).head(10);\n",
    "\n",
    "display('Cantidad de valores en ARS: %d' %len(pricesUSD))\n",
    "display(pricesUSD['place_name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reparamos los precios erróneos encontrados\n",
    "pricesFixed = pricesUSD.copy()\n",
    "valorDolar = np.round(pricesFixed['price_aprox_local_currency'] / pricesFixed['price_aprox_usd'], 2)\n",
    "\n",
    "pricesFixed['price_aprox_local_currency'] = pricesFixed['price_aprox_usd']\n",
    "pricesFixed['price_aprox_usd'] = np.round(pricesFixed['price_aprox_usd'] / valorDolar)\n",
    "pricesFixed['price'] = pricesFixed['price_aprox_usd']\n",
    "\n",
    "pricesFixed['price_usd_per_m2'] = pricesFixed['price'] / pricesFixed['surface_covered_in_m2']\n",
    "\n",
    "pricesFixed.currency = 'ARS'\n",
    "\n",
    "df.iloc[pricesFixed.index] = pricesFixed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9464"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contamos duplicados\n",
    "df.duplicated(['place_with_parent_names','description','title','floor','rooms','surface_total_in_m2'],keep='first').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminamos duplicados\n",
    "df.drop_duplicates(['place_with_parent_names','description','title','floor','rooms','surface_total_in_m2'],keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizamos las variables significativas y eliminamos outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "901\n",
      "\n",
      "count   62771.000\n",
      "mean     1881.659\n",
      "std      1119.417\n",
      "min         0.600\n",
      "25%      1166.667\n",
      "50%      1762.115\n",
      "75%      2421.053\n",
      "max     11346.154\n",
      "Name: price_usd_per_m2, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Variable: PRICE_USD_PER_M2\n",
    "# Pasamos los ceros a nan\n",
    "df[\"price_usd_per_m2\"] = np.where(df[\"price_usd_per_m2\"] == 0, np.nan,df[\"price_usd_per_m2\"])\n",
    "# Calculamos zscore\n",
    "#df[\"price_usd_per_m2_zscore\"] = (df[\"price_usd_per_m2\"] - df[\"price_usd_per_m2\"].mean()) / df[\"price_usd_per_m2\"].std()\n",
    "df[\"price_usd_per_m2_zscore\"] = (df.groupby(['property_type'])[[\"price_usd_per_m2\"]]\n",
    ".apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# Contamos outliers\n",
    "print(df[\"price_usd_per_m2_zscore\"][abs(df[\"price_usd_per_m2_zscore\"]) > 3].count())\n",
    "# Dropeamos las filas con outliers\n",
    "df.drop(df[\"price_usd_per_m2_zscore\"][abs(df['price_usd_per_m2_zscore']) > 3].index,inplace=True)\n",
    "print(\"\")\n",
    "print(df[\"price_usd_per_m2\"].describe())\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"price_usd_per_m2_zscore\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "630\n"
     ]
    }
   ],
   "source": [
    "# Variable: PRICE_USD_PER_M2 - Segunda pasada para limpiar extremos por grupo y zonas\n",
    "# transformamos en zscore por state_name, place_name y property_type la variable \"price_usd_per_m2\"\n",
    "df[\"price_usd_per_m2_zscore\"] = (df.groupby(['state_name','place_name','property_type'])[[\"price_usd_per_m2\"]]\n",
    ".apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# y contamos outliers por sobre los 3 desvíos\n",
    "print(df['price_usd_per_m2_zscore'][abs(df[\"price_usd_per_m2_zscore\"]) > 3].count())\n",
    "df.drop(df[abs(df[\"price_usd_per_m2_zscore\"])>3].index,inplace=True)\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"price_usd_per_m2_zscore\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1481\n",
      "\n",
      "count     91429.000\n",
      "mean     209421.556\n",
      "std      199658.183\n",
      "min        4952.450\n",
      "25%       89733.880\n",
      "50%      144000.000\n",
      "75%      255000.000\n",
      "max     2700000.000\n",
      "Name: price_aprox_usd, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df.join(pd.get_dummies(df.property_type, prefix='property_type')).join(pd.get_dummies(df.state_name, prefix='state_name')).join(pd.get_dummies(df.place_name, prefix='place_name'))# Variable: PRICE_APROX_USD\n",
    "# Pasamos los ceros a nan\n",
    "df[\"price_aprox_usd\"] = np.where(df[\"price_aprox_usd\"] == 0, np.nan,df[\"price_aprox_usd\"])\n",
    "# Calculamos Zscore\n",
    "#df[\"price_aprox_usd_zscore\"] = (df[\"price_aprox_usd\"] - df[\"price_aprox_usd\"].mean()) / df[\"price_aprox_usd\"].std()\n",
    "df[\"price_aprox_usd_zscore\"] = (df.groupby(['property_type'])[[\"price_aprox_usd\"]]\n",
    ".apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# Contamos outliers\n",
    "print(df[\"price_aprox_usd_zscore\"][abs(df[\"price_aprox_usd_zscore\"]) > 3].count())\n",
    "# Dropeamos las filas con outliers\n",
    "df.drop(df[\"price_aprox_usd\"][abs(df['price_aprox_usd_zscore']) > 3].index,inplace=True)\n",
    "print(\"\")\n",
    "print(df[\"price_aprox_usd\"].describe())\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"price_aprox_usd_zscore\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1640\n"
     ]
    }
   ],
   "source": [
    "# Variable: PRICE_APROX_USD - Segunda pasada para limpiar extremos por grupo\n",
    "# transformamos en zscore por state_name, place_name y property_type la variable \"price_usd_per_m2\"\n",
    "df[\"price_aprox_usd_zscore\"] = (df.groupby(['state_name','place_name','property_type'])[[\"price_aprox_usd\"]]\n",
    ".apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# y contamos outliers por sobre los 3 desvíos\n",
    "print(df['price_aprox_usd'][abs(df[\"price_aprox_usd_zscore\"]) > 3].count())\n",
    "df.drop(df[abs(df[\"price_aprox_usd_zscore\"])>3].index,inplace=True)\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"price_aprox_usd_zscore\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137\n",
      "\n",
      "count   70327.000\n",
      "mean      194.109\n",
      "std       324.235\n",
      "min        10.000\n",
      "25%        52.000\n",
      "50%        87.000\n",
      "75%       208.000\n",
      "max      6168.000\n",
      "Name: surface_total_in_m2, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Variable: SURFACE_TOTAL_IN_M2\n",
    "# Pasamos los ceros a nan\n",
    "df[\"surface_total_in_m2\"] = np.where(df[\"surface_total_in_m2\"] == 0, np.nan,df[\"surface_total_in_m2\"])\n",
    "# Calculamos zscore\n",
    "#df[\"surface_total_in_m2_zscore\"] = (df[\"surface_total_in_m2\"] - df[\"surface_total_in_m2\"].mean()) / df[\"surface_total_in_m2\"].std()\n",
    "df[\"surface_total_in_m2_zscore\"] = (df.groupby(['property_type'])[[\"surface_total_in_m2\"]]\n",
    ".apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# Contamos outliers\n",
    "print(df[\"surface_total_in_m2_zscore\"][abs(df[\"surface_total_in_m2_zscore\"]) > 3].count())\n",
    "# Dropeamos las filas con outliers\n",
    "df.drop(df[\"surface_total_in_m2\"][abs(df['surface_total_in_m2_zscore']) > 3].index,inplace=True)\n",
    "print(\"\")\n",
    "print(df[\"surface_total_in_m2\"].describe())\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"surface_total_in_m2_zscore\"], inplace=True)\n",
    "# Como nos vienen muchos valores entre 1 y 25, asumimos es fallido por lo que dropeamos filas < a 25 m2\n",
    "df.drop(df[\"surface_total_in_m2\"][df[\"surface_total_in_m2\"] < 25].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1122\n"
     ]
    }
   ],
   "source": [
    "# Variable: surface_total_in_m2 - Segunda pasada para limpiar extremos por grupo\n",
    "# transformamos en zscore por state_name, place_name y property_type la variable \"price_usd_per_m2\"\n",
    "df[\"surface_total_in_m2_zscore\"] = (df.groupby(['state_name','place_name','property_type'])[[\"surface_total_in_m2\"]]\n",
    ".apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# y contamos outliers por sobre los 3 desvíos\n",
    "print(df['surface_total_in_m2'][abs(df[\"surface_total_in_m2_zscore\"]) > 3].count())\n",
    "df.drop(df[abs(df[\"surface_total_in_m2_zscore\"])>3].index,inplace=True)\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"surface_total_in_m2_zscore\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75\n",
      "\n",
      "count   87454.000\n",
      "mean      117.376\n",
      "std       117.747\n",
      "min         1.000\n",
      "25%        46.000\n",
      "50%        75.000\n",
      "75%       150.000\n",
      "max      2700.000\n",
      "Name: surface_covered_in_m2, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Variable SURFACE_COVERED_IN_M2\n",
    "# Pasamos los ceros a nan\n",
    "df[\"surface_covered_in_m2\"] = np.where(df[\"surface_covered_in_m2\"] == 0, np.nan,df[\"surface_covered_in_m2\"])\n",
    "# Calculamos zscore\n",
    "#df[\"surface_covered_in_m2_zscore\"] = (df[\"surface_covered_in_m2\"] - df[\"surface_covered_in_m2\"].mean()) / df[\"surface_covered_in_m2\"].std()\n",
    "df[\"surface_covered_in_m2_zscore\"] = (df.groupby(['property_type'])[[\"surface_covered_in_m2\"]]\n",
    ".apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# Contamos outliers\n",
    "print(df[\"surface_covered_in_m2_zscore\"][abs(df[\"surface_covered_in_m2_zscore\"]) > 3].count())\n",
    "# Dropeamos las filas con outliers\n",
    "df.drop(df[\"surface_covered_in_m2\"][abs(df['surface_covered_in_m2_zscore']) > 3].index,inplace=True)\n",
    "print(\"\")\n",
    "print(df[\"surface_covered_in_m2\"].describe())\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"surface_covered_in_m2_zscore\"], inplace=True)\n",
    "# Como nos vienen muchos valores entre 1 y 25, asumimos es fallido por lo que dropeamos filas < a 25 m2\n",
    "df.drop(df[\"surface_covered_in_m2\"][df[\"surface_covered_in_m2\"] < 25].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1038\n"
     ]
    }
   ],
   "source": [
    "# Variable: surface_covered_in_m2 - Segunda pasada para limpiar extremos por grupo\n",
    "# transformamos en zscore por state_name, place_name y property_type la variable \"price_usd_per_m2\"\n",
    "df[\"surface_covered_in_m2_zscore\"] = (df.groupby(['state_name','place_name','property_type'])[[\"surface_covered_in_m2\"]]\n",
    ".apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# y contamos outliers por sobre los 3 desvíos\n",
    "print(df['surface_covered_in_m2'][abs(df[\"surface_covered_in_m2_zscore\"]) > 3].count())\n",
    "df.drop(df[abs(df[\"surface_covered_in_m2_zscore\"])>3].index,inplace=True)\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"surface_covered_in_m2_zscore\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "382\n",
      "\n",
      "count   39630.000\n",
      "mean        2.972\n",
      "std         1.498\n",
      "min         1.000\n",
      "25%         2.000\n",
      "50%         3.000\n",
      "75%         4.000\n",
      "max        10.000\n",
      "Name: rooms, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Variable rooms\n",
    "# Pasamos los ceros a nan\n",
    "df[\"rooms\"] = np.where(df[\"rooms\"] == 0, np.nan,df[\"rooms\"])\n",
    "# Calculamos zscore\n",
    "#df[\"rooms_zscore\"] = (df[\"rooms\"] - df[\"rooms\"].mean()) / df[\"rooms\"].std()\n",
    "df[\"rooms_zscore\"] = (df.groupby(['property_type'])[[\"rooms\"]]\n",
    ".apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# Contamos outliers\n",
    "print(df[\"rooms_zscore\"][abs(df[\"rooms_zscore\"]) > 3].count())\n",
    "# Dropeamos las filas con outliers\n",
    "df.drop(df[\"rooms\"][abs(df['rooms_zscore']) > 3].index,inplace=True)\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"rooms_zscore\"], inplace=True)\n",
    "# Como nos vienen muchos valores altos, asumimos es fallido por lo que dropeamos filas > a 11\n",
    "df.drop(df[\"rooms\"][df[\"rooms\"] > 10].index,inplace=True)\n",
    "print(\"\")\n",
    "print(df[\"rooms\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97\n",
      "\n",
      "count    12511.000\n",
      "mean      2778.704\n",
      "std       5824.894\n",
      "min          1.000\n",
      "25%       1000.000\n",
      "50%       1900.000\n",
      "75%       3700.000\n",
      "max     380000.000\n",
      "Name: expenses, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Variable expenses\n",
