{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyze Districts in Madrid to open a cafeteria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Madrid is the capital of Spain and its largest city. It is located in the center of the peninsula, as the capital of Spain Madrid has the seat of government, the Court and is also the official residence of the King and Queen of Spain.\n",
"On the economic level, Madrid is home to the headquarters of many national and international companies. Culturally, Madrid has world-famous museums such as the Prado Museum and the Reina Sofia Museum. Madrid's neighborhoods are full of history and peculiar characteristics that make them unique.\n",
"\n",
"\n",
"Madrid stands out for the infinity of bars, restaurants and cafes in its streets. No doubt in every walk through the city we discover new places and the list of places to go is getting longer and longer.\n",
"This project aims to help by analyzing the characteristics of the districts of Madrid, its impact on the COVID combined with the data and datasets of the city, will determine the best or possible locations for a new restaurant type cafeteria. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data description\n",
"\n",
"The data will be collected from different Madrid official soruces all of them with real and current data "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* COVID datasets distributed by districts: https://datos.comunidad.madrid/catalogo/dataset/covid19_tia_muni_y_distritos/resource/877fa8f5-cd6c-4e44-9df5-0fb60944a841\n",
"* Neighborhoods an districts information: https://datos.gob.es/en/catalogo/a13002908-covid-19-tia-por-municipios-y-distritos-de-madrid1\n",
"* Prominents Madrid's data: https://datos.madrid.es/portal/site/egob/menuitem.ca8ea3bd9f53b2811ff64a46a8a409a0/?vgnextoid=6db862c549810510VgnVCM1000008a4a900aRCRD&vgnextchannel=6db862c549810510VgnVCM1000008a4a900aRCRD&vgnextfmt=default\n",
"* District's numbers: https://www.madrid.es/portales/munimadrid/es/Inicio/El-Ayuntamiento/Estadistica/Distritos-en-cifras/Distritos-en-cifras-Informacion-de-Distritos-/?vgnextfmt=default&vgnextoid=74b33ece5284c310VgnVCM1000000b205a0aRCRD&vgnextchannel=27002d05cb71b310VgnVCM1000000b205a0aRCRD#"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Venue data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data will be extracted from the API Foursquare wich has large numbers of venues throughout the different districts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Import libraries**"
]
},
{
"cell_type": "code",
"execution_count": 434,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"from IPython.display import Image\n",
"from IPython.core.display import HTML\n",
"from pandas.io.json import json_normalize\n",
"import geocoder\n",
"from geopy.geocoders import Nominatim\n",
"import folium\n",
"from folium import plugins\n",
"import matplotlib.cm as cm\n",
"import matplotlib.colors as colors\n",
"from sklearn.preprocessing import StandardScaler\n",
"import numpy as np\n",
"from sklearn.cluster import KMeans \n",
"from sklearn.datasets.samples_generator import make_blobs\n",
"from bs4 import BeautifulSoup\n",
"import requests\n",
"import pandas as pd\n",
"import plotly.express as px\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import silhouette_score"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"warnings.simplefilter('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data analysis and wrangling\n",
"The csv file is taken from the links given above"
]
},
{
"cell_type": "code",
"execution_count": 290,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" OBJECTID_1 \n",
" DISTRICT \n",
" NEIGHBORHOOD \n",
" DISTRICT_LATITUDE \n",
" DISTRICT_LONGITUDE \n",
" Shape_Leng \n",
" Shape_Area \n",
" COD_DIS \n",
" COD_DIS_TX \n",
" BARRIO_MAY \n",
" COD_DISBAR \n",
" COD_BAR \n",
" NUM_BAR \n",
" BARRIO_MT \n",
" COD_DISB \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 60 \n",
" Centro \n",
" Palacio \n",
" 40.415347 \n",
" -3.707371 \n",
" 5754,822748 \n",
" 1469905,684 \n",
" 1 \n",
" 1 \n",
" PALACIO \n",
" 11 \n",
" 11 \n",
" 1 \n",
" PALACIO \n",
" 1_1 \n",
" \n",
" \n",
" 1 \n",
" 50 \n",
" Centro \n",
" Embajadores \n",
" 40.415347 \n",
" -3.707371 \n",
" 4275,227681 \n",
" 1033724,698 \n",
" 1 \n",
" 1 \n",
" EMBAJADORES \n",
" 12 \n",
" 12 \n",
" 2 \n",
" EMBAJADORES \n",
" 1_2 \n",
" \n",
" \n",
" 2 \n",
" 55 \n",
" Centro \n",
" Cortes \n",
" 40.415347 \n",
" -3.707371 \n",
" 3731,07903 \n",
" 591874,1219 \n",
" 1 \n",
" 1 \n",
" CORTES \n",
" 13 \n",
" 13 \n",
" 3 \n",
" CORTES \n",
" 1_3 \n",
" \n",
" \n",
" 3 \n",
" 64 \n",
" Centro \n",
" Justicia \n",
" 40.415347 \n",
" -3.707371 \n",
" 3597,421427 \n",
" 739414,338 \n",
" 1 \n",
" 1 \n",
" JUSTICIA \n",
" 14 \n",
" 14 \n",
" 4 \n",
" JUSTICIA \n",
" 1_4 \n",
" \n",
" \n",
" 4 \n",
" 66 \n",
" Centro \n",
" Universidad \n",
" 40.415347 \n",
" -3.707371 \n",
" 4060,075813 \n",
" 948027,0773 \n",
" 1 \n",
" 1 \n",
" UNIVERSIDAD \n",
" 15 \n",
" 15 \n",
" 5 \n",
" UNIVERSIDAD \n",
" 1_5 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" OBJECTID_1 DISTRICT NEIGHBORHOOD DISTRICT_LATITUDE DISTRICT_LONGITUDE \\\n",
"0 60 Centro Palacio 40.415347 -3.707371 \n",
"1 50 Centro Embajadores 40.415347 -3.707371 \n",
"2 55 Centro Cortes 40.415347 -3.707371 \n",
"3 64 Centro Justicia 40.415347 -3.707371 \n",
"4 66 Centro Universidad 40.415347 -3.707371 \n",
"\n",
" Shape_Leng Shape_Area COD_DIS COD_DIS_TX BARRIO_MAY COD_DISBAR \\\n",
"0 5754,822748 1469905,684 1 1 PALACIO 11 \n",
"1 4275,227681 1033724,698 1 1 EMBAJADORES 12 \n",
"2 3731,07903 591874,1219 1 1 CORTES 13 \n",
"3 3597,421427 739414,338 1 1 JUSTICIA 14 \n",
"4 4060,075813 948027,0773 1 1 UNIVERSIDAD 15 \n",
"\n",
" COD_BAR NUM_BAR BARRIO_MT COD_DISB \n",
"0 11 1 PALACIO 1_1 \n",
"1 12 2 EMBAJADORES 1_2 \n",
"2 13 3 CORTES 1_3 \n",
"3 14 4 JUSTICIA 1_4 \n",
"4 15 5 UNIVERSIDAD 1_5 "
]
},
"execution_count": 290,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_data = pd.read_csv('Barrios.csv', sep=\";\", encoding='latin-1')\n",
"madrid_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 291,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" DISTRICT \n",
" NEIGHBORHOOD \n",
" DISTRICT_LATITUDE \n",
" DISTRICT_LONGITUDE \n",
" COD_DIS \n",
" COD_DIS_TX \n",
" BARRIO_MAY \n",
" COD_DISBAR \n",
" COD_BAR \n",
" NUM_BAR \n",
" BARRIO_MT \n",
" COD_DISB \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Centro \n",
" Palacio \n",
" 40.415347 \n",
" -3.707371 \n",
" 1 \n",
" 1 \n",
" PALACIO \n",
" 11 \n",
" 11 \n",
" 1 \n",
" PALACIO \n",
" 1_1 \n",
" \n",
" \n",
" 1 \n",
" Centro \n",
" Embajadores \n",
" 40.415347 \n",
" -3.707371 \n",
" 1 \n",
" 1 \n",
" EMBAJADORES \n",
" 12 \n",
" 12 \n",
" 2 \n",
" EMBAJADORES \n",
" 1_2 \n",
" \n",
" \n",
" 2 \n",
" Centro \n",
" Cortes \n",
" 40.415347 \n",
" -3.707371 \n",
" 1 \n",
" 1 \n",
" CORTES \n",
" 13 \n",
" 13 \n",
" 3 \n",
" CORTES \n",
" 1_3 \n",
" \n",
" \n",
" 3 \n",
" Centro \n",
" Justicia \n",
" 40.415347 \n",
" -3.707371 \n",
" 1 \n",
" 1 \n",
" JUSTICIA \n",
" 14 \n",
" 14 \n",
" 4 \n",
" JUSTICIA \n",
" 1_4 \n",
" \n",
" \n",
" 4 \n",
" Centro \n",
" Universidad \n",
" 40.415347 \n",
" -3.707371 \n",
" 1 \n",
" 1 \n",
" UNIVERSIDAD \n",
" 15 \n",
" 15 \n",
" 5 \n",
" UNIVERSIDAD \n",
" 1_5 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" DISTRICT NEIGHBORHOOD DISTRICT_LATITUDE DISTRICT_LONGITUDE COD_DIS \\\n",
"0 Centro Palacio 40.415347 -3.707371 1 \n",
"1 Centro Embajadores 40.415347 -3.707371 1 \n",
"2 Centro Cortes 40.415347 -3.707371 1 \n",
"3 Centro Justicia 40.415347 -3.707371 1 \n",
"4 Centro Universidad 40.415347 -3.707371 1 \n",
"\n",
" COD_DIS_TX BARRIO_MAY COD_DISBAR COD_BAR NUM_BAR BARRIO_MT COD_DISB \n",
"0 1 PALACIO 11 11 1 PALACIO 1_1 \n",
"1 1 EMBAJADORES 12 12 2 EMBAJADORES 1_2 \n",
"2 1 CORTES 13 13 3 CORTES 1_3 \n",
"3 1 JUSTICIA 14 14 4 JUSTICIA 1_4 \n",
"4 1 UNIVERSIDAD 15 15 5 UNIVERSIDAD 1_5 "
]
},
"execution_count": 291,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_data.drop(['OBJECTID_1','Shape_Leng','Shape_Area', ], axis = 1, inplace=True)\n",
"madrid_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 292,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" DISTRICT \n",
" NEIGHBORHOOD \n",
" DISTRICT_LATITUDE \n",
" DISTRICT_LONGITUDE \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Centro \n",
" Palacio \n",
" 40.415347 \n",
" -3.707371 \n",
" \n",
" \n",
" 1 \n",
" Centro \n",
" Embajadores \n",
" 40.415347 \n",
" -3.707371 \n",
" \n",
" \n",
" 2 \n",
" Centro \n",
" Cortes \n",
" 40.415347 \n",
" -3.707371 \n",
" \n",
" \n",
" 3 \n",
" Centro \n",
" Justicia \n",
" 40.415347 \n",
" -3.707371 \n",
" \n",
" \n",
" 4 \n",
" Centro \n",
" Universidad \n",
" 40.415347 \n",
" -3.707371 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" DISTRICT NEIGHBORHOOD DISTRICT_LATITUDE DISTRICT_LONGITUDE\n",
"0 Centro Palacio 40.415347 -3.707371 \n",
"1 Centro Embajadores 40.415347 -3.707371 \n",
"2 Centro Cortes 40.415347 -3.707371 \n",
"3 Centro Justicia 40.415347 -3.707371 \n",
"4 Centro Universidad 40.415347 -3.707371 "
]
},
"execution_count": 292,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_data.drop(['COD_DIS','COD_DIS_TX','BARRIO_MAY','COD_DISBAR','COD_BAR','NUM_BAR','BARRIO_MT','COD_DISB'], axis = 1, inplace=True)\n",
"madrid_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Madrid latitude and longitude:"
]
},
{
"cell_type": "code",
"execution_count": 293,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madrid latitude: 40.4948384 \n",
" Madrid longitude: -3.5740806206811313\n"
]
}
],
"source": [
"city = 'Madrid, MAD'\n",
"geolocator = Nominatim(user_agent=\"madrid\")\n",
"location = geolocator.geocode(city)\n",
"latitude = location.latitude\n",
"longitude = location.longitude\n",
"print(\"Madrid latitude: \", location.latitude, \"\\n\", \"Madrid longitude: \", location.longitude)"
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Aeropuerto de Madrid-Barajas Adolfo Suárez, Barajas, Madrid, Área metropolitana de Madrid y Corredor del Henares, Comunidad de Madrid, 28001, España\n"
]
}
],
"source": [
"# this is another way using Nominatim passing the coordinates we can see its name\n",
"\n",
"geolocator = Nominatim(user_agent = \"url\")\n",
"location = geolocator.reverse(\"40.4948384 , -3.5740806206811313\")\n",
"print(location.address)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Madrid map with districts and neighborhoods:**"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"Make this Notebook Trusted to load map: File -> Trust Notebook
"
],
"text/plain": [
""
]
},
"execution_count": 295,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create map of Madrid using latitude and longitude values\n",
"madrid_map = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
"\n",
"# add markers to map\n",
"for lat, lng, district, neighborhood in zip(madrid_data['DISTRICT_LATITUDE'],madrid_data['DISTRICT_LONGITUDE'],madrid_data['DISTRICT'],madrid_data['NEIGHBORHOOD']):\n",
" label = '{}, {}'.format(neighborhood,district)\n",
" label = folium.Popup(label, parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lng],\n",
" radius=5,\n",
" popup=label,\n",
" color='blue',\n",
" fill=True,\n",
" fill_color='#3186cc',\n",
" fill_opacity=0.7,\n",
" parse_html=False).add_to(madrid_map)\n",
"\n",
"madrid_map\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Madrid COVID data**"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" municipio_distrito \n",
" latitude \n",
" longitude \n",
" fecha_informe \n",
" casos_confirmados_activos_ultimos_14dias \n",
" tasa_incidencia_acumulada_activos_ultimos_14dias \n",
" casos_confirmados_ultimos_14dias \n",
" tasa_incidencia_acumulada_ultimos_14dias \n",
" casos_confirmados_totales \n",
" poblacion_distrito \n",
" tasa_incidencia_acumulada_total \n",
" codigo_geometria \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Madrid-Retiro \n",
" 40.408072 \n",
" -3.676729 \n",
" 29/06/2021 12:04 \n",
" NaN \n",
" NaN \n",
" 105.0 \n",
" 87,24 \n",
" 12414.0 \n",
" 120238.0 \n",
" 10314,06 \n",
" 79603.0 \n",
" \n",
" \n",
" 1 \n",
" Madrid-Salamanca \n",
" 40.430000 \n",
" -3.677778 \n",
" 29/06/2021 12:04 \n",
" NaN \n",
" NaN \n",
" 175.0 \n",
" 118,37 \n",
" 16615.0 \n",
" 147349.0 \n",
" 11238,43 \n",
" 79604.0 \n",
" \n",
" \n",
" 2 \n",
" Madrid-Centro \n",
" 40.415347 \n",
" -3.707371 \n",
" 29/06/2021 12:04 \n",
" NaN \n",
" NaN \n",
" 173.0 \n",
" 123,11 \n",
" 15821.0 \n",
" 140360.0 \n",
" 11258,58 \n",
" 79601.0 \n",
" \n",
" \n",
" 3 \n",
" Madrid-Arganzuela \n",
" 40.402733 \n",
" -3.695403 \n",
" 29/06/2021 12:04 \n",
" NaN \n",
" NaN \n",
" 206.0 \n",
" 132,35 \n",
" 16282.0 \n",
" 156010.0 \n",
" 10460,78 \n",
" 79602.0 \n",
" \n",
" \n",
" 4 \n",
" Madrid-Chamartín \n",
" 40.453333 \n",
" -3.677500 \n",
" 29/06/2021 12:04 \n",
" NaN \n",
" NaN \n",
" 182.0 \n",
" 123,35 \n",
" 15996.0 \n",
" 147483.0 \n",
" 10841,14 \n",
" 79605.0 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" municipio_distrito latitude longitude fecha_informe \\\n",
"0 Madrid-Retiro 40.408072 -3.676729 29/06/2021 12:04 \n",
"1 Madrid-Salamanca 40.430000 -3.677778 29/06/2021 12:04 \n",
"2 Madrid-Centro 40.415347 -3.707371 29/06/2021 12:04 \n",
"3 Madrid-Arganzuela 40.402733 -3.695403 29/06/2021 12:04 \n",
"4 Madrid-Chamartín 40.453333 -3.677500 29/06/2021 12:04 \n",
"\n",
" casos_confirmados_activos_ultimos_14dias \\\n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"\n",
" tasa_incidencia_acumulada_activos_ultimos_14dias \\\n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"\n",
" casos_confirmados_ultimos_14dias tasa_incidencia_acumulada_ultimos_14dias \\\n",
"0 105.0 87,24 \n",
"1 175.0 118,37 \n",
"2 173.0 123,11 \n",
"3 206.0 132,35 \n",
"4 182.0 123,35 \n",
"\n",
" casos_confirmados_totales poblacion_distrito \\\n",
"0 12414.0 120238.0 \n",
"1 16615.0 147349.0 \n",
"2 15821.0 140360.0 \n",
"3 16282.0 156010.0 \n",
"4 15996.0 147483.0 \n",
"\n",
" tasa_incidencia_acumulada_total codigo_geometria \n",
"0 10314,06 79603.0 \n",
"1 11238,43 79604.0 \n",
"2 11258,58 79601.0 \n",
"3 10460,78 79602.0 \n",
"4 10841,14 79605.0 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_covid = pd.read_csv('COVID_madrid.csv', sep=\";\", encoding='latin-1')\n",
"madrid_covid.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"madrid_covid.drop(['tasa_incidencia_acumulada_activos_ultimos_14dias','tasa_incidencia_acumulada_ultimos_14dias','codigo_geometria','casos_confirmados_activos_ultimos_14dias'], axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" municipio_distrito \n",
" latitude \n",
" longitude \n",
" fecha_informe \n",
" casos_confirmados_ultimos_14dias \n",
" casos_confirmados_totales \n",
" poblacion_distrito \n",
" tasa_incidencia_acumulada_total \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Madrid-Retiro \n",
" 40.408072 \n",
" -3.676729 \n",
" 29/06/2021 12:04 \n",
" 105.0 \n",
" 12414.0 \n",
" 120238.0 \n",
" 10314,06 \n",
" \n",
" \n",
" 1 \n",
" Madrid-Salamanca \n",
" 40.430000 \n",
" -3.677778 \n",
" 29/06/2021 12:04 \n",
" 175.0 \n",
" 16615.0 \n",
" 147349.0 \n",
" 11238,43 \n",
" \n",
" \n",
" 2 \n",
" Madrid-Centro \n",
" 40.415347 \n",
" -3.707371 \n",
" 29/06/2021 12:04 \n",
" 173.0 \n",
" 15821.0 \n",
" 140360.0 \n",
" 11258,58 \n",
" \n",
" \n",
" 3 \n",
" Madrid-Arganzuela \n",
" 40.402733 \n",
" -3.695403 \n",
" 29/06/2021 12:04 \n",
" 206.0 \n",
" 16282.0 \n",
" 156010.0 \n",
" 10460,78 \n",
" \n",
" \n",
" 4 \n",
" Madrid-Chamartín \n",
" 40.453333 \n",
" -3.677500 \n",
" 29/06/2021 12:04 \n",
" 182.0 \n",
" 15996.0 \n",
" 147483.0 \n",
" 10841,14 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 11509 \n",
" Getafe \n",
" NaN \n",
" NaN \n",
" 26/05/2020 7:00 \n",
" 60.0 \n",
" 1463.0 \n",
" NaN \n",
" 797,82 \n",
" \n",
" \n",
" 11510 \n",
" Ribatejada \n",
" NaN \n",
" NaN \n",
" 26/05/2020 7:00 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 255,43 \n",
" \n",
" \n",
" 11511 \n",
" Villaconejos \n",
" NaN \n",
" NaN \n",
" 26/05/2020 7:00 \n",
" NaN \n",
" 9.0 \n",
" NaN \n",
" 265,64 \n",
" \n",
" \n",
" 11512 \n",
" Valdetorres de Jarama \n",
" NaN \n",
" NaN \n",
" 26/05/2020 7:00 \n",
" NaN \n",
" 18.0 \n",
" NaN \n",
" 400,27 \n",
" \n",
" \n",
" 11513 \n",
" Pozuelo de Alarcón \n",
" NaN \n",
" NaN \n",
" 26/05/2020 7:00 \n",
" 25.0 \n",
" 837.0 \n",
" NaN \n",
" 968,5 \n",
" \n",
" \n",
"
\n",
"
11514 rows × 8 columns
\n",
"
"
],
"text/plain": [
" municipio_distrito latitude longitude fecha_informe \\\n",
"0 Madrid-Retiro 40.408072 -3.676729 29/06/2021 12:04 \n",
"1 Madrid-Salamanca 40.430000 -3.677778 29/06/2021 12:04 \n",
"2 Madrid-Centro 40.415347 -3.707371 29/06/2021 12:04 \n",
"3 Madrid-Arganzuela 40.402733 -3.695403 29/06/2021 12:04 \n",
"4 Madrid-Chamartín 40.453333 -3.677500 29/06/2021 12:04 \n",
"... ... ... ... ... \n",
"11509 Getafe NaN NaN 26/05/2020 7:00 \n",
"11510 Ribatejada NaN NaN 26/05/2020 7:00 \n",
"11511 Villaconejos NaN NaN 26/05/2020 7:00 \n",
"11512 Valdetorres de Jarama NaN NaN 26/05/2020 7:00 \n",
"11513 Pozuelo de Alarcón NaN NaN 26/05/2020 7:00 \n",
"\n",
" casos_confirmados_ultimos_14dias casos_confirmados_totales \\\n",
"0 105.0 12414.0 \n",
"1 175.0 16615.0 \n",
"2 173.0 15821.0 \n",
"3 206.0 16282.0 \n",
"4 182.0 15996.0 \n",
"... ... ... \n",
"11509 60.0 1463.0 \n",
"11510 NaN NaN \n",
"11511 NaN 9.0 \n",
"11512 NaN 18.0 \n",
"11513 25.0 837.0 \n",
"\n",
" poblacion_distrito tasa_incidencia_acumulada_total \n",
"0 120238.0 10314,06 \n",
"1 147349.0 11238,43 \n",
"2 140360.0 11258,58 \n",
"3 156010.0 10460,78 \n",
"4 147483.0 10841,14 \n",
"... ... ... \n",
"11509 NaN 797,82 \n",
"11510 NaN 255,43 \n",
"11511 NaN 265,64 \n",
"11512 NaN 400,27 \n",
"11513 NaN 968,5 \n",
"\n",
