1_Data Exploration and Wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qiv5m3SSXclD"
   },
   "source": [
    "### **Imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "fnssAaoHDBg7"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.sparse import random\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "from sklearn.model_selection import train_test_split\n",
    "import shapely "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gHUa-29Q65Zr"
   },
   "source": [
    "### **Base**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "ReJJuhj6g_aT"
   },
   "outputs": [],
   "source": [
    "dfFull=pd.read_csv('properatti.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qdYjo5Ql9AR0"
   },
   "source": [
    "### **0. Descripcion general**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 496
    },
    "id": "WWoLZcnB9JjD",
    "outputId": "64134d45-ff27-453a-9388-5f10f96ae713"
   },
   "outputs": [
    {
     "data": {
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       "<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>3430787.0</td>\n",
       "      <td>-34.6618237,-58.5088387</td>\n",
       "      <td>-34.661824</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1127.272727</td>\n",
       "      <td>1550.000000</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>3432039.0</td>\n",
       "      <td>-34.9038831,-57.9643295</td>\n",
       "      <td>-34.903883</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>3430787.0</td>\n",
       "      <td>-34.6522615,-58.5229825</td>\n",
       "      <td>-34.652262</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1309.090909</td>\n",
       "      <td>1309.090909</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>sell</td>\n",
       "      <td>PH</td>\n",
       "      <td>Liniers</td>\n",
       "      <td>|Argentina|Capital Federal|Liniers|</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3431333.0</td>\n",
       "      <td>-34.6477969,-58.5164244</td>\n",
       "      <td>-34.647797</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/15boh_venta_ph_lin...</td>\n",
       "      <td>PH 3 ambientes con patio. Hay 3 deptos en lote...</td>\n",
       "      <td>PH 3 amb. cfte. reciclado</td>\n",
       "      <td>https://thumbs4.properati.com/3/DgIfX-85Mog5SP...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>Centro</td>\n",
       "      <td>|Argentina|Buenos Aires Costa Atlántica|Mar de...</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Buenos Aires Costa Atlántica</td>\n",
       "      <td>3435548.0</td>\n",
       "      <td>-38.0026256,-57.5494468</td>\n",
       "      <td>-38.002626</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1828.571429</td>\n",
       "      <td>1828.571429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.properati.com.ar/15bok_venta_depart...</td>\n",
       "      <td>DEPARTAMENTO CON FANTÁSTICA ILUMINACIÓN NATURA...</td>\n",
       "      <td>DEPTO 2 AMB AL CONTRAFRENTE ZONA CENTRO/PLAZA ...</td>\n",
       "      <td>https://thumbs4.properati.com/5/xrRqlNcSI_vs-f...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
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      "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",
       "3           3      sell            PH    Liniers   \n",
       "4           4      sell     apartment     Centro   \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",
       "3                |Argentina|Capital Federal|Liniers|    Argentina   \n",
       "4  |Argentina|Buenos Aires Costa Atlántica|Mar de...    Argentina   \n",
       "\n",
       "                     state_name  geonames_id                  lat-lon  \\\n",
       "0               Capital Federal    3430787.0  -34.6618237,-58.5088387   \n",
       "1        Bs.As. G.B.A. Zona Sur    3432039.0  -34.9038831,-57.9643295   \n",
       "2               Capital Federal    3430787.0  -34.6522615,-58.5229825   \n",
       "3               Capital Federal    3431333.0  -34.6477969,-58.5164244   \n",
       "4  Buenos Aires Costa Atlántica    3435548.0  -38.0026256,-57.5494468   \n",
       "\n",
       "         lat  ...  surface_covered_in_m2  price_usd_per_m2 price_per_m2  \\\n",
       "0 -34.661824  ...                   40.0       1127.272727  1550.000000   \n",
       "1 -34.903883  ...                    NaN               NaN          NaN   \n",
       "2 -34.652262  ...                   55.0       1309.090909  1309.090909   \n",
       "3 -34.647797  ...                    NaN               NaN          NaN   \n",
       "4 -38.002626  ...                   35.0       1828.571429  1828.571429   \n",
       "\n",
       "   floor  rooms  expenses                                      properati_url  \\\n",
       "0    NaN    NaN       NaN  http://www.properati.com.ar/15bo8_venta_ph_mat...   \n",
       "1    NaN    NaN       NaN  http://www.properati.com.ar/15bob_venta_depart...   \n",
       "2    NaN    NaN       NaN  http://www.properati.com.ar/15bod_venta_depart...   \n",
       "3    NaN    NaN       NaN  http://www.properati.com.ar/15boh_venta_ph_lin...   \n",
       "4    NaN    NaN       NaN  http://www.properati.com.ar/15bok_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",
       "3  PH 3 ambientes con patio. Hay 3 deptos en lote...   \n",
       "4  DEPARTAMENTO CON FANTÁSTICA ILUMINACIÓN NATURA...   \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",
       "3                       PH 3 amb. cfte. reciclado      \n",
       "4  DEPTO 2 AMB AL CONTRAFRENTE ZONA CENTRO/PLAZA ...   \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",
       "3  https://thumbs4.properati.com/3/DgIfX-85Mog5SP...  \n",
       "4  https://thumbs4.properati.com/5/xrRqlNcSI_vs-f...  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfFull.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Buv9YgGH9bMw",
    "outputId": "7e14f4d3-180d-48f2-e4c5-f07bc30f8b44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'operation', 'property_type', 'place_name',\n",
       "       'place_with_parent_names', 'country_name', 'state_name', 'geonames_id',\n",
       "       'lat-lon', 'lat', 'lon', 'price', 'currency',\n",
       "       'price_aprox_local_currency', 'price_aprox_usd', 'surface_total_in_m2',\n",
       "       'surface_covered_in_m2', 'price_usd_per_m2', 'price_per_m2', 'floor',\n",
       "       'rooms', 'expenses', 'properati_url', 'description', 'title',\n",
       "       'image_thumbnail'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfFull.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "9xE3TBs09Unp",
    "outputId": "0b773daf-9fbf-479b-f64f-02d9e105f6fa"
   },
   "outputs": [
    {
     "data": {
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       "<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>121220.000000</td>\n",
       "      <td>1.025030e+05</td>\n",
       "      <td>69670.000000</td>\n",
       "      <td>69670.000000</td>\n",
       "      <td>1.008100e+05</td>\n",
       "      <td>1.008100e+05</td>\n",
       "      <td>1.008100e+05</td>\n",
       "      <td>81892.000000</td>\n",
       "      <td>101313.000000</td>\n",
       "      <td>68617.000000</td>\n",
       "      <td>8.765800e+04</td>\n",
       "      <td>7899.000000</td>\n",
       "      <td>47390.000000</td>\n",
       "      <td>1.426200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60609.500000</td>\n",
       "      <td>3.574442e+06</td>\n",
       "      <td>-34.626210</td>\n",
       "      <td>-59.266290</td>\n",
       "      <td>4.685259e+05</td>\n",
       "      <td>4.229397e+06</td>\n",
       "      <td>2.397006e+05</td>\n",
       "      <td>233.795328</td>\n",
       "      <td>133.050181</td>\n",
       "      <td>2160.086916</td>\n",
       "      <td>6.912216e+03</td>\n",
       "      <td>17.452336</td>\n",
       "      <td>3.080840</td>\n",
       "      <td>5.009234e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34993.344153</td>\n",
       "      <td>3.541306e+05</td>\n",
       "      <td>1.980936</td>\n",
       "      <td>2.299922</td>\n",
       "      <td>2.260101e+06</td>\n",
       "      <td>6.904714e+06</td>\n",
       "      <td>3.913239e+05</td>\n",
       "      <td>1782.222147</td>\n",
       "      <td>724.351479</td>\n",
       "      <td>2759.288621</td>\n",
       "      <td>2.837864e+04</td>\n",
       "      <td>120.243621</td>\n",
       "      <td>1.860773</td>\n",
       "      <td>1.204403e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.427208e+06</td>\n",
       "      <td>-54.823985</td>\n",