    "# Pasamos los ceros a nan\n",
    "df[\"expenses\"] = np.where(df[\"expenses\"] == 0, np.nan,df[\"expenses\"])\n",
    "# Calculamos zscore\n",
    "#df[\"expenses_zscore\"] = (df[\"expenses\"] - df[\"expenses\"].mean()) / df[\"expenses\"].std()\n",
    "df[\"expenses_zscore\"] = (df.groupby(['state_name','property_type'])[[\"expenses\"]]\n",
    ".apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# Contamos outliers\n",
    "print(df[\"expenses_zscore\"][abs(df[\"expenses_zscore\"]) > 3].count())\n",
    "# Dropeamos las filas con outliers\n",
    "df.drop(df[\"expenses\"][abs(df['expenses_zscore']) > 3].index,inplace=True)\n",
    "print(\"\")\n",
    "print(df[\"expenses\"].describe())\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"expenses_zscore\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83\n",
      "\n",
      "count   6644.000\n",
      "mean       4.317\n",
      "std        4.278\n",
      "min        1.000\n",
      "25%        2.000\n",
      "50%        3.000\n",
      "75%        6.000\n",
      "max       54.000\n",
      "Name: floor, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Variable floor\n",
    "# Pasamos los ceros a nan\n",
    "df[\"floor\"] = np.where(df[\"floor\"] == 0, np.nan,df[\"floor\"])\n",
    "# Calculamos zscore\n",
    "df[\"floor_zscore\"] = (df[\"floor\"] - df[\"floor\"].mean()) / df[\"floor\"].std()\n",
    "#df[\"floor_zscore\"] = (df.groupby(['state_name'])[[\"floor\"]]\n",
    "#.apply(lambda x: (x - x.mean()) / x.std()))\n",
    "# Contamos outliers\n",
    "print(df[\"floor_zscore\"][abs(df[\"floor_zscore\"]) > 3].count())\n",
    "# Dropeamos las filas con outliers\n",
    "df.drop(df[\"floor\"][abs(df['floor_zscore']) > 3].index,inplace=True)\n",
    "# dropeamos la columna temporal zscore\n",
    "df.drop(columns=[\"floor_zscore\"], inplace=True)\n",
    "# Como nos vienen muchos valores altos, asumimos es fallido por lo que dropeamos filas > a 55 (edif + alto Arg)\n",
    "df.drop(df['floor'][df[\"floor\"] > 55].index,inplace=True)\n",
    "print(\"\")\n",
    "print(df[\"floor\"].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolvemos los faltantes de \"place_name\" (25 casos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rellenamos los valores faltantes en \"place_name\" parseando el campo \"place_with_parent_names\"\n",
    "df[\"place_name\"].fillna(df['place_with_parent_names'].str.extract('^\\|[^|]+\\|[^|]+\\|([^|]+)\\|', expand=True)[0],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descartamos que las otras columnas de precio nos sirvan para imputar superficies donde falten, y que existan datos de precio cuando price_aprox_usd sea nula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['surface_total_in_m2'][(df['surface_total_in_m2'].isnull() & df['price_aprox_usd'].notna() & df['price_usd_per_m2'].notna())].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hay datos de precio x m2 en filas que no tengan datos de superficie?\n",
    "df[\"price_usd_per_m2\"].loc[(df[\"surface_total_in_m2\"].isnull() & df[\"surface_covered_in_m2\"].isnull())].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price_per_m2\"].loc[(df[\"surface_total_in_m2\"].isnull() & df[\"surface_covered_in_m2\"].isnull())].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hay datos de precio en moneda original para los campos en null en \"price_aprox_usd\"?\n",
    "df[\"price\"].loc[df[\"price_aprox_usd\"].isnull()].count()\n",
    "#print(df[\"price_aprox_local_currency\"].loc[df[\"price_aprox_usd\"].isnull()].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limpiamos y rellenamos variable de Superficie - Imputamos \"surface_covered_in_m2\" a \"surface_total_in_m2\" donde la segunda es nula pero hay datos en la primera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180.5609178736814"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# la media de superficie antes de imputar\n",
    "df[\"surface_total_in_m2\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rellenamos\n",
    "df[\"surface_total_in_m2\"] = \\\n",
    "np.where(df[\"surface_total_in_m2\"].isnull(),df[\"surface_covered_in_m2\"],df[\"surface_total_in_m2\"])\n",
    "# limpiamos valores en cero y los pasamos a nan\n",
    "df[\"surface_total_in_m2\"] = np.where(df[\"surface_total_in_m2\"] == 0, np.nan,df[\"surface_total_in_m2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "166.45709861414375"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# la media de superficie despues de imputar\n",
    "df[\"surface_total_in_m2\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chequeamos que la imputación haya sido completa\n",
    "df[\"surface_covered_in_m2\"].loc[df[\"surface_total_in_m2\"].isnull()].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11747"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conteo cuantos m2 en nulos\n",
    "df[\"surface_total_in_m2\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.0\n",
      "7229\n",
      "7230\n",
      "7230\n"
     ]
    }
   ],
   "source": [
    "# Controlamos si en los casos donde no existe el dato de superficie si existen datos de precio\n",
    "print(df[\"price_per_m2\"][df[\"surface_total_in_m2\"].isnull()].sum())\n",
    "print(df[\"price_usd_per_m2\"][df[\"surface_total_in_m2\"].isnull()].sum())\n",
    "print(df[\"price_aprox_usd\"][df[\"surface_total_in_m2\"].isnull()].count())\n",
    "print(df[\"price_aprox_local_currency\"][df[\"surface_total_in_m2\"].isnull()].count())\n",
    "print(df[\"price\"][df[\"surface_total_in_m2\"].isnull()].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropeamos todas las filas que no tienen datos de superficie - 12mil filas\n",
    "df = df[df[\"surface_total_in_m2\"].notna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parseamos el campo \"description\" para rescatar datos de ambientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13436\n"
     ]
    }
   ],
   "source": [
    "# en busca de cantidad de dormitorios\n",
    "df[\"dorm_en_desc\"] = df['description'].str.extract('(\\d+?) (dormitorios|dormitorio)')[0]\n",
    "# contamos cuantos datos ganamos\n",
    "print(df[\"dorm_en_desc\"].loc[df[\"rooms\"].isnull()].count())\n",
    "# imputamos sumando 1 para transformar dormitorios en ambientes\n",
    "df[\"rooms\"] = np.where(df[\"rooms\"].isnull(),pd.to_numeric(df[\"dorm_en_desc\"]) + 1,df[\"rooms\"])\n",
    "# dropeamos la columna intermedia\n",
    "df.drop(columns=[\"dorm_en_desc\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6101\n"
     ]
    }
   ],
   "source": [
    "# en busca de cantidad de ambientes\n",
    "df[\"amb_en_desc\"] = df['description'].str.extract('(\\d+?) (ambientes|ambiente)')[0]\n",
    "# contamos cuantos datos ganamos\n",
    "print(df[\"amb_en_desc\"].loc[df[\"rooms\"].isnull()].count())\n",
    "# imputamos suma\n",
    "df[\"rooms\"] = np.where(df[\"rooms\"].isnull(),pd.to_numeric(df[\"amb_en_desc\"]),df[\"rooms\"])\n",
    "# dropeamos la columna intermedia\n",
    "df.drop(columns=[\"amb_en_desc\"], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parseamos el campo \"title\" para rescatar datos de superficie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "446\n"
     ]
    }
   ],
   "source": [
    "# en busca de cantidad de dormitorios\n",
    "df[\"dorm_en_tit\"] = df['title'].str.extract('(\\d+?) (dormitorios|dormitorio)')[0]\n",
    "# y contamos cuantos datos ganamos\n",
    "print(df[\"dorm_en_tit\"].loc[df[\"rooms\"].isnull()].count())\n",
    "# imputamos sumando 1 para transformar dormitorios en ambientes\n",
    "df[\"rooms\"] = np.where(df[\"rooms\"].isnull(),pd.to_numeric(df[\"dorm_en_tit\"]) + 1,df[\"rooms\"])\n",
    "# dropeamos la columna intermedia\n",
    "df.drop(columns=[\"dorm_en_tit\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "776\n"
     ]
    }
   ],
   "source": [
    "# en busca de cantidad de ambientes\n",
    "df[\"amb_en_tit\"] = df['title'].str.extract('(\\d+?) (ambientes|ambiente)')[0]\n",
    "# contamos cuantos datos ganamos\n",
    "print(df[\"amb_en_tit\"].loc[df[\"rooms\"].isnull()].count())\n",
    "# imputamos\n",
    "df[\"rooms\"] = np.where(df[\"rooms\"].isnull(),pd.to_numeric(df[\"amb_en_tit\"]),df[\"rooms\"])\n",
    "# dropeamos la columna intermedia\n",
    "df.drop(columns=[\"amb_en_tit\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Volvemos a limpiar el máximo de 10 ambientes para la variable rooms\n",
    "df.drop(df[\"rooms\"][df[\"rooms\"] > 10].index,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rellenamos faltantes la variable price usd per m2 cuando hayan datos de precio total y superficie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31878\n",
      "11797\n"
     ]
    }
   ],
   "source": [
    "# recalcular el precio usd por m2 donde este sea null\n",
    "print(df[\"price_usd_per_m2\"].isnull().sum()) # nulos antes\n",
    "df[\"price_usd_per_m2\"] = np.where(df[\"price_usd_per_m2\"].isnull(),np.divide(df[\"price_aprox_usd\"],df[\"surface_total_in_m2\"]),df[\"price_usd_per_m2\"])\n",
    "print(df[\"price_usd_per_m2\"].isnull().sum()) # nulos despues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolvemos los valores faltantes del campo \"price_usd_per_m2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# imputar a los valores faltantes de price_usd_per_m2 con el promedio por provincia, localidad/barrio y tipo de propiedad\n",
    "df[\"price_usd_per_m2\"].fillna(df.groupby(['state_name','property_type'])['price_usd_per_m2'].transform('median'),inplace=True)\n",
    "#df.groupby(['state_name','place_name','property_type'])[['price_usd_per_m2']].transform('median').sort_values('price_usd_per_m2',ascending=True)\n",
    "#df.sort_values('price_usd_per_m2',ascending=False)\n",
    "# dropear remanente que no pudo ser rellenado con promedios\n",
    "df.drop(df[df[\"price_usd_per_m2\"].isnull()].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rellenar precios totales en USD faltantes\n",
    "df['price_aprox_usd'] = np.where(df['price_aprox_usd'].isnull(),df['price_usd_per_m2'] * df['surface_total_in_m2'],df['price_aprox_usd'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación nueva variable Zona recategorizando state_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Capital Federal', 'Buenos Aires Costa Atlántica', 'Entre Ríos',\n",
       "       'Bs.As. G.B.A. Zona Norte', 'Córdoba', 'Bs.As. G.B.A. Zona Oeste',\n",
       "       'Bs.As. G.B.A. Zona Sur', 'Misiones', 'Santa Fe',\n",
       "       'Buenos Aires Interior', 'Neuquén', 'Río Negro', 'San Luis',\n",
       "       'Mendoza', 'Corrientes', 'Chubut', 'Tucumán', 'Chaco', 'Salta',\n",
       "       'San Juan', 'Tierra Del Fuego', 'Santa Cruz', 'La Pampa',\n",
       "       'Santiago Del Estero', 'Catamarca', 'Jujuy', 'La Rioja', 'Formosa'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['state_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Capital & GBA    63132\n",
       "Interior         26589\n",
       "Name: zona, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reemplazos valores y creamos nueva variable\n",
    "df['zona'] = df['state_name'].replace({'Capital Federal':'Capital & GBA', 'Buenos Aires Costa Atlántica':'Interior', 'Entre Ríos':'Interior', 'Bs.As. G.B.A. Zona Norte':'Capital & GBA', 'Córdoba':'Interior', 'Bs.As. G.B.A. Zona Oeste':'Capital & GBA', 'Bs.As. G.B.A. Zona Sur':'Capital & GBA', 'Misiones':'Interior', 'Santa Fe':'Interior', 'Buenos Aires Interior':'Interior', 'Neuquén':'Interior', 'Río Negro':'Interior', 'San Luis':'Interior', 'Mendoza':'Interior', 'Corrientes':'Interior', 'Chubut':'Interior', 'Tucumán':'Interior', 'Chaco':'Interior', 'Salta':'Interior', 'San Juan':'Interior', 'Tierra Del Fuego':'Interior', 'Santa Cruz':'Interior', 'La Pampa':'Interior', 'Santiago Del Estero':'Interior', 'Catamarca':'Interior', 'Jujuy':'Interior', 'La Rioja':'Interior', 'Formosa':'Interior'})\n",
    "df['zona'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limpieza final y evaluación del dataset resultante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nos quedamos solo con los precios en USD\n",