"[11514 rows x 8 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_covid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unnecessary rows are eliminated, leaving only the first 21 rows corresponding to the 21 districts of Madrid which have the most recent case reports. which have the most recent report of cases"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"madrid_covid=madrid_covid.iloc[0:21]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(21, 8)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_covid.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"Madrid-\" is removed from the municipio_distrito column since we only need the name of the district."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"madrid_covid['municipio_distrito']=madrid_covid['municipio_distrito'].replace({'Madrid-':''}, regex=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Columns names:\n",
" * municipio_distrito: district name\n",
" * fecha_informe: date of the report\n",
" * casos_confirmados_ultimos_14dias: confirmed cases for the last 14 days\n",
" * casos_confirmados_totales: total confirmed cases for each district\n",
" * poblacion_distrito: district population\n",
" * tasa_incidencia_acumulada_total: total cumulative incidence rate"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" municipio_distrito \n",
" latitude \n",
" longitude \n",
" fecha_informe \n",
" casos_confirmados_ultimos_14dias \n",
" casos_confirmados_totales \n",
" poblacion_distrito \n",
" tasa_incidencia_acumulada_total \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Retiro \n",
" 40.408072 \n",
" -3.676729 \n",
" 29/06/2021 12:04 \n",
" 105.0 \n",
" 12414.0 \n",
" 120238.0 \n",
" 10314,06 \n",
" \n",
" \n",
" 1 \n",
" Salamanca \n",
" 40.430000 \n",
" -3.677778 \n",
" 29/06/2021 12:04 \n",
" 175.0 \n",
" 16615.0 \n",
" 147349.0 \n",
" 11238,43 \n",
" \n",
" \n",
" 2 \n",
" Centro \n",
" 40.415347 \n",
" -3.707371 \n",
" 29/06/2021 12:04 \n",
" 173.0 \n",
" 15821.0 \n",
" 140360.0 \n",
" 11258,58 \n",
" \n",
" \n",
" 3 \n",
" Arganzuela \n",
" 40.402733 \n",
" -3.695403 \n",
" 29/06/2021 12:04 \n",
" 206.0 \n",
" 16282.0 \n",
" 156010.0 \n",
" 10460,78 \n",
" \n",
" \n",
" 4 \n",
" Chamartín \n",
" 40.453333 \n",
" -3.677500 \n",
" 29/06/2021 12:04 \n",
" 182.0 \n",
" 15996.0 \n",
" 147483.0 \n",
" 10841,14 \n",
" \n",
" \n",
" 5 \n",
" Tetuán \n",
" 40.460556 \n",
" -3.700000 \n",
" 29/06/2021 12:04 \n",
" 128.0 \n",
" 17464.0 \n",
" 161233.0 \n",
" 10821,06 \n",
" \n",
" \n",
" 6 \n",
" Chamberí \n",
" 40.432792 \n",
" -3.697186 \n",
" 29/06/2021 12:04 \n",
" 185.0 \n",
" 16486.0 \n",
" 140516.0 \n",
" 11705,98 \n",
" \n",
" \n",
" 7 \n",
" Fuencarral-El Pardo \n",
" 40.478611 \n",
" -3.709722 \n",
" 29/06/2021 12:04 \n",
" 204.0 \n",
" 24499.0 \n",
" 250655.0 \n",
" 9803,17 \n",
" \n",
" \n",
" 8 \n",
" Moncloa-Aravaca \n",
" 40.435151 \n",
" -3.718765 \n",
" 29/06/2021 12:04 \n",
" 113.0 \n",
" 14199.0 \n",
" 121880.0 \n",
" 11670,67 \n",
" \n",
" \n",
" 9 \n",
" Latina \n",
" 40.402461 \n",
" -3.741294 \n",
" 29/06/2021 12:04 \n",
" 167.0 \n",
" 25016.0 \n",
" 242447.0 \n",
" 10330,66 \n",
" \n",
" \n",
" 10 \n",
" Carabanchel \n",
" 40.383669 \n",
" -3.727989 \n",
" 29/06/2021 12:04 \n",
" 172.0 \n",
" 28794.0 \n",
" 260801.0 \n",
" 11065,25 \n",
" \n",
" \n",
" 11 \n",
" Usera \n",
" 40.381336 \n",
" -3.706856 \n",
" 29/06/2021 12:04 \n",
" 97.0 \n",
" 16452.0 \n",
" 143141.0 \n",
" 11499,02 \n",
" \n",
" \n",
" 12 \n",
" Puente de Vallecas \n",
" 40.398204 \n",
" -3.669059 \n",
" 29/06/2021 12:04 \n",
" 168.0 \n",
" 32344.0 \n",
" 241901.0 \n",
" 13425,98 \n",
" \n",
" \n",
" 13 \n",
" San Blas - Canillejas \n",
" 40.426001 \n",
" -3.612764 \n",
" 29/06/2021 12:04 \n",
" 152.0 \n",
" 16636.0 \n",
" 161696.0 \n",
" 10322,28 \n",
" \n",
" \n",
" 14 \n",
" Barajas \n",
" 40.470196 \n",
" -3.584890 \n",
" 29/06/2021 12:04 \n",
" 67.0 \n",
" 4907.0 \n",
" 50320.0 \n",
" 9817,34 \n",
" \n",
" \n",
" 15 \n",
" Moratalaz \n",
" 40.409869 \n",
" -3.644436 \n",
" 29/06/2021 12:04 \n",
" 90.0 \n",
" 10609.0 \n",
" 95375.0 \n",
" 11101,11 \n",
" \n",
" \n",
" 16 \n",
" Ciudad Lineal \n",
" 40.453333 \n",
" -3.650000 \n",
" 29/06/2021 12:04 \n",
" 155.0 \n",
" 24269.0 \n",
" 220118.0 \n",
" 11036,83 \n",
" \n",
" \n",
" 17 \n",
" Hortaleza \n",
" 40.469457 \n",
" -3.640482 \n",
" 29/06/2021 12:04 \n",
" 206.0 \n",
" 19116.0 \n",
" 194529.0 \n",
" 9893,13 \n",
" \n",
" \n",
" 18 \n",
" Villaverde \n",
" 40.345925 \n",
" -3.709356 \n",
" 29/06/2021 12:04 \n",
" 94.0 \n",
" 17744.0 \n",
" 155149.0 \n",
" 11494,39 \n",
" \n",
" \n",
" 19 \n",
" Villa de Vallecas \n",
" 40.379600 \n",
" -3.621350 \n",
" 29/06/2021 12:04 \n",
" 54.0 \n",
" 11923.0 \n",
" 115495.0 \n",
" 10417,38 \n",
" \n",
" \n",
" 20 \n",
" Vicálvaro \n",
" 40.404200 \n",
" -3.608060 \n",
" 29/06/2021 12:04 \n",
" 48.0 \n",
" 7441.0 \n",
" 74577.0 \n",
" 10054,86 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" municipio_distrito latitude longitude fecha_informe \\\n",
"0 Retiro 40.408072 -3.676729 29/06/2021 12:04 \n",
"1 Salamanca 40.430000 -3.677778 29/06/2021 12:04 \n",
"2 Centro 40.415347 -3.707371 29/06/2021 12:04 \n",
"3 Arganzuela 40.402733 -3.695403 29/06/2021 12:04 \n",
"4 Chamartín 40.453333 -3.677500 29/06/2021 12:04 \n",
"5 Tetuán 40.460556 -3.700000 29/06/2021 12:04 \n",
"6 Chamberí 40.432792 -3.697186 29/06/2021 12:04 \n",
"7 Fuencarral-El Pardo 40.478611 -3.709722 29/06/2021 12:04 \n",
"8 Moncloa-Aravaca 40.435151 -3.718765 29/06/2021 12:04 \n",
"9 Latina 40.402461 -3.741294 29/06/2021 12:04 \n",
"10 Carabanchel 40.383669 -3.727989 29/06/2021 12:04 \n",
"11 Usera 40.381336 -3.706856 29/06/2021 12:04 \n",
"12 Puente de Vallecas 40.398204 -3.669059 29/06/2021 12:04 \n",
"13 San Blas - Canillejas 40.426001 -3.612764 29/06/2021 12:04 \n",
"14 Barajas 40.470196 -3.584890 29/06/2021 12:04 \n",
"15 Moratalaz 40.409869 -3.644436 29/06/2021 12:04 \n",
"16 Ciudad Lineal 40.453333 -3.650000 29/06/2021 12:04 \n",
"17 Hortaleza 40.469457 -3.640482 29/06/2021 12:04 \n",
"18 Villaverde 40.345925 -3.709356 29/06/2021 12:04 \n",
"19 Villa de Vallecas 40.379600 -3.621350 29/06/2021 12:04 \n",
"20 Vicálvaro 40.404200 -3.608060 29/06/2021 12:04 \n",
"\n",
" casos_confirmados_ultimos_14dias casos_confirmados_totales \\\n",
"0 105.0 12414.0 \n",
"1 175.0 16615.0 \n",
"2 173.0 15821.0 \n",
"3 206.0 16282.0 \n",
"4 182.0 15996.0 \n",
"5 128.0 17464.0 \n",
"6 185.0 16486.0 \n",
"7 204.0 24499.0 \n",
"8 113.0 14199.0 \n",
"9 167.0 25016.0 \n",
"10 172.0 28794.0 \n",
"11 97.0 16452.0 \n",
"12 168.0 32344.0 \n",
"13 152.0 16636.0 \n",
"14 67.0 4907.0 \n",
"15 90.0 10609.0 \n",
"16 155.0 24269.0 \n",
"17 206.0 19116.0 \n",
"18 94.0 17744.0 \n",
"19 54.0 11923.0 \n",
"20 48.0 7441.0 \n",
"\n",
" poblacion_distrito tasa_incidencia_acumulada_total \n",
"0 120238.0 10314,06 \n",
"1 147349.0 11238,43 \n",
"2 140360.0 11258,58 \n",
"3 156010.0 10460,78 \n",
"4 147483.0 10841,14 \n",
"5 161233.0 10821,06 \n",
"6 140516.0 11705,98 \n",
"7 250655.0 9803,17 \n",
"8 121880.0 11670,67 \n",
"9 242447.0 10330,66 \n",
"10 260801.0 11065,25 \n",
"11 143141.0 11499,02 \n",
"12 241901.0 13425,98 \n",
"13 161696.0 10322,28 \n",
"14 50320.0 9817,34 \n",
"15 95375.0 11101,11 \n",
"16 220118.0 11036,83 \n",
"17 194529.0 9893,13 \n",
"18 155149.0 11494,39 \n",
"19 115495.0 10417,38 \n",
"20 74577.0 10054,86 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_covid"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"madrid_covid.set_index('municipio_distrito')['casos_confirmados_totales'].plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 487,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean: 17382.238095238095, Median: 16486.0, Mode: 0 4907.0 \n",
"1 7441.0 \n",
"2 10609.0\n",
"3 11923.0\n",
"4 12414.0\n",
"5 14199.0\n",
"6 15821.0\n",
"7 15996.0\n",
"8 16282.0\n",
"9 16452.0\n",
"10 16486.0\n",
"11 16615.0\n",
"12 16636.0\n",
"13 17464.0\n",
"14 17744.0\n",
"15 19116.0\n",
"16 24269.0\n",
"17 24499.0\n",
"18 25016.0\n",
"19 28794.0\n",
"20 32344.0\n",
"dtype: float64\n"
]
}
],
"source": [
"mean = madrid_covid[\"casos_confirmados_totales\"].mean()\n",
"median = madrid_covid[\"casos_confirmados_totales\"].median()\n",
"mode = madrid_covid[\"casos_confirmados_totales\"].mode()\n",
"print(\"Mean: {}, Median: {}, Mode: {}\".format(mean, median, mode))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Make this Notebook Trusted to load map: File -> Trust Notebook
"
],
"text/plain": [
""
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create map of Madrid using latitude and longitude values\n",
"district_map = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
"\n",
"# add markers to map\n",
"for lat, lng, district, confirmed in zip(madrid_covid['latitude'],madrid_covid['longitude'],madrid_covid['municipio_distrito'], madrid_covid['casos_confirmados_totales']):\n",
" label = '{}, {} Confirmed cases'.format(district, confirmed)\n",
" label = folium.Popup(label, parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lng],\n",
" radius=10,\n",
" popup=label,\n",
" color='blue',\n",
" fill=True,\n",
" fill_color='#3186cc',\n",
" fill_opacity=0.7,\n",
" parse_html=False).add_to(district_map)\n",
"\n",
"district_map"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Retiro\n",
"1 Salamanca\n",
"2 Centro\n",
"3 Arganzuela\n",
"4 Chamartín\n",
"5 Tetuán\n",
"6 Chamberí\n",
"7 Fuencarral-El Pardo\n",
"8 Moncloa-Aravaca\n",
"9 Latina\n",
"10 Carabanchel\n",
"11 Usera\n",
"12 Puente de Vallecas\n",
"13 San Blas - Canillejas\n",
"14 Barajas\n",
"15 Moratalaz\n",
"16 Ciudad Lineal\n",
"17 Hortaleza\n",
"18 Villaverde\n",
"19 Villa de Vallecas\n",
"20 Vicálvaro\n",
"Name: municipio_distrito, dtype: object"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_covid['municipio_distrito']"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" municipio_distrito \n",
" latitude \n",
" longitude \n",
" fecha_informe \n",
" casos_confirmados_ultimos_14dias \n",
" casos_confirmados_totales \n",
" poblacion_distrito \n",
" tasa_incidencia_acumulada_total \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Retiro \n",
" 40.408072 \n",
" -3.676729 \n",
" 29/06/2021 12:04 \n",
" 105.0 \n",
" 12414.0 \n",
" 120238.0 \n",
" 10314,06 \n",
" \n",
" \n",
" 1 \n",
" Salamanca \n",
" 40.430000 \n",
" -3.677778 \n",
" 29/06/2021 12:04 \n",
" 175.0 \n",
" 16615.0 \n",
" 147349.0 \n",
" 11238,43 \n",
" \n",
" \n",
" 2 \n",
" Centro \n",
" 40.415347 \n",
" -3.707371 \n",
" 29/06/2021 12:04 \n",
" 173.0 \n",
" 15821.0 \n",
" 140360.0 \n",
" 11258,58 \n",
" \n",
" \n",
" 3 \n",
" Arganzuela \n",
" 40.402733 \n",
" -3.695403 \n",
" 29/06/2021 12:04 \n",
" 206.0 \n",
" 16282.0 \n",
" 156010.0 \n",
" 10460,78 \n",
" \n",
" \n",
" 4 \n",
" Chamartín \n",
" 40.453333 \n",
" -3.677500 \n",
" 29/06/2021 12:04 \n",
" 182.0 \n",
" 15996.0 \n",
" 147483.0 \n",
" 10841,14 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" municipio_distrito latitude longitude fecha_informe \\\n",
"0 Retiro 40.408072 -3.676729 29/06/2021 12:04 \n",
"1 Salamanca 40.430000 -3.677778 29/06/2021 12:04 \n",
"2 Centro 40.415347 -3.707371 29/06/2021 12:04 \n",
"3 Arganzuela 40.402733 -3.695403 29/06/2021 12:04 \n",
"4 Chamartín 40.453333 -3.677500 29/06/2021 12:04 \n",
"\n",
" casos_confirmados_ultimos_14dias casos_confirmados_totales \\\n",
"0 105.0 12414.0 \n",
"1 175.0 16615.0 \n",
"2 173.0 15821.0 \n",
"3 206.0 16282.0 \n",
"4 182.0 15996.0 \n",
"\n",
" poblacion_distrito tasa_incidencia_acumulada_total \n",
"0 120238.0 10314,06 \n",
"1 147349.0 11238,43 \n",
"2 140360.0 11258,58 \n",
"3 156010.0 10460,78 \n",
"4 147483.0 10841,14 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_covid.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function that extracts the **category of the venue**"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def categories_type(row):\n",
" categories_list = row['venue.categories']\n",
" if len(categories_list) == 0:\n",
" return None\n",
" else:\n",
" return categories_list[0]['name']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"foursquare_venues=pd.DataFrame()\n",
"mdc = []"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"for latitude, longitude, distr, conf in zip(madrid_covid['latitude'],madrid_covid['longitude'],madrid_covid['municipio_distrito'],madrid_covid['casos_confirmados_totales']):\n",
" url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
" CLIENT_ID, \n",
" CLIENT_SECRET, \n",
" VERSION, \n",
" latitude, \n",
" longitude, \n",
" radius, \n",
" LIMIT)\n",
" results = requests.get(url).json()\n",
" venues = pd.json_normalize(results['response']['groups'][0]['items'])\n",
" filtered_columns = ['venue.id', 'venue.location.lat', 'venue.location.lng', 'venue.name', 'venue.categories']\n",
" venues_df = venues[filtered_columns]\n",
" venues_df['venue.categories'] = venues_df.apply(categories_type, axis=1)\n",
" venues_df['district']=distr\n",
" venues_df['confirmed']=conf\n",
" mdc.append(venues_df) \n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Venues from the API Foursquare with each district and COVID confirmed cases"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" venue.id \n",
" venue.location.lat \n",
" venue.location.lng \n",
" venue.name \n",
" venue.categories \n",
" district \n",
" confirmed \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 4da9810d8154abafc2948f8e \n",
" 40.410987 \n",
" -3.680377 \n",
" Rosaleda del Retiro \n",
" Garden \n",
" Retiro \n",
" 12414.0 \n",
" \n",
" \n",
" 1 \n",
" 4d28cb43342d6dcb624dfcca \n",
" 40.404065 \n",
" -3.677902 \n",
" El Rincón de Fogg \n",
" Burger Joint \n",
" Retiro \n",
" 12414.0 \n",
" \n",
" \n",
" 2 \n",
" 4bc201b0f8219c741615b410 \n",
" 40.407648 \n",
" -3.680188 \n",
" Don Giovanni \n",
" Italian Restaurant \n",
" Retiro \n",
" 12414.0 \n",
" \n",
" \n",
" 3 \n",
" 4e6276311838ad3d0eceb6c9 \n",
" 40.412714 \n",
" -3.677826 \n",
" Jardines de Cecilio Rodríguez \n",
" Garden \n",
" Retiro \n",
" 12414.0 \n",
" \n",
" \n",
" 4 \n",
" 4bc8a39e6501c9b6b88f4029 \n",
" 40.406407 \n",
" -3.681303 \n",
" EFTI \n",
" Art Gallery \n",
" Retiro \n",
" 12414.0 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" venue.id venue.location.lat venue.location.lng \\\n",
"0 4da9810d8154abafc2948f8e 40.410987 -3.680377 \n",
"1 4d28cb43342d6dcb624dfcca 40.404065 -3.677902 \n",
"2 4bc201b0f8219c741615b410 40.407648 -3.680188 \n",
"3 4e6276311838ad3d0eceb6c9 40.412714 -3.677826 \n",
"4 4bc8a39e6501c9b6b88f4029 40.406407 -3.681303 \n",
"\n",
" venue.name venue.categories district confirmed \n",
"0 Rosaleda del Retiro Garden Retiro 12414.0 \n",
"1 El Rincón de Fogg Burger Joint Retiro 12414.0 \n",
"2 Don Giovanni Italian Restaurant Retiro 12414.0 \n",
"3 Jardines de Cecilio Rodríguez Garden Retiro 12414.0 \n",
"4 EFTI Art Gallery Retiro 12414.0 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"foursquare_venues = pd.concat(mdc)\n",
"foursquare_venues.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1898, 7)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"foursquare_venues.shape"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1898 venues were returned by Foursquare.\n"
]
}
],
"source": [
"print('{} venues were returned by Foursquare.'.format(foursquare_venues.shape[0]))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Spanish Restaurant 174\n",
"Restaurant 109\n",
"Hotel 72\n",
"Park 71\n",
"Tapas Restaurant 65\n",
" ... \n",
"Soup Place 1\n",
"Motorcycle Shop 1\n",
"Board Shop 1\n",
"Arcade 1\n",
"Gas Station 1\n",
"Name: venue.categories, Length: 214, dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"foursquare_venues['venue.categories'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 467,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" venue.id \n",
" venue.location.lat \n",
" venue.location.lng \n",
" venue.name \n",
" venue.categories \n",
" district \n",
" confirmed \n",
" \n",
" \n",
" \n",
" \n",
" 25 \n",
" 4b7137e0f964a520f23c2de3 \n",
" 40.479636 \n",
" -3.707357 \n",
" Starbucks CC La Vaguada \n",
" Cafeteria \n",
" Fuencarral-El Pardo \n",
" 24499.0 \n",
" \n",
" \n",
" 28 \n",
" 4b6d7fddf964a52088782ce3 \n",
" 40.429069 \n",
" -3.715079 \n",
" Starbucks Princesa 40 \n",
" Cafeteria \n",
" Moncloa-Aravaca \n",
" 14199.0 \n",
" \n",
" \n",
" 14 \n",
" 59ea4524ccad6b7e88d32a86 \n",
" 40.391304 \n",
" -3.701123 \n",
" Starbucks Plaza Río 2 \n",
" Cafeteria \n",
" Usera \n",
" 16452.0 \n",
" \n",
" \n",
" 92 \n",
" 4b673c58f964a520fd422be3 \n",
" 40.462823 \n",
" -3.636758 \n",
" Starbucks CC Dreams \n",
" Cafeteria \n",
" Ciudad Lineal \n",
" 24269.0 \n",
" \n",
" \n",
" 23 \n",
" 4b673c58f964a520fd422be3 \n",
" 40.462823 \n",
" -3.636758 \n",
" Starbucks CC Dreams \n",
" Cafeteria \n",
" Hortaleza \n",
" 19116.0 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" venue.id venue.location.lat venue.location.lng \\\n",
"25 4b7137e0f964a520f23c2de3 40.479636 -3.707357 \n",
"28 4b6d7fddf964a52088782ce3 40.429069 -3.715079 \n",
"14 59ea4524ccad6b7e88d32a86 40.391304 -3.701123 \n",
"92 4b673c58f964a520fd422be3 40.462823 -3.636758 \n",
"23 4b673c58f964a520fd422be3 40.462823 -3.636758 \n",
"\n",
" venue.name venue.categories district confirmed \n",
"25 Starbucks CC La Vaguada Cafeteria Fuencarral-El Pardo 24499.0 \n",
"28 Starbucks Princesa 40 Cafeteria Moncloa-Aravaca 14199.0 \n",
"14 Starbucks Plaza Río 2 Cafeteria Usera 16452.0 \n",
"92 Starbucks CC Dreams Cafeteria Ciudad Lineal 24269.0 \n",
"23 Starbucks CC Dreams Cafeteria Hortaleza 19116.0 "
]
},
"execution_count": 467,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# cafeteria venues\n",
"venues_cafeteria = foursquare_venues[foursquare_venues['venue.categories'] == 'Cafeteria']\n",
"venues_cafeteria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets start by exploring just the first district in our dataframe using Foursquare API."
