Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa = pd.read_csv(\"fotocasa.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Antiquity', 'Bathrooms', 'Community_Expenses', 'Condition', 'Deposit',\n",
       "       'Elevator', 'Emissions', 'Energy_Consumption', 'Extra', 'Floor',\n",
       "       'Furnished', 'Heating', 'Neighborhood', 'Orientation', 'Parking',\n",
       "       'Pets', 'Photos', 'Price', 'Rooms', 'Size', 'Title', 'Type',\n",
       "       'Water_Heater', 'url'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Antiquity             62.042081\n",
       "Bathrooms              0.640820\n",
       "Community_Expenses    54.565844\n",
       "Condition             50.197586\n",
       "Deposit               81.918189\n",
       "Elevator              27.160098\n",
       "Extra                  3.140019\n",
       "Floor                 33.205169\n",
       "Furnished             32.265300\n",
       "Heating               63.120795\n",
       "Orientation           75.104133\n",
       "Parking               78.137349\n",
       "Pets                  93.260707\n",
       "Rooms                  5.756702\n",
       "Size                   0.469935\n",
       "Water_Heater          69.411513\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa.isna().sum()[fotocasa.isna().sum()/len(fotocasa)*100 > 0]/len(fotocasa)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  4  cleaned columns:\n",
      "Index(['Emissions', 'Energy_Consumption', 'Neighborhood', 'Photos', 'Price',\n",
      "       'Title', 'Type', 'url'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "cleaned_columns = len(fotocasa.isna().sum()[fotocasa.isna().sum()/len(fotocasa)*100 == 0])\n",
    "print(\"There are \", cleaned_columns-4, \" cleaned columns:\")\n",
    "print(fotocasa.isna().sum()[fotocasa.isna().sum()/len(fotocasa)*100 == 0].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clean_price(x):\n",
    "    if x!= \"A consultar\":\n",
    "        return x.replace(\".\",\"\").rstrip(\"€\")\n",
    "    else:\n",
    "        return np.nan\n",
    "fotocasa[\"Price\"].apply(clean_price).isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa[\"Price\"] = fotocasa[\"Price\"].apply(clean_price)\n",
    "fotocasa = fotocasa.dropna(axis=0, subset = [\"Price\"])\n",
    "fotocasa[\"Price\"] = fotocasa[\"Price\"].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting district and borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "districts = [\"Centro\",\"Arganzuela\",\"Retiro\",\"Salamanca\",\"Chamartín\",\"Tetuán\",\"Chamberí\",\"Fuencarral\",\n",
    "             \"Moncloa\",\"Latina\",\"Carabanchel\",\"Usera\",\"Puente de Vallecas\",\"Moratalaz\",\"Ciudad Lineal\",\n",
    "             \"Hortaleza\",\"Villaverde\",\"Villa de Vallecas\",\"Vicálvaro\",\"San Blas\",\"Barajas\"]\n",
    "boroughs = [\"El Pardo\", \"Fuentelareina\", \"Peñagrande\", \"Pilar\", \"La Paz\", \"Valverde\", \"Mirasierra\",\n",
    "                \"El Goloso\", \"Casa de Campo\", \"Argüelles\", \"Ciudad Universitaria\", \"Valdezarza\", \n",
    "                \"Valdemarín\", \"El Plantío\", \"Aravaca\", \"Bellas Vistas\", \"Cuatro Caminos\", \"Castillejos\", \n",
    "                \"Almenara\", \"Valdeacederas\", \"Berruguete\", \"Palomas\", \"Piovera\", \"Canillas\", \n",
    "                \"Pinar del Rey\", \"Apóstol Santiago\", \"Valdefuentes\", \"El Viso\", \"Prosperidad\", \n",
    "                \"Ciudad Jardín\", \"Hispanoamérica\", \"Nueva España\", \"Castilla\", \"Ventas\", \"Pueblo Nuevo\", \n",
    "                \"Quintana\", \"Concepción\", \"San Pascual\", \"San Juan Bautista\", \"Colina\", \"Atalaya\", \n",
    "                \"Costillares\", \"Gaztambide\", \"Arapiles\", \"Trafalgar\", \"Almagro\", \"Ríos Rosas\", \n",
    "                \"Vallehermoso\",\"Recoletos\", \"Goya\", \"Fuente del Berro\", \"Guindalera\", \"Lista\", \n",
    "                \"Castellana\", \"Simancas\", \"Hellín\", \"Amposta\", \"Arcos\", \"Rosas\", \"Rejas\", \"Canillejas\", \n",
    "                \"Salvador\", \"Alameda de Osuna\", \"Aeropuerto\", \"Casco Histórico de Barajas\", \"Timón\", \n",
    "                \"Corralejos\", \"Palacio\", \"Embajadores\", \"Cortes\", \"Justicia\", \"Universidad\", \"Sol\", \n",