    "df.drop(columns=[\"price\",\"price_aprox_local_currency\",\"price_per_m2\",\"currency\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limpiamos el Dataset de Columnas innecesarias\n",
    "df.drop(columns=[\"Unnamed: 0\",\"operation\",\"place_with_parent_names\",\"country_name\",\"lat-lon\",\"image_thumbnail\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zona                     89721\n",
       "price_usd_per_m2         89721\n",
       "place_name               89721\n",
       "state_name               89721\n",
       "price_aprox_usd          89721\n",
       "surface_total_in_m2      89721\n",
       "title                    89721\n",
       "property_type            89721\n",
       "properati_url            89721\n",
       "description              89720\n",
       "surface_covered_in_m2    83887\n",
       "geonames_id              74770\n",
       "rooms                    56308\n",
       "lon                      51889\n",
       "lat                      51889\n",
       "expenses                 12069\n",
       "floor                     6216\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mapeamos los valores válidos con los que nos quedamos\n",
    "df.notna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>geonames_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>price_aprox_usd</th>\n",
       "      <th>surface_total_in_m2</th>\n",
       "      <th>surface_covered_in_m2</th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "      <th>floor</th>\n",
       "      <th>rooms</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>74770.000</td>\n",
       "      <td>51889.000</td>\n",
       "      <td>51889.000</td>\n",
       "      <td>89721.000</td>\n",
       "      <td>89721.000</td>\n",
       "      <td>83887.000</td>\n",
       "      <td>89721.000</td>\n",
       "      <td>6216.000</td>\n",
       "      <td>56308.000</td>\n",
       "      <td>12069.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3561684.280</td>\n",
       "      <td>-34.730</td>\n",
       "      <td>-59.076</td>\n",
       "      <td>208761.667</td>\n",
       "      <td>166.431</td>\n",
       "      <td>116.192</td>\n",
       "      <td>1773.945</td>\n",
       "      <td>4.277</td>\n",
       "      <td>3.013</td>\n",
       "      <td>2802.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>367957.304</td>\n",
       "      <td>1.841</td>\n",
       "      <td>2.053</td>\n",
       "      <td>202348.918</td>\n",
       "      <td>242.566</td>\n",
       "      <td>110.208</td>\n",
       "      <td>932.112</td>\n",
       "      <td>4.227</td>\n",
       "      <td>1.308</td>\n",
       "      <td>5912.947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3427208.000</td>\n",
       "      <td>-54.820</td>\n",
       "      <td>-71.665</td>\n",
       "      <td>1323.332</td>\n",
       "      <td>25.000</td>\n",
       "      <td>25.000</td>\n",
       "      <td>8.062</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3430234.000</td>\n",
       "      <td>-34.666</td>\n",
       "      <td>-58.690</td>\n",
       "      <td>92000.000</td>\n",
       "      <td>52.000</td>\n",
       "      <td>47.000</td>\n",
       "      <td>1156.069</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3433775.000</td>\n",
       "      <td>-34.599</td>\n",
       "      <td>-58.473</td>\n",
       "      <td>145000.000</td>\n",
       "      <td>85.000</td>\n",
       "      <td>76.000</td>\n",
       "      <td>1666.667</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1900.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3436397.000</td>\n",
       "      <td>-34.473</td>\n",
       "      <td>-58.395</td>\n",
       "      <td>250000.000</td>\n",
       "      <td>190.000</td>\n",
       "      <td>150.000</td>\n",
       "      <td>2300.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>3800.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6948895.000</td>\n",
       "      <td>-23.137</td>\n",
       "      <td>-54.556</td>\n",
       "      <td>7664487.633</td>\n",
       "      <td>6168.000</td>\n",
       "      <td>2700.000</td>\n",
       "      <td>16666.667</td>\n",
       "      <td>54.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>380000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       geonames_id       lat       lon  price_aprox_usd  surface_total_in_m2  \\\n",
       "count    74770.000 51889.000 51889.000        89721.000            89721.000   \n",
       "mean   3561684.280   -34.730   -59.076       208761.667              166.431   \n",
       "std     367957.304     1.841     2.053       202348.918              242.566   \n",
       "min    3427208.000   -54.820   -71.665         1323.332               25.000   \n",
       "25%    3430234.000   -34.666   -58.690        92000.000               52.000   \n",
       "50%    3433775.000   -34.599   -58.473       145000.000               85.000   \n",
       "75%    3436397.000   -34.473   -58.395       250000.000              190.000   \n",
       "max    6948895.000   -23.137   -54.556      7664487.633             6168.000   \n",
       "\n",
       "       surface_covered_in_m2  price_usd_per_m2    floor     rooms   expenses  \n",
       "count              83887.000         89721.000 6216.000 56308.000  12069.000  \n",
       "mean                 116.192          1773.945    4.277     3.013   2802.150  \n",
       "std                  110.208           932.112    4.227     1.308   5912.947  \n",
       "min                   25.000             8.062    1.000     1.000      1.000  \n",
       "25%                   47.000          1156.069    2.000     2.000   1000.000  \n",
       "50%                   76.000          1666.667    3.000     3.000   1900.000  \n",
       "75%                  150.000          2300.000    6.000     4.000   3800.000  \n",
       "max                 2700.000         16666.667   54.000    10.000 380000.000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>price_usd_per_m2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capital Federal</th>\n",
       "      <td>2483.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catamarca</th>\n",
       "      <td>1890.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bs.As. G.B.A. Zona Norte</th>\n",
       "      <td>1775.907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Río Negro</th>\n",
       "      <td>1637.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Juan</th>\n",
       "      <td>1566.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neuquén</th>\n",
       "      <td>1549.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buenos Aires Costa Atlántica</th>\n",
       "      <td>1545.373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chubut</th>\n",
       "      <td>1531.872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formosa</th>\n",
       "      <td>1512.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santa Fe</th>\n",
       "      <td>1480.111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              price_usd_per_m2\n",
       "state_name                                    \n",
       "Capital Federal                       2483.071\n",
       "Catamarca                             1890.291\n",
       "Bs.As. G.B.A. Zona Norte              1775.907\n",
       "Río Negro                             1637.077\n",
       "San Juan                              1566.091\n",
       "Neuquén                               1549.860\n",
       "Buenos Aires Costa Atlántica          1545.373\n",
       "Chubut                                1531.872\n",
       "Formosa                               1512.175\n",
       "Santa Fe                              1480.111"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['state_name'])[['price_usd_per_m2']].mean().sort_values(by=['price_usd_per_m2'],ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_name</th>\n",
       "      <th>place_name</th>\n",
       "      <th>property_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Buenos Aires Interior</th>\n",
       "      <th>Pergamino</th>\n",
       "      <th>store</th>\n",
       "      <td>10000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Córdoba</th>\n",
       "      <th>Cosquín</th>\n",
       "      <th>store</th>\n",
       "      <td>7500.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capital Federal</th>\n",
       "      <th>Puerto Madero</th>\n",
       "      <th>store</th>\n",
       "      <td>5517.406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>La Pampa</th>\n",
       "      <th>Perú</th>\n",
       "      <th>apartment</th>\n",
       "      <td>5200.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Córdoba</th>\n",
       "      <th>Oliva</th>\n",
       "      <th>house</th>\n",
       "      <td>5196.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capital Federal</th>\n",
       "      <th>Puerto Madero</th>\n",
       "      <th>apartment</th>\n",
       "      <td>5194.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bs.As. G.B.A. Zona Norte</th>\n",
       "      <th>Barrio Los Alisos</th>\n",
       "      <th>store</th>\n",
       "      <td>5050.505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catamarca</th>\n",
       "      <th>Manantiales</th>\n",
       "      <th>apartment</th>\n",
       "      <td>4964.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mendoza</th>\n",
       "      <th>Capilla del Rosario</th>\n",
       "      <th>house</th>\n",
       "      <td>4375.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bs.As. G.B.A. Zona Norte</th>\n",
       "      <th>Pilar Golf Country Club</th>\n",
       "      <th>apartment</th>\n",
       "      <td>3753.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capital Federal</th>\n",
       "      <th>Palermo Chico</th>\n",
       "      <th>apartment</th>\n",
       "      <td>3702.818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bs.As. G.B.A. Zona Norte</th>\n",
       "      <th>Nordelta</th>\n",
       "      <th>store</th>\n",
       "      <td>3645.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capital Federal</th>\n",
       "      <th>Palermo</th>\n",
       "      <th>store</th>\n",
       "      <td>3637.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bs.As. G.B.A. Zona Sur</th>\n",
       "      <th>GreenVille Polo &amp; Resort</th>\n",
       "      <th>apartment</th>\n",
       "      <td>3620.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capital Federal</th>\n",
       "      <th>Recoleta</th>\n",
       "      <th>store</th>\n",
       "      <td>3582.630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 price_usd_per_m2\n",
       "state_name               place_name               property_type                  \n",
       "Buenos Aires Interior    Pergamino                store                 10000.000\n",
       "Córdoba                  Cosquín                  store                  7500.000\n",
       "Capital Federal          Puerto Madero            store                  5517.406\n",
       "La Pampa                 Perú                     apartment              5200.000\n",
       "Córdoba                  Oliva                    house                  5196.633\n",
       "Capital Federal          Puerto Madero            apartment              5194.173\n",
       "Bs.As. G.B.A. Zona Norte Barrio Los Alisos        store                  5050.505\n",
       "Catamarca                Manantiales              apartment              4964.115\n",
       "Mendoza                  Capilla del Rosario      house                  4375.000\n",
       "Bs.As. G.B.A. Zona Norte Pilar Golf Country Club  apartment              3753.521\n",
       "Capital Federal          Palermo Chico            apartment              3702.818\n",
       "Bs.As. G.B.A. Zona Norte Nordelta                 store                  3645.375\n",
       "Capital Federal          Palermo                  store                  3637.016\n",
       "Bs.As. G.B.A. Zona Sur   GreenVille Polo & Resort apartment              3620.968\n",