]
},
{
"cell_type": "code",
"execution_count": 385,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The district is Centro and it's district's coordinates are 40.415347 latitude and -3.7073709999999997 longitude\n"
]
}
],
"source": [
"district_name = madrid_data.loc[0, 'District']\n",
"district_lat = madrid_data.loc[0, 'District_Latitude']\n",
"district_long = madrid_data.loc[0, 'District_Longitude']\n",
"\n",
"print(\"The district is {} and it's district's coordinates are {} latitude and {} longitude\".format(district_name,\n",
" district_lat, district_long))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 386,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'https://api.foursquare.com/v2/venues/explore?&client_id=IYJ1TPEJFEWISEQRDNZNXO4HJQ0MTMY0AVDGBJBOLUEYAGH0&client_secret=HSVKU5JZT2OEKVIW33NFPEGPDFT4Z1OLKCY2FDFRFO023T4B&v=20180604&ll=40.415347,-3.7073709999999997&radius=1000&limit=100'"
]
},
"execution_count": 386,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# API Foursquare\n",
"LIMIT = 100\n",
"radius = 1000\n",
"url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
" CLIENT_ID, \n",
" CLIENT_SECRET, \n",
" VERSION, \n",
" district_lat, \n",
" district_long, \n",
" radius, \n",
" LIMIT)\n",
"url"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function extracts the categories of venues"
]
},
{
"cell_type": "code",
"execution_count": 388,
"metadata": {},
"outputs": [],
"source": [
"def get_category_type(row):\n",
" try:\n",
" categories_list = row['categories']\n",
" except:\n",
" categories_list = row['venue.categories']\n",
" if len(categories_list) == 0:\n",
" return None\n",
" else:\n",
" return categories_list[0]['name']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cleaning the json and structure it into a pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": 389,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" name \n",
" categories \n",
" lat \n",
" lng \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Mesón El Águila \n",
" Spanish Restaurant \n",
" 40.401573 \n",
" -3.607245 \n",
" \n",
" \n",
" 1 \n",
" Heladería Sienna - Sienna Winter Café \n",
" Ice Cream Shop \n",
" 40.402532 \n",
" -3.606221 \n",
" \n",
" \n",
" 2 \n",
" Parque de Valdebernardo \n",
" Park \n",
" 40.397950 \n",
" -3.606945 \n",
" \n",
" \n",
" 3 \n",
" Ahorramás \n",
" Grocery Store \n",
" 40.404239 \n",
" -3.605553 \n",
" \n",
" \n",
" 4 \n",
" La Madrina \n",
" Pizza Place \n",
" 40.398139 \n",
" -3.606602 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" name categories lat \\\n",
"0 Mesón El Águila Spanish Restaurant 40.401573 \n",
"1 Heladería Sienna - Sienna Winter Café Ice Cream Shop 40.402532 \n",
"2 Parque de Valdebernardo Park 40.397950 \n",
"3 Ahorramás Grocery Store 40.404239 \n",
"4 La Madrina Pizza Place 40.398139 \n",
"\n",
" lng \n",
"0 -3.607245 \n",
"1 -3.606221 \n",
"2 -3.606945 \n",
"3 -3.605553 \n",
"4 -3.606602 "
]
},
"execution_count": 389,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"venues = results['response']['groups'][0]['items']\n",
"\n",
"nearby_venues = pd.json_normalize(venues) # flatten JSON\n",
"\n",
"# filter columns\n",
"filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
"nearby_venues = nearby_venues.loc[:, filtered_columns]\n",
"\n",
"# filter the category for each row\n",
"nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n",
"\n",
"# clean columns\n",
"nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n",
"\n",
"nearby_venues.head()"
]
},
{
"cell_type": "code",
"execution_count": 390,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"72 venues were returned for Centro by Foursquare\n"
]
}
],
"source": [
"print(\"{} venues were returned for {} by Foursquare\".format(len(nearby_venues), district_name))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Getting nearby venues for all districts by creating the function getNearbyVenues.This function extracts the venues data for the districts in Madrid"
]
},
{
"cell_type": "code",
"execution_count": 397,
"metadata": {},
"outputs": [],
"source": [
"def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
" \n",
" venues_list=[]\n",
" for name, lat, lng in zip(names, latitudes, longitudes):\n",
" print(name)\n",
" \n",
" # create the API request URL\n",
" url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
" CLIENT_ID, \n",
" CLIENT_SECRET, \n",
" VERSION, \n",
" lat, \n",
" lng, \n",
" radius, \n",
" LIMIT)\n",
" \n",
" # make the GET request\n",
" results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
" \n",
" # return only relevant information for each nearby venue\n",
" venues_list.append([(\n",
" name, \n",
" lat, \n",
" lng, \n",
" v['venue']['name'], \n",
" v['venue']['location']['lat'], \n",
" v['venue']['location']['lng'], \n",
" v['venue']['categories'][0]['name']) for v in results])\n",
"\n",
" nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
" nearby_venues.columns = ['District',\n",
" 'Latitude', \n",
" 'Longitude', \n",
" 'Venue', \n",
" 'Venue Latitude', \n",
" 'Venue Longitude', \n",
" 'Venue Category']\n",
" \n",
" return(nearby_venues)"
]
},
{
"cell_type": "code",
"execution_count": 398,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Centro\n",
"Centro\n",
"Centro\n",
"Centro\n",
"Centro\n",
"Centro\n",
"Arganzuela\n",
"Arganzuela\n",
"Arganzuela\n",
"Arganzuela\n",
"Arganzuela\n",
"Arganzuela\n",
"Arganzuela\n",
"Retiro\n",
"Retiro\n",
"Retiro\n",
"Retiro\n",
"Retiro\n",
"Retiro\n",
"Salamanca\n",
"Salamanca\n",
"Salamanca\n",
"Salamanca\n",
"Salamanca\n",
"Salamanca\n",
"Chamartin\n",
"Chamartin\n",
"Chamartin\n",
"Chamartin\n",
"Chamartin\n",
"Chamartin\n",
"Tetuan\n",
"Tetuan\n",
"Tetuan\n",
"Tetuan\n",
"Tetuan\n",
"Tetuan\n",
"Chamberi\n",
"Chamberi\n",
"Chamberi\n",
"Chamberi\n",
"Chamberi\n",
"Chamberi\n",
"Fuencarral - El Pardo\n",
"Fuencarral - El Pardo\n",
"Fuencarral - El Pardo\n",
"Fuencarral - El Pardo\n",
"Fuencarral - El Pardo\n",
"Fuencarral - El Pardo\n",
"Fuencarral - El Pardo\n",
"Fuencarral - El Pardo\n",
"Moncloa - Aravaca\n",
"Moncloa - Aravaca\n",
"Moncloa - Aravaca\n",
"Moncloa - Aravaca\n",
"Moncloa - Aravaca\n",
"Moncloa - Aravaca\n",
"Moncloa - Aravaca\n",
"Latina\n",
"Latina\n",
"Latina\n",
"Latina\n",
"Latina\n",
"Latina\n",
"Latina\n",
"Carabanchel\n",
"Carabanchel\n",
"Carabanchel\n",
"Carabanchel\n",
"Carabanchel\n",
"Carabanchel\n",
"Carabanchel\n",
"Usera\n",
"Usera\n",
"Usera\n",
"Usera\n",
"Usera\n",
"Usera\n",
"Usera\n",
"Puente de Vallecas\n",
"Puente de Vallecas\n",
"Puente de Vallecas\n",
"Puente de Vallecas\n",
"Puente de Vallecas\n",
"Puente de Vallecas\n",
"Moratalaz\n",
"Moratalaz\n",
"Moratalaz\n",
"Moratalaz\n",
"Moratalaz\n",
"Moratalaz\n",
"Ciudad Lineal\n",
"Ciudad Lineal\n",
"Ciudad Lineal\n",
"Ciudad Lineal\n",
"Ciudad Lineal\n",
"Ciudad Lineal\n",
"Ciudad Lineal\n",
"Ciudad Lineal\n",
"Ciudad Lineal\n",
"Hortaleza\n",
"Hortaleza\n",
"Hortaleza\n",
"Hortaleza\n",
"Hortaleza\n",
"Hortaleza\n",
"Villaverde\n",
"Villaverde\n",
"Villaverde\n",
"Villaverde\n",
"Villaverde\n",
"Villa de Vallecas\n",
"Villa de Vallecas\n",
"Villa de Vallecas\n",
"Vicalvaro\n",
"Vicalvaro\n",
"Vicalvaro\n",
"Vicalvaro\n",
"San Blas - Canillejas\n",
"San Blas - Canillejas\n",
"San Blas - Canillejas\n",
"San Blas - Canillejas\n",
"San Blas - Canillejas\n",
"San Blas - Canillejas\n",
"San Blas - Canillejas\n",
"San Blas - Canillejas\n",
"Barajas\n",
"Barajas\n",
"Barajas\n",
"Barajas\n",
"Barajas\n"
]
}
],
"source": [
"# we run the above function on each district and create a new dataframe called madrid_venues.\n",
"madrid_venues = getNearbyVenues(names=madrid_data['District'],\n",
" latitudes=madrid_data['District_Latitude'],\n",
" longitudes=madrid_data['District_Longitude']\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 399,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4707, 7)\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" District \n",
" Latitude \n",
" Longitude \n",
" Venue \n",
" Venue Latitude \n",
" Venue Longitude \n",
" Venue Category \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Centro \n",
" 40.415347 \n",
" -3.707371 \n",
" Plaza Mayor \n",
" 40.415527 \n",
" -3.707506 \n",
" Plaza \n",
" \n",
" \n",
" 1 \n",
" Centro \n",
" 40.415347 \n",
" -3.707371 \n",
" Mercado de San Miguel \n",
" 40.415443 \n",
" -3.708943 \n",
" Market \n",
" \n",
" \n",
" 2 \n",
" Centro \n",
" 40.415347 \n",
" -3.707371 \n",
" The Hat Madrid \n",
" 40.414343 \n",
" -3.707120 \n",
" Hotel \n",
" \n",
" \n",
" 3 \n",
" Centro \n",
" 40.415347 \n",
" -3.707371 \n",
" La Taberna de Mister Pinkleton \n",
" 40.414536 \n",
" -3.708108 \n",
" Other Nightlife \n",
" \n",
" \n",
" 4 \n",
" Centro \n",
" 40.415347 \n",
" -3.707371 \n",
" Plaza Menor \n",
" 40.414192 \n",
" -3.708494 \n",
" Lounge \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" District Latitude Longitude Venue \\\n",
"0 Centro 40.415347 -3.707371 Plaza Mayor \n",
"1 Centro 40.415347 -3.707371 Mercado de San Miguel \n",
"2 Centro 40.415347 -3.707371 The Hat Madrid \n",
"3 Centro 40.415347 -3.707371 La Taberna de Mister Pinkleton \n",
"4 Centro 40.415347 -3.707371 Plaza Menor \n",
"\n",
" Venue Latitude Venue Longitude Venue Category \n",
"0 40.415527 -3.707506 Plaza \n",
"1 40.415443 -3.708943 Market \n",
"2 40.414343 -3.707120 Hotel \n",
"3 40.414536 -3.708108 Other Nightlife \n",
"4 40.414192 -3.708494 Lounge "
]
},
"execution_count": 399,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(madrid_venues.shape)\n",
"madrid_venues.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Counting venues grouped by district:"
]
},
{
"cell_type": "code",
"execution_count": 400,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" Latitude \n",
" Longitude \n",
" Venue \n",
" Venue Latitude \n",
" Venue Longitude \n",
" Venue Category \n",
" \n",
" \n",
" District \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" Arganzuela \n",
" 595 \n",
" 595 \n",
" 595 \n",
" 595 \n",
" 595 \n",
" 595 \n",
" \n",
" \n",
" Barajas \n",
" 155 \n",
" 155 \n",
" 155 \n",
" 155 \n",
" 155 \n",
" 155 \n",
" \n",
" \n",
" Carabanchel \n",
" 63 \n",
" 63 \n",
" 63 \n",
" 63 \n",
" 63 \n",
" 63 \n",
" \n",
" \n",
" Centro \n",
" 600 \n",
" 600 \n",
" 600 \n",
" 600 \n",
" 600 \n",
" 600 \n",
" \n",
" \n",
" Chamartin \n",
" 336 \n",
" 336 \n",
" 336 \n",
" 336 \n",
" 336 \n",
" 336 \n",
" \n",
" \n",
" Chamberi \n",
" 600 \n",
" 600 \n",
" 600 \n",
" 600 \n",
" 600 \n",
" 600 \n",
" \n",
" \n",
" Ciudad Lineal \n",
" 234 \n",
" 234 \n",
" 234 \n",
" 234 \n",
" 234 \n",
" 234 \n",
" \n",
" \n",
" Fuencarral - El Pardo \n",
" 312 \n",
" 312 \n",
" 312 \n",
" 312 \n",
" 312 \n",
" 312 \n",
" \n",
" \n",
" Hortaleza \n",
" 108 \n",
" 108 \n",
" 108 \n",
" 108 \n",
" 108 \n",
" 108 \n",
" \n",
" \n",
" Latina \n",
" 77 \n",
" 77 \n",
" 77 \n",
" 77 \n",
" 77 \n",
" 77 \n",
" \n",
" \n",
" Moncloa - Aravaca \n",
" 392 \n",
" 392 \n",
" 392 \n",
" 392 \n",
" 392 \n",
" 392 \n",
" \n",
" \n",
" Moratalaz \n",
" 72 \n",
" 72 \n",
" 72 \n",
" 72 \n",
" 72 \n",
" 72 \n",
" \n",
" \n",
" Puente de Vallecas \n",
" 156 \n",
" 156 \n",
" 156 \n",
" 156 \n",
" 156 \n",
" 156 \n",
" \n",
" \n",
" Retiro \n",
" 246 \n",
" 246 \n",
" 246 \n",
" 246 \n",
" 246 \n",
" 246 \n",
" \n",
" \n",
" Salamanca \n",
" 312 \n",
" 312 \n",
" 312 \n",
" 312 \n",
" 312 \n",
" 312 \n",
" \n",
" \n",
" San Blas - Canillejas \n",
" 80 \n",
" 80 \n",
" 80 \n",
" 80 \n",
" 80 \n",
" 80 \n",
" \n",
" \n",
" Tetuan \n",
" 192 \n",
" 192 \n",
" 192 \n",
" 192 \n",
" 192 \n",
" 192 \n",
" \n",
" \n",
" Usera \n",
" 84 \n",
" 84 \n",
" 84 \n",
" 84 \n",
" 84 \n",
" 84 \n",
" \n",
" \n",
" Vicalvaro \n",
" 44 \n",
" 44 \n",
" 44 \n",
" 44 \n",
" 44 \n",
" 44 \n",
" \n",
" \n",
" Villa de Vallecas \n",
" 24 \n",
" 24 \n",
" 24 \n",
" 24 \n",
" 24 \n",
" 24 \n",
" \n",
" \n",
" Villaverde \n",
" 25 \n",
" 25 \n",
" 25 \n",
" 25 \n",
" 25 \n",
" 25 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Latitude Longitude Venue Venue Latitude \\\n",
"District \n",
"Arganzuela 595 595 595 595 \n",
"Barajas 155 155 155 155 \n",
"Carabanchel 63 63 63 63 \n",
"Centro 600 600 600 600 \n",
"Chamartin 336 336 336 336 \n",
"Chamberi 600 600 600 600 \n",
"Ciudad Lineal 234 234 234 234 \n",
"Fuencarral - El Pardo 312 312 312 312 \n",
"Hortaleza 108 108 108 108 \n",
"Latina 77 77 77 77 \n",
"Moncloa - Aravaca 392 392 392 392 \n",
"Moratalaz 72 72 72 72 \n",
"Puente de Vallecas 156 156 156 156 \n",
"Retiro 246 246 246 246 \n",
"Salamanca 312 312 312 312 \n",
"San Blas - Canillejas 80 80 80 80 \n",
"Tetuan 192 192 192 192 \n",
"Usera 84 84 84 84 \n",
"Vicalvaro 44 44 44 44 \n",
"Villa de Vallecas 24 24 24 24 \n",
"Villaverde 25 25 25 25 \n",
"\n",
" Venue Longitude Venue Category \n",
"District \n",
"Arganzuela 595 595 \n",
"Barajas 155 155 \n",
"Carabanchel 63 63 \n",
"Centro 600 600 \n",
"Chamartin 336 336 \n",
"Chamberi 600 600 \n",
"Ciudad Lineal 234 234 \n",
"Fuencarral - El Pardo 312 312 \n",
"Hortaleza 108 108 \n",
"Latina 77 77 \n",
"Moncloa - Aravaca 392 392 \n",
"Moratalaz 72 72 \n",
"Puente de Vallecas 156 156 \n",
"Retiro 246 246 \n",
"Salamanca 312 312 \n",
"San Blas - Canillejas 80 80 \n",
"Tetuan 192 192 \n",
"Usera 84 84 \n",
"Vicalvaro 44 44 \n",
"Villa de Vallecas 24 24 \n",
"Villaverde 25 25 "
]
},
"execution_count": 400,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_venues.groupby('District').count()"
]
},
{
"cell_type": "code",
"execution_count": 401,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 165 unique categories\n"
]
}
],
"source": [
"print(\"There are {} unique categories\".format(len(madrid_venues['Venue Category'].unique())))"
]
},
{
"cell_type": "code",
"execution_count": 402,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" District \n",
" American Restaurant \n",
" Arepa Restaurant \n",
" Argentinian Restaurant \n",
" Art Gallery \n",
" Art Museum \n",
" Art Studio \n",
" Arts & Crafts Store \n",
" Asian Restaurant \n",
" Athletics & Sports \n",
" Auto Workshop \n",
" BBQ Joint \n",
" Bakery \n",
" Bar \n",
" Beer Bar \n",
" Beer Garden \n",
" Big Box Store \n",
" Bistro \n",
" Board Shop \n",
" Bookstore \n",
" Boutique \n",
" Boxing Gym \n",
" Brazilian Restaurant \n",
" Breakfast Spot \n",
" Brewery \n",
" Bubble Tea Shop \n",
" Burger Joint \n",
" Burrito Place \n",
" Bus Line \n",
" Bus Station \n",
" Cafeteria \n",
" Café \n",
" Candy Store \n",
" Cantonese Restaurant \n",
" Chinese Restaurant \n",
" Chocolate Shop \n",
" Circus \n",
" Clothing Store \n",
" Cocktail Bar \n",
" Coffee Shop \n",
" Concert Hall \n",
" Cosmetics Shop \n",
" Creperie \n",
" Cuban Restaurant \n",
" Cupcake Shop \n",
" Dance Studio \n",
" Department Store \n",
" Dessert Shop \n",
" Diner \n",
" Dog Run \n",
" Donut Shop \n",
" Dumpling Restaurant \n",
" Eastern European Restaurant \n",
" Electronics Store \n",
" Fabric Shop \n",
" Falafel Restaurant \n",
" Farmers Market \n",
" Fast Food Restaurant \n",
" Fish Market \n",
" Flea Market \n",
" Food \n",
" Food & Drink Shop \n",
" Food Stand \n",
" Food Truck \n",
" French Restaurant \n",
" Fried Chicken Joint \n",
" Frozen Yogurt Shop \n",
" Furniture / Home Store \n",
" Garden \n",
" Gas Station \n",
" Gastropub \n",
" General Entertainment \n",
" German Restaurant \n",
" Gourmet Shop \n",
" Greek Restaurant \n",
" Grocery Store \n",
" Gym \n",
" Gym / Fitness Center \n",
" Gymnastics Gym \n",
" Health & Beauty Service \n",
" Health Food Store \n",
" Herbs & Spices Store \n",
" Historic Site \n",
" History Museum \n",
" Hostel \n",
" Hot Dog Joint \n",
" Hotel \n",
" Ice Cream Shop \n",
" Indian Restaurant \n",
" Indie Movie Theater \n",
" Indie Theater \n",
" Italian Restaurant \n",
" Japanese Restaurant \n",
" Juice Bar \n",
" Kebab Restaurant \n",
" Korean Restaurant \n",
" Latin American Restaurant \n",
" Lebanese Restaurant \n",
" Liquor Store \n",
" Lounge \n",
" Market \n",
" Mediterranean Restaurant \n",
" Metro Station \n",
" Mexican Restaurant \n",
" Middle Eastern Restaurant \n",
" Mobile Phone Shop \n",
" Motorcycle Shop \n",
" Movie Theater \n",
" Multiplex \n",
" Museum \n",
" Music Venue \n",
" Nightclub \n",
" Noodle House \n",
" Opera House \n",
" Other Great Outdoors \n",
" Other Nightlife \n",
" Outdoors & Recreation \n",
" Park \n",
" Pastry Shop \n",
" Peruvian Restaurant \n",
" Pet Store \n",
" Pharmacy \n",
" Pie Shop \n",
" Pizza Place \n",
" Platform \n",
" Playground \n",
" Plaza \n",
" Polish Restaurant \n",
" Pub \n",
" Ramen Restaurant \n",
" Resort \n",
" Restaurant \n",
" Road \n",
" Salad Place \n",
" Sandwich Place \n",
" Scenic Lookout \n",
" Science Museum \n",
" Seafood Restaurant \n",
" Shopping Mall \n",
" Smoothie Shop \n",
" Snack Place \n",
" Soccer Field \n",
" South American Restaurant \n",
" Spa \n",
" Spanish Restaurant \n",
" Sporting Goods Shop \n",
" Steakhouse \n",
" Supermarket \n",
" Sushi Restaurant \n",
" Tapas Restaurant \n",
" Tattoo Parlor \n",
" Tea Room \n",
" Thai Restaurant \n",
" Theater \n",
" Theme Restaurant \n",
" Thrift / Vintage Store \n",
" Toy / Game Store \n",
" Trade School \n",
" Train \n",
" Train Station \n",
" Udon Restaurant \n",
" Vegetarian / Vegan Restaurant \n",
" Video Game Store \n",
" Wine Bar \n",
" Wine Shop \n",
" Women's Store \n",
" \n",
" \n",
" \n",
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},
"execution_count": 402,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# one hot encoding\n",
"madrid_onehot = pd.get_dummies(madrid_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
"\n",
"# add disctrict column back to dataframe\n",
"madrid_onehot['District'] = madrid_venues['District'] \n",
"\n",
"# move neighborhood column to the first column\n",
"fixed_columns = [madrid_onehot.columns[-1]] + list(madrid_onehot.columns[:-1])\n",
"madrid_onehot = madrid_onehot[fixed_columns]\n",
"\n",
"madrid_onehot.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 403,
"metadata": {},
"outputs": [
{
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" \n",
" 7 \n",
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" \n",
" \n",
" 8 \n",
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" \n",
" \n",
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" \n",
" \n",
" 10 \n",
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" \n",
" \n",
" 11 \n",
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" \n",
" \n",
" 12 \n",
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" \n",
" \n",
" 13 \n",
" Retiro \n",
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" \n",
" \n",
" 14 \n",
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" \n",
" \n",
" 15 \n",
" San Blas - Canillejas \n",
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" \n",
" \n",
" 16 \n",
" Tetuan \n",
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"text/plain": [
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"\n",
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"\n",
" Arts & Crafts Store Asian Restaurant Athletics & Sports Auto Workshop \\\n",
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"\n",
" BBQ Joint Bakery Bar Beer Bar Beer Garden Big Box Store \\\n",
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" Bistro Board Shop Bookstore Boutique Boxing Gym \\\n",
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" Brazilian Restaurant Breakfast Spot Brewery Bubble Tea Shop \\\n",
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" Burger Joint Burrito Place Bus Line Bus Station Cafeteria Café \\\n",
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" Candy Store Cantonese Restaurant Chinese Restaurant Chocolate Shop \\\n",
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" Circus Clothing Store Cocktail Bar Coffee Shop Concert Hall \\\n",
"0 0.011765 0.000000 0.000000 0.011765 0.011765 \n",
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" Cosmetics Shop Creperie Cuban Restaurant Cupcake Shop Dance Studio \\\n",
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" Department Store Dessert Shop Diner Dog Run Donut Shop \\\n",
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" Dumpling Restaurant Eastern European Restaurant Electronics Store \\\n",
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" Fabric Shop Falafel Restaurant Farmers Market Fast Food Restaurant \\\n",
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" Food Truck French Restaurant Fried Chicken Joint Frozen Yogurt Shop \\\n",
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" Furniture / Home Store Garden Gas Station Gastropub \\\n",
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" Health & Beauty Service Health Food Store Herbs & Spices Store \\\n",
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"\n",
" Ice Cream Shop Indian Restaurant Indie Movie Theater Indie Theater \\\n",
"0 0.000000 0.00000 0.00 0.011765 \n",
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"\n",
" Italian Restaurant Japanese Restaurant Juice Bar Kebab Restaurant \\\n",
"0 0.011765 0.000000 0.000000 0.000000 \n",
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" Korean Restaurant Latin American Restaurant Lebanese Restaurant \\\n",
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" Liquor Store Lounge Market Mediterranean Restaurant Metro Station \\\n",
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" Mexican Restaurant Middle Eastern Restaurant Mobile Phone Shop \\\n",
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" Motorcycle Shop Movie Theater Multiplex Museum Music Venue \\\n",
"0 0.00000 0.011765 0.00 0.011765 0.011765 \n",
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" Nightclub Noodle House Opera House Other Great Outdoors \\\n",
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" Other Nightlife Outdoors & Recreation Park Pastry Shop \\\n",
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" Peruvian Restaurant Pet Store Pharmacy Pie Shop Pizza Place Platform \\\n",
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" Playground Plaza Polish Restaurant Pub Ramen Restaurant \\\n",
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" Resort Restaurant Road Salad Place Sandwich Place Scenic Lookout \\\n",
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" Science Museum Seafood Restaurant Shopping Mall Smoothie Shop \\\n",
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"\n",
" Snack Place Soccer Field South American Restaurant Spa \\\n",
"0 0.011765 0.000000 0.000000 0.00 \n",
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"\n",