       "      <td>-75.678931</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.510204e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30304.750000</td>\n",
       "      <td>3.430234e+06</td>\n",
       "      <td>-34.669065</td>\n",
       "      <td>-58.727040</td>\n",
       "      <td>1.100000e+05</td>\n",
       "      <td>1.583309e+06</td>\n",
       "      <td>8.973388e+04</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1218.181818</td>\n",
       "      <td>1.550000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60609.500000</td>\n",
       "      <td>3.433910e+06</td>\n",
       "      <td>-34.597985</td>\n",
       "      <td>-58.480128</td>\n",
       "      <td>1.850000e+05</td>\n",
       "      <td>2.558452e+06</td>\n",
       "      <td>1.450000e+05</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>2.213115e+03</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90914.250000</td>\n",
       "      <td>3.836668e+06</td>\n",
       "      <td>-34.441299</td>\n",
       "      <td>-58.395908</td>\n",
       "      <td>4.200000e+05</td>\n",
       "      <td>4.675792e+06</td>\n",
       "      <td>2.650000e+05</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2486.411765</td>\n",
       "      <td>3.355549e+03</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>121219.000000</td>\n",
       "      <td>6.948895e+06</td>\n",
       "      <td>4.545843</td>\n",
       "      <td>-53.733330</td>\n",
       "      <td>6.500000e+08</td>\n",
       "      <td>8.212711e+08</td>\n",
       "      <td>4.654544e+07</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>187000.000000</td>\n",
       "      <td>206333.333333</td>\n",
       "      <td>4.000000e+06</td>\n",
       "      <td>3150.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.000150e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0   geonames_id           lat           lon         price  \\\n",
       "count  121220.000000  1.025030e+05  69670.000000  69670.000000  1.008100e+05   \n",
       "mean    60609.500000  3.574442e+06    -34.626210    -59.266290  4.685259e+05   \n",
       "std     34993.344153  3.541306e+05      1.980936      2.299922  2.260101e+06   \n",
       "min         0.000000  3.427208e+06    -54.823985    -75.678931  0.000000e+00   \n",
       "25%     30304.750000  3.430234e+06    -34.669065    -58.727040  1.100000e+05   \n",
       "50%     60609.500000  3.433910e+06    -34.597985    -58.480128  1.850000e+05   \n",
       "75%     90914.250000  3.836668e+06    -34.441299    -58.395908  4.200000e+05   \n",
       "max    121219.000000  6.948895e+06      4.545843    -53.733330  6.500000e+08   \n",
       "\n",
       "       price_aprox_local_currency  price_aprox_usd  surface_total_in_m2  \\\n",
       "count                1.008100e+05     1.008100e+05         81892.000000   \n",
       "mean                 4.229397e+06     2.397006e+05           233.795328   \n",
       "std                  6.904714e+06     3.913239e+05          1782.222147   \n",
       "min                  0.000000e+00     0.000000e+00             0.000000   \n",
       "25%                  1.583309e+06     8.973388e+04            50.000000   \n",
       "50%                  2.558452e+06     1.450000e+05            84.000000   \n",
       "75%                  4.675792e+06     2.650000e+05           200.000000   \n",
       "max                  8.212711e+08     4.654544e+07        200000.000000   \n",
       "\n",
       "       surface_covered_in_m2  price_usd_per_m2  price_per_m2        floor  \\\n",
       "count          101313.000000      68617.000000  8.765800e+04  7899.000000   \n",
       "mean              133.050181       2160.086916  6.912216e+03    17.452336   \n",
       "std               724.351479       2759.288621  2.837864e+04   120.243621   \n",
       "min                 0.000000          0.600000  1.510204e+00     1.000000   \n",
       "25%                45.000000       1218.181818  1.550000e+03     1.000000   \n",
       "50%                75.000000       1800.000000  2.213115e+03     3.000000   \n",
       "75%               150.000000       2486.411765  3.355549e+03     6.000000   \n",
       "max            187000.000000     206333.333333  4.000000e+06  3150.000000   \n",
       "\n",
       "              rooms      expenses  \n",
       "count  47390.000000  1.426200e+04  \n",
       "mean       3.080840  5.009234e+03  \n",
       "std        1.860773  1.204403e+05  \n",
       "min        1.000000  1.000000e+00  \n",
       "25%        2.000000  1.000000e+03  \n",
       "50%        3.000000  2.000000e+03  \n",
       "75%        4.000000  4.000000e+03  \n",
       "max       32.000000  1.000150e+07  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfFull.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "AIzStxLwg9tM"
   },
   "outputs": [],
   "source": [
    "#Dropeamos columna 'Unnamed' porque repite axis \n",
    "dfFull = dfFull.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-v3zXzk46_30"
   },
   "source": [
    "### **1. Limpieza de datos**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2KuirEeA7Sw9"
   },
   "source": [
    "   #### **Valores duplicados, nulos y outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VENtq8fD-iIQ",
    "outputId": "8a3f540b-d6f8-4231-a4dd-f87217935862"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Nulos por columna \n",
    "dfFull.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3IjaPXfF7pEo"
   },
   "source": [
    "**- state_name: valores nulos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "qrRlIPXRhzbR"
   },
   "outputs": [],
   "source": [
    "dfCapi = dfFull[dfFull['state_name'] == 'Capital Federal']\n",
    "dfOtro = dfFull[dfFull['state_name'] != 'Capital Federal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "7ZUnHFETiOXK"
   },
   "outputs": [],
   "source": [
    "#Nulos Capi por columna\n",
    "NulosCapi = round(((dfCapi.isnull().sum() / dfCapi.isnull().count())*100),0).reset_index()\n",
    "NulosCapi.columns = ['columna', 'porcentaje_nulos']\n",
    "NulosCapi['Tag'] = 'CABA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "cgbe82Zs_gfy"
   },
   "outputs": [],
   "source": [
    "#Nulos Otro por columna\n",
    "NulosOtro = round(((dfOtro.isnull().sum() / dfOtro.isnull().count())*100),0).reset_index()\n",
    "NulosOtro.columns = ['columna', 'porcentaje_nulos']\n",
    "NulosOtro['Tag'] = 'Otros'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "Bhhbbqml_mF3"
   },
   "outputs": [],
   "source": [
    "#Unimos NulosCapi y NulosOtro y nos quedasmo con las columnas que tengan al menos un valor nulo\n",
    "NulosCross = pd.concat([NulosCapi,NulosOtro],axis=0)\n",
    "NulosCross_SoloNulos = NulosCross[(NulosCross['porcentaje_nulos'] > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>columna</th>\n",
       "      <th>porcentaje_nulos</th>\n",
       "      <th>Tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>geonames_id</td>\n",
       "      <td>4.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lat-lon</td>\n",
       "      <td>26.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lat</td>\n",
       "      <td>26.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lon</td>\n",
       "      <td>26.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>price</td>\n",
       "      <td>10.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>currency</td>\n",
       "      <td>10.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>price_aprox_local_currency</td>\n",
       "      <td>10.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>price_aprox_usd</td>\n",
       "      <td>10.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>surface_total_in_m2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>surface_covered_in_m2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>price_usd_per_m2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>price_per_m2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>floor</td>\n",
       "      <td>91.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>rooms</td>\n",
       "      <td>53.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>expenses</td>\n",
       "      <td>80.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>image_thumbnail</td>\n",
       "      <td>2.0</td>\n",