    "                \"Pacífico\", \"Adelfas\", \"Estrella\", \"Ibiza\", \"Jerónimos\", \"Niño Jesús\", \"Imperial\", \n",
    "                \"Acacias\", \"Chopera\", \"Legazpi\", \"Delicias\", \"Palos de Moguer\", \"Atocha\", \"Pavones\", \n",
    "                \"Horcajo\", \"Marroquina\", \"Media Legua\", \"Fontarrón\", \"Vinateros\", \"Villaverde Alto\", \n",
    "                \"San Cristobal\", \"Butarque\", \"Los Rosales\", \"Los Ángeles\", \"Orcasitas\", \"Orcasur\", \n",
    "                \"San Fermín\", \"Almendrales\", \"Moscardó\", \"Zofío\", \"Pradolongo\", \"Cármenes\", \n",
    "                \"Puerta del Ángel\", \"Lucero\", \"Aluche\", \"Campamento\", \"Cuatro Vientos\", \"Águilas\", \n",
    "                \"Comillas\", \"Opañel\", \"San Isidro\", \"Vista Alegre\", \"Puerta Bonita\", \"Buenavista\", \n",
    "                \"Abrantes\", \"Casco Histórico de Vicálvaro\", \"Valdebernardo\", \"Valderrivas\", \"El Cañaveral\",\n",
    "                \"Entrevías\", \"San Diego\", \"Palomeras Bajas\", \"Palomeras Sureste\", \"Portazgo\", \"Numancia\",\n",
    "                \"Casco Histórico de Vallecas\", \"Santa Eugenia\", \"Ensanche de Vallecas\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  27  missing value(s)\n"
     ]
    }
   ],
   "source": [
    "from unidecode import unidecode\n",
    "\n",
    "def get_district(x):\n",
    "    comparison = [unidecode(i).lower() in unidecode(x).lower() for i in districts]\n",
    "    try: \n",
    "        idx = comparison.index(True)\n",
    "        return districts[idx]\n",
    "    except:\n",
    "        return np.nan\n",
    "fotocasa[\"District\"] = fotocasa[\"Neighborhood\"].apply(get_district)\n",
    "print(\"There are \", fotocasa[\"District\"].isna().sum(), \" missing value(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  333  missing value(s)\n"
     ]
    }
   ],
   "source": [
    "def get_borough(x):\n",
    "    comparison = [unidecode(i).lower() in unidecode(x).lower() for i in boroughs]\n",
    "    try: \n",
    "        idx = comparison.index(True)\n",
    "        return boroughs[idx]\n",
    "    except:\n",
    "        return np.nan\n",
    "fotocasa[\"Borough\"] = fotocasa[\"Title\"].apply(get_borough)\n",
    "print(\"There are \", fotocasa[\"Borough\"].isna().sum(), \" missing value(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Sanchinarro': 93,\n",
       " 'Tablas': 60,\n",
       " 'Manoteras': 36,\n",
       " 'Carabanchel': 30,\n",
       " 'Capital': 29,\n",
       " 'Villaverde': 28,\n",
       " 'Montecarmelo': 24,\n",
       " 'Ambroz': 12,\n",
       " 'Fresno': 11,\n",
       " 'Plantío': 8,\n",
       " 'Usera': 1,\n",
       " 'Fuentelarreina': 1}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seeing missing boroughs\n",
    "def clean_title(x):\n",
    "    return x.split(\",\")[-1].split(\" \")[-1]\n",
    "missing_boroughs = fotocasa.loc[fotocasa[\"Borough\"].isna(),\"Title\"].apply(clean_title).to_list()\n",
    "missing_boroughs_dict = {}\n",
    "for missing in missing_boroughs:\n",
    "    if missing in missing_boroughs_dict:\n",
    "        missing_boroughs_dict[missing] += 1\n",
    "    else:\n",
    "        missing_boroughs_dict[missing] = 1\n",
    "{k: v for k, v in sorted(missing_boroughs_dict.items(), key=lambda item: item[1], reverse = True)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  30  missing value(s)\n"
     ]
    }
   ],
   "source": [
    "def get_borough(x):\n",
    "    x = x.replace(\"  \",\" \")\n",
    "    if \"Sanchinarro\".lower() in x.lower():\n",
    "        return \"Valdefuentes\"\n",
    "    elif \"Tablas\".lower() in x.lower():\n",
    "        return \"Valverde\"\n",
    "    elif unidecode(\"San Andrés de Villaverde\").lower() in unidecode(x).lower():\n",
    "        return \"Villaverde Alto\"\n",
    "    elif \"Montecarmelo\".lower() in x.lower():\n",
    "        return \"El Goloso\"\n",
    "    elif \"Manoteras\".lower() in x.lower():\n",
    "        return \"Apóstol Santiago\"\n",
    "    elif \"PAU\" in x:\n",
    "        return \"Buenavista\"\n",
    "    elif \"Ambroz\".lower() in x.lower():\n",
    "        return \"Casco Histórico de Vicálvaro\"\n",
    "    elif \"Arroyo del Fresno\".lower() in x.lower():\n",
    "        return \"Mirasierra\"\n",
    "    elif \"Fuentelarreina\".lower() in x.lower():\n",