" Spanish Restaurant Sporting Goods Shop Steakhouse Supermarket \\\n",
"0 0.094118 0.000000 0.000000 0.011765 \n",
"1 0.096774 0.000000 0.000000 0.032258 \n",
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"\n",
" Sushi Restaurant Tapas Restaurant Tattoo Parlor Tea Room \\\n",
"0 0.000000 0.047059 0.011765 0.00 \n",
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"\n",
" Thai Restaurant Theater Theme Restaurant Thrift / Vintage Store \\\n",
"0 0.011765 0.000000 0.011765 0.011765 \n",
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" Toy / Game Store Trade School Train Train Station Udon Restaurant \\\n",
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"\n",
" Vegetarian / Vegan Restaurant Video Game Store Wine Bar Wine Shop \\\n",
"0 0.00 0.000000 0.000000 0.000000 \n",
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"8 0.00 0.000000 0.000000 0.000000 \n",
"9 0.00 0.000000 0.000000 0.000000 \n",
"10 0.00 0.000000 0.000000 0.000000 \n",
"11 0.00 0.000000 0.000000 0.000000 \n",
"12 0.00 0.000000 0.000000 0.000000 \n",
"13 0.00 0.000000 0.000000 0.000000 \n",
"14 0.00 0.000000 0.000000 0.019231 \n",
"15 0.00 0.000000 0.000000 0.000000 \n",
"16 0.00 0.000000 0.000000 0.000000 \n",
"17 0.00 0.000000 0.000000 0.000000 \n",
"18 0.00 0.000000 0.000000 0.000000 \n",
"19 0.00 0.000000 0.000000 0.000000 \n",
"20 0.00 0.000000 0.000000 0.000000 \n",
"\n",
" Women's Store \n",
"0 0.000000 \n",
"1 0.000000 \n",
"2 0.000000 \n",
"3 0.000000 \n",
"4 0.000000 \n",
"5 0.000000 \n",
"6 0.000000 \n",
"7 0.000000 \n",
"8 0.000000 \n",
"9 0.000000 \n",
"10 0.017857 \n",
"11 0.000000 \n",
"12 0.000000 \n",
"13 0.000000 \n",
"14 0.000000 \n",
"15 0.000000 \n",
"16 0.000000 \n",
"17 0.000000 \n",
"18 0.000000 \n",
"19 0.000000 \n",
"20 0.000000 "
]
},
"execution_count": 403,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_grouped = madrid_onehot.groupby('District').mean().reset_index()\n",
"madrid_grouped"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Displaying the top 5 venues of all districts"
]
},
{
"cell_type": "code",
"execution_count": 404,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----Arganzuela----\n",
" venue freq\n",
"0 Restaurant 0.12\n",
"1 Spanish Restaurant 0.09\n",
"2 Grocery Store 0.07\n",
"3 Bakery 0.06\n",
"4 Tapas Restaurant 0.05\n",
"\n",
"\n",
"----Barajas----\n",
" venue freq\n",
"0 Hotel 0.19\n",
"1 Restaurant 0.13\n",
"2 Spanish Restaurant 0.10\n",
"3 Coffee Shop 0.06\n",
"4 Tapas Restaurant 0.06\n",
"\n",
"\n",
"----Carabanchel----\n",
" venue freq\n",
"0 Plaza 0.11\n",
"1 Bakery 0.11\n",
"2 Fast Food Restaurant 0.11\n",
"3 Nightclub 0.11\n",
"4 Soccer Field 0.11\n",
"\n",
"\n",
"----Centro----\n",
" venue freq\n",
"0 Spanish Restaurant 0.13\n",
"1 Tapas Restaurant 0.12\n",
"2 Plaza 0.10\n",
"3 Ice Cream Shop 0.04\n",
"4 Hostel 0.04\n",
"\n",
"\n",
"----Chamartin----\n",
" venue freq\n",
"0 Restaurant 0.14\n",
"1 Spanish Restaurant 0.14\n",
"2 Café 0.05\n",
"3 Bakery 0.05\n",
"4 Grocery Store 0.05\n",
"\n",
"\n",
"----Chamberi----\n",
" venue freq\n",
"0 Spanish Restaurant 0.12\n",
"1 Restaurant 0.07\n",
"2 Bar 0.06\n",
"3 Brewery 0.05\n",
"4 Italian Restaurant 0.04\n",
"\n",
"\n",
"----Ciudad Lineal----\n",
" venue freq\n",
"0 Spanish Restaurant 0.12\n",
"1 Restaurant 0.12\n",
"2 Grocery Store 0.08\n",
"3 Argentinian Restaurant 0.08\n",
"4 Park 0.08\n",
"\n",
"\n",
"----Fuencarral - El Pardo----\n",
" venue freq\n",
"0 Clothing Store 0.13\n",
"1 Italian Restaurant 0.05\n",
"2 Fast Food Restaurant 0.05\n",
"3 Tapas Restaurant 0.05\n",
"4 Burger Joint 0.05\n",
"\n",
"\n",
"----Hortaleza----\n",
" venue freq\n",
"0 Breakfast Spot 0.17\n",
"1 Pizza Place 0.11\n",
"2 Supermarket 0.11\n",
"3 Pub 0.06\n",
"4 Bakery 0.06\n",
"\n",
"\n",
"----Latina----\n",
" venue freq\n",
"0 Pizza Place 0.18\n",
"1 Fast Food Restaurant 0.09\n",
"2 Metro Station 0.09\n",
"3 Arts & Crafts Store 0.09\n",
"4 Asian Restaurant 0.09\n",
"\n",
"\n",
"----Moncloa - Aravaca----\n",
" venue freq\n",
"0 Restaurant 0.07\n",
"1 Tapas Restaurant 0.05\n",
"2 Spanish Restaurant 0.05\n",
"3 Pizza Place 0.04\n",
"4 Japanese Restaurant 0.04\n",
"\n",
"\n",
"----Moratalaz----\n",
" venue freq\n",
"0 Bar 0.17\n",
"1 Ice Cream Shop 0.08\n",
"2 Bakery 0.08\n",
"3 Pizza Place 0.08\n",
"4 Park 0.08\n",
"\n",
"\n",
"----Puente de Vallecas----\n",
" venue freq\n",
"0 Fast Food Restaurant 0.12\n",
"1 Gym 0.08\n",
"2 Café 0.08\n",
"3 Hotel 0.08\n",
"4 Supermarket 0.08\n",
"\n",
"\n",
"----Retiro----\n",
" venue freq\n",
"0 Spanish Restaurant 0.15\n",
"1 Museum 0.05\n",
"2 Bar 0.05\n",
"3 Supermarket 0.05\n",
"4 Tapas Restaurant 0.05\n",
"\n",
"\n",
"----Salamanca----\n",
" venue freq\n",
"0 Spanish Restaurant 0.17\n",
"1 Restaurant 0.13\n",
"2 Mediterranean Restaurant 0.06\n",
"3 Seafood Restaurant 0.06\n",
"4 Clothing Store 0.04\n",
"\n",
"\n",
"----San Blas - Canillejas----\n",
" venue freq\n",
"0 Metro Station 0.2 \n",
"1 Snack Place 0.1 \n",
"2 Pizza Place 0.1 \n",
"3 Supermarket 0.1 \n",
"4 Shopping Mall 0.1 \n",
"\n",
"\n",
"----Tetuan----\n",
" venue freq\n",
"0 Spanish Restaurant 0.16\n",
"1 Chinese Restaurant 0.06\n",
"2 Brazilian Restaurant 0.06\n",
"3 Supermarket 0.06\n",
"4 Grocery Store 0.06\n",
"\n",
"\n",
"----Usera----\n",
" venue freq\n",
"0 Spanish Restaurant 0.17\n",
"1 Seafood Restaurant 0.17\n",
"2 Chinese Restaurant 0.17\n",
"3 Asian Restaurant 0.08\n",
"4 Noodle House 0.08\n",
"\n",
"\n",
"----Vicalvaro----\n",
" venue freq\n",
"0 Pizza Place 0.18\n",
"1 Spanish Restaurant 0.18\n",
"2 Grocery Store 0.09\n",
"3 Sandwich Place 0.09\n",
"4 Fast Food Restaurant 0.09\n",
"\n",
"\n",
"----Villa de Vallecas----\n",
" venue freq\n",
"0 Breakfast Spot 0.12\n",
"1 Grocery Store 0.12\n",
"2 Park 0.12\n",
"3 Sandwich Place 0.12\n",
"4 Food 0.12\n",
"\n",
"\n",
"----Villaverde----\n",
" venue freq\n",
"0 Spanish Restaurant 0.2 \n",
"1 Pizza Place 0.2 \n",
"2 Plaza 0.2 \n",
"3 Diner 0.2 \n",
"4 Train 0.2 \n",
"\n",
"\n"
]
}
],
"source": [
"num_top_venues = 5\n",
"\n",
"for n in madrid_grouped['District']:\n",
" print(\"----\"+n+\"----\")\n",
" temp = madrid_grouped[madrid_grouped['District']==n].T.reset_index()\n",
" temp.columns = ['venue', 'freq']\n",
" temp = temp.iloc[1:]\n",
" temp['freq'] = temp['freq'].astype(float)\n",
" temp = temp.round({'freq': 2})\n",
" print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
" print('\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Putting the previous into a pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": 405,
"metadata": {},
"outputs": [],
"source": [
"def return_most_common_venues(row, num_top_venues):\n",
" row_categories = row.iloc[1:]\n",
" row_categories_sorted = row_categories.sort_values(ascending=False)\n",
" \n",
" return row_categories_sorted.index.values[0:num_top_venues]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create the new dataframe and display the top 10 venues for each neighborhood"
]
},
{
"cell_type": "code",
"execution_count": 406,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" District \n",
" 1st Most Common Venue \n",
" 2nd Most Common Venue \n",
" 3rd Most Common Venue \n",
" 4th Most Common Venue \n",
" 5th Most Common Venue \n",
" 6th Most Common Venue \n",
" 7th Most Common Venue \n",
" 8th Most Common Venue \n",
" 9th Most Common Venue \n",
" 10th Most Common Venue \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Arganzuela \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Bakery \n",
" Tapas Restaurant \n",
" Gym / Fitness Center \n",
" Beer Garden \n",
" Market \n",
" Falafel Restaurant \n",
" Burger Joint \n",
" \n",
" \n",
" 1 \n",
" Barajas \n",
" Hotel \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Coffee Shop \n",
" Tapas Restaurant \n",
" Bar \n",
" Fast Food Restaurant \n",
" Brewery \n",
" Mexican Restaurant \n",
" Café \n",
" \n",
" \n",
" 2 \n",
" Carabanchel \n",
" Pizza Place \n",
" Nightclub \n",
" Fast Food Restaurant \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Bakery \n",
" Soccer Field \n",
" Metro Station \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 3 \n",
" Centro \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 4 \n",
" Chamartin \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bakery \n",
" Grocery Store \n",
" Tapas Restaurant \n",
" Café \n",
" Gastropub \n",
" Park \n",
" Coffee Shop \n",
" Japanese Restaurant \n",
" \n",
" \n",
" 5 \n",
" Chamberi \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bar \n",
" Brewery \n",
" Italian Restaurant \n",
" Japanese Restaurant \n",
" Café \n",
" Plaza \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" \n",
" \n",
" 6 \n",
" Ciudad Lineal \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Park \n",
" Grocery Store \n",
" Argentinian Restaurant \n",
" Gastropub \n",
" Tapas Restaurant \n",
" Pharmacy \n",
" Café \n",
" Bus Line \n",
" \n",
" \n",
" 7 \n",
" Fuencarral - El Pardo \n",
" Clothing Store \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Fast Food Restaurant \n",
" Italian Restaurant \n",
" Shopping Mall \n",
" Spanish Restaurant \n",
" Big Box Store \n",
" Flea Market \n",
" Boutique \n",
" \n",
" \n",
" 8 \n",
" Hortaleza \n",
" Breakfast Spot \n",
" Supermarket \n",
" Pizza Place \n",
" Sandwich Place \n",
" Pub \n",
" Spanish Restaurant \n",
" Bakery \n",
" Plaza \n",
" Chinese Restaurant \n",
" Restaurant \n",
" \n",
" \n",
" 9 \n",
" Latina \n",
" Pizza Place \n",
" Fast Food Restaurant \n",
" Park \n",
" Grocery Store \n",
" Train Station \n",
" Arts & Crafts Store \n",
" Asian Restaurant \n",
" Falafel Restaurant \n",
" Metro Station \n",
" Bakery \n",
" \n",
" \n",
" 10 \n",
" Moncloa - Aravaca \n",
" Restaurant \n",
" Tapas Restaurant \n",
" Spanish Restaurant \n",
" Pizza Place \n",
" Italian Restaurant \n",
" Pub \n",
" Bakery \n",
" Bar \n",
" Coffee Shop \n",
" Mediterranean Restaurant \n",
" \n",
" \n",
" 11 \n",
" Moratalaz \n",
" Bar \n",
" Pizza Place \n",
" Ice Cream Shop \n",
" Food Truck \n",
" Café \n",
" Brewery \n",
" Nightclub \n",
" Bakery \n",
" Soccer Field \n",
" Plaza \n",
" \n",
" \n",
" 12 \n",
" Puente de Vallecas \n",
" Fast Food Restaurant \n",
" Gym \n",
" Grocery Store \n",
" Café \n",
" Hotel \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Burger Joint \n",
" Breakfast Spot \n",
" \n",
" \n",
" 13 \n",
" Retiro \n",
" Spanish Restaurant \n",
" Museum \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Gym \n",
" Farmers Market \n",
" Boutique \n",
" Brewery \n",
" Food & Drink Shop \n",
" \n",
" \n",
" 14 \n",
" Salamanca \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Mediterranean Restaurant \n",
" Seafood Restaurant \n",
" Burger Joint \n",
" Clothing Store \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" Supermarket \n",
" Plaza \n",
" \n",
" \n",
" 15 \n",
" San Blas - Canillejas \n",
" Metro Station \n",
" Asian Restaurant \n",
" Shopping Mall \n",
" Supermarket \n",
" Snack Place \n",
" Pizza Place \n",
" Gas Station \n",
" Grocery Store \n",
" Gym \n",
" Fabric Shop \n",
" \n",
" \n",
" 16 \n",
" Tetuan \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Brazilian Restaurant \n",
" Chinese Restaurant \n",
" Supermarket \n",
" Breakfast Spot \n",
" Clothing Store \n",
" Resort \n",
" Cafeteria \n",
" Seafood Restaurant \n",
" \n",
" \n",
" 17 \n",
" Usera \n",
" Spanish Restaurant \n",
" Seafood Restaurant \n",
" Chinese Restaurant \n",
" Theater \n",
" Asian Restaurant \n",
" Bubble Tea Shop \n",
" Noodle House \n",
" Fast Food Restaurant \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 18 \n",
" Vicalvaro \n",
" Pizza Place \n",
" Spanish Restaurant \n",
" Beer Bar \n",
" Fast Food Restaurant \n",
" Grocery Store \n",
" Ice Cream Shop \n",
" Sandwich Place \n",
" Breakfast Spot \n",
" Café \n",
" Fish Market \n",
" \n",
" \n",
" 19 \n",
" Villa de Vallecas \n",
" Breakfast Spot \n",
" Park \n",
" Platform \n",
" Grocery Store \n",
" Spanish Restaurant \n",
" Food \n",
" Soccer Field \n",
" Sandwich Place \n",
" Eastern European Restaurant \n",
" Fast Food Restaurant \n",
" \n",
" \n",
" 20 \n",
" Villaverde \n",
" Pizza Place \n",
" Train \n",
" Plaza \n",
" Diner \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Dog Run \n",
" Farmers Market \n",
" Falafel Restaurant \n",
" Fabric Shop \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" District 1st Most Common Venue 2nd Most Common Venue \\\n",
"0 Arganzuela Restaurant Spanish Restaurant \n",
"1 Barajas Hotel Restaurant \n",
"2 Carabanchel Pizza Place Nightclub \n",
"3 Centro Spanish Restaurant Tapas Restaurant \n",
"4 Chamartin Spanish Restaurant Restaurant \n",
"5 Chamberi Spanish Restaurant Restaurant \n",
"6 Ciudad Lineal Restaurant Spanish Restaurant \n",
"7 Fuencarral - El Pardo Clothing Store Burger Joint \n",
"8 Hortaleza Breakfast Spot Supermarket \n",
"9 Latina Pizza Place Fast Food Restaurant \n",
"10 Moncloa - Aravaca Restaurant Tapas Restaurant \n",
"11 Moratalaz Bar Pizza Place \n",
"12 Puente de Vallecas Fast Food Restaurant Gym \n",
"13 Retiro Spanish Restaurant Museum \n",
"14 Salamanca Spanish Restaurant Restaurant \n",
"15 San Blas - Canillejas Metro Station Asian Restaurant \n",
"16 Tetuan Spanish Restaurant Grocery Store \n",
"17 Usera Spanish Restaurant Seafood Restaurant \n",
"18 Vicalvaro Pizza Place Spanish Restaurant \n",
"19 Villa de Vallecas Breakfast Spot Park \n",
"20 Villaverde Pizza Place Train \n",
"\n",
" 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue \\\n",
"0 Grocery Store Bakery Tapas Restaurant \n",
"1 Spanish Restaurant Coffee Shop Tapas Restaurant \n",
"2 Fast Food Restaurant Burger Joint Tapas Restaurant \n",
"3 Plaza Hostel Ice Cream Shop \n",
"4 Bakery Grocery Store Tapas Restaurant \n",
"5 Bar Brewery Italian Restaurant \n",
"6 Park Grocery Store Argentinian Restaurant \n",
"7 Tapas Restaurant Fast Food Restaurant Italian Restaurant \n",
"8 Pizza Place Sandwich Place Pub \n",
"9 Park Grocery Store Train Station \n",
"10 Spanish Restaurant Pizza Place Italian Restaurant \n",
"11 Ice Cream Shop Food Truck Café \n",
"12 Grocery Store Café Hotel \n",
"13 Supermarket Tapas Restaurant Bar \n",
"14 Mediterranean Restaurant Seafood Restaurant Burger Joint \n",
"15 Shopping Mall Supermarket Snack Place \n",
"16 Brazilian Restaurant Chinese Restaurant Supermarket \n",
"17 Chinese Restaurant Theater Asian Restaurant \n",
"18 Beer Bar Fast Food Restaurant Grocery Store \n",
"19 Platform Grocery Store Spanish Restaurant \n",
"20 Plaza Diner Spanish Restaurant \n",
"\n",
" 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n",
"0 Gym / Fitness Center Beer Garden Market \n",
"1 Bar Fast Food Restaurant Brewery \n",
"2 Bakery Soccer Field Metro Station \n",
"3 Bookstore Hotel Pastry Shop \n",
"4 Café Gastropub Park \n",
"5 Japanese Restaurant Café Plaza \n",
"6 Gastropub Tapas Restaurant Pharmacy \n",
"7 Shopping Mall Spanish Restaurant Big Box Store \n",
"8 Spanish Restaurant Bakery Plaza \n",
"9 Arts & Crafts Store Asian Restaurant Falafel Restaurant \n",
"10 Pub Bakery Bar \n",
"11 Brewery Nightclub Bakery \n",
"12 Supermarket Tapas Restaurant Bar \n",
"13 Gym Farmers Market Boutique \n",
"14 Clothing Store Mexican Restaurant Tapas Restaurant \n",
"15 Pizza Place Gas Station Grocery Store \n",
"16 Breakfast Spot Clothing Store Resort \n",
"17 Bubble Tea Shop Noodle House Fast Food Restaurant \n",
"18 Ice Cream Shop Sandwich Place Breakfast Spot \n",
"19 Food Soccer Field Sandwich Place \n",
"20 Grocery Store Dog Run Farmers Market \n",
"\n",
" 9th Most Common Venue 10th Most Common Venue \n",
"0 Falafel Restaurant Burger Joint \n",
"1 Mexican Restaurant Café \n",
"2 Plaza Diner \n",
"3 Cocktail Bar Gym / Fitness Center \n",
"4 Coffee Shop Japanese Restaurant \n",
"5 Mexican Restaurant Tapas Restaurant \n",
"6 Café Bus Line \n",
"7 Flea Market Boutique \n",
"8 Chinese Restaurant Restaurant \n",
"9 Metro Station Bakery \n",
"10 Coffee Shop Mediterranean Restaurant \n",
"11 Soccer Field Plaza \n",
"12 Burger Joint Breakfast Spot \n",
"13 Brewery Food & Drink Shop \n",
"14 Supermarket Plaza \n",
"15 Gym Fabric Shop \n",
"16 Cafeteria Seafood Restaurant \n",
"17 Plaza Diner \n",
"18 Café Fish Market \n",
"19 Eastern European Restaurant Fast Food Restaurant \n",
"20 Falafel Restaurant Fabric Shop "
]
},
"execution_count": 406,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_top_venues = 10\n",
"\n",
"indicators = ['st', 'nd', 'rd']\n",
"\n",
"# create columns according to number of top venues\n",
"columns = ['District']\n",
"for ind in np.arange(num_top_venues):\n",
" try:\n",
" columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
" except:\n",
" columns.append('{}th Most Common Venue'.format(ind+1))\n",
"\n",
"# create a new dataframe\n",
"neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
"neighborhoods_venues_sorted['District'] = madrid_grouped['District']\n",
"\n",
"for ind in np.arange(madrid_grouped.shape[0]):\n",
" neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(madrid_grouped.iloc[ind, :], num_top_venues)\n",
"\n",
"neighborhoods_venues_sorted"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster Districts\n",
"\n",
"Run k-means to cluster districts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following is to determine how many clusters should we use, for this we will use the Silhouette Score - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html"
]
},
{
"cell_type": "code",
"execution_count": 407,
"metadata": {},
"outputs": [],
"source": [
"def plot(x, y):\n",
" fig = plt.figure(figsize=(12,6))\n",
" plt.plot(x, y, 'o-')\n",
" plt.xlabel('Number of clusters')\n",
" plt.ylabel('Silhouette Scores')\n",
" plt.title('Checking Optimum Number of Clusters')\n",
" ax = plt.gca()\n",
" ax.spines['right'].set_visible(False)\n",
" ax.spines['top'].set_visible(False)"
]
},
{
"cell_type": "code",
"execution_count": 408,
"metadata": {},
"outputs": [],
"source": [
"maxk = 15\n",
"scores = []\n",
"kval = []\n",
"\n",
"for k in range(2, maxk+1):\n",
" cl_df = madrid_grouped.drop('District', axis=1)\n",
" kmeans = KMeans(n_clusters=k, init=\"k-means++\", random_state=0).fit_predict(cl_df) #Choose any random_state\n",
" \n",
" score = silhouette_score(cl_df, kmeans, metric='euclidean', random_state=0)\n",
" kval.append(k)\n",
" scores.append(score)"
]
},
{
"cell_type": "code",
"execution_count": 409,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.1903125726032766, 0.1620096375806738, 0.1940931157107913, 0.18574859973350114, 0.15799897660811957, 0.14931739548789957, 0.11691430041648038, 0.15547870618207524, 0.14334680215501786, 0.041599156450781376, 0.04124564442032303, 0.04168433945809237, 0.029124446627720606, 0.032561698923450386]\n",
"[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print(scores)\n",
"print(kval)\n",
"plot(kval, scores)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Acording to the previous we can use 3 or 4 clusters given that it provides the highest silhouette score. Also note that it decreases as the number of clusters increases "
]
},
{
"cell_type": "code",
"execution_count": 410,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 1, 0, 0, 0, 0, 0, 0, 3])"
]
},
"execution_count": 410,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# set number of clusters\n",
"kclusters = 4\n",
"\n",
"madrid_grouped_clustering = madrid_grouped.drop('District', 1)\n",
"\n",
"# run k-means clustering\n",
"\n",
"kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(madrid_grouped_clustering)\n",
"\n",
"# check cluster labels generated for each row in the dataframe\n",
"\n",
"kmeans.labels_[0:10]"
]
},
{
"cell_type": "code",
"execution_count": 411,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" District \n",
" Neighborhood \n",
" District_Latitude \n",
" District_Longitude \n",
" Cluster Labels \n",
" 1st Most Common Venue \n",
" 2nd Most Common Venue \n",
" 3rd Most Common Venue \n",
" 4th Most Common Venue \n",
" 5th Most Common Venue \n",
" 6th Most Common Venue \n",
" 7th Most Common Venue \n",
" 8th Most Common Venue \n",
" 9th Most Common Venue \n",
" 10th Most Common Venue \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Centro \n",
" Palacio \n",
" 40.415347 \n",
" -3.707371 \n",
" 0 \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 1 \n",
" Centro \n",
" Embajadores \n",
" 40.415347 \n",
" -3.707371 \n",
" 0 \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 2 \n",
" Centro \n",
" Cortes \n",
" 40.415347 \n",
" -3.707371 \n",
" 0 \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 3 \n",
" Centro \n",
" Justicia \n",
" 40.415347 \n",
" -3.707371 \n",
" 0 \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 4 \n",
" Centro \n",
" Universidad \n",
" 40.415347 \n",
" -3.707371 \n",
" 0 \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" District Neighborhood District_Latitude District_Longitude \\\n",
"0 Centro Palacio 40.415347 -3.707371 \n",
"1 Centro Embajadores 40.415347 -3.707371 \n",
"2 Centro Cortes 40.415347 -3.707371 \n",
"3 Centro Justicia 40.415347 -3.707371 \n",
"4 Centro Universidad 40.415347 -3.707371 \n",
"\n",
" Cluster Labels 1st Most Common Venue 2nd Most Common Venue \\\n",
"0 0 Spanish Restaurant Tapas Restaurant \n",
"1 0 Spanish Restaurant Tapas Restaurant \n",
"2 0 Spanish Restaurant Tapas Restaurant \n",
"3 0 Spanish Restaurant Tapas Restaurant \n",
"4 0 Spanish Restaurant Tapas Restaurant \n",
"\n",
" 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue \\\n",
"0 Plaza Hostel Ice Cream Shop \n",
"1 Plaza Hostel Ice Cream Shop \n",
"2 Plaza Hostel Ice Cream Shop \n",
"3 Plaza Hostel Ice Cream Shop \n",
"4 Plaza Hostel Ice Cream Shop \n",
"\n",
" 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n",
"0 Bookstore Hotel Pastry Shop \n",
"1 Bookstore Hotel Pastry Shop \n",
"2 Bookstore Hotel Pastry Shop \n",
"3 Bookstore Hotel Pastry Shop \n",
"4 Bookstore Hotel Pastry Shop \n",
"\n",
" 9th Most Common Venue 10th Most Common Venue \n",
"0 Cocktail Bar Gym / Fitness Center \n",
"1 Cocktail Bar Gym / Fitness Center \n",
"2 Cocktail Bar Gym / Fitness Center \n",
"3 Cocktail Bar Gym / Fitness Center \n",
"4 Cocktail Bar Gym / Fitness Center "
]
},
"execution_count": 411,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# add clustering labels\n",
"neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
"\n",
"madrid_merged = madrid_data\n",
"\n",
"# merge manhattan_grouped with manhattan_data to add latitude/longitude for each neighborhood\n",
"madrid_merged = madrid_merged.join(neighborhoods_venues_sorted.set_index('District'), on='District')\n",
"\n",
"madrid_merged.head() # check the last columns!"