       "      <td>CABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>geonames_id</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lat-lon</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lat</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lon</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>price</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>currency</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>price_aprox_local_currency</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>price_aprox_usd</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>surface_total_in_m2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>surface_covered_in_m2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>price_usd_per_m2</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>price_per_m2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>floor</td>\n",
       "      <td>94.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>rooms</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>expenses</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>image_thumbnail</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Otros</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       columna  porcentaje_nulos    Tag\n",
       "6                  geonames_id               4.0   CABA\n",
       "7                      lat-lon              26.0   CABA\n",
       "8                          lat              26.0   CABA\n",
       "9                          lon              26.0   CABA\n",
       "10                       price              10.0   CABA\n",
       "11                    currency              10.0   CABA\n",
       "12  price_aprox_local_currency              10.0   CABA\n",
       "13             price_aprox_usd              10.0   CABA\n",
       "14         surface_total_in_m2              18.0   CABA\n",
       "15       surface_covered_in_m2               7.0   CABA\n",
       "16            price_usd_per_m2              27.0   CABA\n",
       "17                price_per_m2              15.0   CABA\n",
       "18                       floor              91.0   CABA\n",
       "19                       rooms              53.0   CABA\n",
       "20                    expenses              80.0   CABA\n",
       "24             image_thumbnail               2.0   CABA\n",
       "6                  geonames_id              20.0  Otros\n",
       "7                      lat-lon              48.0  Otros\n",
       "8                          lat              48.0  Otros\n",
       "9                          lon              48.0  Otros\n",
       "10                       price              19.0  Otros\n",
       "11                    currency              19.0  Otros\n",
       "12  price_aprox_local_currency              19.0  Otros\n",
       "13             price_aprox_usd              19.0  Otros\n",
       "14         surface_total_in_m2              38.0  Otros\n",
       "15       surface_covered_in_m2              20.0  Otros\n",
       "16            price_usd_per_m2              49.0  Otros\n",
       "17                price_per_m2              32.0  Otros\n",
       "18                       floor              94.0  Otros\n",
       "19                       rooms              64.0  Otros\n",
       "20                    expenses              91.0  Otros\n",
       "24             image_thumbnail               3.0  Otros"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NulosCross_SoloNulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "id": "Cyd6O2kK_q43",
    "outputId": "c9f2cebe-0934-4f7b-c81f-0d2be79416a0"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plotly.com"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Tag=CABA<br>columna=%{x}<br>porcentaje_nulos=%{y}<extra></extra>",
         "legendgroup": "CABA",
         "marker": {
          "color": "lightseagreen"
         },
         "name": "CABA",
         "offsetgroup": "CABA",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
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          "price_aprox_usd",
          "surface_total_in_m2",
          "surface_covered_in_m2",
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         "xaxis": "x",
         "y": [
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         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Tag=Otros<br>columna=%{x}<br>porcentaje_nulos=%{y}<extra></extra>",
         "legendgroup": "Otros",
         "marker": {
          "color": "gray"
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         "name": "Otros",
         "offsetgroup": "Otros",
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         "xaxis": "x",
         "y": [
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         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "group",
        "height": 400,
        "legend": {
         "title": {
          "text": "Tag"
         },
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficamos % de nulos por columna para CABA vs resto\n",
    "fig = px.bar(data_frame = NulosCross_SoloNulos, x = \"columna\", y = \"porcentaje_nulos\", \n",
    "             color = \"Tag\",\n",
    "             color_discrete_map={'CABA': 'lightseagreen','Otros': 'gray'},\n",
    "             barmode='group',\n",
    "             title=\"Porcentaje de nulos por variable - CABA vs Otros\",\n",
    "             width=800, height=400)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m16md5ClAux3"
   },
   "source": [
    "**Como CABA tiene menos valores nulos que el resto de las regiones nos vamos a quedar con CABA para el resto del análisis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QfPItK7fXETz"
   },
   "source": [
    "**Elimación de duplicados**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HgKmj2fYXCrW",
    "outputId": "010bb417-0124-490e-8a9c-19248b8f8219"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ARIEL~1.DAP\\AppData\\Local\\Temp/ipykernel_15864/889814540.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Eliminar duplicados\n",
    "dfCapi.drop_duplicates(subset=[\"price_aprox_usd\",\"description\",\"place_name\"], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xtU27VUbwgVU"
   },
   "source": [
    "**- Correlaciones entre variables**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2QGZyzpFw0V8"
   },
   "source": [
    "Se ve una relación entre superficie y precio total. Después obviamente hay relación entre las variables de precio (precio en pesos y precio en usd) y entre las variables de superficie (surface covered y surface total) dado que deberian ser similares - ej si aumenta surface covered, tambien surface total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = dfCapi.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
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       "            background-color:  #de6c83;\n",
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       "            max-width:  80px;\n",
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       "            background-color:  #eabbc4;\n",
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       "            background-color:  #e48fa0;\n",
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       "            background-color:  #e0778c;\n",
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       "            background-color:  #e38a9c;\n",
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       "            background-color:  #e0798d;\n",
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       "            background-color:  #e17c90;\n",
       "            color:  #000000;\n",
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       "            background-color:  #e7a7b4;\n",
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       "        }#T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col4,#T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col5{\n",
       "            background-color:  #ebc2ca;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col6{\n",
       "            background-color:  #e17d91;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col7{\n",
       "            background-color:  #edd0d6;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col10{\n",