       "Capital Federal          Recoleta                 store                  3582.630"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['state_name','place_name','property_type'])[['price_usd_per_m2']].mean().sort_values(by=['price_usd_per_m2'],ascending=False).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>property_type</th>\n",
       "      <th>place_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>geonames_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>price_aprox_usd</th>\n",
       "      <th>surface_total_in_m2</th>\n",
       "      <th>surface_covered_in_m2</th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "      <th>...</th>\n",
       "      <th>place_name_Virrey del Pino</th>\n",
       "      <th>place_name_Virreyes</th>\n",
       "      <th>place_name_Vistalba</th>\n",
       "      <th>place_name_Wenceslao Escalante</th>\n",
       "      <th>place_name_Wilde</th>\n",
       "      <th>place_name_William Morris</th>\n",
       "      <th>place_name_Yerba Buena</th>\n",
       "      <th>place_name_Zelaya</th>\n",
       "      <th>place_name_Zárate</th>\n",
       "      <th>place_name_coordenadas 34.255511</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PH</td>\n",
       "      <td>Mataderos</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3430787.000</td>\n",
       "      <td>-34.662</td>\n",
       "      <td>-58.509</td>\n",
       "      <td>62000.000</td>\n",
       "      <td>55.000</td>\n",
       "      <td>40.000</td>\n",
       "      <td>1127.273</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Mataderos</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3430787.000</td>\n",
       "      <td>-34.652</td>\n",
       "      <td>-58.523</td>\n",
       "      <td>72000.000</td>\n",
       "      <td>55.000</td>\n",
       "      <td>55.000</td>\n",
       "      <td>1309.091</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Buenos Aires Costa Atlántica</td>\n",
       "      <td>3435548.000</td>\n",
       "      <td>-38.003</td>\n",
       "      <td>-57.549</td>\n",
       "      <td>64000.000</td>\n",
       "      <td>35.000</td>\n",
       "      <td>35.000</td>\n",
       "      <td>1828.571</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>house</td>\n",
       "      <td>Gualeguaychú</td>\n",
       "      <td>Entre Ríos</td>\n",
       "      <td>3433657.000</td>\n",
       "      <td>-33.014</td>\n",
       "      <td>-58.520</td>\n",
       "      <td>29724.348</td>\n",
       "      <td>53.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>560.837</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PH</td>\n",
       "      <td>Munro</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3430511.000</td>\n",
       "      <td>-34.533</td>\n",
       "      <td>-58.522</td>\n",
       "      <td>130000.000</td>\n",
       "      <td>106.000</td>\n",
       "      <td>78.000</td>\n",
       "      <td>1226.415</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Belgrano</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3436077.000</td>\n",
       "      <td>-34.560</td>\n",
       "      <td>-58.443</td>\n",
       "      <td>138000.000</td>\n",
       "      <td>45.000</td>\n",
       "      <td>40.000</td>\n",
       "      <td>3066.667</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Belgrano</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3436077.000</td>\n",
       "      <td>-34.560</td>\n",
       "      <td>-58.443</td>\n",
       "      <td>195000.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>house</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860259.000</td>\n",
       "      <td>-31.420</td>\n",
       "      <td>-64.189</td>\n",
       "      <td>70000.000</td>\n",
       "      <td>1514.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>46.235</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Martínez</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3430813.000</td>\n",
       "      <td>-34.486</td>\n",
       "      <td>-58.504</td>\n",
       "      <td>115000.000</td>\n",
       "      <td>39.000</td>\n",
       "      <td>36.000</td>\n",
       "      <td>2948.718</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Palermo Soho</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3430234.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>111700.000</td>\n",
       "      <td>50.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>2234.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Palermo Soho</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3430234.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>147900.000</td>\n",
       "      <td>42.000</td>\n",
       "      <td>31.000</td>\n",
       "      <td>3521.429</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>house</td>\n",
       "      <td>Bella Vista</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3436061.000</td>\n",
       "      <td>-34.588</td>\n",
       "      <td>-58.699</td>\n",
       "      <td>250000.000</td>\n",
       "      <td>164.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1524.390</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PH</td>\n",
       "      <td>Mataderos</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3430787.000</td>\n",
       "      <td>-34.652</td>\n",
       "      <td>-58.502</td>\n",
       "      <td>239000.000</td>\n",
       "      <td>140.000</td>\n",
       "      <td>98.000</td>\n",
       "      <td>1707.143</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Barrio El Golf</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>nan</td>\n",
       "      <td>-34.409</td>\n",
       "      <td>-58.634</td>\n",
       "      <td>315000.000</td>\n",
       "      <td>176.000</td>\n",
       "      <td>78.000</td>\n",
       "      <td>1789.773</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>house</td>\n",
       "      <td>Tigre</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3427753.000</td>\n",
       "      <td>-34.425</td>\n",
       "      <td>-58.580</td>\n",
       "      <td>570000.000</td>\n",
       "      <td>270.000</td>\n",
       "      <td>270.000</td>\n",
       "      <td>2111.111</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Palermo</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3430234.000</td>\n",
       "      <td>-34.581</td>\n",
       "      <td>-58.406</td>\n",
       "      <td>350000.000</td>\n",
       "      <td>104.000</td>\n",
       "      <td>96.000</td>\n",
       "      <td>3365.385</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>house</td>\n",
       "      <td>Villa Giardino</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3832729.000</td>\n",
       "      <td>-31.053</td>\n",
       "      <td>-64.499</td>\n",
       "      <td>119711.480</td>\n",
       "      <td>148.000</td>\n",
       "      <td>148.000</td>\n",
       "      <td>808.861</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Palermo</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3430234.000</td>\n",
       "      <td>-34.591</td>\n",
       "      <td>-58.412</td>\n",
       "      <td>270500.000</td>\n",
       "      <td>118.000</td>\n",
       "      <td>73.000</td>\n",
       "      <td>2292.373</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>house</td>\n",
       "      <td>General Rodríguez</td>\n",
       "      <td>Bs.As. G.B.A. Zona Oeste</td>\n",
       "      <td>3433780.000</td>\n",
       "      <td>-34.650</td>\n",
       "      <td>-58.966</td>\n",
       "      <td>290000.000</td>\n",
       "      <td>200.000</td>\n",
       "      <td>200.000</td>\n",
       "      <td>1450.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Pilar</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>82000.000</td>\n",
       "      <td>48.000</td>\n",
       "      <td>42.000</td>\n",
       "      <td>1708.333</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>house</td>\n",
       "      <td>Barrio San Gabriel</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>nan</td>\n",
       "      <td>-34.364</td>\n",
       "      <td>-58.690</td>\n",
       "      <td>265000.000</td>\n",
       "      <td>230.000</td>\n",
       "      <td>190.000</td>\n",
       "      <td>1152.174</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>house</td>\n",
       "      <td>Estancias del Pilar</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>nan</td>\n",
       "      <td>-34.479</td>\n",
       "      <td>-58.985</td>\n",
       "      <td>650000.000</td>\n",
       "      <td>425.000</td>\n",
       "      <td>425.000</td>\n",
       "      <td>1529.412</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Flores</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3433918.000</td>\n",
       "      <td>-34.635</td>\n",
       "      <td>-58.474</td>\n",
       "      <td>75000.000</td>\n",
       "      <td>43.000</td>\n",
       "      <td>43.000</td>\n",
       "      <td>1744.186</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>house</td>\n",
       "      <td>Pilar</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3429979.000</td>\n",
       "      <td>-34.454</td>\n",
       "      <td>-58.840</td>\n",
       "      <td>178000.000</td>\n",
       "      <td>150.000</td>\n",
       "      <td>150.000</td>\n",
       "      <td>1186.667</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>house</td>\n",
       "      <td>Munro</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3430511.000</td>\n",
       "      <td>-34.542</td>\n",
       "      <td>-58.540</td>\n",
       "      <td>130000.000</td>\n",
       "      <td>207.000</td>\n",
       "      <td>103.000</td>\n",
       "      <td>628.019</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860259.000</td>\n",
       "      <td>-31.405</td>\n",
       "      <td>-64.156</td>\n",
       "      <td>30846.020</td>\n",
       "      <td>50.000</td>\n",
       "      <td>50.000</td>\n",
       "      <td>616.920</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Barrio El Golf</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>nan</td>\n",
       "      <td>-34.409</td>\n",
       "      <td>-58.634</td>\n",
       "      <td>325000.000</td>\n",
       "      <td>186.000</td>\n",
       "      <td>83.000</td>\n",
       "      <td>1747.312</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PH</td>\n",
       "      <td>Munro</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3430511.000</td>\n",
       "      <td>-34.527</td>\n",
       "      <td>-58.513</td>\n",
       "      <td>169000.000</td>\n",
       "      <td>172.000</td>\n",
       "      <td>160.000</td>\n",
       "      <td>982.558</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Boedo</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3436003.000</td>\n",
       "      <td>-34.622</td>\n",
       "      <td>-58.422</td>\n",
       "      <td>145000.000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>77.000</td>\n",
       "      <td>1812.500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>house</td>\n",
       "      <td>Pilar</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>nan</td>\n",
       "      <td>-34.463</td>\n",
       "      <td>-58.902</td>\n",
       "      <td>400000.000</td>\n",
       "      <td>400.000</td>\n",
       "      <td>400.000</td>\n",
       "      <td>1000.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121148</th>\n",
       "      <td>PH</td>\n",
       "      <td>Flores</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3433918.000</td>\n",
       "      <td>-34.630</td>\n",
       "      <td>-58.457</td>\n",
       "      <td>229000.000</td>\n",
       "      <td>185.000</td>\n",
       "      <td>185.000</td>\n",
       "      <td>1237.838</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121149</th>\n",
       "      <td>house</td>\n",
       "      <td>Caballito</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3435874.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>485000.000</td>\n",
       "      <td>195.000</td>\n",
       "      <td>195.000</td>\n",