]
},
{
"cell_type": "code",
"execution_count": 488,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" District \n",
" Neighborhood \n",
" District_Latitude \n",
" District_Longitude \n",
" \n",
" \n",
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" 32 \n",
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" Cuatro Caminos \n",
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" \n",
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" Tetuan \n",
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" 35 \n",
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" 37 \n",
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" \n",
" \n",
" 38 \n",
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" 40.432792 \n",
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" \n",
" \n",
" 39 \n",
" Chamberi \n",
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" \n",
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" 40 \n",
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" \n",
" \n",
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" \n",
" \n",
" 43 \n",
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" \n",
" \n",
" 44 \n",
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" \n",
" \n",
" 45 \n",
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" \n",
" \n",
" 46 \n",
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" \n",
" \n",
" 47 \n",
" Fuencarral - El Pardo \n",
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" \n",
" \n",
" 48 \n",
" Fuencarral - El Pardo \n",
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" \n",
" \n",
" 49 \n",
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" \n",
" 50 \n",
" Fuencarral - El Pardo \n",
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" \n",
" \n",
" 51 \n",
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" \n",
" \n",
" 52 \n",
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" \n",
" \n",
" 53 \n",
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" \n",
" \n",
" 54 \n",
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" \n",
" \n",
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" \n",
" \n",
" 56 \n",
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" \n",
" \n",
" 57 \n",
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" \n",
" \n",
" 58 \n",
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" \n",
" 59 \n",
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" \n",
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" \n",
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" \n",
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" \n",
" \n",
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" \n",
" \n",
" 65 \n",
" Carabanchel \n",
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" \n",
" \n",
" 66 \n",
" Carabanchel \n",
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" \n",
" 67 \n",
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" \n",
" \n",
" 68 \n",
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" \n",
" \n",
" 69 \n",
" Carabanchel \n",
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" \n",
" \n",
" 70 \n",
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" \n",
" 71 \n",
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" \n",
" \n",
" 72 \n",
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" \n",
" \n",
" 73 \n",
" Usera \n",
" Orcasur \n",
" 40.381336 \n",
" -3.706856 \n",
" \n",
" \n",
" 74 \n",
" Usera \n",
" San Fermín \n",
" 40.381336 \n",
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" \n",
" \n",
" 75 \n",
" Usera \n",
" Almendrales \n",
" 40.381336 \n",
" -3.706856 \n",
" \n",
" \n",
" 76 \n",
" Usera \n",
" Moscardó \n",
" 40.381336 \n",
" -3.706856 \n",
" \n",
" \n",
" 77 \n",
" Usera \n",
" Zofío \n",
" 40.381336 \n",
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" \n",
" \n",
" 78 \n",
" Usera \n",
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" \n",
" \n",
" 79 \n",
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" \n",
" \n",
" 80 \n",
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" -3.669059 \n",
" \n",
" \n",
" 81 \n",
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" \n",
" \n",
" 82 \n",
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" \n",
" \n",
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" \n",
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" 88 \n",
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" \n",
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" \n",
" 90 \n",
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" 92 \n",
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" \n",
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" \n",
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" 96 \n",
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" \n",
" \n",
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" \n",
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" 98 \n",
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" \n",
" \n",
" 99 \n",
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" \n",
" \n",
" 100 \n",
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" \n",
" \n",
" 101 \n",
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" \n",
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" 102 \n",
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" \n",
" \n",
" 103 \n",
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" 40.469457 \n",
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" \n",
" \n",
" 104 \n",
" Hortaleza \n",
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" 40.469457 \n",
" -3.640482 \n",
" \n",
" \n",
" 105 \n",
" Hortaleza \n",
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" 40.469457 \n",
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" \n",
" \n",
" 106 \n",
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" \n",
" \n",
" 107 \n",
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" San Cristóbal \n",
" 40.345925 \n",
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" \n",
" \n",
" 108 \n",
" Villaverde \n",
" Butarque \n",
" 40.345925 \n",
" -3.709356 \n",
" \n",
" \n",
" 109 \n",
" Villaverde \n",
" Los Rosales \n",
" 40.345925 \n",
" -3.709356 \n",
" \n",
" \n",
" 110 \n",
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" 40.345925 \n",
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" \n",
" \n",
" 111 \n",
" Villa de Vallecas \n",
" Casco Histórico de Vallecas \n",
" 40.379600 \n",
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" \n",
" \n",
" 112 \n",
" Villa de Vallecas \n",
" Santa Eugenia \n",
" 40.379600 \n",
" -3.621350 \n",
" \n",
" \n",
" 113 \n",
" Villa de Vallecas \n",
" Ensanche de Vallecas \n",
" 40.379600 \n",
" -3.621350 \n",
" \n",
" \n",
" 114 \n",
" Vicalvaro \n",
" Casco Histórico de Vicálvaro \n",
" 40.404200 \n",
" -3.608060 \n",
" \n",
" \n",
" 115 \n",
" Vicalvaro \n",
" Valdebernardo \n",
" 40.404200 \n",
" -3.608060 \n",
" \n",
" \n",
" 116 \n",
" Vicalvaro \n",
" Valderrivas \n",
" 40.404200 \n",
" -3.608060 \n",
" \n",
" \n",
" 117 \n",
" Vicalvaro \n",
" El Cañaveral \n",
" 40.404200 \n",
" -3.608060 \n",
" \n",
" \n",
" 118 \n",
" San Blas - Canillejas \n",
" Simancas \n",
" 40.426001 \n",
" -3.612764 \n",
" \n",
" \n",
" 119 \n",
" San Blas - Canillejas \n",
" Hellín \n",
" 40.426001 \n",
" -3.612764 \n",
" \n",
" \n",
" 120 \n",
" San Blas - Canillejas \n",
" Amposta \n",
" 40.426001 \n",
" -3.612764 \n",
" \n",
" \n",
" 121 \n",
" San Blas - Canillejas \n",
" Arcos \n",
" 40.426001 \n",
" -3.612764 \n",
" \n",
" \n",
" 122 \n",
" San Blas - Canillejas \n",
" Rosas \n",
" 40.426001 \n",
" -3.612764 \n",
" \n",
" \n",
" 123 \n",
" San Blas - Canillejas \n",
" Rejas \n",
" 40.426001 \n",
" -3.612764 \n",
" \n",
" \n",
" 124 \n",
" San Blas - Canillejas \n",
" Canillejas \n",
" 40.426001 \n",
" -3.612764 \n",
" \n",
" \n",
" 125 \n",
" San Blas - Canillejas \n",
" Salvador \n",
" 40.426001 \n",
" -3.612764 \n",
" \n",
" \n",
" 126 \n",
" Barajas \n",
" Alameda de Osuna \n",
" 40.470196 \n",
" -3.584890 \n",
" \n",
" \n",
" 127 \n",
" Barajas \n",
" Aeropuerto \n",
" 40.470196 \n",
" -3.584890 \n",
" \n",
" \n",
" 128 \n",
" Barajas \n",
" Casco Histórico de Barajas \n",
" 40.470196 \n",
" -3.584890 \n",
" \n",
" \n",
" 129 \n",
" Barajas \n",
" Timón \n",
" 40.470196 \n",
" -3.584890 \n",
" \n",
" \n",
" 130 \n",
" Barajas \n",
" Corralejos \n",
" 40.470196 \n",
" -3.584890 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" District Neighborhood \\\n",
"0 Centro Palacio \n",
"1 Centro Embajadores \n",
"2 Centro Cortes \n",
"3 Centro Justicia \n",
"4 Centro Universidad \n",
"5 Centro Sol \n",
"6 Arganzuela Imperial \n",
"7 Arganzuela Acacias \n",
"8 Arganzuela Chopera \n",
"9 Arganzuela Legazpi \n",
"10 Arganzuela Delicias \n",
"11 Arganzuela Palos de Moguer \n",
"12 Arganzuela Atocha \n",
"13 Retiro Pacífico \n",
"14 Retiro Adelfas \n",
"15 Retiro Estrella \n",
"16 Retiro Ibiza \n",
"17 Retiro Jerónimos \n",
"18 Retiro Niño Jesús \n",
"19 Salamanca Recoletos \n",
"20 Salamanca Goya \n",
"21 Salamanca Fuente del Berro \n",
"22 Salamanca Guindalera \n",
"23 Salamanca Lista \n",
"24 Salamanca Castellana \n",
"25 Chamartin El Viso \n",
"26 Chamartin Prosperidad \n",
"27 Chamartin Ciudad Jardín \n",
"28 Chamartin Hispanoamérica \n",
"29 Chamartin Nueva España \n",
"30 Chamartin Castilla \n",
"31 Tetuan Bellas Vistas \n",
"32 Tetuan Cuatro Caminos \n",
"33 Tetuan Castillejos \n",
"34 Tetuan Almenara \n",
"35 Tetuan Valdeacederas \n",
"36 Tetuan Berruguete \n",
"37 Chamberi Gaztambide \n",
"38 Chamberi Arapiles \n",
"39 Chamberi Trafalgar \n",
"40 Chamberi Almagro \n",
"41 Chamberi Rios Rosas \n",
"42 Chamberi Vallehermoso \n",
"43 Fuencarral - El Pardo El Pardo \n",
"44 Fuencarral - El Pardo Fuentelareina \n",
"45 Fuencarral - El Pardo Peñagrande \n",
"46 Fuencarral - El Pardo Pilar \n",
"47 Fuencarral - El Pardo La Paz \n",
"48 Fuencarral - El Pardo Valverde \n",
"49 Fuencarral - El Pardo Mirasierra \n",
"50 Fuencarral - El Pardo El Goloso \n",
"51 Moncloa - Aravaca Casa de Campo \n",
"52 Moncloa - Aravaca Argüelles \n",
"53 Moncloa - Aravaca Ciudad Universitaria \n",
"54 Moncloa - Aravaca Valdezarza \n",
"55 Moncloa - Aravaca Valdemarín \n",
"56 Moncloa - Aravaca El Plantío \n",
"57 Moncloa - Aravaca Aravaca \n",
"58 Latina Cármenes \n",
"59 Latina Puerta del Ángel \n",
"60 Latina Lucero \n",
"61 Latina Aluche \n",
"62 Latina Campamento \n",
"63 Latina Cuatro Vientos \n",
"64 Latina Águilas \n",
"65 Carabanchel Comillas \n",
"66 Carabanchel Opañel \n",
"67 Carabanchel San Isidro \n",
"68 Carabanchel Vista Alegre \n",
"69 Carabanchel Puerta Bonita \n",
"70 Carabanchel Buenavista \n",
"71 Carabanchel Abrantes \n",
"72 Usera Orcasitas \n",
"73 Usera Orcasur \n",
"74 Usera San Fermín \n",
"75 Usera Almendrales \n",
"76 Usera Moscardó \n",
"77 Usera Zofío \n",
"78 Usera Pradolongo \n",
"79 Puente de Vallecas Entrevías \n",
"80 Puente de Vallecas San Diego \n",
"81 Puente de Vallecas Palomeras Bajas \n",
"82 Puente de Vallecas Palomeras Sureste \n",
"83 Puente de Vallecas Portazgo \n",
"84 Puente de Vallecas Numancia \n",
"85 Moratalaz Pavones \n",
"86 Moratalaz Horcajo \n",
"87 Moratalaz Marroquina \n",
"88 Moratalaz Media Legua \n",
"89 Moratalaz Fontarrón \n",
"90 Moratalaz Vinateros \n",
"91 Ciudad Lineal Ventas \n",
"92 Ciudad Lineal Pueblo Nuevo \n",
"93 Ciudad Lineal Quintana \n",
"94 Ciudad Lineal Concepción \n",
"95 Ciudad Lineal San Pascual \n",
"96 Ciudad Lineal San Juan Bautista \n",
"97 Ciudad Lineal Colina \n",
"98 Ciudad Lineal Atalaya \n",
"99 Ciudad Lineal Costillares \n",
"100 Hortaleza Palomas \n",
"101 Hortaleza Piovera \n",
"102 Hortaleza Canillas \n",
"103 Hortaleza Pinar del Rey \n",
"104 Hortaleza Apostol Santiago \n",
"105 Hortaleza Valdefuentes \n",
"106 Villaverde Villaverde Alto, Casco Histórico de Villaverde \n",
"107 Villaverde San Cristóbal \n",
"108 Villaverde Butarque \n",
"109 Villaverde Los Rosales \n",
"110 Villaverde Los Ángeles \n",
"111 Villa de Vallecas Casco Histórico de Vallecas \n",
"112 Villa de Vallecas Santa Eugenia \n",
"113 Villa de Vallecas Ensanche de Vallecas \n",
"114 Vicalvaro Casco Histórico de Vicálvaro \n",
"115 Vicalvaro Valdebernardo \n",
"116 Vicalvaro Valderrivas \n",
"117 Vicalvaro El Cañaveral \n",
"118 San Blas - Canillejas Simancas \n",
"119 San Blas - Canillejas Hellín \n",
"120 San Blas - Canillejas Amposta \n",
"121 San Blas - Canillejas Arcos \n",
"122 San Blas - Canillejas Rosas \n",
"123 San Blas - Canillejas Rejas \n",
"124 San Blas - Canillejas Canillejas \n",
"125 San Blas - Canillejas Salvador \n",
"126 Barajas Alameda de Osuna \n",
"127 Barajas Aeropuerto \n",
"128 Barajas Casco Histórico de Barajas \n",
"129 Barajas Timón \n",
"130 Barajas Corralejos \n",
"\n",
" District_Latitude District_Longitude \n",
"0 40.415347 -3.707371 \n",
"1 40.415347 -3.707371 \n",
"2 40.415347 -3.707371 \n",
"3 40.415347 -3.707371 \n",
"4 40.415347 -3.707371 \n",
"5 40.415347 -3.707371 \n",
"6 40.402733 -3.695403 \n",
"7 40.402733 -3.695403 \n",
"8 40.402733 -3.695403 \n",
"9 40.402733 -3.695403 \n",
"10 40.402733 -3.695403 \n",
"11 40.402733 -3.695403 \n",
"12 40.402733 -3.695403 \n",
"13 40.408072 -3.676729 \n",
"14 40.408072 -3.676729 \n",
"15 40.408072 -3.676729 \n",
"16 40.408072 -3.676729 \n",
"17 40.408072 -3.676729 \n",
"18 40.408072 -3.676729 \n",
"19 40.430000 -3.677778 \n",
"20 40.430000 -3.677778 \n",
"21 40.430000 -3.677778 \n",
"22 40.430000 -3.677778 \n",
"23 40.430000 -3.677778 \n",
"24 40.430000 -3.677778 \n",
"25 40.453333 -3.677500 \n",
"26 40.453333 -3.677500 \n",
"27 40.453333 -3.677500 \n",
"28 40.453333 -3.677500 \n",
"29 40.453333 -3.677500 \n",
"30 40.453333 -3.677500 \n",
"31 40.460556 -3.700000 \n",
"32 40.460556 -3.700000 \n",
"33 40.460556 -3.700000 \n",
"34 40.460556 -3.700000 \n",
"35 40.460556 -3.700000 \n",
"36 40.460556 -3.700000 \n",
"37 40.432792 -3.697186 \n",
"38 40.432792 -3.697186 \n",
"39 40.432792 -3.697186 \n",
"40 40.432792 -3.697186 \n",
"41 40.432792 -3.697186 \n",
"42 40.432792 -3.697186 \n",
"43 40.478611 -3.709722 \n",
"44 40.478611 -3.709722 \n",
"45 40.478611 -3.709722 \n",
"46 40.478611 -3.709722 \n",
"47 40.478611 -3.709722 \n",
"48 40.478611 -3.709722 \n",
"49 40.478611 -3.709722 \n",
"50 40.478611 -3.709722 \n",
"51 40.435151 -3.718765 \n",
"52 40.435151 -3.718765 \n",
"53 40.435151 -3.718765 \n",
"54 40.435151 -3.718765 \n",
"55 40.435151 -3.718765 \n",
"56 40.435151 -3.718765 \n",
"57 40.435151 -3.718765 \n",
"58 40.402461 -3.741294 \n",
"59 40.402461 -3.741294 \n",
"60 40.402461 -3.741294 \n",
"61 40.402461 -3.741294 \n",
"62 40.402461 -3.741294 \n",
"63 40.402461 -3.741294 \n",
"64 40.402461 -3.741294 \n",
"65 40.383669 -3.727989 \n",
"66 40.383669 -3.727989 \n",
"67 40.383669 -3.727989 \n",
"68 40.383669 -3.727989 \n",
"69 40.383669 -3.727989 \n",
"70 40.383669 -3.727989 \n",
"71 40.383669 -3.727989 \n",
"72 40.381336 -3.706856 \n",
"73 40.381336 -3.706856 \n",
"74 40.381336 -3.706856 \n",
"75 40.381336 -3.706856 \n",
"76 40.381336 -3.706856 \n",
"77 40.381336 -3.706856 \n",
"78 40.381336 -3.706856 \n",
"79 40.398204 -3.669059 \n",
"80 40.398204 -3.669059 \n",
"81 40.398204 -3.669059 \n",
"82 40.398204 -3.669059 \n",
"83 40.398204 -3.669059 \n",
"84 40.398204 -3.669059 \n",
"85 40.409869 -3.644436 \n",
"86 40.409869 -3.644436 \n",
"87 40.409869 -3.644436 \n",
"88 40.409869 -3.644436 \n",
"89 40.409869 -3.644436 \n",
"90 40.409869 -3.644436 \n",
"91 40.453333 -3.650000 \n",
"92 40.453333 -3.650000 \n",
"93 40.453333 -3.650000 \n",
"94 40.453333 -3.650000 \n",
"95 40.453333 -3.650000 \n",
"96 40.453333 -3.650000 \n",
"97 40.453333 -3.650000 \n",
"98 40.453333 -3.650000 \n",
"99 40.453333 -3.650000 \n",
"100 40.469457 -3.640482 \n",
"101 40.469457 -3.640482 \n",
"102 40.469457 -3.640482 \n",
"103 40.469457 -3.640482 \n",
"104 40.469457 -3.640482 \n",
"105 40.469457 -3.640482 \n",
"106 40.345925 -3.709356 \n",
"107 40.345925 -3.709356 \n",
"108 40.345925 -3.709356 \n",
"109 40.345925 -3.709356 \n",
"110 40.345925 -3.709356 \n",
"111 40.379600 -3.621350 \n",
"112 40.379600 -3.621350 \n",
"113 40.379600 -3.621350 \n",
"114 40.404200 -3.608060 \n",
"115 40.404200 -3.608060 \n",
"116 40.404200 -3.608060 \n",
"117 40.404200 -3.608060 \n",
"118 40.426001 -3.612764 \n",
"119 40.426001 -3.612764 \n",
"120 40.426001 -3.612764 \n",
"121 40.426001 -3.612764 \n",
"122 40.426001 -3.612764 \n",
"123 40.426001 -3.612764 \n",
"124 40.426001 -3.612764 \n",
"125 40.426001 -3.612764 \n",
"126 40.470196 -3.584890 \n",
"127 40.470196 -3.584890 \n",
"128 40.470196 -3.584890 \n",
"129 40.470196 -3.584890 \n",
"130 40.470196 -3.584890 "
]
},
"execution_count": 488,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Had to do this the column's names were giving error\n",
"madrid_data = madrid_data.rename(columns={'DISTRICT': 'District', 'NEIGHBORHOOD':'Neighborhood', 'DISTRICT_LATITUDE':'District_Latitude',\n",
" 'DISTRICT_LONGITUDE':'District_Longitude'})\n",
"madrid_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let's visualize the resulting clusters"
]
},
{
"cell_type": "code",
"execution_count": 414,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Make this Notebook Trusted to load map: File -> Trust Notebook
"
],
"text/plain": [
""
]
},
"execution_count": 414,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create map\n",
"map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
"\n",
"# set color scheme for the clusters\n",
"x = np.arange(kclusters)\n",
"ys = [i+x+(i*x)**2 for i in range(kclusters)]\n",
"colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
"rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
"\n",
"# add markers to the map\n",
"markers_colors = []\n",
"for lat, lon, poi, cluster in zip(madrid_merged['District_Latitude'], madrid_merged['District_Longitude'], madrid_merged['District'], madrid_merged['Cluster Labels']):\n",
" label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lon],\n",
" radius=5,\n",
" popup=label,\n",
" color=rainbow[cluster-1],\n",
" fill=True,\n",
" fill_color=rainbow[cluster-1],\n",
" fill_opacity=0.7).add_to(map_clusters)\n",
" \n",
"map_clusters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 1"
]
},
{
"cell_type": "code",
"execution_count": 421,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" District \n",
" Neighborhood \n",
" 1st Most Common Venue \n",
" 2nd Most Common Venue \n",
" 3rd Most Common Venue \n",
" 4th Most Common Venue \n",
" 5th Most Common Venue \n",
" 6th Most Common Venue \n",
" 7th Most Common Venue \n",
" 8th Most Common Venue \n",
" 9th Most Common Venue \n",
" 10th Most Common Venue \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Centro \n",
" Palacio \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 1 \n",
" Centro \n",
" Embajadores \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 2 \n",
" Centro \n",
" Cortes \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 3 \n",
" Centro \n",
" Justicia \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 4 \n",
" Centro \n",
" Universidad \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 5 \n",
" Centro \n",
" Sol \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 6 \n",
" Arganzuela \n",
" Imperial \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Bakery \n",
" Tapas Restaurant \n",
" Gym / Fitness Center \n",
" Beer Garden \n",
" Market \n",
" Falafel Restaurant \n",
" Burger Joint \n",
" \n",
" \n",
" 7 \n",
" Arganzuela \n",
" Acacias \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Bakery \n",
" Tapas Restaurant \n",
" Gym / Fitness Center \n",
" Beer Garden \n",
" Market \n",
" Falafel Restaurant \n",
" Burger Joint \n",
" \n",
" \n",
" 8 \n",
" Arganzuela \n",
" Chopera \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Bakery \n",
" Tapas Restaurant \n",
" Gym / Fitness Center \n",
" Beer Garden \n",
" Market \n",
" Falafel Restaurant \n",
" Burger Joint \n",
" \n",
" \n",
" 9 \n",
" Arganzuela \n",
" Legazpi \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Bakery \n",
" Tapas Restaurant \n",
" Gym / Fitness Center \n",
" Beer Garden \n",
" Market \n",
" Falafel Restaurant \n",
" Burger Joint \n",
" \n",
" \n",
" 10 \n",
" Arganzuela \n",
" Delicias \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Bakery \n",
" Tapas Restaurant \n",
" Gym / Fitness Center \n",
" Beer Garden \n",
" Market \n",
" Falafel Restaurant \n",
" Burger Joint \n",
" \n",
" \n",
" 11 \n",
" Arganzuela \n",
" Palos de Moguer \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Bakery \n",
" Tapas Restaurant \n",
" Gym / Fitness Center \n",
" Beer Garden \n",
" Market \n",
" Falafel Restaurant \n",
" Burger Joint \n",
" \n",
" \n",
" 12 \n",
" Arganzuela \n",
" Atocha \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Bakery \n",
" Tapas Restaurant \n",
" Gym / Fitness Center \n",
" Beer Garden \n",
" Market \n",
" Falafel Restaurant \n",
" Burger Joint \n",
" \n",
" \n",
" 13 \n",
" Retiro \n",