       "            background-color:  #e48e9f;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }</style><table id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9\" ><caption>Hover to magify</caption><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >geonames_id</th>        <th class=\"col_heading level0 col1\" >lat</th>        <th class=\"col_heading level0 col2\" >lon</th>        <th class=\"col_heading level0 col3\" >price</th>        <th class=\"col_heading level0 col4\" >price_aprox_local_currency</th>        <th class=\"col_heading level0 col5\" >price_aprox_usd</th>        <th class=\"col_heading level0 col6\" >surface_total_in_m2</th>        <th class=\"col_heading level0 col7\" >surface_covered_in_m2</th>        <th class=\"col_heading level0 col8\" >price_usd_per_m2</th>        <th class=\"col_heading level0 col9\" >price_per_m2</th>        <th class=\"col_heading level0 col10\" >floor</th>        <th class=\"col_heading level0 col11\" >rooms</th>        <th class=\"col_heading level0 col12\" >expenses</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row0\" class=\"row_heading level0 row0\" >geonames_id</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col1\" class=\"data row0 col1\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col2\" class=\"data row0 col2\" >0.12</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col3\" class=\"data row0 col3\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col4\" class=\"data row0 col4\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col5\" class=\"data row0 col5\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col6\" class=\"data row0 col6\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col7\" class=\"data row0 col7\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col8\" class=\"data row0 col8\" >-0.05</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col9\" class=\"data row0 col9\" >-0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col10\" class=\"data row0 col10\" >-0.02</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col11\" class=\"data row0 col11\" >0.02</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row0_col12\" class=\"data row0 col12\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row1\" class=\"row_heading level0 row1\" >lat</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col0\" class=\"data row1 col0\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col2\" class=\"data row1 col2\" >0.40</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col3\" class=\"data row1 col3\" >0.07</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col4\" class=\"data row1 col4\" >0.07</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col5\" class=\"data row1 col5\" >0.07</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col6\" class=\"data row1 col6\" >-0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col8\" class=\"data row1 col8\" >0.08</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col9\" class=\"data row1 col9\" >0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col10\" class=\"data row1 col10\" >0.05</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row1_col12\" class=\"data row1 col12\" >-0.03</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row2\" class=\"row_heading level0 row2\" >lon</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col0\" class=\"data row2 col0\" >0.12</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col1\" class=\"data row2 col1\" >0.40</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col3\" class=\"data row2 col3\" >0.05</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col4\" class=\"data row2 col4\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col5\" class=\"data row2 col5\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col6\" class=\"data row2 col6\" >0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col7\" class=\"data row2 col7\" >0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col8\" class=\"data row2 col8\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col9\" class=\"data row2 col9\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col10\" class=\"data row2 col10\" >-0.03</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col11\" class=\"data row2 col11\" >0.03</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row2_col12\" class=\"data row2 col12\" >-0.04</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row3\" class=\"row_heading level0 row3\" >price</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col0\" class=\"data row3 col0\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col1\" class=\"data row3 col1\" >0.07</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col2\" class=\"data row3 col2\" >0.05</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col4\" class=\"data row3 col4\" >0.54</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col5\" class=\"data row3 col5\" >0.54</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col6\" class=\"data row3 col6\" >0.37</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col7\" class=\"data row3 col7\" >0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col8\" class=\"data row3 col8\" >0.16</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col9\" class=\"data row3 col9\" >0.42</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col10\" class=\"data row3 col10\" >0.07</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col11\" class=\"data row3 col11\" >0.17</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row3_col12\" class=\"data row3 col12\" >0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row4\" class=\"row_heading level0 row4\" >price_aprox_local_currency</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col0\" class=\"data row4 col0\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col1\" class=\"data row4 col1\" >0.07</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col2\" class=\"data row4 col2\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col3\" class=\"data row4 col3\" >0.54</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col5\" class=\"data row4 col5\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col6\" class=\"data row4 col6\" >0.69</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col7\" class=\"data row4 col7\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col8\" class=\"data row4 col8\" >0.29</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col9\" class=\"data row4 col9\" >0.08</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col10\" class=\"data row4 col10\" >0.05</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col11\" class=\"data row4 col11\" >0.25</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row4_col12\" class=\"data row4 col12\" >0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row5\" class=\"row_heading level0 row5\" >price_aprox_usd</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col0\" class=\"data row5 col0\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col1\" class=\"data row5 col1\" >0.07</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col2\" class=\"data row5 col2\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col3\" class=\"data row5 col3\" >0.54</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col4\" class=\"data row5 col4\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col6\" class=\"data row5 col6\" >0.69</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col7\" class=\"data row5 col7\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col8\" class=\"data row5 col8\" >0.29</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col9\" class=\"data row5 col9\" >0.08</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col10\" class=\"data row5 col10\" >0.05</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col11\" class=\"data row5 col11\" >0.25</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row5_col12\" class=\"data row5 col12\" >0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row6\" class=\"row_heading level0 row6\" >surface_total_in_m2</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col0\" class=\"data row6 col0\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col1\" class=\"data row6 col1\" >-0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col2\" class=\"data row6 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col3\" class=\"data row6 col3\" >0.37</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col4\" class=\"data row6 col4\" >0.69</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col5\" class=\"data row6 col5\" >0.69</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col7\" class=\"data row6 col7\" >0.87</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col8\" class=\"data row6 col8\" >-0.02</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col9\" class=\"data row6 col9\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col10\" class=\"data row6 col10\" >-0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col11\" class=\"data row6 col11\" >0.03</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row6_col12\" class=\"data row6 col12\" >0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row7\" class=\"row_heading level0 row7\" >surface_covered_in_m2</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col0\" class=\"data row7 col0\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col1\" class=\"data row7 