    "        return \"Fuentelareina\"\n",
    "    elif \"Valderribas\".lower() in x.lower():\n",
    "        return \"Valderrivas\"\n",
    "    else:\n",
    "        comparison = [unidecode(i).lower() in unidecode(x).lower() for i in boroughs]\n",
    "        try: \n",
    "            idx = comparison.index(True)\n",
    "            return boroughs[idx]\n",
    "        except:\n",
    "            return np.nan\n",
    "fotocasa[\"Borough\"] = fotocasa[\"Title\"].apply(get_borough)\n",
    "print(\"There are \", fotocasa[\"Borough\"].isna().sum(), \" missing value(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Piso de alquiler en Usera\n",
      "Piso de alquiler en Calle Calle de Cartagena, 87  Madrid Capital\n",
      "Loft de alquiler en Calle Miguel Yuste, 21  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de Marcelino Roa Vázquez, 51  Madrid Capital\n",
      "Ático de alquiler en Calle de Santa Eulalia, 12  Madrid Capital\n",
      "Piso de alquiler en Calle de Los Ceramistas, 2  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de Padilla, 67  Madrid Capital\n",
      "Piso de alquiler en Paseo Paseo de Santa María de la Cabeza, 16  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de Corazón de María, 57  Madrid Capital\n",
      "Piso de alquiler en Calle Fuencarral, 43  Madrid Capital\n",
      "Piso de alquiler en Calle de Blasco de Garay, 63  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de Palomares, .  Madrid Capital\n",
      "Piso de alquiler en Calle Milán, 10  Madrid Capital\n",
      "Piso de alquiler en Calle Camino de la Zarzuela, 49  Madrid Capital\n",
      "Piso de alquiler en Alameda Calle de Carretas, .  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de Juan Bravo, 57  Madrid Capital\n",
      "Piso de alquiler en Plaza Bastero  17, .  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de Sánchez Barcáiztegui, 18  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de Covarrubias, 29  Madrid Capital\n",
      "Piso de alquiler en Calle Calle del Doctor Drumen, .  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de San Blas, 3  Madrid Capital\n",
      "Piso de alquiler en Avenida Avenida de Concha Espina, 15  Madrid Capital\n",
      "Ático de alquiler en Avenida de Francisco Pi y Margall, 1d  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de Antonio Vicent, .  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de Ágata, 4  Madrid Capital\n",
      "Piso de alquiler en Paseo Paseo del General Martínez Campos, 51  Madrid Capital\n",
      "Piso de alquiler en  Madrid Capital\n",
      "Piso de alquiler en Calle Calle del Tesoro, 19  Madrid Capital\n",
      "Piso de alquiler en  Madrid Capital\n",
      "Piso de alquiler en Calle Calle de Antonio Vicent, .  Madrid Capital\n"
     ]
    }
   ],
   "source": [
    "titles = fotocasa.loc[fotocasa[\"Borough\"].isna(),\"Title\"].values\n",
    "for title in titles:\n",
    "    print(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa = fotocasa.dropna(axis=0, subset = [\"District\",\"Borough\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Antiquity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30 a 50 años      739\n",
       "50 a 70 años      709\n",
       "10 a 20 años      536\n",
       "70 a 100 años     485\n",
       "+ 100 años        434\n",
       "20 a 30 años      316\n",
       "Menos de 1 año    120\n",
       "1 a 5 años        119\n",
       "5 a 10 años        84\n",
       "Name: Antiquity, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Antiquity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_antiquity(x):\n",
    "    try:\n",
    "        return x.rstrip(\" años\").replace(\" \",\"\").replace(\"a\",\"-\").replace(\"Menosde\",\"<\")\n",
    "    except AttributeError:\n",
    "        return x\n",
    "    \n",
    "fotocasa[\"Antiquity\"] = fotocasa[\"Antiquity\"].apply(clean_antiquity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30-50     739\n",
       "50-70     709\n",
       "10-20     536\n",
       "70-100    485\n",
       "+100      434\n",
       "20-30     316\n",
       "<1        120\n",
       "1-5       119\n",
       "5-10       84\n",