" Pacífico \n",
" Spanish Restaurant \n",
" Museum \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Gym \n",
" Farmers Market \n",
" Boutique \n",
" Brewery \n",
" Food & Drink Shop \n",
" \n",
" \n",
" 14 \n",
" Retiro \n",
" Adelfas \n",
" Spanish Restaurant \n",
" Museum \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Gym \n",
" Farmers Market \n",
" Boutique \n",
" Brewery \n",
" Food & Drink Shop \n",
" \n",
" \n",
" 15 \n",
" Retiro \n",
" Estrella \n",
" Spanish Restaurant \n",
" Museum \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Gym \n",
" Farmers Market \n",
" Boutique \n",
" Brewery \n",
" Food & Drink Shop \n",
" \n",
" \n",
" 16 \n",
" Retiro \n",
" Ibiza \n",
" Spanish Restaurant \n",
" Museum \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Gym \n",
" Farmers Market \n",
" Boutique \n",
" Brewery \n",
" Food & Drink Shop \n",
" \n",
" \n",
" 17 \n",
" Retiro \n",
" Jerónimos \n",
" Spanish Restaurant \n",
" Museum \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Gym \n",
" Farmers Market \n",
" Boutique \n",
" Brewery \n",
" Food & Drink Shop \n",
" \n",
" \n",
" 18 \n",
" Retiro \n",
" Niño Jesús \n",
" Spanish Restaurant \n",
" Museum \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Gym \n",
" Farmers Market \n",
" Boutique \n",
" Brewery \n",
" Food & Drink Shop \n",
" \n",
" \n",
" 19 \n",
" Salamanca \n",
" Recoletos \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Mediterranean Restaurant \n",
" Seafood Restaurant \n",
" Burger Joint \n",
" Clothing Store \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" Supermarket \n",
" Plaza \n",
" \n",
" \n",
" 20 \n",
" Salamanca \n",
" Goya \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Mediterranean Restaurant \n",
" Seafood Restaurant \n",
" Burger Joint \n",
" Clothing Store \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" Supermarket \n",
" Plaza \n",
" \n",
" \n",
" 21 \n",
" Salamanca \n",
" Fuente del Berro \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Mediterranean Restaurant \n",
" Seafood Restaurant \n",
" Burger Joint \n",
" Clothing Store \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" Supermarket \n",
" Plaza \n",
" \n",
" \n",
" 22 \n",
" Salamanca \n",
" Guindalera \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Mediterranean Restaurant \n",
" Seafood Restaurant \n",
" Burger Joint \n",
" Clothing Store \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" Supermarket \n",
" Plaza \n",
" \n",
" \n",
" 23 \n",
" Salamanca \n",
" Lista \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Mediterranean Restaurant \n",
" Seafood Restaurant \n",
" Burger Joint \n",
" Clothing Store \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" Supermarket \n",
" Plaza \n",
" \n",
" \n",
" 24 \n",
" Salamanca \n",
" Castellana \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Mediterranean Restaurant \n",
" Seafood Restaurant \n",
" Burger Joint \n",
" Clothing Store \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" Supermarket \n",
" Plaza \n",
" \n",
" \n",
" 25 \n",
" Chamartin \n",
" El Viso \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bakery \n",
" Grocery Store \n",
" Tapas Restaurant \n",
" Café \n",
" Gastropub \n",
" Park \n",
" Coffee Shop \n",
" Japanese Restaurant \n",
" \n",
" \n",
" 26 \n",
" Chamartin \n",
" Prosperidad \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bakery \n",
" Grocery Store \n",
" Tapas Restaurant \n",
" Café \n",
" Gastropub \n",
" Park \n",
" Coffee Shop \n",
" Japanese Restaurant \n",
" \n",
" \n",
" 27 \n",
" Chamartin \n",
" Ciudad Jardín \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bakery \n",
" Grocery Store \n",
" Tapas Restaurant \n",
" Café \n",
" Gastropub \n",
" Park \n",
" Coffee Shop \n",
" Japanese Restaurant \n",
" \n",
" \n",
" 28 \n",
" Chamartin \n",
" Hispanoamérica \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bakery \n",
" Grocery Store \n",
" Tapas Restaurant \n",
" Café \n",
" Gastropub \n",
" Park \n",
" Coffee Shop \n",
" Japanese Restaurant \n",
" \n",
" \n",
" 29 \n",
" Chamartin \n",
" Nueva España \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bakery \n",
" Grocery Store \n",
" Tapas Restaurant \n",
" Café \n",
" Gastropub \n",
" Park \n",
" Coffee Shop \n",
" Japanese Restaurant \n",
" \n",
" \n",
" 30 \n",
" Chamartin \n",
" Castilla \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bakery \n",
" Grocery Store \n",
" Tapas Restaurant \n",
" Café \n",
" Gastropub \n",
" Park \n",
" Coffee Shop \n",
" Japanese Restaurant \n",
" \n",
" \n",
" 31 \n",
" Tetuan \n",
" Bellas Vistas \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Brazilian Restaurant \n",
" Chinese Restaurant \n",
" Supermarket \n",
" Breakfast Spot \n",
" Clothing Store \n",
" Resort \n",
" Cafeteria \n",
" Seafood Restaurant \n",
" \n",
" \n",
" 32 \n",
" Tetuan \n",
" Cuatro Caminos \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Brazilian Restaurant \n",
" Chinese Restaurant \n",
" Supermarket \n",
" Breakfast Spot \n",
" Clothing Store \n",
" Resort \n",
" Cafeteria \n",
" Seafood Restaurant \n",
" \n",
" \n",
" 33 \n",
" Tetuan \n",
" Castillejos \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Brazilian Restaurant \n",
" Chinese Restaurant \n",
" Supermarket \n",
" Breakfast Spot \n",
" Clothing Store \n",
" Resort \n",
" Cafeteria \n",
" Seafood Restaurant \n",
" \n",
" \n",
" 34 \n",
" Tetuan \n",
" Almenara \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Brazilian Restaurant \n",
" Chinese Restaurant \n",
" Supermarket \n",
" Breakfast Spot \n",
" Clothing Store \n",
" Resort \n",
" Cafeteria \n",
" Seafood Restaurant \n",
" \n",
" \n",
" 35 \n",
" Tetuan \n",
" Valdeacederas \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Brazilian Restaurant \n",
" Chinese Restaurant \n",
" Supermarket \n",
" Breakfast Spot \n",
" Clothing Store \n",
" Resort \n",
" Cafeteria \n",
" Seafood Restaurant \n",
" \n",
" \n",
" 36 \n",
" Tetuan \n",
" Berruguete \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Brazilian Restaurant \n",
" Chinese Restaurant \n",
" Supermarket \n",
" Breakfast Spot \n",
" Clothing Store \n",
" Resort \n",
" Cafeteria \n",
" Seafood Restaurant \n",
" \n",
" \n",
" 37 \n",
" Chamberi \n",
" Gaztambide \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bar \n",
" Brewery \n",
" Italian Restaurant \n",
" Japanese Restaurant \n",
" Café \n",
" Plaza \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" \n",
" \n",
" 38 \n",
" Chamberi \n",
" Arapiles \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bar \n",
" Brewery \n",
" Italian Restaurant \n",
" Japanese Restaurant \n",
" Café \n",
" Plaza \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" \n",
" \n",
" 39 \n",
" Chamberi \n",
" Trafalgar \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bar \n",
" Brewery \n",
" Italian Restaurant \n",
" Japanese Restaurant \n",
" Café \n",
" Plaza \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" \n",
" \n",
" 40 \n",
" Chamberi \n",
" Almagro \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bar \n",
" Brewery \n",
" Italian Restaurant \n",
" Japanese Restaurant \n",
" Café \n",
" Plaza \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" \n",
" \n",
" 41 \n",
" Chamberi \n",
" Rios Rosas \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bar \n",
" Brewery \n",
" Italian Restaurant \n",
" Japanese Restaurant \n",
" Café \n",
" Plaza \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" \n",
" \n",
" 42 \n",
" Chamberi \n",
" Vallehermoso \n",
" Spanish Restaurant \n",
" Restaurant \n",
" Bar \n",
" Brewery \n",
" Italian Restaurant \n",
" Japanese Restaurant \n",
" Café \n",
" Plaza \n",
" Mexican Restaurant \n",
" Tapas Restaurant \n",
" \n",
" \n",
" 43 \n",
" Fuencarral - El Pardo \n",
" El Pardo \n",
" Clothing Store \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Fast Food Restaurant \n",
" Italian Restaurant \n",
" Shopping Mall \n",
" Spanish Restaurant \n",
" Big Box Store \n",
" Flea Market \n",
" Boutique \n",
" \n",
" \n",
" 44 \n",
" Fuencarral - El Pardo \n",
" Fuentelareina \n",
" Clothing Store \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Fast Food Restaurant \n",
" Italian Restaurant \n",
" Shopping Mall \n",
" Spanish Restaurant \n",
" Big Box Store \n",
" Flea Market \n",
" Boutique \n",
" \n",
" \n",
" 45 \n",
" Fuencarral - El Pardo \n",
" Peñagrande \n",
" Clothing Store \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Fast Food Restaurant \n",
" Italian Restaurant \n",
" Shopping Mall \n",
" Spanish Restaurant \n",
" Big Box Store \n",
" Flea Market \n",
" Boutique \n",
" \n",
" \n",
" 46 \n",
" Fuencarral - El Pardo \n",
" Pilar \n",
" Clothing Store \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Fast Food Restaurant \n",
" Italian Restaurant \n",
" Shopping Mall \n",
" Spanish Restaurant \n",
" Big Box Store \n",
" Flea Market \n",
" Boutique \n",
" \n",
" \n",
" 47 \n",
" Fuencarral - El Pardo \n",
" La Paz \n",
" Clothing Store \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Fast Food Restaurant \n",
" Italian Restaurant \n",
" Shopping Mall \n",
" Spanish Restaurant \n",
" Big Box Store \n",
" Flea Market \n",
" Boutique \n",
" \n",
" \n",
" 48 \n",
" Fuencarral - El Pardo \n",
" Valverde \n",
" Clothing Store \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Fast Food Restaurant \n",
" Italian Restaurant \n",
" Shopping Mall \n",
" Spanish Restaurant \n",
" Big Box Store \n",
" Flea Market \n",
" Boutique \n",
" \n",
" \n",
" 49 \n",
" Fuencarral - El Pardo \n",
" Mirasierra \n",
" Clothing Store \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Fast Food Restaurant \n",
" Italian Restaurant \n",
" Shopping Mall \n",
" Spanish Restaurant \n",
" Big Box Store \n",
" Flea Market \n",
" Boutique \n",
" \n",
" \n",
" 50 \n",
" Fuencarral - El Pardo \n",
" El Goloso \n",
" Clothing Store \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Fast Food Restaurant \n",
" Italian Restaurant \n",
" Shopping Mall \n",
" Spanish Restaurant \n",
" Big Box Store \n",
" Flea Market \n",
" Boutique \n",
" \n",
" \n",
" 51 \n",
" Moncloa - Aravaca \n",
" Casa de Campo \n",
" Restaurant \n",
" Tapas Restaurant \n",
" Spanish Restaurant \n",
" Pizza Place \n",
" Italian Restaurant \n",
" Pub \n",
" Bakery \n",
" Bar \n",
" Coffee Shop \n",
" Mediterranean Restaurant \n",
" \n",
" \n",
" 52 \n",
" Moncloa - Aravaca \n",
" Argüelles \n",
" Restaurant \n",
" Tapas Restaurant \n",
" Spanish Restaurant \n",
" Pizza Place \n",
" Italian Restaurant \n",
" Pub \n",
" Bakery \n",
" Bar \n",
" Coffee Shop \n",
" Mediterranean Restaurant \n",
" \n",
" \n",
" 53 \n",
" Moncloa - Aravaca \n",
" Ciudad Universitaria \n",
" Restaurant \n",
" Tapas Restaurant \n",
" Spanish Restaurant \n",
" Pizza Place \n",
" Italian Restaurant \n",
" Pub \n",
" Bakery \n",
" Bar \n",
" Coffee Shop \n",
" Mediterranean Restaurant \n",
" \n",
" \n",
" 54 \n",
" Moncloa - Aravaca \n",
" Valdezarza \n",
" Restaurant \n",
" Tapas Restaurant \n",
" Spanish Restaurant \n",
" Pizza Place \n",
" Italian Restaurant \n",
" Pub \n",
" Bakery \n",
" Bar \n",
" Coffee Shop \n",
" Mediterranean Restaurant \n",
" \n",
" \n",
" 55 \n",
" Moncloa - Aravaca \n",
" Valdemarín \n",
" Restaurant \n",
" Tapas Restaurant \n",
" Spanish Restaurant \n",
" Pizza Place \n",
" Italian Restaurant \n",
" Pub \n",
" Bakery \n",
" Bar \n",
" Coffee Shop \n",
" Mediterranean Restaurant \n",
" \n",
" \n",
" 56 \n",
" Moncloa - Aravaca \n",
" El Plantío \n",
" Restaurant \n",
" Tapas Restaurant \n",
" Spanish Restaurant \n",
" Pizza Place \n",
" Italian Restaurant \n",
" Pub \n",
" Bakery \n",
" Bar \n",
" Coffee Shop \n",
" Mediterranean Restaurant \n",
" \n",
" \n",
" 57 \n",
" Moncloa - Aravaca \n",
" Aravaca \n",
" Restaurant \n",
" Tapas Restaurant \n",
" Spanish Restaurant \n",
" Pizza Place \n",
" Italian Restaurant \n",
" Pub \n",
" Bakery \n",
" Bar \n",
" Coffee Shop \n",
" Mediterranean Restaurant \n",
" \n",
" \n",
" 72 \n",
" Usera \n",
" Orcasitas \n",
" Spanish Restaurant \n",
" Seafood Restaurant \n",
" Chinese Restaurant \n",
" Theater \n",
" Asian Restaurant \n",
" Bubble Tea Shop \n",
" Noodle House \n",
" Fast Food Restaurant \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 73 \n",
" Usera \n",
" Orcasur \n",
" Spanish Restaurant \n",
" Seafood Restaurant \n",
" Chinese Restaurant \n",
" Theater \n",
" Asian Restaurant \n",
" Bubble Tea Shop \n",
" Noodle House \n",
" Fast Food Restaurant \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 74 \n",
" Usera \n",
" San Fermín \n",
" Spanish Restaurant \n",
" Seafood Restaurant \n",
" Chinese Restaurant \n",
" Theater \n",
" Asian Restaurant \n",
" Bubble Tea Shop \n",
" Noodle House \n",
" Fast Food Restaurant \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 75 \n",
" Usera \n",
" Almendrales \n",
" Spanish Restaurant \n",
" Seafood Restaurant \n",
" Chinese Restaurant \n",
" Theater \n",
" Asian Restaurant \n",
" Bubble Tea Shop \n",
" Noodle House \n",
" Fast Food Restaurant \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 76 \n",
" Usera \n",
" Moscardó \n",
" Spanish Restaurant \n",
" Seafood Restaurant \n",
" Chinese Restaurant \n",
" Theater \n",
" Asian Restaurant \n",
" Bubble Tea Shop \n",
" Noodle House \n",
" Fast Food Restaurant \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 77 \n",
" Usera \n",
" Zofío \n",
" Spanish Restaurant \n",
" Seafood Restaurant \n",
" Chinese Restaurant \n",
" Theater \n",
" Asian Restaurant \n",
" Bubble Tea Shop \n",
" Noodle House \n",
" Fast Food Restaurant \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 78 \n",
" Usera \n",
" Pradolongo \n",
" Spanish Restaurant \n",
" Seafood Restaurant \n",
" Chinese Restaurant \n",
" Theater \n",
" Asian Restaurant \n",
" Bubble Tea Shop \n",
" Noodle House \n",
" Fast Food Restaurant \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 79 \n",
" Puente de Vallecas \n",
" Entrevías \n",
" Fast Food Restaurant \n",
" Gym \n",
" Grocery Store \n",
" Café \n",
" Hotel \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Burger Joint \n",
" Breakfast Spot \n",
" \n",
" \n",
" 80 \n",
" Puente de Vallecas \n",
" San Diego \n",
" Fast Food Restaurant \n",
" Gym \n",
" Grocery Store \n",
" Café \n",
" Hotel \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Burger Joint \n",
" Breakfast Spot \n",
" \n",
" \n",
" 81 \n",
" Puente de Vallecas \n",
" Palomeras Bajas \n",
" Fast Food Restaurant \n",
" Gym \n",
" Grocery Store \n",
" Café \n",
" Hotel \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Burger Joint \n",
" Breakfast Spot \n",
" \n",
" \n",
" 82 \n",
" Puente de Vallecas \n",
" Palomeras Sureste \n",
" Fast Food Restaurant \n",
" Gym \n",
" Grocery Store \n",
" Café \n",
" Hotel \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Burger Joint \n",
" Breakfast Spot \n",
" \n",
" \n",
" 83 \n",
" Puente de Vallecas \n",
" Portazgo \n",
" Fast Food Restaurant \n",
" Gym \n",
" Grocery Store \n",
" Café \n",
" Hotel \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Burger Joint \n",
" Breakfast Spot \n",
" \n",
" \n",
" 84 \n",
" Puente de Vallecas \n",
" Numancia \n",
" Fast Food Restaurant \n",
" Gym \n",
" Grocery Store \n",
" Café \n",
" Hotel \n",
" Supermarket \n",
" Tapas Restaurant \n",
" Bar \n",
" Burger Joint \n",
" Breakfast Spot \n",
" \n",
" \n",
" 91 \n",
" Ciudad Lineal \n",
" Ventas \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Park \n",
" Grocery Store \n",
" Argentinian Restaurant \n",
" Gastropub \n",
" Tapas Restaurant \n",
" Pharmacy \n",
" Café \n",
" Bus Line \n",
" \n",
" \n",
" 92 \n",
" Ciudad Lineal \n",
" Pueblo Nuevo \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Park \n",
" Grocery Store \n",
" Argentinian Restaurant \n",
" Gastropub \n",
" Tapas Restaurant \n",
" Pharmacy \n",
" Café \n",
" Bus Line \n",
" \n",
" \n",
" 93 \n",
" Ciudad Lineal \n",
" Quintana \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Park \n",
" Grocery Store \n",
" Argentinian Restaurant \n",
" Gastropub \n",
" Tapas Restaurant \n",
" Pharmacy \n",
" Café \n",
" Bus Line \n",
" \n",
" \n",
" 94 \n",
" Ciudad Lineal \n",
" Concepción \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Park \n",
" Grocery Store \n",
" Argentinian Restaurant \n",
" Gastropub \n",
" Tapas Restaurant \n",
" Pharmacy \n",
" Café \n",
" Bus Line \n",
" \n",
" \n",
" 95 \n",
" Ciudad Lineal \n",
" San Pascual \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Park \n",
" Grocery Store \n",
" Argentinian Restaurant \n",
" Gastropub \n",
" Tapas Restaurant \n",
" Pharmacy \n",
" Café \n",
" Bus Line \n",
" \n",
" \n",
" 96 \n",
" Ciudad Lineal \n",
" San Juan Bautista \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Park \n",
" Grocery Store \n",
" Argentinian Restaurant \n",
" Gastropub \n",
" Tapas Restaurant \n",
" Pharmacy \n",
" Café \n",
" Bus Line \n",
" \n",
" \n",
" 97 \n",
" Ciudad Lineal \n",
" Colina \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Park \n",
" Grocery Store \n",
" Argentinian Restaurant \n",
" Gastropub \n",
" Tapas Restaurant \n",
" Pharmacy \n",
" Café \n",
" Bus Line \n",
" \n",
" \n",
" 98 \n",
" Ciudad Lineal \n",
" Atalaya \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Park \n",
" Grocery Store \n",
" Argentinian Restaurant \n",
" Gastropub \n",
" Tapas Restaurant \n",
" Pharmacy \n",
" Café \n",
" Bus Line \n",
" \n",
" \n",
" 99 \n",
" Ciudad Lineal \n",
" Costillares \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Park \n",
" Grocery Store \n",
" Argentinian Restaurant \n",
" Gastropub \n",
" Tapas Restaurant \n",
" Pharmacy \n",
" Café \n",
" Bus Line \n",
" \n",
" \n",
" 100 \n",
" Hortaleza \n",
" Palomas \n",
" Breakfast Spot \n",
" Supermarket \n",
" Pizza Place \n",
" Sandwich Place \n",
" Pub \n",
" Spanish Restaurant \n",
" Bakery \n",
" Plaza \n",
" Chinese Restaurant \n",
" Restaurant \n",
" \n",
" \n",
" 101 \n",
" Hortaleza \n",
" Piovera \n",
" Breakfast Spot \n",
" Supermarket \n",
" Pizza Place \n",
" Sandwich Place \n",
" Pub \n",
" Spanish Restaurant \n",
" Bakery \n",
" Plaza \n",
" Chinese Restaurant \n",
" Restaurant \n",
" \n",
" \n",
" 102 \n",
" Hortaleza \n",
" Canillas \n",
" Breakfast Spot \n",
" Supermarket \n",
" Pizza Place \n",
" Sandwich Place \n",
" Pub \n",
" Spanish Restaurant \n",
" Bakery \n",
" Plaza \n",
" Chinese Restaurant \n",
" Restaurant \n",
" \n",
" \n",
" 103 \n",
" Hortaleza \n",
" Pinar del Rey \n",
" Breakfast Spot \n",
" Supermarket \n",
" Pizza Place \n",
" Sandwich Place \n",
" Pub \n",
" Spanish Restaurant \n",
" Bakery \n",
" Plaza \n",
" Chinese Restaurant \n",
" Restaurant \n",
" \n",
" \n",
" 104 \n",
" Hortaleza \n",
" Apostol Santiago \n",
" Breakfast Spot \n",
" Supermarket \n",
" Pizza Place \n",
" Sandwich Place \n",
" Pub \n",
" Spanish Restaurant \n",
" Bakery \n",
" Plaza \n",
" Chinese Restaurant \n",
" Restaurant \n",
" \n",
" \n",
" 105 \n",
" Hortaleza \n",
" Valdefuentes \n",
" Breakfast Spot \n",
" Supermarket \n",
" Pizza Place \n",
" Sandwich Place \n",
" Pub \n",
" Spanish Restaurant \n",
" Bakery \n",
" Plaza \n",
" Chinese Restaurant \n",
" Restaurant \n",
" \n",
" \n",
" 111 \n",
" Villa de Vallecas \n",
" Casco Histórico de Vallecas \n",
" Breakfast Spot \n",
" Park \n",
" Platform \n",
" Grocery Store \n",
" Spanish Restaurant \n",
" Food \n",
" Soccer Field \n",
" Sandwich Place \n",
" Eastern European Restaurant \n",
" Fast Food Restaurant \n",
" \n",
" \n",
" 112 \n",
" Villa de Vallecas \n",
" Santa Eugenia \n",
" Breakfast Spot \n",
" Park \n",
" Platform \n",
" Grocery Store \n",
" Spanish Restaurant \n",
" Food \n",
" Soccer Field \n",
" Sandwich Place \n",
" Eastern European Restaurant \n",
" Fast Food Restaurant \n",
" \n",
" \n",
" 113 \n",
" Villa de Vallecas \n",
" Ensanche de Vallecas \n",
" Breakfast Spot \n",
" Park \n",
" Platform \n",
" Grocery Store \n",
" Spanish Restaurant \n",
" Food \n",
" Soccer Field \n",
" Sandwich Place \n",
" Eastern European Restaurant \n",
" Fast Food Restaurant \n",
" \n",
" \n",
" 114 \n",
" Vicalvaro \n",
" Casco Histórico de Vicálvaro \n",
" Pizza Place \n",
" Spanish Restaurant \n",
" Beer Bar \n",
" Fast Food Restaurant \n",
" Grocery Store \n",
" Ice Cream Shop \n",
" Sandwich Place \n",
" Breakfast Spot \n",
" Café \n",
" Fish Market \n",
" \n",
" \n",
" 115 \n",
" Vicalvaro \n",
" Valdebernardo \n",
" Pizza Place \n",
" Spanish Restaurant \n",
" Beer Bar \n",
" Fast Food Restaurant \n",
" Grocery Store \n",
" Ice Cream Shop \n",
" Sandwich Place \n",
" Breakfast Spot \n",
" Café \n",
" Fish Market \n",
" \n",
" \n",
" 116 \n",
" Vicalvaro \n",
" Valderrivas \n",
" Pizza Place \n",
" Spanish Restaurant \n",
" Beer Bar \n",
" Fast Food Restaurant \n",
" Grocery Store \n",
" Ice Cream Shop \n",
" Sandwich Place \n",
" Breakfast Spot \n",
" Café \n",
" Fish Market \n",
" \n",
" \n",
" 117 \n",
" Vicalvaro \n",
" El Cañaveral \n",