col1\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col2\" class=\"data row7 col2\" >0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col3\" class=\"data row7 col3\" >0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col4\" class=\"data row7 col4\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col5\" class=\"data row7 col5\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col6\" class=\"data row7 col6\" >0.87</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col8\" class=\"data row7 col8\" >-0.06</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col9\" class=\"data row7 col9\" >-0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col10\" class=\"data row7 col10\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col11\" class=\"data row7 col11\" >0.30</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row7_col12\" class=\"data row7 col12\" >0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row8\" class=\"row_heading level0 row8\" >price_usd_per_m2</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col0\" class=\"data row8 col0\" >-0.05</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col1\" class=\"data row8 col1\" >0.08</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col2\" class=\"data row8 col2\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col3\" class=\"data row8 col3\" >0.16</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col4\" class=\"data row8 col4\" >0.29</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col5\" class=\"data row8 col5\" >0.29</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col6\" class=\"data row8 col6\" >-0.02</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col7\" class=\"data row8 col7\" >-0.06</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col9\" class=\"data row8 col9\" >0.23</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col10\" class=\"data row8 col10\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col11\" class=\"data row8 col11\" >-0.18</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row8_col12\" class=\"data row8 col12\" >0.02</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row9\" class=\"row_heading level0 row9\" >price_per_m2</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col0\" class=\"data row9 col0\" >-0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col1\" class=\"data row9 col1\" >0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col2\" class=\"data row9 col2\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col3\" class=\"data row9 col3\" >0.42</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col4\" class=\"data row9 col4\" >0.08</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col5\" class=\"data row9 col5\" >0.08</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col6\" class=\"data row9 col6\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col7\" class=\"data row9 col7\" >-0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col8\" class=\"data row9 col8\" >0.23</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col10\" class=\"data row9 col10\" >0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col11\" class=\"data row9 col11\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row9_col12\" class=\"data row9 col12\" >0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row10\" class=\"row_heading level0 row10\" >floor</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col0\" class=\"data row10 col0\" >-0.02</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col1\" class=\"data row10 col1\" >0.05</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col2\" class=\"data row10 col2\" >-0.03</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col3\" class=\"data row10 col3\" >0.07</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col4\" class=\"data row10 col4\" >0.05</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col5\" class=\"data row10 col5\" >0.05</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col6\" class=\"data row10 col6\" >-0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col7\" class=\"data row10 col7\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col8\" class=\"data row10 col8\" >0.09</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col9\" class=\"data row10 col9\" >0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col11\" class=\"data row10 col11\" >-0.02</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row11\" class=\"row_heading level0 row11\" >rooms</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col0\" class=\"data row11 col0\" >0.02</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col1\" class=\"data row11 col1\" >0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col2\" class=\"data row11 col2\" >0.03</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col3\" class=\"data row11 col3\" >0.17</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col4\" class=\"data row11 col4\" >0.25</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col5\" class=\"data row11 col5\" >0.25</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col6\" class=\"data row11 col6\" >0.03</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col7\" class=\"data row11 col7\" >0.30</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col8\" class=\"data row11 col8\" >-0.18</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col9\" class=\"data row11 col9\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col10\" class=\"data row11 col10\" >-0.02</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row11_col12\" class=\"data row11 col12\" >-0.02</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9level0_row12\" class=\"row_heading level0 row12\" >expenses</th>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col0\" class=\"data row12 col0\" >-0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col1\" class=\"data row12 col1\" >-0.03</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col2\" class=\"data row12 col2\" >-0.04</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col3\" class=\"data row12 col3\" >0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col4\" class=\"data row12 col4\" >0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col5\" class=\"data row12 col5\" >0.01</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col6\" class=\"data row12 col6\" >0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col7\" class=\"data row12 col7\" >0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col8\" class=\"data row12 col8\" >0.02</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col9\" class=\"data row12 col9\" >0.00</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col11\" class=\"data row12 col11\" >-0.02</td>\n",
       "                        <td id=\"T_6c0081c7_8f8c_11ec_a82b_902e16a033a9row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x18f3cdab760>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmap=sns.diverging_palette(5, 250, as_cmap=True)\n",
    "\n",
    "def magnify():\n",
    "    return [dict(selector=\"th\",\n",
    "                 props=[(\"font-size\", \"7pt\")]),\n",
    "            dict(selector=\"td\",\n",
    "                 props=[('padding', \"0em 0em\")])\n",
    "]\n",
    "\n",
    "corr.style.background_gradient(cmap, axis=1)\\\n",
    "    .set_properties(**{'max-width': '80px', 'font-size': '10pt'})\\\n",
    "    .set_caption(\"Hover to magify\")\\\n",
    "    .set_precision(2)\\\n",
    "    .set_table_styles(magnify())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 717
    },
    "id": "6Nk3X-m_wgk-",
    "outputId": "501d1031-e754-4430-f973-aa8c927eff48",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ARIEL~1.DAP\\AppData\\Local\\Temp/ipykernel_15864/387532550.py:3: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,8))       \n",
    "cor = dfCapi.corr().abs()\n",
    "cor.values[[np.arange(6)]*2] = 0\n",
    "ax = plt.axes()\n",
    "sns.heatmap(cor,cmap='Blues', alpha=0.5,ax=ax);\n",
    "ax.set_title('Correlacion entre variables', fontsize=20)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "with sns.axes_style(\"white\"):\n",
    "    f, ax = plt.subplots(figsize=(16, 8))\n",
    "    ax = sns.heatmap(corr, cmap = \"Blues\", alpha=0.5, mask=mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Yn8IF2VE_7B3"
   },
   "source": [
    "Nos vamos a focalizar en limpiar (outliers) y completar (nulos) variables como Rooms, Superficie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FZnpgCstA744"
   },
   "source": [
    "**- Valores nulos para CABA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "Cr9tyZadrzc1"
   },
   "outputs": [],
   "source": [