       "Name: Antiquity, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Antiquity\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding missing values with the average of the borough and then district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for borough in fotocasa.loc[fotocasa[\"Antiquity\"].isna(),\"Borough\"].unique():\n",
    "    filter_borough = fotocasa[\"Borough\"] == borough\n",
    "    try:\n",
    "        # Antiquity\n",
    "        frequent_antiquity = fotocasa.loc[filter_borough, \"Antiquity\"].mode().loc[0]\n",
    "        fotocasa.loc[fotocasa[\"Antiquity\"].isna() & filter_borough,\"Antiquity\"] = frequent_antiquity\n",
    "    except KeyError:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for district in fotocasa.loc[fotocasa[\"Antiquity\"].isna(),\"District\"].unique():\n",
    "    filter_district = fotocasa[\"District\"] == district\n",
    "    try:\n",
    "        # Antiquity\n",
    "        frequent_antiquity = fotocasa.loc[filter_district, \"Antiquity\"].mode().loc[0]\n",
    "        fotocasa.loc[fotocasa[\"Antiquity\"].isna() & filter_district,\"Antiquity\"] = frequent_antiquity\n",
    "    except KeyError:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Antiquity\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bathrooms and rooms\n",
    "At least one bathroom and room per property"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa[\"Bathrooms\"] = fotocasa[\"Bathrooms\"].fillna(1).astype(int)\n",
    "fotocasa[\"Rooms\"] = fotocasa[\"Rooms\"].fillna(1).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Size\n",
    "We will use the average size per borough as we did with the antiquity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for borough in fotocasa.loc[fotocasa[\"Size\"].isna(),\"District\"].unique():\n",
    "    filter_borough = fotocasa[\"Borough\"] == borough\n",
    "    try:\n",
    "        # Antiquity\n",
    "        frequent_antiquity = fotocasa.loc[filter_borough, \"Size\"].mean()\n",
    "        fotocasa.loc[fotocasa[\"Size\"].isna() & filter_borough,\"Size\"] = frequent_antiquity\n",
    "    except KeyError:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for district in fotocasa.loc[fotocasa[\"Size\"].isna(),\"District\"].unique():\n",
    "    filter_district = fotocasa[\"District\"] == district\n",
    "    try:\n",
    "        # Antiquity\n",
    "        frequent_antiquity = fotocasa.loc[filter_district, \"Size\"].mode().loc[0]\n",
    "        fotocasa.loc[fotocasa[\"Size\"].isna() & filter_district, \"Size\"] = frequent_antiquity\n",
    "    except KeyError:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa[\"Size\"] = fotocasa[\"Size\"].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_extras = fotocasa[\"Extra\"].fillna(\"\").apply(lambda x: x+\"-\").sum().split(\"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Armarios': 6550,\n",
       " 'Aire acondicionado': 5893,\n",
       " 'Cocina Equipada': 5530,\n",
       " 'Electrodomésticos': 4923,\n",
       " 'Nevera': 4523,\n",
       " 'Lavadora': 4493,\n",
       " 'Calefacción': 4360,\n",
       " 'Horno': 3508,\n",
       " 'Puerta Blindada': 3507,\n",
       " 'Parquet': 3179,\n",
       " 'Microondas': 3035,\n",
       " 'Serv. portería': 3024,\n",
       " 'Terraza': 2128,\n",
       " 'Suite ': 1612,\n",
       " ' con baño': 1612,\n",
       " 'TV': 1359,\n",
       " 'Balcón': 1336,\n",
       " 'Trastero': 1329,\n",
       " 'Cocina Office': 1279,\n",
       " 'Videoportero': 1199,\n",
       " 'Z. Comunitaria': 1120,\n",
       " 'Piscina comunitaria': 1031,\n",
       " 'Internet': 1014,\n",
       " 'Lavadero': 858,\n",
       " 'Piscina': 800,\n",
       " 'Gres Cerámica': 761,\n",
       " 'Zona Infantil': 557,\n",
       " 'Ascensor interior': 545,\n",
       " 'Jardín Privado': 503,\n",
       " 'Patio': 497,\n",
       " 'Zona Deportiva': 393,\n",
       " '': 291,\n",
       " 'Baño de huéspedes': 121,\n",
       " 'Gimnasio': 114,\n",
       " 'Alarma': 81,\n",
       " 'Domótica': 71,\n",
       " 'Energía Solar': 55,\n",
       " 'Cuarto para el servicio': 52,\n",
       " 'Cuarto lavado plancha': 47,\n",
       " 'Sauna': 28,\n",
       " 'Pista de Tenis': 20,\n",
       " 'Jacuzzi': 19,\n",
       " 'Muebles de diseño': 16,\n",
       " 'Porche cubierto': 15,\n",
       " 'Bodega': 11,\n",
       " 'Casa de invitados': 3,\n",
       " 'Agua Caliente': 1,\n",
       " 'Música ambiental': 1}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extras_dict = {}\n",