       "      <td>2487.179</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121150</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Paternal</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3430087.000</td>\n",
       "      <td>-34.609</td>\n",
       "      <td>-58.476</td>\n",
       "      <td>95000.000</td>\n",
       "      <td>52.000</td>\n",
       "      <td>52.000</td>\n",
       "      <td>1826.923</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121151</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Flores</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3433918.000</td>\n",
       "      <td>-34.630</td>\n",
       "      <td>-58.461</td>\n",
       "      <td>120000.000</td>\n",
       "      <td>72.000</td>\n",
       "      <td>72.000</td>\n",
       "      <td>1666.667</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121152</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Caballito</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3435874.000</td>\n",
       "      <td>-34.623</td>\n",
       "      <td>-58.455</td>\n",
       "      <td>74500.000</td>\n",
       "      <td>33.000</td>\n",
       "      <td>33.000</td>\n",
       "      <td>2257.576</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121153</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Barrio Norte</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3436109.000</td>\n",
       "      <td>-34.599</td>\n",
       "      <td>-58.403</td>\n",
       "      <td>165000.000</td>\n",
       "      <td>91.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>1813.187</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121154</th>\n",
       "      <td>house</td>\n",
       "      <td>La Plata</td>\n",
       "      <td>Bs.As. G.B.A. Zona Sur</td>\n",
       "      <td>3432039.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>295000.000</td>\n",
       "      <td>205.000</td>\n",
       "      <td>205.000</td>\n",
       "      <td>1439.024</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121155</th>\n",
       "      <td>house</td>\n",
       "      <td>Pilar</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3429979.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>170000.000</td>\n",
       "      <td>130.000</td>\n",
       "      <td>130.000</td>\n",
       "      <td>1307.692</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121158</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Recoleta</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3429595.000</td>\n",
       "      <td>-34.587</td>\n",
       "      <td>-58.397</td>\n",
       "      <td>165000.000</td>\n",
       "      <td>44.000</td>\n",
       "      <td>39.000</td>\n",
       "      <td>3750.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121159</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860255.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>79807.070</td>\n",
       "      <td>43.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1855.978</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121160</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Belgrano</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3436077.000</td>\n",
       "      <td>-34.564</td>\n",
       "      <td>-58.443</td>\n",
       "      <td>410000.000</td>\n",
       "      <td>157.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>2611.465</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121161</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860255.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>79694.900</td>\n",
       "      <td>40.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1992.372</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121163</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860255.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>79134.060</td>\n",
       "      <td>44.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1798.501</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121164</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860255.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>83957.260</td>\n",
       "      <td>71.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1182.497</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121166</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860255.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>78629.310</td>\n",
       "      <td>49.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1604.680</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121182</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860255.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>79134.060</td>\n",
       "      <td>46.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1720.306</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121189</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860255.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>150192.080</td>\n",
       "      <td>93.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1614.969</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121190</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860255.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>152211.090</td>\n",
       "      <td>100.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1522.111</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121193</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860255.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>104259.550</td>\n",
       "      <td>136.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>766.614</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121196</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>3860255.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>83957.260</td>\n",
       "      <td>52.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1614.563</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121207</th>\n",
       "      <td>house</td>\n",
       "      <td>Escobar</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3434130.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>242727.273</td>\n",
       "      <td>178.000</td>\n",
       "      <td>178.000</td>\n",
       "      <td>1363.636</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121210</th>\n",
       "      <td>house</td>\n",
       "      <td>Martínez</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3430813.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>163636.364</td>\n",
       "      <td>120.000</td>\n",
       "      <td>120.000</td>\n",
       "      <td>1363.636</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121211</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Acassuso</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3436508.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>471500.000</td>\n",
       "      <td>205.000</td>\n",
       "      <td>205.000</td>\n",
       "      <td>2300.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121212</th>\n",
       "      <td>house</td>\n",
       "      <td>Acassuso</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3436508.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>572727.273</td>\n",
       "      <td>420.000</td>\n",
       "      <td>420.000</td>\n",
       "      <td>1363.636</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121213</th>\n",
       "      <td>house</td>\n",
       "      <td>Martínez</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3430813.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>818181.818</td>\n",
       "      <td>600.000</td>\n",
       "      <td>600.000</td>\n",
       "      <td>1363.636</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121214</th>\n",
       "      <td>store</td>\n",
       "      <td>San Isidro</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3428983.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>171211.088</td>\n",
       "      <td>123.000</td>\n",
       "      <td>123.000</td>\n",
       "      <td>1391.960</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121216</th>\n",
       "      <td>house</td>\n",
       "      <td>Beccar</td>\n",
       "      <td>Bs.As. G.B.A. Zona Norte</td>\n",
       "      <td>3436080.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>498000.000</td>\n",
       "      <td>360.000</td>\n",
       "      <td>360.000</td>\n",
       "      <td>1383.333</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121217</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Villa Urquiza</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3433775.000</td>\n",
       "      <td>-34.571</td>\n",
       "      <td>-58.476</td>\n",
       "      <td>131500.000</td>\n",
       "      <td>46.000</td>\n",
       "      <td>39.000</td>\n",
       "      <td>2858.696</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121218</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Plaza Colón</td>\n",
       "      <td>Buenos Aires Costa Atlántica</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>95900.000</td>\n",
       "      <td>48.000</td>\n",
       "      <td>48.000</td>\n",
       "      <td>1997.917</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121219</th>\n",
       "      <td>apartment</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3433955.000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>129000.000</td>\n",
       "      <td>77.000</td>\n",
       "      <td>77.000</td>\n",
       "      <td>1675.325</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89721 rows × 1022 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       property_type           place_name                    state_name  \\\n",
       "0                 PH            Mataderos               Capital Federal   \n",
       "2          apartment            Mataderos               Capital Federal   \n",
       "4          apartment               Centro  Buenos Aires Costa Atlántica   \n",
       "5              house         Gualeguaychú                    Entre Ríos   \n",
       "6                 PH                Munro      Bs.As. G.B.A. Zona Norte   \n",
       "7          apartment             Belgrano               Capital Federal   \n",
       "8          apartment             Belgrano               Capital Federal   \n",
       "10             house              Córdoba                       Córdoba   \n",
       "12         apartment             Martínez      Bs.As. G.B.A. Zona Norte   \n",
       "13         apartment         Palermo Soho               Capital Federal   \n",
       "14         apartment         Palermo Soho               Capital Federal   \n",
       "15             house          Bella Vista      Bs.As. G.B.A. Zona Norte   \n",
       "16                PH            Mataderos               Capital Federal   \n",
       "17         apartment       Barrio El Golf      Bs.As. G.B.A. Zona Norte   \n",
       "18             house                Tigre      Bs.As. G.B.A. Zona Norte   \n",
       "19         apartment              Palermo               Capital Federal   \n",
       "20             house       Villa Giardino                       Córdoba   \n",
       "21         apartment              Palermo               Capital Federal   \n",
       "22             house    General Rodríguez      Bs.As. G.B.A. Zona Oeste   \n",
       "23         apartment                Pilar      Bs.As. G.B.A. Zona Norte   \n",
       "26             house   Barrio San Gabriel      Bs.As. G.B.A. Zona Norte   \n",
       "27             house  Estancias del Pilar      Bs.As. G.B.A. Zona Norte   \n",
       "29         apartment               Flores               Capital Federal   \n",
       "30             house                Pilar      Bs.As. G.B.A. Zona Norte   \n",
       "31             house                Munro      Bs.As. G.B.A. Zona Norte   \n",
       "32         apartment              Córdoba                       Córdoba   \n",
       "33         apartment       Barrio El Golf      Bs.As. G.B.A. Zona Norte   \n",
       "34                PH                Munro      Bs.As. G.B.A. Zona Norte   \n",