    "#Funcion para graficar el porcentaje de valores nulos\n",
    "\n",
    "def porcentajes_nulos(df, h=800):\n",
    "    per_cant_null = (df.isnull().sum() / df.shape[0]).sort_values()\n",
    "    \n",
    "    fig = px.bar(per_cant_null, x=per_cant_null.values, y=per_cant_null.index, \n",
    "                 height=h, \n",
    "                 color_discrete_sequence =['lightseagreen']*len(df),\n",
    "                 orientation=\"h\", \n",
    "                 title=\"Cantidad de valores nulos por campo\",\n",
    "                 labels={\n",
    "                     \"x\": \"Porcentaje (%)\",\n",
    "                     \"index\": \"Campo\",\n",
    "                 })    \n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 817
    },
    "id": "33a8WFS_joCU",
    "outputId": "88dda49c-dfa0-4339-9c7a-a214e9a631ac"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plotly.com"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Porcentaje (%)=%{x}<br>Campo=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "lightseagreen"
         },
         "name": "",
         "offsetgroup": "",
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     "metadata": {},
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   "source": [
    "porcentajes_nulos(dfCapi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dQuej_TIBgZN"
   },
   "source": [
    "Floor y expenses tiene casi la totalidad de valores nulos (candidatos a no utilizarse)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tj73NlvOCWs5"
   },
   "source": [
    "Rooms tiene el 50% de datos nulos pero quizás lo podemos completar con con el campo 'description' y completandolo con la info de los registros que sí tienen el dato (ej con la media)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "90yJlgfUDEHM"
   },
   "source": [
    "**- Columnas de precio: precio vs precio en usd**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "7N6fAZ_KjoRt",
    "outputId": "7d7e0a0c-eb33-4e9c-81a7-b8e9cc87fb8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.00     26639\n",
       "17.83     1085\n",
       "17.14       24\n",
       "17.63        5\n",
       "3.24         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Chequeo si la columna precio se corresponde con la columna del precio en dolar\n",
    "serie = dfCapi[\"price\"] / dfCapi[\"price_aprox_usd\"]\n",
    "serie.round(2).value_counts()\n",
    "#Y hay correspondencia así que que estos datos no hay que convertirlos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bnY-CdoyDbs7"
   },
   "source": [
    "**- Rooms:** Completamos esta columna con description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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     "name": "stderr",
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     "text": [
      "C:\\Users\\ariel.dapia\\anaconda3\\envs\\dhdsblend2021\\lib\\site-packages\\pandas\\core\\indexing.py:1596: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "C:\\Users\\ariel.dapia\\anaconda3\\envs\\dhdsblend2021\\lib\\site-packages\\pandas\\core\\indexing.py:1743: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "C:\\Users\\ariel.dapia\\anaconda3\\envs\\dhdsblend2021\\lib\\site-packages\\pandas\\core\\series.py:4576: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
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   "source": [
    "#REGEX para rooms\n",
    "\n",
    "patron = \"(?P<cant_rooms>\\d+)(\\s?)(amb)\"\n",
    "regex = re.compile(patron, flags=re.IGNORECASE)\n",
    "dfCapi.loc[:,\"rooms_reg\"] = dfCapi.rooms\n",
    "dfCapi.loc[:,\"rooms_reg\"] = dfCapi.apply(lambda x: np.int(regex.search(x.description).group(\"cant_rooms\"))\n",
    "                                         if ((regex.search(x.description) != None) & (np.isnan(x.rooms_reg)))\n",
    "                                         else x.rooms_reg, axis=1)\n",
    "\n",
    "patron = \"(?P<cant_rooms>un|mono|dos|tres|cuatro)(-?)(\\s?)(amb)\"\n",
    "regex = re.compile(patron, flags=re.IGNORECASE)\n",
    "dfCapi.loc[:,\"rooms_reg\"] = dfCapi.apply(lambda x: regex.search(x.description).group(\"cant_rooms\").lower() \n",
    "                                         if ((regex.search(x.description) != None) & (np.isnan(x.rooms_reg))) \n",
    "                                         else x.rooms_reg, axis=1)\n",
    "dfCapi[\"rooms_reg\"].replace({\"un\":1, \"mono\":1, \"dos\": 2, \"tres\": 3, \"cuatro\":4}, inplace=True)\n"
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   ],
   "source": [
    "dfRooms = dfCapi.loc[:,['rooms','rooms_reg']]\n",
    "porcentajes_nulos(dfRooms,400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gZquu2vgDN-n"
   },
   "source": [
    "**- Barrios:**\n",
    "algunos tienen place_name = 'Capital Federal'. Los tratamos de reemplzar con la descripción y lat-lon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lH3uu6cSnzw_"
   },
   "source": [
    "Hicimos un grafico de burbujas con superficie y m2 por barrio y vimos que habían varias propiedades con place_name = Capital Federal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "j2ESJtq-n3QG"
   },
   "outputs": [],
   "source": [
    "Burbujas = dfCapi.groupby(['place_name']).aggregate({'place_name':'count', 'surface_total_in_m2':'mean','price_usd_per_m2':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "AskZiX-in3Sf"
   },
   "outputs": [],
   "source": [
    "Burbujas = Burbujas.rename(columns={'place_name':'Count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "0dRph3Zzn3VE",
    "outputId": "38127033-ebef-497b-d5bd-f43af0732bc0"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>place_name</th>\n",
       "      <th>Count</th>\n",
       "      <th>surface_total_in_m2</th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abasto</td>\n",
       "      <td>100</td>\n",
       "      <td>95.047619</td>\n",
       "      <td>2048.205347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Agronomía</td>\n",
       "      <td>40</td>\n",
       "      <td>102.592593</td>\n",
       "      <td>2065.200228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Almagro</td>\n",
       "      <td>1056</td>\n",
       "      <td>79.348264</td>\n",
       "      <td>2762.905303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Balvanera</td>\n",
       "      <td>642</td>\n",
       "      <td>98.407098</td>\n",
       "      <td>1956.755333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Barracas</td>\n",
       "      <td>406</td>\n",
       "      <td>117.833333</td>\n",
       "      <td>1978.223779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Villa Riachuelo</td>\n",
       "      <td>6</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>1110.690766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Villa Santa Rita</td>\n",
       "      <td>56</td>\n",
       "      <td>129.423077</td>\n",
       "      <td>1871.349906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Villa Soldati</td>\n",
       "      <td>14</td>\n",
       "      <td>3313.666667</td>\n",
       "      <td>736.929399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Villa Urquiza</td>\n",
       "      <td>1415</td>\n",
       "      <td>72.777388</td>\n",
       "      <td>2501.582595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Villa del Parque</td>\n",
       "      <td>433</td>\n",
       "      <td>95.403125</td>\n",
       "      <td>2183.736414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 4 columns</p>\n",
       "</div>"
      ],
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       "          place_name  Count  surface_total_in_m2  price_usd_per_m2\n",
       "0             Abasto    100            95.047619       2048.205347\n",
       "1          Agronomía     40           102.592593       2065.200228\n",
       "2            Almagro   1056            79.348264       2762.905303\n",
       "3          Balvanera    642            98.407098       1956.755333\n",
       "4           Barracas    406           117.833333       1978.223779\n",
       "..               ...    ...                  ...               ...\n",
       "57   Villa Riachuelo      6           226.000000       1110.690766\n",
       "58  Villa Santa Rita     56           129.423077       1871.349906\n",
       "59     Villa Soldati     14          3313.666667        736.929399\n",
       "60     Villa Urquiza   1415            72.777388       2501.582595\n",
       "61  Villa del Parque    433            95.403125       2183.736414\n",
       "\n",
       "[62 rows x 4 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfBub = pd.DataFrame(Burbujas)\n",