    "for extra in all_extras:\n",
    "    if extra in extras_dict:\n",
    "        extras_dict[extra] += 1\n",
    "    else:\n",
    "        extras_dict[extra] = 1\n",
    "{k: v for k, v in sorted(extras_dict.items(), key=lambda item: item[1], reverse = True)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5893"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Air_Conditioner\"] = fotocasa[\"Extra\"].fillna(\"\").apply(lambda x: int(\"Aire acondicionado\" in x))\n",
    "fotocasa[\"Air_Conditioner\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1540"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Community_Zone\"] = fotocasa[\"Extra\"].fillna(\"\") \\\n",
    "    .apply(lambda x: int((\"comunitaria\" in x.lower()) or (\"Zona\" in x)))\n",
    "fotocasa[\"Community_Zone\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3024"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Concierge\"] = fotocasa[\"Extra\"].fillna(\"\").apply(lambda x: int(\"Serv. portería\" in x))\n",
    "fotocasa[\"Concierge\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7982"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Equipped_Kitchen\"] = fotocasa[\"Extra\"].fillna(\"\") \\\n",
    "    .apply(lambda x: int((\"Cocina Equipada\" in x) or (\"Electrodomésticos\" in x) or (\"Nevera\" in x) \\\n",
    "                        or (\"Horno\" in x)))\n",
    "fotocasa[\"Equipped_Kitchen\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1612"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Ensuite\"] = fotocasa[\"Extra\"].fillna(\"\").apply(lambda x: int(\"Suite\" in x))\n",
    "fotocasa[\"Ensuite\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "504"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Garden\"] = fotocasa[\"Extra\"].fillna(\"\").apply(lambda x: int((\"Jardín\" in x) or (\"Porche\" in x)))\n",
    "fotocasa[\"Garden\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1388"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Swimming_Pool\"] = fotocasa[\"Extra\"].fillna(\"\").apply(lambda x: int(\"Piscina\" in x))\n",
    "fotocasa[\"Swimming_Pool\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2128"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Terrace\"] = fotocasa[\"Extra\"].fillna(\"\").apply(lambda x: int(\"Terraza\" in x))\n",
    "fotocasa[\"Terrace\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa[\"Len_Extras\"] = fotocasa[\"Extra\"].fillna(\"\").apply(lambda x: len(x.split(\"-\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Furnished"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sí    4255\n",
       "No    2058\n",
       "Name: Furnished, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Furnished\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa[\"Furnished\"] = fotocasa[\"Furnished\"].map({\"Sí\": \"1\", \"No\": \"0\"}).fillna(\"0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5068\n",
       "1    4255\n",
       "Name: Furnished, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Furnished\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elevator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sí    6797\n",
       "Name: Elevator, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Elevator\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa[\"Elevator\"] = fotocasa[\"Elevator\"].map({\"Sí\": \"1\", \"No\": \"0\"}).fillna(\"0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    6797\n",
       "0    2526\n",
       "Name: Elevator, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Elevator\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Energy consumption and emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd657bb3d90>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "f, ax = plt.subplots(nrows = 2, figsize = (10,8))\n",
    "sns.barplot(x = fotocasa[\"Energy_Consumption\"].value_counts().index, \n",
    "            y = fotocasa[\"Energy_Consumption\"].value_counts().reset_index()[\"Energy_Consumption\"],\n",
    "            order = [\"En trámite\", \"Exento\", \"G\", \"F\", \"E\", \"D\", \"C\", \"B\", \"A\"], ax=ax[0])\n",
    "sns.barplot(x = fotocasa[\"Emissions\"].value_counts().index, \n",
    "            y = fotocasa[\"Emissions\"].value_counts().reset_index()[\"Emissions\"],\n",