       "35         apartment                Boedo               Capital Federal   \n",
       "36             house                Pilar      Bs.As. G.B.A. Zona Norte   \n",
       "...              ...                  ...                           ...   \n",
       "121148            PH               Flores               Capital Federal   \n",
       "121149         house            Caballito               Capital Federal   \n",
       "121150     apartment             Paternal               Capital Federal   \n",
       "121151     apartment               Flores               Capital Federal   \n",
       "121152     apartment            Caballito               Capital Federal   \n",
       "121153     apartment         Barrio Norte               Capital Federal   \n",
       "121154         house             La Plata        Bs.As. G.B.A. Zona Sur   \n",
       "121155         house                Pilar      Bs.As. G.B.A. Zona Norte   \n",
       "121158     apartment             Recoleta               Capital Federal   \n",
       "121159     apartment              Córdoba                       Córdoba   \n",
       "121160     apartment             Belgrano               Capital Federal   \n",
       "121161     apartment              Córdoba                       Córdoba   \n",
       "121163     apartment              Córdoba                       Córdoba   \n",
       "121164     apartment              Córdoba                       Córdoba   \n",
       "121166     apartment              Córdoba                       Córdoba   \n",
       "121182     apartment              Córdoba                       Córdoba   \n",
       "121189     apartment              Córdoba                       Córdoba   \n",
       "121190     apartment              Córdoba                       Córdoba   \n",
       "121193     apartment              Córdoba                       Córdoba   \n",
       "121196     apartment              Córdoba                       Córdoba   \n",
       "121207         house              Escobar      Bs.As. G.B.A. Zona Norte   \n",
       "121210         house             Martínez      Bs.As. G.B.A. Zona Norte   \n",
       "121211     apartment             Acassuso      Bs.As. G.B.A. Zona Norte   \n",
       "121212         house             Acassuso      Bs.As. G.B.A. Zona Norte   \n",
       "121213         house             Martínez      Bs.As. G.B.A. Zona Norte   \n",
       "121214         store           San Isidro      Bs.As. G.B.A. Zona Norte   \n",
       "121216         house               Beccar      Bs.As. G.B.A. Zona Norte   \n",
       "121217     apartment        Villa Urquiza               Capital Federal   \n",
       "121218     apartment          Plaza Colón  Buenos Aires Costa Atlántica   \n",
       "121219     apartment      Capital Federal               Capital Federal   \n",
       "\n",
       "        geonames_id     lat     lon  price_aprox_usd  surface_total_in_m2  \\\n",
       "0       3430787.000 -34.662 -58.509        62000.000               55.000   \n",
       "2       3430787.000 -34.652 -58.523        72000.000               55.000   \n",
       "4       3435548.000 -38.003 -57.549        64000.000               35.000   \n",
       "5       3433657.000 -33.014 -58.520        29724.348               53.000   \n",
       "6       3430511.000 -34.533 -58.522       130000.000              106.000   \n",
       "7       3436077.000 -34.560 -58.443       138000.000               45.000   \n",
       "8       3436077.000 -34.560 -58.443       195000.000               65.000   \n",
       "10      3860259.000 -31.420 -64.189        70000.000             1514.000   \n",
       "12      3430813.000 -34.486 -58.504       115000.000               39.000   \n",
       "13      3430234.000     nan     nan       111700.000               50.000   \n",
       "14      3430234.000     nan     nan       147900.000               42.000   \n",
       "15      3436061.000 -34.588 -58.699       250000.000              164.000   \n",
       "16      3430787.000 -34.652 -58.502       239000.000              140.000   \n",
       "17              nan -34.409 -58.634       315000.000              176.000   \n",
       "18      3427753.000 -34.425 -58.580       570000.000              270.000   \n",
       "19      3430234.000 -34.581 -58.406       350000.000              104.000   \n",
       "20      3832729.000 -31.053 -64.499       119711.480              148.000   \n",
       "21      3430234.000 -34.591 -58.412       270500.000              118.000   \n",
       "22      3433780.000 -34.650 -58.966       290000.000              200.000   \n",
       "23              nan     nan     nan        82000.000               48.000   \n",
       "26              nan -34.364 -58.690       265000.000              230.000   \n",
       "27              nan -34.479 -58.985       650000.000              425.000   \n",
       "29      3433918.000 -34.635 -58.474        75000.000               43.000   \n",
       "30      3429979.000 -34.454 -58.840       178000.000              150.000   \n",
       "31      3430511.000 -34.542 -58.540       130000.000              207.000   \n",
       "32      3860259.000 -31.405 -64.156        30846.020               50.000   \n",
       "33              nan -34.409 -58.634       325000.000              186.000   \n",
       "34      3430511.000 -34.527 -58.513       169000.000              172.000   \n",
       "35      3436003.000 -34.622 -58.422       145000.000               80.000   \n",
       "36              nan -34.463 -58.902       400000.000              400.000   \n",
       "...             ...     ...     ...              ...                  ...   \n",
       "121148  3433918.000 -34.630 -58.457       229000.000              185.000   \n",
       "121149  3435874.000     nan     nan       485000.000              195.000   \n",
       "121150  3430087.000 -34.609 -58.476        95000.000               52.000   \n",
       "121151  3433918.000 -34.630 -58.461       120000.000               72.000   \n",
       "121152  3435874.000 -34.623 -58.455        74500.000               33.000   \n",
       "121153  3436109.000 -34.599 -58.403       165000.000               91.000   \n",
       "121154  3432039.000     nan     nan       295000.000              205.000   \n",
       "121155  3429979.000     nan     nan       170000.000              130.000   \n",
       "121158  3429595.000 -34.587 -58.397       165000.000               44.000   \n",
       "121159  3860255.000     nan     nan        79807.070               43.000   \n",
       "121160  3436077.000 -34.564 -58.443       410000.000              157.000   \n",
       "121161  3860255.000     nan     nan        79694.900               40.000   \n",
       "121163  3860255.000     nan     nan        79134.060               44.000   \n",
       "121164  3860255.000     nan     nan        83957.260               71.000   \n",
       "121166  3860255.000     nan     nan        78629.310               49.000   \n",
       "121182  3860255.000     nan     nan        79134.060               46.000   \n",
       "121189  3860255.000     nan     nan       150192.080               93.000   \n",
       "121190  3860255.000     nan     nan       152211.090              100.000   \n",
       "121193  3860255.000     nan     nan       104259.550              136.000   \n",
       "121196  3860255.000     nan     nan        83957.260               52.000   \n",
       "121207  3434130.000     nan     nan       242727.273              178.000   \n",
       "121210  3430813.000     nan     nan       163636.364              120.000   \n",
       "121211  3436508.000     nan     nan       471500.000              205.000   \n",
       "121212  3436508.000     nan     nan       572727.273              420.000   \n",
       "121213  3430813.000     nan     nan       818181.818              600.000   \n",
       "121214  3428983.000     nan     nan       171211.088              123.000   \n",
       "121216  3436080.000     nan     nan       498000.000              360.000   \n",
       "121217  3433775.000 -34.571 -58.476       131500.000               46.000   \n",
       "121218          nan     nan     nan        95900.000               48.000   \n",
       "121219  3433955.000     nan     nan       129000.000               77.000   \n",
       "\n",
       "        surface_covered_in_m2  price_usd_per_m2  \\\n",
       "0                      40.000          1127.273   \n",
       "2                      55.000          1309.091   \n",
       "4                      35.000          1828.571   \n",
       "5                         nan           560.837   \n",
       "6                      78.000          1226.415   \n",
       "7                      40.000          3066.667   \n",
       "8                      60.000          3000.000   \n",
       "10                        nan            46.235   \n",
       "12                     36.000          2948.718   \n",
       "13                     30.000          2234.000   \n",
       "14                     31.000          3521.429   \n",
       "15                        nan          1524.390   \n",
       "16                     98.000          1707.143   \n",
       "17                     78.000          1789.773   \n",
       "18                    270.000          2111.111   \n",
       "19                     96.000          3365.385   \n",
       "20                    148.000           808.861   \n",
       "21                     73.000          2292.373   \n",
       "22                    200.000          1450.000   \n",
       "23                     42.000          1708.333   \n",
       "26                    190.000          1152.174   \n",
       "27                    425.000          1529.412   \n",
       "29                     43.000          1744.186   \n",
       "30                    150.000          1186.667   \n",
       "31                    103.000           628.019   \n",
       "32                     50.000           616.920   \n",
       "33                     83.000          1747.312   \n",
       "34                    160.000           982.558   \n",
       "35                     77.000          1812.500   \n",
       "36                    400.000          1000.000   \n",
       "...                       ...               ...   \n",
       "121148                185.000          1237.838   \n",
       "121149                195.000          2487.179   \n",
       "121150                 52.000          1826.923   \n",
       "121151                 72.000          1666.667   \n",
       "121152                 33.000          2257.576   \n",
       "121153                 65.000          1813.187   \n",
       "121154                205.000          1439.024   \n",
       "121155                130.000          1307.692   \n",
       "121158                 39.000          3750.000   \n",
       "121159                    nan          1855.978   \n",
       "121160                    nan          2611.465   \n",
       "121161                    nan          1992.372   \n",
       "121163                    nan          1798.501   \n",
       "121164                    nan          1182.497   \n",
       "121166                    nan          1604.680   \n",
       "121182                    nan          1720.306   \n",
       "121189                    nan          1614.969   \n",
       "121190                    nan          1522.111   \n",
       "121193                    nan           766.614   \n",
       "121196                    nan          1614.563   \n",
       "121207                178.000          1363.636   \n",
       "121210                120.000          1363.636   \n",
       "121211                205.000          2300.000   \n",
       "121212                420.000          1363.636   \n",
       "121213                600.000          1363.636   \n",
       "121214                123.000          1391.960   \n",
       "121216                360.000          1383.333   \n",
       "121217                 39.000          2858.696   \n",