    "dfBub.reset_index(inplace=True)\n",
    "dfBub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 717
    },
    "id": "ZTiGvKh-n-da",
    "outputId": "437d28d0-2c59-47b9-9879-0f56b681b110"
   },
   "outputs": [
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       "                            \n",
       "var gd = document.getElementById('7062440c-3dad-4063-b9f3-35361e9b38cb');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(data_frame = dfBub, x = \"surface_total_in_m2\", y = \"price_usd_per_m2\",\n",
    "                 size = \"Count\", color = \"place_name\", opacity = 0.6,\n",
    "                 hover_name=\"place_name\", width=700, height=700)\n",
    "\n",
    "fig.update_xaxes(rangeslider_visible=True)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wRE6Q1zh1DCI"
   },
   "source": [
    "El tamaño de las burbujas representa la cantidad de propiedades. Es interesante ver cómo Puerto Madero y Palermo Chico estan bien alejadas del resto de los barrios, con alta superficie y costo por m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QOiBnCY5BMty"
   },
   "source": [
    "Ahora vamos a reemplazar los casos con place_name = 'Capital Federal' a partir de la latitud y longitud de las propiedades. Vamos a utilizar un csv que contiene info geografica de cada barrio ('https://raw.githubusercontent.com/francodegio/DigitalHouse/master/barrios.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XM1dfJmPCzp6",
    "outputId": "4999f8aa-bfc4-4641-a8cc-febf70a75f0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Abasto' 'Agronomía' 'Almagro' 'Balvanera' 'Barracas' 'Barrio Norte'\n",
      " 'Belgrano' 'Boca' 'Boedo' 'Caballito' 'Capital Federal' 'Catalinas'\n",
      " 'Centro / Microcentro' 'Chacarita' 'Coghlan' 'Colegiales' 'Congreso'\n",
      " 'Constitución' 'Flores' 'Floresta' 'Las Cañitas' 'Liniers' 'Mataderos'\n",
      " 'Monserrat' 'Monte Castro' 'Nuñez' 'Once' 'Palermo' 'Palermo Chico'\n",
      " 'Palermo Hollywood' 'Palermo Soho' 'Palermo Viejo' 'Parque Avellaneda'\n",
      " 'Parque Centenario' 'Parque Chacabuco' 'Parque Chas' 'Parque Patricios'\n",
      " 'Paternal' 'Pompeya' 'Puerto Madero' 'Recoleta' 'Retiro' 'Saavedra'\n",
      " 'San Cristobal' 'San Nicolás' 'San Telmo' 'Tribunales' 'Velez Sarsfield'\n",
      " 'Versalles' 'Villa Crespo' 'Villa Devoto' 'Villa General Mitre'\n",
      " 'Villa Lugano' 'Villa Luro' 'Villa Ortuzar' 'Villa Pueyrredón'\n",
      " 'Villa Real' 'Villa Riachuelo' 'Villa Santa Rita' 'Villa Soldati'\n",
      " 'Villa Urquiza' 'Villa del Parque']\n",
      "\n",
      "\n",
      "|Argentina|Capital Federal|    1013\n",
      "Name: place_with_parent_names, dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Lista de barrios\n",
    "place = np.sort(dfCapi.place_name.unique())\n",
    "print(place) #Llama la atencion el barrio Capital Federal y otros barrios no formales\n",
    "print(\"\\n\")\n",
    "\n",
    "#Me fijo si para los casos de place_name = capital federal, podria usar el campo place with parents \n",
    "print(dfCapi[dfCapi[\"place_name\"] == \"Capital Federal\"]['place_with_parent_names'].value_counts())\n",
    "print(\"\\n\")\n",
    "#La respuesta es NO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Sy2aEMLPjoeO",
    "outputId": "9636a712-a5bb-4c8c-968b-0131d94a63d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "345\n"
     ]
    }
   ],
   "source": [
    "#Para una proporcion de ellos podriamos usar las coordenadas\n",
    "print(dfCapi[dfCapi[\"place_name\"] == \"Capital Federal\"]['lat-lon'].notnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "vDWFSaMZqKOl"
   },
   "outputs": [],
   "source": [
    "# Creamos GeoDataframes que nos permitan analizar dentro que poligono (barrio) se encuentran las propiedades \n",
    "#con place name = \"Capital Federal\", a partir de los campos Lat y Lon\n",
    "barrios=pd.read_csv('https://raw.githubusercontent.com/francodegio/DigitalHouse/master/barrios.csv',encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8fuMGtMHEYd4",
    "outputId": "5ce268b3-5290-4cb2-91d9-d5eaa8eefee6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ariel.dapia\\anaconda3\\envs\\dhdsblend2021\\lib\\site-packages\\pandas\\core\\generic.py:5170: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Ponemos todo en minuscula\n",
    "barrios.BARRIO=barrios.BARRIO.apply(str.lower)\n",
    "barrios=barrios.rename(columns={\"BARRIO\": \"barrio\"})\n",
    "dfCapi.place_name = dfCapi.place_name.apply(str.lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "id": "T3jgcFmKDU5Y",
    "outputId": "df711e70-1b3e-4001-db38-8685101f1240"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "  <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</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",
       "      <th>rooms_reg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>place_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>abasto</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-34.603732</td>\n",
       "      <td>-58.411528</td>\n",
       "      <td>184995.947368</td>\n",
       "      <td>2.989968e+06</td>\n",
       "      <td>169456.103895</td>\n",
       "      <td>95.047619</td>\n",
       "      <td>75.626374</td>\n",
       "      <td>2048.205347</td>\n",
       "      <td>2573.271558</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.647059</td>\n",
       "      <td>1922.400000</td>\n",
       "      <td>3.146341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agronomía</th>\n",
       "      <td>3436487.0</td>\n",
       "      <td>-34.592870</td>\n",
       "      <td>-58.485720</td>\n",
       "      <td>170054.861111</td>\n",
       "      <td>3.000533e+06</td>\n",
       "      <td>170054.861111</td>\n",
       "      <td>102.592593</td>\n",
       "      <td>87.342105</td>\n",
       "      <td>2065.200228</td>\n",
       "      <td>2665.513373</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3019.125000</td>\n",
       "      <td>2.973684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>almagro</th>\n",
       "      <td>3436397.0</td>\n",
       "      <td>-34.608143</td>\n",
       "      <td>-58.421318</td>\n",
       "      <td>225355.004301</td>\n",
       "      <td>3.045516e+06</td>\n",
       "      <td>172604.270871</td>\n",
       "      <td>79.348264</td>\n",
       "      <td>75.038424</td>\n",
       "      <td>2762.905303</td>\n",
       "      <td>4457.424330</td>\n",
       "      <td>4.361702</td>\n",
       "      <td>2.540741</td>\n",
       "      <td>2395.741007</td>\n",
       "      <td>2.441081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balvanera</th>\n",
       "      <td>6693228.0</td>\n",
       "      <td>-34.609790</td>\n",
       "      <td>-58.402820</td>\n",
       "      <td>169973.251645</td>\n",
       "      <td>2.833453e+06</td>\n",
       "      <td>160585.631184</td>\n",
       "      <td>98.407098</td>\n",
       "      <td>98.850420</td>\n",
       "      <td>1956.755333</td>\n",
       "      <td>2452.584236</td>\n",
       "      <td>5.914286</td>\n",
       "      <td>2.881068</td>\n",
       "      <td>2448.841270</td>\n",
       "      <td>2.778378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>barracas</th>\n",
       "      <td>3436134.0</td>\n",
       "      <td>-34.638086</td>\n",
       "      <td>-58.374490</td>\n",
       "      <td>192569.402532</td>\n",
       "      <td>3.307664e+06</td>\n",
       "      <td>187461.500886</td>\n",
       "      <td>117.833333</td>\n",
       "      <td>101.478378</td>\n",
       "      <td>1978.223779</td>\n",
       "      <td>2372.392671</td>\n",
       "      <td>13.920000</td>\n",
       "      <td>2.925439</td>\n",
       "      <td>2450.947368</td>\n",
       "      <td>2.831395</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>villa real</th>\n",
       "      <td>3427383.0</td>\n",
       "      <td>-34.620126</td>\n",
       "      <td>-58.524726</td>\n",
       "      <td>187378.378378</td>\n",
       "      <td>3.306198e+06</td>\n",
       "      <td>187378.378378</td>\n",
       "      <td>153.888889</td>\n",
       "      <td>102.945946</td>\n",
       "      <td>1467.577619</td>\n",
       "      <td>2034.242961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.294118</td>\n",
       "      <td>3750.000000</td>\n",
       "      <td>3.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>villa riachuelo</th>\n",
       "      <td>3427379.0</td>\n",
       "      <td>-34.693764</td>\n",
       "      <td>-58.469724</td>\n",
       "      <td>160000.000000</td>\n",
       "      <td>2.823120e+06</td>\n",