    "            order = [\"En trámite\", \"Exento\", \"G\", \"F\", \"E\", \"D\", \"C\", \"B\", \"A\"], ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G             5661\n",
       "E             1647\n",
       "D              628\n",
       "A              360\n",
       "C              358\n",
       "B              272\n",
       "F              232\n",
       "En trámite     106\n",
       "Exento          59\n",
       "Name: Energy_Consumption, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Energy_Consumption\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution is the same, therefore:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa.loc[fotocasa[\"Energy_Consumption\"]==\"En trámite\",\"Energy_Consumption\"] = \\\n",
    "  fotocasa.loc[fotocasa[\"Energy_Consumption\"]==\"En trámite\",\"Emissions\"]  \n",
    "\n",
    "fotocasa.loc[fotocasa[\"Emissions\"]==\"En trámite\",\"Emissions\"] = \\\n",
    "  fotocasa.loc[fotocasa[\"Emissions\"]==\"En trámite\",\"Energy_Consumption\"]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6555b5700>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(nrows = 2, figsize = (10,8))\n",
    "sns.barplot(x = fotocasa[\"Energy_Consumption\"].value_counts().index, \n",
    "            y = fotocasa[\"Energy_Consumption\"].value_counts().reset_index()[\"Energy_Consumption\"],\n",
    "            order = [\"En trámite\", \"Exento\", \"G\", \"F\", \"E\", \"D\", \"C\", \"B\", \"A\"], ax=ax[0])\n",
    "sns.barplot(x = fotocasa[\"Emissions\"].value_counts().index, \n",
    "            y = fotocasa[\"Emissions\"].value_counts().reset_index()[\"Emissions\"],\n",
    "            order = [\"En trámite\", \"Exento\", \"G\", \"F\", \"E\", \"D\", \"C\", \"B\", \"A\"], ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G             61.010404\n",
       "E             17.848332\n",
       "D              6.768208\n",
       "A              3.872144\n",
       "C              3.850692\n",
       "B              2.917516\n",
       "F              2.595731\n",
       "Exento         0.632844\n",
       "En trámite     0.504130\n",
       "Name: Energy_Consumption, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Energy_Consumption\"].value_counts()/len(fotocasa)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa.loc[fotocasa[\"Energy_Consumption\"]==\"En trámite\",\"Energy_Consumption\"] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa = fotocasa.dropna(axis=0, subset = [\"Energy_Consumption\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emissions             0\n",
       "Energy_Consumption    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa.isna().sum().loc[[\"Emissions\",\"Energy_Consumption\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1ª planta                    1154\n",
       "2ª planta                    1116\n",
       "3ª planta                    1014\n",
       "4ª planta                     818\n",
       "5ª planta                     563\n",
       "Bajos                         483\n",
       "6ª planta                     348\n",
       "7ª planta                     191\n",
       "Sótano                         91\n",
       "Subsótano                      91\n",
       "8ª planta                      83\n",
       "Entresuelo                     79\n",
       "Principal                      45\n",
       "9ª planta                      38\n",
       "10ª planta                     22\n",
       "12ª planta                     18\n",
       "11ª planta                     18\n",
       "A partir de la 15ª planta      16\n",
       "13ª planta                     11\n",
       "14ª planta                      8\n",
       "15ª planta                      7\n",
       "Otro                            1\n",
       "Name: Floor, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Floor\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_floor(x):\n",
    "    try:\n",
    "        return x.replace(\"ª planta\",\"\")\n",
    "    except:\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y = fotocasa[\"Price\"], x = fotocasa[\"Floor\"].apply(clean_floor),\n",
    "    order = [\"Subsótano\",\"Sótano\",\"Bajos\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\n",
    "             \"15\",\"A partir de la 15\",\"Entresuelo\",\"Principal\",\"Otro\"]) \n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_floor(x):\n",