       "121218                 48.000          1997.917   \n",
       "121219                 77.000          1675.325   \n",
       "\n",
       "                      ...                 place_name_Virrey del Pino  \\\n",
       "0                     ...                                          0   \n",
       "2                     ...                                          0   \n",
       "4                     ...                                          0   \n",
       "5                     ...                                          0   \n",
       "6                     ...                                          0   \n",
       "7                     ...                                          0   \n",
       "8                     ...                                          0   \n",
       "10                    ...                                          0   \n",
       "12                    ...                                          0   \n",
       "13                    ...                                          0   \n",
       "14                    ...                                          0   \n",
       "15                    ...                                          0   \n",
       "16                    ...                                          0   \n",
       "17                    ...                                          0   \n",
       "18                    ...                                          0   \n",
       "19                    ...                                          0   \n",
       "20                    ...                                          0   \n",
       "21                    ...                                          0   \n",
       "22                    ...                                          0   \n",
       "23                    ...                                          0   \n",
       "26                    ...                                          0   \n",
       "27                    ...                                          0   \n",
       "29                    ...                                          0   \n",
       "30                    ...                                          0   \n",
       "31                    ...                                          0   \n",
       "32                    ...                                          0   \n",
       "33                    ...                                          0   \n",
       "34                    ...                                          0   \n",
       "35                    ...                                          0   \n",
       "36                    ...                                          0   \n",
       "...                   ...                                        ...   \n",
       "121148                ...                                          0   \n",
       "121149                ...                                          0   \n",
       "121150                ...                                          0   \n",
       "121151                ...                                          0   \n",
       "121152                ...                                          0   \n",
       "121153                ...                                          0   \n",
       "121154                ...                                          0   \n",
       "121155                ...                                          0   \n",
       "121158                ...                                          0   \n",
       "121159                ...                                          0   \n",
       "121160                ...                                          0   \n",
       "121161                ...                                          0   \n",
       "121163                ...                                          0   \n",
       "121164                ...                                          0   \n",
       "121166                ...                                          0   \n",
       "121182                ...                                          0   \n",
       "121189                ...                                          0   \n",
       "121190                ...                                          0   \n",
       "121193                ...                                          0   \n",
       "121196                ...                                          0   \n",
       "121207                ...                                          0   \n",
       "121210                ...                                          0   \n",
       "121211                ...                                          0   \n",
       "121212                ...                                          0   \n",
       "121213                ...                                          0   \n",
       "121214                ...                                          0   \n",
       "121216                ...                                          0   \n",
       "121217                ...                                          0   \n",
       "121218                ...                                          0   \n",
       "121219                ...                                          0   \n",
       "\n",
       "        place_name_Virreyes  place_name_Vistalba  \\\n",
       "0                         0                    0   \n",
       "2                         0                    0   \n",
       "4                         0                    0   \n",
       "5                         0                    0   \n",
       "6                         0                    0   \n",
       "7                         0                    0   \n",
       "8                         0                    0   \n",
       "10                        0                    0   \n",
       "12                        0                    0   \n",
       "13                        0                    0   \n",
       "14                        0                    0   \n",
       "15                        0                    0   \n",
       "16                        0                    0   \n",
       "17                        0                    0   \n",
       "18                        0                    0   \n",
       "19                        0                    0   \n",
       "20                        0                    0   \n",
       "21                        0                    0   \n",
       "22                        0                    0   \n",
       "23                        0                    0   \n",
       "26                        0                    0   \n",
       "27                        0                    0   \n",
       "29                        0                    0   \n",
       "30                        0                    0   \n",
       "31                        0                    0   \n",
       "32                        0                    0   \n",
       "33                        0                    0   \n",
       "34                        0                    0   \n",
       "35                        0                    0   \n",
       "36                        0                    0   \n",
       "...                     ...                  ...   \n",
       "121148                    0                    0   \n",
       "121149                    0                    0   \n",
       "121150                    0                    0   \n",
       "121151                    0                    0   \n",
       "121152                    0                    0   \n",
       "121153                    0                    0   \n",
       "121154                    0                    0   \n",
       "121155                    0                    0   \n",
       "121158                    0                    0   \n",
       "121159                    0                    0   \n",
       "121160                    0                    0   \n",
       "121161                    0                    0   \n",
       "121163                    0                    0   \n",
       "121164                    0                    0   \n",
       "121166                    0                    0   \n",
       "121182                    0                    0   \n",
       "121189                    0                    0   \n",
       "121190                    0                    0   \n",
       "121193                    0                    0   \n",
       "121196                    0                    0   \n",
       "121207                    0                    0   \n",
       "121210                    0                    0   \n",
       "121211                    0                    0   \n",
       "121212                    0                    0   \n",
       "121213                    0                    0   \n",
       "121214                    0                    0   \n",
       "121216                    0                    0   \n",
       "121217                    0                    0   \n",
       "121218                    0                    0   \n",
       "121219                    0                    0   \n",
       "\n",
       "       place_name_Wenceslao Escalante place_name_Wilde  \\\n",
       "0                                   0                0   \n",
       "2                                   0                0   \n",
       "4                                   0                0   \n",
       "5                                   0                0   \n",
       "6                                   0                0   \n",
       "7                                   0                0   \n",
       "8                                   0                0   \n",
       "10                                  0                0   \n",
       "12                                  0                0   \n",
       "13                                  0                0   \n",
       "14                                  0                0   \n",
       "15                                  0                0   \n",
       "16                                  0                0   \n",
       "17                                  0                0   \n",
       "18                                  0                0   \n",
       "19                                  0                0   \n",
       "20                                  0                0   \n",
       "21                                  0                0   \n",
       "22                                  0                0   \n",
       "23                                  0                0   \n",
       "26                                  0                0   \n",
       "27                                  0                0   \n",
       "29                                  0                0   \n",
       "30                                  0                0   \n",
       "31                                  0                0   \n",
       "32                                  0                0   \n",
       "33                                  0                0   \n",
       "34                                  0                0   \n",
       "35                                  0                0   \n",
       "36                                  0                0   \n",
       "...                               ...              ...   \n",
       "121148                              0                0   \n",
       "121149                              0                0   \n",
       "121150                              0                0   \n",
       "121151                              0                0   \n",
       "121152                              0                0   \n",
       "121153                              0                0   \n",
       "121154                              0                0   \n",
       "121155                              0                0   \n",
       "121158                              0                0   \n",