" Pizza Place \n",
" Spanish Restaurant \n",
" Beer Bar \n",
" Fast Food Restaurant \n",
" Grocery Store \n",
" Ice Cream Shop \n",
" Sandwich Place \n",
" Breakfast Spot \n",
" Café \n",
" Fish Market \n",
" \n",
" \n",
" 126 \n",
" Barajas \n",
" Alameda de Osuna \n",
" Hotel \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Coffee Shop \n",
" Tapas Restaurant \n",
" Bar \n",
" Fast Food Restaurant \n",
" Brewery \n",
" Mexican Restaurant \n",
" Café \n",
" \n",
" \n",
" 127 \n",
" Barajas \n",
" Aeropuerto \n",
" Hotel \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Coffee Shop \n",
" Tapas Restaurant \n",
" Bar \n",
" Fast Food Restaurant \n",
" Brewery \n",
" Mexican Restaurant \n",
" Café \n",
" \n",
" \n",
" 128 \n",
" Barajas \n",
" Casco Histórico de Barajas \n",
" Hotel \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Coffee Shop \n",
" Tapas Restaurant \n",
" Bar \n",
" Fast Food Restaurant \n",
" Brewery \n",
" Mexican Restaurant \n",
" Café \n",
" \n",
" \n",
" 129 \n",
" Barajas \n",
" Timón \n",
" Hotel \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Coffee Shop \n",
" Tapas Restaurant \n",
" Bar \n",
" Fast Food Restaurant \n",
" Brewery \n",
" Mexican Restaurant \n",
" Café \n",
" \n",
" \n",
" 130 \n",
" Barajas \n",
" Corralejos \n",
" Hotel \n",
" Restaurant \n",
" Spanish Restaurant \n",
" Coffee Shop \n",
" Tapas Restaurant \n",
" Bar \n",
" Fast Food Restaurant \n",
" Brewery \n",
" Mexican Restaurant \n",
" Café \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" District Neighborhood \\\n",
"0 Centro Palacio \n",
"1 Centro Embajadores \n",
"2 Centro Cortes \n",
"3 Centro Justicia \n",
"4 Centro Universidad \n",
"5 Centro Sol \n",
"6 Arganzuela Imperial \n",
"7 Arganzuela Acacias \n",
"8 Arganzuela Chopera \n",
"9 Arganzuela Legazpi \n",
"10 Arganzuela Delicias \n",
"11 Arganzuela Palos de Moguer \n",
"12 Arganzuela Atocha \n",
"13 Retiro Pacífico \n",
"14 Retiro Adelfas \n",
"15 Retiro Estrella \n",
"16 Retiro Ibiza \n",
"17 Retiro Jerónimos \n",
"18 Retiro Niño Jesús \n",
"19 Salamanca Recoletos \n",
"20 Salamanca Goya \n",
"21 Salamanca Fuente del Berro \n",
"22 Salamanca Guindalera \n",
"23 Salamanca Lista \n",
"24 Salamanca Castellana \n",
"25 Chamartin El Viso \n",
"26 Chamartin Prosperidad \n",
"27 Chamartin Ciudad Jardín \n",
"28 Chamartin Hispanoamérica \n",
"29 Chamartin Nueva España \n",
"30 Chamartin Castilla \n",
"31 Tetuan Bellas Vistas \n",
"32 Tetuan Cuatro Caminos \n",
"33 Tetuan Castillejos \n",
"34 Tetuan Almenara \n",
"35 Tetuan Valdeacederas \n",
"36 Tetuan Berruguete \n",
"37 Chamberi Gaztambide \n",
"38 Chamberi Arapiles \n",
"39 Chamberi Trafalgar \n",
"40 Chamberi Almagro \n",
"41 Chamberi Rios Rosas \n",
"42 Chamberi Vallehermoso \n",
"43 Fuencarral - El Pardo El Pardo \n",
"44 Fuencarral - El Pardo Fuentelareina \n",
"45 Fuencarral - El Pardo Peñagrande \n",
"46 Fuencarral - El Pardo Pilar \n",
"47 Fuencarral - El Pardo La Paz \n",
"48 Fuencarral - El Pardo Valverde \n",
"49 Fuencarral - El Pardo Mirasierra \n",
"50 Fuencarral - El Pardo El Goloso \n",
"51 Moncloa - Aravaca Casa de Campo \n",
"52 Moncloa - Aravaca Argüelles \n",
"53 Moncloa - Aravaca Ciudad Universitaria \n",
"54 Moncloa - Aravaca Valdezarza \n",
"55 Moncloa - Aravaca Valdemarín \n",
"56 Moncloa - Aravaca El Plantío \n",
"57 Moncloa - Aravaca Aravaca \n",
"72 Usera Orcasitas \n",
"73 Usera Orcasur \n",
"74 Usera San Fermín \n",
"75 Usera Almendrales \n",
"76 Usera Moscardó \n",
"77 Usera Zofío \n",
"78 Usera Pradolongo \n",
"79 Puente de Vallecas Entrevías \n",
"80 Puente de Vallecas San Diego \n",
"81 Puente de Vallecas Palomeras Bajas \n",
"82 Puente de Vallecas Palomeras Sureste \n",
"83 Puente de Vallecas Portazgo \n",
"84 Puente de Vallecas Numancia \n",
"91 Ciudad Lineal Ventas \n",
"92 Ciudad Lineal Pueblo Nuevo \n",
"93 Ciudad Lineal Quintana \n",
"94 Ciudad Lineal Concepción \n",
"95 Ciudad Lineal San Pascual \n",
"96 Ciudad Lineal San Juan Bautista \n",
"97 Ciudad Lineal Colina \n",
"98 Ciudad Lineal Atalaya \n",
"99 Ciudad Lineal Costillares \n",
"100 Hortaleza Palomas \n",
"101 Hortaleza Piovera \n",
"102 Hortaleza Canillas \n",
"103 Hortaleza Pinar del Rey \n",
"104 Hortaleza Apostol Santiago \n",
"105 Hortaleza Valdefuentes \n",
"111 Villa de Vallecas Casco Histórico de Vallecas \n",
"112 Villa de Vallecas Santa Eugenia \n",
"113 Villa de Vallecas Ensanche de Vallecas \n",
"114 Vicalvaro Casco Histórico de Vicálvaro \n",
"115 Vicalvaro Valdebernardo \n",
"116 Vicalvaro Valderrivas \n",
"117 Vicalvaro El Cañaveral \n",
"126 Barajas Alameda de Osuna \n",
"127 Barajas Aeropuerto \n",
"128 Barajas Casco Histórico de Barajas \n",
"129 Barajas Timón \n",
"130 Barajas Corralejos \n",
"\n",
" 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue \\\n",
"0 Spanish Restaurant Tapas Restaurant Plaza \n",
"1 Spanish Restaurant Tapas Restaurant Plaza \n",
"2 Spanish Restaurant Tapas Restaurant Plaza \n",
"3 Spanish Restaurant Tapas Restaurant Plaza \n",
"4 Spanish Restaurant Tapas Restaurant Plaza \n",
"5 Spanish Restaurant Tapas Restaurant Plaza \n",
"6 Restaurant Spanish Restaurant Grocery Store \n",
"7 Restaurant Spanish Restaurant Grocery Store \n",
"8 Restaurant Spanish Restaurant Grocery Store \n",
"9 Restaurant Spanish Restaurant Grocery Store \n",
"10 Restaurant Spanish Restaurant Grocery Store \n",
"11 Restaurant Spanish Restaurant Grocery Store \n",
"12 Restaurant Spanish Restaurant Grocery Store \n",
"13 Spanish Restaurant Museum Supermarket \n",
"14 Spanish Restaurant Museum Supermarket \n",
"15 Spanish Restaurant Museum Supermarket \n",
"16 Spanish Restaurant Museum Supermarket \n",
"17 Spanish Restaurant Museum Supermarket \n",
"18 Spanish Restaurant Museum Supermarket \n",
"19 Spanish Restaurant Restaurant Mediterranean Restaurant \n",
"20 Spanish Restaurant Restaurant Mediterranean Restaurant \n",
"21 Spanish Restaurant Restaurant Mediterranean Restaurant \n",
"22 Spanish Restaurant Restaurant Mediterranean Restaurant \n",
"23 Spanish Restaurant Restaurant Mediterranean Restaurant \n",
"24 Spanish Restaurant Restaurant Mediterranean Restaurant \n",
"25 Spanish Restaurant Restaurant Bakery \n",
"26 Spanish Restaurant Restaurant Bakery \n",
"27 Spanish Restaurant Restaurant Bakery \n",
"28 Spanish Restaurant Restaurant Bakery \n",
"29 Spanish Restaurant Restaurant Bakery \n",
"30 Spanish Restaurant Restaurant Bakery \n",
"31 Spanish Restaurant Grocery Store Brazilian Restaurant \n",
"32 Spanish Restaurant Grocery Store Brazilian Restaurant \n",
"33 Spanish Restaurant Grocery Store Brazilian Restaurant \n",
"34 Spanish Restaurant Grocery Store Brazilian Restaurant \n",
"35 Spanish Restaurant Grocery Store Brazilian Restaurant \n",
"36 Spanish Restaurant Grocery Store Brazilian Restaurant \n",
"37 Spanish Restaurant Restaurant Bar \n",
"38 Spanish Restaurant Restaurant Bar \n",
"39 Spanish Restaurant Restaurant Bar \n",
"40 Spanish Restaurant Restaurant Bar \n",
"41 Spanish Restaurant Restaurant Bar \n",
"42 Spanish Restaurant Restaurant Bar \n",
"43 Clothing Store Burger Joint Tapas Restaurant \n",
"44 Clothing Store Burger Joint Tapas Restaurant \n",
"45 Clothing Store Burger Joint Tapas Restaurant \n",
"46 Clothing Store Burger Joint Tapas Restaurant \n",
"47 Clothing Store Burger Joint Tapas Restaurant \n",
"48 Clothing Store Burger Joint Tapas Restaurant \n",
"49 Clothing Store Burger Joint Tapas Restaurant \n",
"50 Clothing Store Burger Joint Tapas Restaurant \n",
"51 Restaurant Tapas Restaurant Spanish Restaurant \n",
"52 Restaurant Tapas Restaurant Spanish Restaurant \n",
"53 Restaurant Tapas Restaurant Spanish Restaurant \n",
"54 Restaurant Tapas Restaurant Spanish Restaurant \n",
"55 Restaurant Tapas Restaurant Spanish Restaurant \n",
"56 Restaurant Tapas Restaurant Spanish Restaurant \n",
"57 Restaurant Tapas Restaurant Spanish Restaurant \n",
"72 Spanish Restaurant Seafood Restaurant Chinese Restaurant \n",
"73 Spanish Restaurant Seafood Restaurant Chinese Restaurant \n",
"74 Spanish Restaurant Seafood Restaurant Chinese Restaurant \n",
"75 Spanish Restaurant Seafood Restaurant Chinese Restaurant \n",
"76 Spanish Restaurant Seafood Restaurant Chinese Restaurant \n",
"77 Spanish Restaurant Seafood Restaurant Chinese Restaurant \n",
"78 Spanish Restaurant Seafood Restaurant Chinese Restaurant \n",
"79 Fast Food Restaurant Gym Grocery Store \n",
"80 Fast Food Restaurant Gym Grocery Store \n",
"81 Fast Food Restaurant Gym Grocery Store \n",
"82 Fast Food Restaurant Gym Grocery Store \n",
"83 Fast Food Restaurant Gym Grocery Store \n",
"84 Fast Food Restaurant Gym Grocery Store \n",
"91 Restaurant Spanish Restaurant Park \n",
"92 Restaurant Spanish Restaurant Park \n",
"93 Restaurant Spanish Restaurant Park \n",
"94 Restaurant Spanish Restaurant Park \n",
"95 Restaurant Spanish Restaurant Park \n",
"96 Restaurant Spanish Restaurant Park \n",
"97 Restaurant Spanish Restaurant Park \n",
"98 Restaurant Spanish Restaurant Park \n",
"99 Restaurant Spanish Restaurant Park \n",
"100 Breakfast Spot Supermarket Pizza Place \n",
"101 Breakfast Spot Supermarket Pizza Place \n",
"102 Breakfast Spot Supermarket Pizza Place \n",
"103 Breakfast Spot Supermarket Pizza Place \n",
"104 Breakfast Spot Supermarket Pizza Place \n",
"105 Breakfast Spot Supermarket Pizza Place \n",
"111 Breakfast Spot Park Platform \n",
"112 Breakfast Spot Park Platform \n",
"113 Breakfast Spot Park Platform \n",
"114 Pizza Place Spanish Restaurant Beer Bar \n",
"115 Pizza Place Spanish Restaurant Beer Bar \n",
"116 Pizza Place Spanish Restaurant Beer Bar \n",
"117 Pizza Place Spanish Restaurant Beer Bar \n",
"126 Hotel Restaurant Spanish Restaurant \n",
"127 Hotel Restaurant Spanish Restaurant \n",
"128 Hotel Restaurant Spanish Restaurant \n",
"129 Hotel Restaurant Spanish Restaurant \n",
"130 Hotel Restaurant Spanish Restaurant \n",
"\n",
" 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue \\\n",
"0 Hostel Ice Cream Shop Bookstore \n",
"1 Hostel Ice Cream Shop Bookstore \n",
"2 Hostel Ice Cream Shop Bookstore \n",
"3 Hostel Ice Cream Shop Bookstore \n",
"4 Hostel Ice Cream Shop Bookstore \n",
"5 Hostel Ice Cream Shop Bookstore \n",
"6 Bakery Tapas Restaurant Gym / Fitness Center \n",
"7 Bakery Tapas Restaurant Gym / Fitness Center \n",
"8 Bakery Tapas Restaurant Gym / Fitness Center \n",
"9 Bakery Tapas Restaurant Gym / Fitness Center \n",
"10 Bakery Tapas Restaurant Gym / Fitness Center \n",
"11 Bakery Tapas Restaurant Gym / Fitness Center \n",
"12 Bakery Tapas Restaurant Gym / Fitness Center \n",
"13 Tapas Restaurant Bar Gym \n",
"14 Tapas Restaurant Bar Gym \n",
"15 Tapas Restaurant Bar Gym \n",
"16 Tapas Restaurant Bar Gym \n",
"17 Tapas Restaurant Bar Gym \n",
"18 Tapas Restaurant Bar Gym \n",
"19 Seafood Restaurant Burger Joint Clothing Store \n",
"20 Seafood Restaurant Burger Joint Clothing Store \n",
"21 Seafood Restaurant Burger Joint Clothing Store \n",
"22 Seafood Restaurant Burger Joint Clothing Store \n",
"23 Seafood Restaurant Burger Joint Clothing Store \n",
"24 Seafood Restaurant Burger Joint Clothing Store \n",
"25 Grocery Store Tapas Restaurant Café \n",
"26 Grocery Store Tapas Restaurant Café \n",
"27 Grocery Store Tapas Restaurant Café \n",
"28 Grocery Store Tapas Restaurant Café \n",
"29 Grocery Store Tapas Restaurant Café \n",
"30 Grocery Store Tapas Restaurant Café \n",
"31 Chinese Restaurant Supermarket Breakfast Spot \n",
"32 Chinese Restaurant Supermarket Breakfast Spot \n",
"33 Chinese Restaurant Supermarket Breakfast Spot \n",
"34 Chinese Restaurant Supermarket Breakfast Spot \n",
"35 Chinese Restaurant Supermarket Breakfast Spot \n",
"36 Chinese Restaurant Supermarket Breakfast Spot \n",
"37 Brewery Italian Restaurant Japanese Restaurant \n",
"38 Brewery Italian Restaurant Japanese Restaurant \n",
"39 Brewery Italian Restaurant Japanese Restaurant \n",
"40 Brewery Italian Restaurant Japanese Restaurant \n",
"41 Brewery Italian Restaurant Japanese Restaurant \n",
"42 Brewery Italian Restaurant Japanese Restaurant \n",
"43 Fast Food Restaurant Italian Restaurant Shopping Mall \n",
"44 Fast Food Restaurant Italian Restaurant Shopping Mall \n",
"45 Fast Food Restaurant Italian Restaurant Shopping Mall \n",
"46 Fast Food Restaurant Italian Restaurant Shopping Mall \n",
"47 Fast Food Restaurant Italian Restaurant Shopping Mall \n",
"48 Fast Food Restaurant Italian Restaurant Shopping Mall \n",
"49 Fast Food Restaurant Italian Restaurant Shopping Mall \n",
"50 Fast Food Restaurant Italian Restaurant Shopping Mall \n",
"51 Pizza Place Italian Restaurant Pub \n",
"52 Pizza Place Italian Restaurant Pub \n",
"53 Pizza Place Italian Restaurant Pub \n",
"54 Pizza Place Italian Restaurant Pub \n",
"55 Pizza Place Italian Restaurant Pub \n",
"56 Pizza Place Italian Restaurant Pub \n",
"57 Pizza Place Italian Restaurant Pub \n",
"72 Theater Asian Restaurant Bubble Tea Shop \n",
"73 Theater Asian Restaurant Bubble Tea Shop \n",
"74 Theater Asian Restaurant Bubble Tea Shop \n",
"75 Theater Asian Restaurant Bubble Tea Shop \n",
"76 Theater Asian Restaurant Bubble Tea Shop \n",
"77 Theater Asian Restaurant Bubble Tea Shop \n",
"78 Theater Asian Restaurant Bubble Tea Shop \n",
"79 Café Hotel Supermarket \n",
"80 Café Hotel Supermarket \n",
"81 Café Hotel Supermarket \n",
"82 Café Hotel Supermarket \n",
"83 Café Hotel Supermarket \n",
"84 Café Hotel Supermarket \n",
"91 Grocery Store Argentinian Restaurant Gastropub \n",
"92 Grocery Store Argentinian Restaurant Gastropub \n",
"93 Grocery Store Argentinian Restaurant Gastropub \n",
"94 Grocery Store Argentinian Restaurant Gastropub \n",
"95 Grocery Store Argentinian Restaurant Gastropub \n",
"96 Grocery Store Argentinian Restaurant Gastropub \n",
"97 Grocery Store Argentinian Restaurant Gastropub \n",
"98 Grocery Store Argentinian Restaurant Gastropub \n",
"99 Grocery Store Argentinian Restaurant Gastropub \n",
"100 Sandwich Place Pub Spanish Restaurant \n",
"101 Sandwich Place Pub Spanish Restaurant \n",
"102 Sandwich Place Pub Spanish Restaurant \n",
"103 Sandwich Place Pub Spanish Restaurant \n",
"104 Sandwich Place Pub Spanish Restaurant \n",
"105 Sandwich Place Pub Spanish Restaurant \n",
"111 Grocery Store Spanish Restaurant Food \n",
"112 Grocery Store Spanish Restaurant Food \n",
"113 Grocery Store Spanish Restaurant Food \n",
"114 Fast Food Restaurant Grocery Store Ice Cream Shop \n",
"115 Fast Food Restaurant Grocery Store Ice Cream Shop \n",
"116 Fast Food Restaurant Grocery Store Ice Cream Shop \n",
"117 Fast Food Restaurant Grocery Store Ice Cream Shop \n",
"126 Coffee Shop Tapas Restaurant Bar \n",
"127 Coffee Shop Tapas Restaurant Bar \n",
"128 Coffee Shop Tapas Restaurant Bar \n",
"129 Coffee Shop Tapas Restaurant Bar \n",
"130 Coffee Shop Tapas Restaurant Bar \n",
"\n",
" 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue \\\n",
"0 Hotel Pastry Shop Cocktail Bar \n",
"1 Hotel Pastry Shop Cocktail Bar \n",
"2 Hotel Pastry Shop Cocktail Bar \n",
"3 Hotel Pastry Shop Cocktail Bar \n",
"4 Hotel Pastry Shop Cocktail Bar \n",
"5 Hotel Pastry Shop Cocktail Bar \n",
"6 Beer Garden Market Falafel Restaurant \n",
"7 Beer Garden Market Falafel Restaurant \n",
"8 Beer Garden Market Falafel Restaurant \n",
"9 Beer Garden Market Falafel Restaurant \n",
"10 Beer Garden Market Falafel Restaurant \n",
"11 Beer Garden Market Falafel Restaurant \n",
"12 Beer Garden Market Falafel Restaurant \n",
"13 Farmers Market Boutique Brewery \n",
"14 Farmers Market Boutique Brewery \n",
"15 Farmers Market Boutique Brewery \n",
"16 Farmers Market Boutique Brewery \n",
"17 Farmers Market Boutique Brewery \n",
"18 Farmers Market Boutique Brewery \n",
"19 Mexican Restaurant Tapas Restaurant Supermarket \n",
"20 Mexican Restaurant Tapas Restaurant Supermarket \n",
"21 Mexican Restaurant Tapas Restaurant Supermarket \n",
"22 Mexican Restaurant Tapas Restaurant Supermarket \n",
"23 Mexican Restaurant Tapas Restaurant Supermarket \n",
"24 Mexican Restaurant Tapas Restaurant Supermarket \n",
"25 Gastropub Park Coffee Shop \n",
"26 Gastropub Park Coffee Shop \n",
"27 Gastropub Park Coffee Shop \n",
"28 Gastropub Park Coffee Shop \n",
"29 Gastropub Park Coffee Shop \n",
"30 Gastropub Park Coffee Shop \n",
"31 Clothing Store Resort Cafeteria \n",
"32 Clothing Store Resort Cafeteria \n",
"33 Clothing Store Resort Cafeteria \n",
"34 Clothing Store Resort Cafeteria \n",
"35 Clothing Store Resort Cafeteria \n",
"36 Clothing Store Resort Cafeteria \n",
"37 Café Plaza Mexican Restaurant \n",
"38 Café Plaza Mexican Restaurant \n",
"39 Café Plaza Mexican Restaurant \n",
"40 Café Plaza Mexican Restaurant \n",
"41 Café Plaza Mexican Restaurant \n",
"42 Café Plaza Mexican Restaurant \n",
"43 Spanish Restaurant Big Box Store Flea Market \n",
"44 Spanish Restaurant Big Box Store Flea Market \n",
"45 Spanish Restaurant Big Box Store Flea Market \n",
"46 Spanish Restaurant Big Box Store Flea Market \n",
"47 Spanish Restaurant Big Box Store Flea Market \n",
"48 Spanish Restaurant Big Box Store Flea Market \n",
"49 Spanish Restaurant Big Box Store Flea Market \n",
"50 Spanish Restaurant Big Box Store Flea Market \n",
"51 Bakery Bar Coffee Shop \n",
"52 Bakery Bar Coffee Shop \n",
"53 Bakery Bar Coffee Shop \n",
"54 Bakery Bar Coffee Shop \n",
"55 Bakery Bar Coffee Shop \n",
"56 Bakery Bar Coffee Shop \n",
"57 Bakery Bar Coffee Shop \n",
"72 Noodle House Fast Food Restaurant Plaza \n",
"73 Noodle House Fast Food Restaurant Plaza \n",
"74 Noodle House Fast Food Restaurant Plaza \n",
"75 Noodle House Fast Food Restaurant Plaza \n",
"76 Noodle House Fast Food Restaurant Plaza \n",
"77 Noodle House Fast Food Restaurant Plaza \n",
"78 Noodle House Fast Food Restaurant Plaza \n",
"79 Tapas Restaurant Bar Burger Joint \n",
"80 Tapas Restaurant Bar Burger Joint \n",
"81 Tapas Restaurant Bar Burger Joint \n",
"82 Tapas Restaurant Bar Burger Joint \n",
"83 Tapas Restaurant Bar Burger Joint \n",
"84 Tapas Restaurant Bar Burger Joint \n",
"91 Tapas Restaurant Pharmacy Café \n",
"92 Tapas Restaurant Pharmacy Café \n",
"93 Tapas Restaurant Pharmacy Café \n",
"94 Tapas Restaurant Pharmacy Café \n",
"95 Tapas Restaurant Pharmacy Café \n",
"96 Tapas Restaurant Pharmacy Café \n",
"97 Tapas Restaurant Pharmacy Café \n",
"98 Tapas Restaurant Pharmacy Café \n",
"99 Tapas Restaurant Pharmacy Café \n",
"100 Bakery Plaza Chinese Restaurant \n",
"101 Bakery Plaza Chinese Restaurant \n",
"102 Bakery Plaza Chinese Restaurant \n",
"103 Bakery Plaza Chinese Restaurant \n",
"104 Bakery Plaza Chinese Restaurant \n",
"105 Bakery Plaza Chinese Restaurant \n",
"111 Soccer Field Sandwich Place Eastern European Restaurant \n",
"112 Soccer Field Sandwich Place Eastern European Restaurant \n",
"113 Soccer Field Sandwich Place Eastern European Restaurant \n",
"114 Sandwich Place Breakfast Spot Café \n",
"115 Sandwich Place Breakfast Spot Café \n",
"116 Sandwich Place Breakfast Spot Café \n",
"117 Sandwich Place Breakfast Spot Café \n",
"126 Fast Food Restaurant Brewery Mexican Restaurant \n",
"127 Fast Food Restaurant Brewery Mexican Restaurant \n",
"128 Fast Food Restaurant Brewery Mexican Restaurant \n",
"129 Fast Food Restaurant Brewery Mexican Restaurant \n",
"130 Fast Food Restaurant Brewery Mexican Restaurant \n",
"\n",
" 10th Most Common Venue \n",
"0 Gym / Fitness Center \n",
"1 Gym / Fitness Center \n",
"2 Gym / Fitness Center \n",
"3 Gym / Fitness Center \n",
"4 Gym / Fitness Center \n",
"5 Gym / Fitness Center \n",
"6 Burger Joint \n",
"7 Burger Joint \n",
"8 Burger Joint \n",
"9 Burger Joint \n",
"10 Burger Joint \n",
"11 Burger Joint \n",
"12 Burger Joint \n",
"13 Food & Drink Shop \n",
"14 Food & Drink Shop \n",
"15 Food & Drink Shop \n",
"16 Food & Drink Shop \n",
"17 Food & Drink Shop \n",
"18 Food & Drink Shop \n",
"19 Plaza \n",
"20 Plaza \n",
"21 Plaza \n",
"22 Plaza \n",
"23 Plaza \n",
"24 Plaza \n",
"25 Japanese Restaurant \n",
"26 Japanese Restaurant \n",
"27 Japanese Restaurant \n",
"28 Japanese Restaurant \n",
"29 Japanese Restaurant \n",
"30 Japanese Restaurant \n",
"31 Seafood Restaurant \n",
"32 Seafood Restaurant \n",
"33 Seafood Restaurant \n",
"34 Seafood Restaurant \n",
"35 Seafood Restaurant \n",
"36 Seafood Restaurant \n",
"37 Tapas Restaurant \n",
"38 Tapas Restaurant \n",
"39 Tapas Restaurant \n",
"40 Tapas Restaurant \n",
"41 Tapas Restaurant \n",
"42 Tapas Restaurant \n",
"43 Boutique \n",
"44 Boutique \n",
"45 Boutique \n",
"46 Boutique \n",
"47 Boutique \n",
"48 Boutique \n",
"49 Boutique \n",
"50 Boutique \n",
"51 Mediterranean Restaurant \n",
"52 Mediterranean Restaurant \n",
"53 Mediterranean Restaurant \n",
"54 Mediterranean Restaurant \n",
"55 Mediterranean Restaurant \n",
"56 Mediterranean Restaurant \n",
"57 Mediterranean Restaurant \n",
"72 Diner \n",
"73 Diner \n",
"74 Diner \n",
"75 Diner \n",
"76 Diner \n",
"77 Diner \n",
"78 Diner \n",
"79 Breakfast Spot \n",
"80 Breakfast Spot \n",
"81 Breakfast Spot \n",
"82 Breakfast Spot \n",
"83 Breakfast Spot \n",
"84 Breakfast Spot \n",
"91 Bus Line \n",
"92 Bus Line \n",
"93 Bus Line \n",