    "    try:\n",
    "        x = x.replace(\"ª planta\",\"\")\n",
    "        if x in [\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\"]:\n",
    "            return x\n",
    "        elif x==\"A partir de la 15\":\n",
    "            return \"+15\"\n",
    "        elif x==\"Subsótano\":\n",
    "            return \"Sótano\"\n",
    "        elif x==\"Otro\":\n",
    "            return np.nan\n",
    "        else:\n",
    "            return x\n",
    "    except AttributeError:\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa[\"Floor\"] = fotocasa[\"Floor\"].apply(clean_floor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputing the mode does not preserve the distribution. KNN imputer will be used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3062"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa[\"Floor\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputing missing values with K-neighbors classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "imputer = Pipeline([('dummy',OneHotEncoder(handle_unknown = \"ignore\")),\n",
    "                    ('knc',KNeighborsClassifier(n_neighbors = 10))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_columns = list(fotocasa.isna().sum()[fotocasa.isna().sum()/len(fotocasa)*100 == 0].index)\n",
    "cleaned_columns.remove(\"Title\")\n",
    "cleaned_columns.remove(\"Neighborhood\")\n",
    "cleaned_columns.remove(\"url\")\n",
    "cleaned_columns.remove(\"Photos\")\n",
    "cleaned_columns.remove(\"Len_Extras\")\n",
    "cleaned_columns.append(\"Floor\")\n",
    "fotocasa_cleaned = fotocasa[sorted(cleaned_columns)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_miss = fotocasa_cleaned[\"Floor\"].isna()\n",
    "# Fitting\n",
    "imputer.fit(fotocasa_cleaned[~filter_miss].drop([\"Floor\",\"Price\"], axis=1), \n",
    "            fotocasa_cleaned.loc[~filter_miss,\"Floor\"])\n",
    "pd.options.mode.chained_assignment = None\n",
    "fotocasa_cleaned.loc[filter_miss.values,\"Floor\"] = \\\n",
    "    imputer.predict(fotocasa_cleaned[filter_miss].drop([\"Floor\",\"Price\"], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(ncols = 2, figsize = (15,10), sharey = True)\n",
    "sns.barplot(x = fotocasa[\"Floor\"].value_counts().index,\n",
    "    y = fotocasa[\"Floor\"].value_counts().values/len(fotocasa[~fotocasa[\"Floor\"].isna()]),\n",
    "    order = [\"Sótano\",\"Bajos\",\"Principal\",\"Entresuelo\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\n",
    "             \"11\",\"12\",\"13\",\"14\",\"15\",\"+15\"],\n",
    "           ax = ax[0]) \n",
    "sns.barplot(x = fotocasa_cleaned[\"Floor\"].value_counts().index,\n",
    "    y = fotocasa_cleaned[\"Floor\"].value_counts().values/len(fotocasa_cleaned),\n",
    "    order = [\"Sótano\",\"Bajos\",\"Principal\",\"Entresuelo\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\n",
    "             \"11\",\"12\",\"13\",\"14\",\"15\",\"+15\"],\n",
    "           ax = ax[1]) \n",
    "for i in range(2):\n",
    "    for tick in ax[i].get_xticklabels():\n",
    "        tick.set_rotation(90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good imputing. We observe the same shape distribution in the data withouth replacement than in the data with replacement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1             2163\n",
       "2             1982\n",
       "3             1490\n",
       "4             1116\n",
       "5              713\n",
       "Bajos          622\n",
       "6              390\n",
       "Sótano         235\n",
       "7              211\n",
       "8               84\n",
       "Entresuelo      81\n",
       "Principal       46\n",
       "9               38\n",
       "10              26\n",
       "11              18\n",
       "12              18\n",
       "+15             17\n",
       "13              11\n",
       "14               8\n",
       "15               7\n",
       "Name: Floor, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fotocasa_cleaned[\"Floor\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "fotocasa_cleaned.to_csv(\"fotocasa_cleaned.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes: Valderribas is included in Valdebernardo and Atocha in Delicias."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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