       "121159                              0                0   \n",
       "121160                              0                0   \n",
       "121161                              0                0   \n",
       "121163                              0                0   \n",
       "121164                              0                0   \n",
       "121166                              0                0   \n",
       "121182                              0                0   \n",
       "121189                              0                0   \n",
       "121190                              0                0   \n",
       "121193                              0                0   \n",
       "121196                              0                0   \n",
       "121207                              0                0   \n",
       "121210                              0                0   \n",
       "121211                              0                0   \n",
       "121212                              0                0   \n",
       "121213                              0                0   \n",
       "121214                              0                0   \n",
       "121216                              0                0   \n",
       "121217                              0                0   \n",
       "121218                              0                0   \n",
       "121219                              0                0   \n",
       "\n",
       "       place_name_William Morris place_name_Yerba Buena  place_name_Zelaya  \\\n",
       "0                              0                      0                  0   \n",
       "2                              0                      0                  0   \n",
       "4                              0                      0                  0   \n",
       "5                              0                      0                  0   \n",
       "6                              0                      0                  0   \n",
       "7                              0                      0                  0   \n",
       "8                              0                      0                  0   \n",
       "10                             0                      0                  0   \n",
       "12                             0                      0                  0   \n",
       "13                             0                      0                  0   \n",
       "14                             0                      0                  0   \n",
       "15                             0                      0                  0   \n",
       "16                             0                      0                  0   \n",
       "17                             0                      0                  0   \n",
       "18                             0                      0                  0   \n",
       "19                             0                      0                  0   \n",
       "20                             0                      0                  0   \n",
       "21                             0                      0                  0   \n",
       "22                             0                      0                  0   \n",
       "23                             0                      0                  0   \n",
       "26                             0                      0                  0   \n",
       "27                             0                      0                  0   \n",
       "29                             0                      0                  0   \n",
       "30                             0                      0                  0   \n",
       "31                             0                      0                  0   \n",
       "32                             0                      0                  0   \n",
       "33                             0                      0                  0   \n",
       "34                             0                      0                  0   \n",
       "35                             0                      0                  0   \n",
       "36                             0                      0                  0   \n",
       "...                          ...                    ...                ...   \n",
       "121148                         0                      0                  0   \n",
       "121149                         0                      0                  0   \n",
       "121150                         0                      0                  0   \n",
       "121151                         0                      0                  0   \n",
       "121152                         0                      0                  0   \n",
       "121153                         0                      0                  0   \n",
       "121154                         0                      0                  0   \n",
       "121155                         0                      0                  0   \n",
       "121158                         0                      0                  0   \n",
       "121159                         0                      0                  0   \n",
       "121160                         0                      0                  0   \n",
       "121161                         0                      0                  0   \n",
       "121163                         0                      0                  0   \n",
       "121164                         0                      0                  0   \n",
       "121166                         0                      0                  0   \n",
       "121182                         0                      0                  0   \n",
       "121189                         0                      0                  0   \n",
       "121190                         0                      0                  0   \n",
       "121193                         0                      0                  0   \n",
       "121196                         0                      0                  0   \n",
       "121207                         0                      0                  0   \n",
       "121210                         0                      0                  0   \n",
       "121211                         0                      0                  0   \n",
       "121212                         0                      0                  0   \n",
       "121213                         0                      0                  0   \n",
       "121214                         0                      0                  0   \n",
       "121216                         0                      0                  0   \n",
       "121217                         0                      0                  0   \n",
       "121218                         0                      0                  0   \n",
       "121219                         0                      0                  0   \n",
       "\n",
       "        place_name_Zárate  place_name_coordenadas 34.255511  \n",
       "0                       0                                 0  \n",
       "2                       0                                 0  \n",
       "4                       0                                 0  \n",
       "5                       0                                 0  \n",
       "6                       0                                 0  \n",
       "7                       0                                 0  \n",
       "8                       0                                 0  \n",
       "10                      0                                 0  \n",
       "12                      0                                 0  \n",
       "13                      0                                 0  \n",
       "14                      0                                 0  \n",
       "15                      0                                 0  \n",
       "16                      0                                 0  \n",
       "17                      0                                 0  \n",
       "18                      0                                 0  \n",
       "19                      0                                 0  \n",
       "20                      0                                 0  \n",
       "21                      0                                 0  \n",
       "22                      0                                 0  \n",
       "23                      0                                 0  \n",
       "26                      0                                 0  \n",
       "27                      0                                 0  \n",
       "29                      0                                 0  \n",
       "30                      0                                 0  \n",
       "31                      0                                 0  \n",
       "32                      0                                 0  \n",
       "33                      0                                 0  \n",
       "34                      0                                 0  \n",
       "35                      0                                 0  \n",
       "36                      0                                 0  \n",
       "...                   ...                               ...  \n",
       "121148                  0                                 0  \n",
       "121149                  0                                 0  \n",
       "121150                  0                                 0  \n",
       "121151                  0                                 0  \n",
       "121152                  0                                 0  \n",
       "121153                  0                                 0  \n",
       "121154                  0                                 0  \n",
       "121155                  0                                 0  \n",
       "121158                  0                                 0  \n",
       "121159                  0                                 0  \n",
       "121160                  0                                 0  \n",
       "121161                  0                                 0  \n",
       "121163                  0                                 0  \n",
       "121164                  0                                 0  \n",
       "121166                  0                                 0  \n",
       "121182                  0                                 0  \n",
       "121189                  0                                 0  \n",
       "121190                  0                                 0  \n",
       "121193                  0                                 0  \n",
       "121196                  0                                 0  \n",
       "121207                  0                                 0  \n",
       "121210                  0                                 0  \n",
       "121211                  0                                 0  \n",
       "121212                  0                                 0  \n",
       "121213                  0                                 0  \n",
       "121214                  0                                 0  \n",
       "121216                  0                                 0  \n",
       "121217                  0                                 0  \n",
       "121218                  0                                 0  \n",
       "121219                  0                                 0  \n",
       "\n",
       "[89721 rows x 1022 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dummies para place_name preparatorio p/regresión\n",
    "df.join(pd.get_dummies(df.property_type, prefix='property_type')).join(pd.get_dummies(df.state_name, prefix='state_name')).join(pd.get_dummies(df.place_name, prefix='place_name'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle('dataset.pkl')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Pre-Analisis.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}