       "      <td>160000.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>91.166667</td>\n",
       "      <td>1110.690766</td>\n",
       "      <td>2938.233398</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>villa santa rita</th>\n",
       "      <td>6693230.0</td>\n",
       "      <td>-34.615461</td>\n",
       "      <td>-58.480298</td>\n",
       "      <td>178864.545455</td>\n",
       "      <td>3.155975e+06</td>\n",
       "      <td>178864.545455</td>\n",
       "      <td>129.423077</td>\n",
       "      <td>62.388889</td>\n",
       "      <td>1871.349906</td>\n",
       "      <td>38539.016206</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>2.589744</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>2.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>villa soldati</th>\n",
       "      <td>3427366.0</td>\n",
       "      <td>-34.662783</td>\n",
       "      <td>-58.445758</td>\n",
       "      <td>765079.357143</td>\n",
       "      <td>1.232170e+07</td>\n",
       "      <td>698330.988571</td>\n",
       "      <td>3313.666667</td>\n",
       "      <td>945.538462</td>\n",
       "      <td>736.929399</td>\n",
       "      <td>2188.063752</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>3.727273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>villa urquiza</th>\n",
       "      <td>3433775.0</td>\n",
       "      <td>-34.572991</td>\n",
       "      <td>-58.482989</td>\n",
       "      <td>487818.211884</td>\n",
       "      <td>3.041284e+06</td>\n",
       "      <td>172364.395582</td>\n",
       "      <td>72.777388</td>\n",
       "      <td>64.330182</td>\n",
       "      <td>2501.582595</td>\n",
       "      <td>8675.385150</td>\n",
       "      <td>47.782609</td>\n",
       "      <td>2.330724</td>\n",
       "      <td>2105.715116</td>\n",
       "      <td>4.129382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  geonames_id        lat        lon          price  \\\n",
       "place_name                                                           \n",
       "abasto                    NaN -34.603732 -58.411528  184995.947368   \n",
       "agronomía           3436487.0 -34.592870 -58.485720  170054.861111   \n",
       "almagro             3436397.0 -34.608143 -58.421318  225355.004301   \n",
       "balvanera           6693228.0 -34.609790 -58.402820  169973.251645   \n",
       "barracas            3436134.0 -34.638086 -58.374490  192569.402532   \n",
       "...                       ...        ...        ...            ...   \n",
       "villa real          3427383.0 -34.620126 -58.524726  187378.378378   \n",
       "villa riachuelo     3427379.0 -34.693764 -58.469724  160000.000000   \n",
       "villa santa rita    6693230.0 -34.615461 -58.480298  178864.545455   \n",
       "villa soldati       3427366.0 -34.662783 -58.445758  765079.357143   \n",
       "villa urquiza       3433775.0 -34.572991 -58.482989  487818.211884   \n",
       "\n",
       "                  price_aprox_local_currency  price_aprox_usd  \\\n",
       "place_name                                                      \n",
       "abasto                          2.989968e+06    169456.103895   \n",
       "agronomía                       3.000533e+06    170054.861111   \n",
       "almagro                         3.045516e+06    172604.270871   \n",
       "balvanera                       2.833453e+06    160585.631184   \n",
       "barracas                        3.307664e+06    187461.500886   \n",
       "...                                      ...              ...   \n",
       "villa real                      3.306198e+06    187378.378378   \n",
       "villa riachuelo                 2.823120e+06    160000.000000   \n",
       "villa santa rita                3.155975e+06    178864.545455   \n",
       "villa soldati                   1.232170e+07    698330.988571   \n",
       "villa urquiza                   3.041284e+06    172364.395582   \n",
       "\n",
       "                  surface_total_in_m2  surface_covered_in_m2  \\\n",
       "place_name                                                     \n",
       "abasto                      95.047619              75.626374   \n",
       "agronomía                  102.592593              87.342105   \n",
       "almagro                     79.348264              75.038424   \n",
       "balvanera                   98.407098              98.850420   \n",
       "barracas                   117.833333             101.478378   \n",
       "...                               ...                    ...   \n",
       "villa real                 153.888889             102.945946   \n",
       "villa riachuelo            226.000000              91.166667   \n",
       "villa santa rita           129.423077              62.388889   \n",
       "villa soldati             3313.666667             945.538462   \n",
       "villa urquiza               72.777388              64.330182   \n",
       "\n",
       "                  price_usd_per_m2  price_per_m2      floor     rooms  \\\n",
       "place_name                                                              \n",
       "abasto                 2048.205347   2573.271558   5.000000  2.647059   \n",
       "agronomía              2065.200228   2665.513373  17.500000  3.000000   \n",
       "almagro                2762.905303   4457.424330   4.361702  2.540741   \n",
       "balvanera              1956.755333   2452.584236   5.914286  2.881068   \n",
       "barracas               1978.223779   2372.392671  13.920000  2.925439   \n",
       "...                            ...           ...        ...       ...   \n",
       "villa real             1467.577619   2034.242961        NaN  3.294118   \n",
       "villa riachuelo        1110.690766   2938.233398  12.000000  3.666667   \n",
       "villa santa rita       1871.349906  38539.016206   5.700000  2.589744   \n",
       "villa soldati           736.929399   2188.063752   4.666667  3.727273   \n",
       "villa urquiza          2501.582595   8675.385150  47.782609  2.330724   \n",
       "\n",
       "                     expenses  rooms_reg  \n",
       "place_name                                \n",
       "abasto            1922.400000   3.146341  \n",
       "agronomía         3019.125000   2.973684  \n",
       "almagro           2395.741007   2.441081  \n",
       "balvanera         2448.841270   2.778378  \n",
       "barracas          2450.947368   2.831395  \n",
       "...                       ...        ...  \n",
       "villa real        3750.000000   3.212121  \n",
       "villa riachuelo           NaN   3.600000  \n",
       "villa santa rita   800.000000   2.680000  \n",
       "villa soldati        1.000000   3.727273  \n",
       "villa urquiza     2105.715116   4.129382  \n",
       "\n",
       "[62 rows x 14 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Armamos una pivot con los place_name de nuestro data frame para checkear si matchean con el csv barrios\n",
    "dfCapi_grouped=dfCapi.groupby('place_name').mean()\n",
    "dfCapi_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D0zgZgcqDDH3",
    "outputId": "2cded82d-90b3-4777-f476-a71e0e3642d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constitucion\n",
      "villa gral. mitre\n",
      "agronomia\n",
      "nueva pompeya\n",
      "villa pueyrredon\n",
      "san nicolas\n"
     ]
    }
   ],
   "source": [
    "for i,x in barrios.iterrows():\n",
    "    try:\n",
    "        j=dfCapi_grouped.index.get_loc(x.barrio)\n",
    "    except:\n",
    "        # printeo los que no encuentra\n",
    "        print(x.barrio) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "-YNxAagxEg6f"
   },
   "outputs": [],
   "source": [
    "#Vamos a reemplazar estos valores en barrios para que coincidan con nuestr df \n",
    "barrios['barrio'].replace({\"nueva pompeya\": \"pompeya\",\"constitucion\": \"constitución\", \"villa gral. mitre\": \"villa general mitre\", \"agronomia\": \"agronomía\", \"villa pueyrredon\": \"villa pueyrredón\", \"san nicolas\": \"san nicolás\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "odOL-YX_E_t4"
   },
   "outputs": [],
   "source": [
    "#Checkeamos que el replace haya sido exitoso\n",
    "for i,x in barrios.iterrows():\n",
    "    try:\n",
    "        j=dfCapi_grouped.index.get_loc(x.barrio)\n",
    "    except:\n",
    "        # printeo los que no encuentra\n",
    "        print(x.barrio) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dfCapi.to_csv(\"parte1.csv\")\n",
    "# barrios.to_csv(\"barrios.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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