"94 Bus Line \n",
"95 Bus Line \n",
"96 Bus Line \n",
"97 Bus Line \n",
"98 Bus Line \n",
"99 Bus Line \n",
"100 Restaurant \n",
"101 Restaurant \n",
"102 Restaurant \n",
"103 Restaurant \n",
"104 Restaurant \n",
"105 Restaurant \n",
"111 Fast Food Restaurant \n",
"112 Fast Food Restaurant \n",
"113 Fast Food Restaurant \n",
"114 Fish Market \n",
"115 Fish Market \n",
"116 Fish Market \n",
"117 Fish Market \n",
"126 Café \n",
"127 Café \n",
"128 Café \n",
"129 Café \n",
"130 Café "
]
},
"execution_count": 421,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_merged.loc[madrid_merged['Cluster Labels'] == 0, madrid_merged.columns[[0] + [1] + list(range(5, madrid_merged.shape[1]))]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 2"
]
},
{
"cell_type": "code",
"execution_count": 422,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" District \n",
" Neighborhood \n",
" 1st Most Common Venue \n",
" 2nd Most Common Venue \n",
" 3rd Most Common Venue \n",
" 4th Most Common Venue \n",
" 5th Most Common Venue \n",
" 6th Most Common Venue \n",
" 7th Most Common Venue \n",
" 8th Most Common Venue \n",
" 9th Most Common Venue \n",
" 10th Most Common Venue \n",
" \n",
" \n",
" \n",
" \n",
" 65 \n",
" Carabanchel \n",
" Comillas \n",
" Pizza Place \n",
" Nightclub \n",
" Fast Food Restaurant \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Bakery \n",
" Soccer Field \n",
" Metro Station \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 66 \n",
" Carabanchel \n",
" Opañel \n",
" Pizza Place \n",
" Nightclub \n",
" Fast Food Restaurant \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Bakery \n",
" Soccer Field \n",
" Metro Station \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 67 \n",
" Carabanchel \n",
" San Isidro \n",
" Pizza Place \n",
" Nightclub \n",
" Fast Food Restaurant \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Bakery \n",
" Soccer Field \n",
" Metro Station \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 68 \n",
" Carabanchel \n",
" Vista Alegre \n",
" Pizza Place \n",
" Nightclub \n",
" Fast Food Restaurant \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Bakery \n",
" Soccer Field \n",
" Metro Station \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 69 \n",
" Carabanchel \n",
" Puerta Bonita \n",
" Pizza Place \n",
" Nightclub \n",
" Fast Food Restaurant \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Bakery \n",
" Soccer Field \n",
" Metro Station \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 70 \n",
" Carabanchel \n",
" Buenavista \n",
" Pizza Place \n",
" Nightclub \n",
" Fast Food Restaurant \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Bakery \n",
" Soccer Field \n",
" Metro Station \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 71 \n",
" Carabanchel \n",
" Abrantes \n",
" Pizza Place \n",
" Nightclub \n",
" Fast Food Restaurant \n",
" Burger Joint \n",
" Tapas Restaurant \n",
" Bakery \n",
" Soccer Field \n",
" Metro Station \n",
" Plaza \n",
" Diner \n",
" \n",
" \n",
" 85 \n",
" Moratalaz \n",
" Pavones \n",
" Bar \n",
" Pizza Place \n",
" Ice Cream Shop \n",
" Food Truck \n",
" Café \n",
" Brewery \n",
" Nightclub \n",
" Bakery \n",
" Soccer Field \n",
" Plaza \n",
" \n",
" \n",
" 86 \n",
" Moratalaz \n",
" Horcajo \n",
" Bar \n",
" Pizza Place \n",
" Ice Cream Shop \n",
" Food Truck \n",
" Café \n",
" Brewery \n",
" Nightclub \n",
" Bakery \n",
" Soccer Field \n",
" Plaza \n",
" \n",
" \n",
" 87 \n",
" Moratalaz \n",
" Marroquina \n",
" Bar \n",
" Pizza Place \n",
" Ice Cream Shop \n",
" Food Truck \n",
" Café \n",
" Brewery \n",
" Nightclub \n",
" Bakery \n",
" Soccer Field \n",
" Plaza \n",
" \n",
" \n",
" 88 \n",
" Moratalaz \n",
" Media Legua \n",
" Bar \n",
" Pizza Place \n",
" Ice Cream Shop \n",
" Food Truck \n",
" Café \n",
" Brewery \n",
" Nightclub \n",
" Bakery \n",
" Soccer Field \n",
" Plaza \n",
" \n",
" \n",
" 89 \n",
" Moratalaz \n",
" Fontarrón \n",
" Bar \n",
" Pizza Place \n",
" Ice Cream Shop \n",
" Food Truck \n",
" Café \n",
" Brewery \n",
" Nightclub \n",
" Bakery \n",
" Soccer Field \n",
" Plaza \n",
" \n",
" \n",
" 90 \n",
" Moratalaz \n",
" Vinateros \n",
" Bar \n",
" Pizza Place \n",
" Ice Cream Shop \n",
" Food Truck \n",
" Café \n",
" Brewery \n",
" Nightclub \n",
" Bakery \n",
" Soccer Field \n",
" Plaza \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" District Neighborhood 1st Most Common Venue 2nd Most Common Venue \\\n",
"65 Carabanchel Comillas Pizza Place Nightclub \n",
"66 Carabanchel Opañel Pizza Place Nightclub \n",
"67 Carabanchel San Isidro Pizza Place Nightclub \n",
"68 Carabanchel Vista Alegre Pizza Place Nightclub \n",
"69 Carabanchel Puerta Bonita Pizza Place Nightclub \n",
"70 Carabanchel Buenavista Pizza Place Nightclub \n",
"71 Carabanchel Abrantes Pizza Place Nightclub \n",
"85 Moratalaz Pavones Bar Pizza Place \n",
"86 Moratalaz Horcajo Bar Pizza Place \n",
"87 Moratalaz Marroquina Bar Pizza Place \n",
"88 Moratalaz Media Legua Bar Pizza Place \n",
"89 Moratalaz Fontarrón Bar Pizza Place \n",
"90 Moratalaz Vinateros Bar Pizza Place \n",
"\n",
" 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue \\\n",
"65 Fast Food Restaurant Burger Joint Tapas Restaurant \n",
"66 Fast Food Restaurant Burger Joint Tapas Restaurant \n",
"67 Fast Food Restaurant Burger Joint Tapas Restaurant \n",
"68 Fast Food Restaurant Burger Joint Tapas Restaurant \n",
"69 Fast Food Restaurant Burger Joint Tapas Restaurant \n",
"70 Fast Food Restaurant Burger Joint Tapas Restaurant \n",
"71 Fast Food Restaurant Burger Joint Tapas Restaurant \n",
"85 Ice Cream Shop Food Truck Café \n",
"86 Ice Cream Shop Food Truck Café \n",
"87 Ice Cream Shop Food Truck Café \n",
"88 Ice Cream Shop Food Truck Café \n",
"89 Ice Cream Shop Food Truck Café \n",
"90 Ice Cream Shop Food Truck Café \n",
"\n",
" 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n",
"65 Bakery Soccer Field Metro Station \n",
"66 Bakery Soccer Field Metro Station \n",
"67 Bakery Soccer Field Metro Station \n",
"68 Bakery Soccer Field Metro Station \n",
"69 Bakery Soccer Field Metro Station \n",
"70 Bakery Soccer Field Metro Station \n",
"71 Bakery Soccer Field Metro Station \n",
"85 Brewery Nightclub Bakery \n",
"86 Brewery Nightclub Bakery \n",
"87 Brewery Nightclub Bakery \n",
"88 Brewery Nightclub Bakery \n",
"89 Brewery Nightclub Bakery \n",
"90 Brewery Nightclub Bakery \n",
"\n",
" 9th Most Common Venue 10th Most Common Venue \n",
"65 Plaza Diner \n",
"66 Plaza Diner \n",
"67 Plaza Diner \n",
"68 Plaza Diner \n",
"69 Plaza Diner \n",
"70 Plaza Diner \n",
"71 Plaza Diner \n",
"85 Soccer Field Plaza \n",
"86 Soccer Field Plaza \n",
"87 Soccer Field Plaza \n",
"88 Soccer Field Plaza \n",
"89 Soccer Field Plaza \n",
"90 Soccer Field Plaza "
]
},
"execution_count": 422,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_merged.loc[madrid_merged['Cluster Labels'] == 1, madrid_merged.columns[[0] + [1] + list(range(5, madrid_merged.shape[1]))]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 3"
]
},
{
"cell_type": "code",
"execution_count": 424,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" District \n",
" Neighborhood \n",
" 1st Most Common Venue \n",
" 2nd Most Common Venue \n",
" 3rd Most Common Venue \n",
" 4th Most Common Venue \n",
" 5th Most Common Venue \n",
" 6th Most Common Venue \n",
" 7th Most Common Venue \n",
" 8th Most Common Venue \n",
" 9th Most Common Venue \n",
" 10th Most Common Venue \n",
" \n",
" \n",
" \n",
" \n",
" 106 \n",
" Villaverde \n",
" Villaverde Alto, Casco Histórico de Villaverde \n",
" Pizza Place \n",
" Train \n",
" Plaza \n",
" Diner \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Dog Run \n",
" Farmers Market \n",
" Falafel Restaurant \n",
" Fabric Shop \n",
" \n",
" \n",
" 107 \n",
" Villaverde \n",
" San Cristóbal \n",
" Pizza Place \n",
" Train \n",
" Plaza \n",
" Diner \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Dog Run \n",
" Farmers Market \n",
" Falafel Restaurant \n",
" Fabric Shop \n",
" \n",
" \n",
" 108 \n",
" Villaverde \n",
" Butarque \n",
" Pizza Place \n",
" Train \n",
" Plaza \n",
" Diner \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Dog Run \n",
" Farmers Market \n",
" Falafel Restaurant \n",
" Fabric Shop \n",
" \n",
" \n",
" 109 \n",
" Villaverde \n",
" Los Rosales \n",
" Pizza Place \n",
" Train \n",
" Plaza \n",
" Diner \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Dog Run \n",
" Farmers Market \n",
" Falafel Restaurant \n",
" Fabric Shop \n",
" \n",
" \n",
" 110 \n",
" Villaverde \n",
" Los Ángeles \n",
" Pizza Place \n",
" Train \n",
" Plaza \n",
" Diner \n",
" Spanish Restaurant \n",
" Grocery Store \n",
" Dog Run \n",
" Farmers Market \n",
" Falafel Restaurant \n",
" Fabric Shop \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" District Neighborhood \\\n",
"106 Villaverde Villaverde Alto, Casco Histórico de Villaverde \n",
"107 Villaverde San Cristóbal \n",
"108 Villaverde Butarque \n",
"109 Villaverde Los Rosales \n",
"110 Villaverde Los Ángeles \n",
"\n",
" 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue \\\n",
"106 Pizza Place Train Plaza \n",
"107 Pizza Place Train Plaza \n",
"108 Pizza Place Train Plaza \n",
"109 Pizza Place Train Plaza \n",
"110 Pizza Place Train Plaza \n",
"\n",
" 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue \\\n",
"106 Diner Spanish Restaurant Grocery Store \n",
"107 Diner Spanish Restaurant Grocery Store \n",
"108 Diner Spanish Restaurant Grocery Store \n",
"109 Diner Spanish Restaurant Grocery Store \n",
"110 Diner Spanish Restaurant Grocery Store \n",
"\n",
" 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue \\\n",
"106 Dog Run Farmers Market Falafel Restaurant \n",
"107 Dog Run Farmers Market Falafel Restaurant \n",
"108 Dog Run Farmers Market Falafel Restaurant \n",
"109 Dog Run Farmers Market Falafel Restaurant \n",
"110 Dog Run Farmers Market Falafel Restaurant \n",
"\n",
" 10th Most Common Venue \n",
"106 Fabric Shop \n",
"107 Fabric Shop \n",
"108 Fabric Shop \n",
"109 Fabric Shop \n",
"110 Fabric Shop "
]
},
"execution_count": 424,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_merged.loc[madrid_merged['Cluster Labels'] == 2, madrid_merged.columns[[0] + [1] + list(range(5, madrid_merged.shape[1]))]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 4"
]
},
{
"cell_type": "code",
"execution_count": 425,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" 2nd Most Common Venue \n",
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" 5th Most Common Venue \n",
" 6th Most Common Venue \n",
" 7th Most Common Venue \n",
" 8th Most Common Venue \n",
" 9th Most Common Venue \n",
" 10th Most Common Venue \n",
" \n",
" \n",
" \n",
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" 58 \n",
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" Falafel Restaurant \n",
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" \n",
" \n",
" 60 \n",
" Latina \n",
" Lucero \n",
" Pizza Place \n",
" Fast Food Restaurant \n",
" Park \n",
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" Arts & Crafts Store \n",
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" Falafel Restaurant \n",
" Metro Station \n",
" Bakery \n",
" \n",
" \n",
" 61 \n",
" Latina \n",
" Aluche \n",
" Pizza Place \n",
" Fast Food Restaurant \n",
" Park \n",
" Grocery Store \n",
" Train Station \n",
" Arts & Crafts Store \n",
" Asian Restaurant \n",
" Falafel Restaurant \n",
" Metro Station \n",
" Bakery \n",
" \n",
" \n",
" 62 \n",
" Latina \n",
" Campamento \n",
" Pizza Place \n",
" Fast Food Restaurant \n",
" Park \n",
" Grocery Store \n",
" Train Station \n",
" Arts & Crafts Store \n",
" Asian Restaurant \n",
" Falafel Restaurant \n",
" Metro Station \n",
" Bakery \n",
" \n",
" \n",
" 63 \n",
" Latina \n",
" Cuatro Vientos \n",
" Pizza Place \n",
" Fast Food Restaurant \n",
" Park \n",
" Grocery Store \n",
" Train Station \n",
" Arts & Crafts Store \n",
" Asian Restaurant \n",
" Falafel Restaurant \n",
" Metro Station \n",
" Bakery \n",
" \n",
" \n",
" 64 \n",
" Latina \n",
" Águilas \n",
" Pizza Place \n",
" Fast Food Restaurant \n",
" Park \n",
" Grocery Store \n",
" Train Station \n",
" Arts & Crafts Store \n",
" Asian Restaurant \n",
" Falafel Restaurant \n",
" Metro Station \n",
" Bakery \n",
" \n",
" \n",
" 118 \n",
" San Blas - Canillejas \n",
" Simancas \n",
" Metro Station \n",
" Asian Restaurant \n",
" Shopping Mall \n",
" Supermarket \n",
" Snack Place \n",
" Pizza Place \n",
" Gas Station \n",
" Grocery Store \n",
" Gym \n",
" Fabric Shop \n",
" \n",
" \n",
" 119 \n",
" San Blas - Canillejas \n",
" Hellín \n",
" Metro Station \n",
" Asian Restaurant \n",
" Shopping Mall \n",
" Supermarket \n",
" Snack Place \n",
" Pizza Place \n",
" Gas Station \n",
" Grocery Store \n",
" Gym \n",
" Fabric Shop \n",
" \n",
" \n",
" 120 \n",
" San Blas - Canillejas \n",
" Amposta \n",
" Metro Station \n",
" Asian Restaurant \n",
" Shopping Mall \n",
" Supermarket \n",
" Snack Place \n",
" Pizza Place \n",
" Gas Station \n",
" Grocery Store \n",
" Gym \n",
" Fabric Shop \n",
" \n",
" \n",
" 121 \n",
" San Blas - Canillejas \n",
" Arcos \n",
" Metro Station \n",
" Asian Restaurant \n",
" Shopping Mall \n",
" Supermarket \n",
" Snack Place \n",
" Pizza Place \n",
" Gas Station \n",
" Grocery Store \n",
" Gym \n",
" Fabric Shop \n",
" \n",
" \n",
" 122 \n",
" San Blas - Canillejas \n",
" Rosas \n",
" Metro Station \n",
" Asian Restaurant \n",
" Shopping Mall \n",
" Supermarket \n",
" Snack Place \n",
" Pizza Place \n",
" Gas Station \n",
" Grocery Store \n",
" Gym \n",
" Fabric Shop \n",
" \n",
" \n",
" 123 \n",
" San Blas - Canillejas \n",
" Rejas \n",
" Metro Station \n",
" Asian Restaurant \n",
" Shopping Mall \n",
" Supermarket \n",
" Snack Place \n",
" Pizza Place \n",
" Gas Station \n",
" Grocery Store \n",
" Gym \n",
" Fabric Shop \n",
" \n",
" \n",
" 124 \n",
" San Blas - Canillejas \n",
" Canillejas \n",
" Metro Station \n",
" Asian Restaurant \n",
" Shopping Mall \n",
" Supermarket \n",
" Snack Place \n",
" Pizza Place \n",
" Gas Station \n",
" Grocery Store \n",
" Gym \n",
" Fabric Shop \n",
" \n",
" \n",
" 125 \n",
" San Blas - Canillejas \n",
" Salvador \n",
" Metro Station \n",
" Asian Restaurant \n",
" Shopping Mall \n",
" Supermarket \n",
" Snack Place \n",
" Pizza Place \n",
" Gas Station \n",
" Grocery Store \n",
" Gym \n",
" Fabric Shop \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" District Neighborhood 1st Most Common Venue \\\n",
"58 Latina Cármenes Pizza Place \n",
"59 Latina Puerta del Ángel Pizza Place \n",
"60 Latina Lucero Pizza Place \n",
"61 Latina Aluche Pizza Place \n",
"62 Latina Campamento Pizza Place \n",
"63 Latina Cuatro Vientos Pizza Place \n",
"64 Latina Águilas Pizza Place \n",
"118 San Blas - Canillejas Simancas Metro Station \n",
"119 San Blas - Canillejas Hellín Metro Station \n",
"120 San Blas - Canillejas Amposta Metro Station \n",
"121 San Blas - Canillejas Arcos Metro Station \n",
"122 San Blas - Canillejas Rosas Metro Station \n",
"123 San Blas - Canillejas Rejas Metro Station \n",
"124 San Blas - Canillejas Canillejas Metro Station \n",
"125 San Blas - Canillejas Salvador Metro Station \n",
"\n",
" 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue \\\n",
"58 Fast Food Restaurant Park Grocery Store \n",
"59 Fast Food Restaurant Park Grocery Store \n",
"60 Fast Food Restaurant Park Grocery Store \n",
"61 Fast Food Restaurant Park Grocery Store \n",
"62 Fast Food Restaurant Park Grocery Store \n",
"63 Fast Food Restaurant Park Grocery Store \n",
"64 Fast Food Restaurant Park Grocery Store \n",
"118 Asian Restaurant Shopping Mall Supermarket \n",
"119 Asian Restaurant Shopping Mall Supermarket \n",
"120 Asian Restaurant Shopping Mall Supermarket \n",
"121 Asian Restaurant Shopping Mall Supermarket \n",
"122 Asian Restaurant Shopping Mall Supermarket \n",
"123 Asian Restaurant Shopping Mall Supermarket \n",
"124 Asian Restaurant Shopping Mall Supermarket \n",
"125 Asian Restaurant Shopping Mall Supermarket \n",
"\n",
" 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue \\\n",
"58 Train Station Arts & Crafts Store Asian Restaurant \n",
"59 Train Station Arts & Crafts Store Asian Restaurant \n",
"60 Train Station Arts & Crafts Store Asian Restaurant \n",
"61 Train Station Arts & Crafts Store Asian Restaurant \n",
"62 Train Station Arts & Crafts Store Asian Restaurant \n",
"63 Train Station Arts & Crafts Store Asian Restaurant \n",
"64 Train Station Arts & Crafts Store Asian Restaurant \n",
"118 Snack Place Pizza Place Gas Station \n",
"119 Snack Place Pizza Place Gas Station \n",
"120 Snack Place Pizza Place Gas Station \n",
"121 Snack Place Pizza Place Gas Station \n",
"122 Snack Place Pizza Place Gas Station \n",
"123 Snack Place Pizza Place Gas Station \n",
"124 Snack Place Pizza Place Gas Station \n",
"125 Snack Place Pizza Place Gas Station \n",
"\n",
" 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue \n",
"58 Falafel Restaurant Metro Station Bakery \n",
"59 Falafel Restaurant Metro Station Bakery \n",
"60 Falafel Restaurant Metro Station Bakery \n",
"61 Falafel Restaurant Metro Station Bakery \n",
"62 Falafel Restaurant Metro Station Bakery \n",
"63 Falafel Restaurant Metro Station Bakery \n",
"64 Falafel Restaurant Metro Station Bakery \n",
"118 Grocery Store Gym Fabric Shop \n",
"119 Grocery Store Gym Fabric Shop \n",
"120 Grocery Store Gym Fabric Shop \n",
"121 Grocery Store Gym Fabric Shop \n",
"122 Grocery Store Gym Fabric Shop \n",
"123 Grocery Store Gym Fabric Shop \n",
"124 Grocery Store Gym Fabric Shop \n",
"125 Grocery Store Gym Fabric Shop "
]
},
"execution_count": 425,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"madrid_merged.loc[madrid_merged['Cluster Labels'] == 3, madrid_merged.columns[[0] + [1] + list(range(5, madrid_merged.shape[1]))]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results and discussion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By segmenting into clusters we can distinguish the different districts of the city of Madrid and their respective venues according to Foursquare, by using Silhouette Score the best number of clusters were 3 and 4, in this exercise we used 4 clusters.\n",
"\n",
"When analyzing the different clusters, cluster number 1 has in fourth, sixth, seventh, ninth and tenth place categories cafeteria and derivatives, it is also cluster number 1 the longest one where more districts and neighborhoods are located.\n",
"According to the above Madrid is a city where there are quite a few cafeteria type places partly also because of its strong tourist area especially in the central and most touristic part of the city, districts such as Chamartin, Barajas, Hortaleza, Puente de Vallecas have quite a few venues in cafeteria.\n",
"\n",
"A good option for a possible interested in starting a cafeteria type project would be in the mentioned districts due to its touristic nature, for example Moratalaz presents in the second cluster a fifth place in cafeteria venues."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**This is the cluster number 1 wich has the most cafeteria venue:**"
]
},
{
"cell_type": "code",
"execution_count": 469,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" District \n",
" Neighborhood \n",
" District_Latitude \n",
" District_Longitude \n",
" 1st Most Common Venue \n",
" 2nd Most Common Venue \n",
" 3rd Most Common Venue \n",
" 4th Most Common Venue \n",
" 5th Most Common Venue \n",
" 6th Most Common Venue \n",
" 7th Most Common Venue \n",
" 8th Most Common Venue \n",
" 9th Most Common Venue \n",
" 10th Most Common Venue \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Centro \n",
" Palacio \n",
" 40.415347 \n",
" -3.707371 \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 1 \n",
" Centro \n",
" Embajadores \n",
" 40.415347 \n",
" -3.707371 \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 2 \n",
" Centro \n",
" Cortes \n",
" 40.415347 \n",
" -3.707371 \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 3 \n",
" Centro \n",
" Justicia \n",
" 40.415347 \n",
" -3.707371 \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
" 4 \n",
" Centro \n",
" Universidad \n",
" 40.415347 \n",
" -3.707371 \n",
" Spanish Restaurant \n",
" Tapas Restaurant \n",
" Plaza \n",
" Hostel \n",
" Ice Cream Shop \n",
" Bookstore \n",
" Hotel \n",
" Pastry Shop \n",
" Cocktail Bar \n",
" Gym / Fitness Center \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" District Neighborhood District_Latitude District_Longitude \\\n",
"0 Centro Palacio 40.415347 -3.707371 \n",
"1 Centro Embajadores 40.415347 -3.707371 \n",
"2 Centro Cortes 40.415347 -3.707371 \n",
"3 Centro Justicia 40.415347 -3.707371 \n",
"4 Centro Universidad 40.415347 -3.707371 \n",
"\n",
" 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue \\\n",
"0 Spanish Restaurant Tapas Restaurant Plaza \n",
"1 Spanish Restaurant Tapas Restaurant Plaza \n",
"2 Spanish Restaurant Tapas Restaurant Plaza \n",
"3 Spanish Restaurant Tapas Restaurant Plaza \n",
"4 Spanish Restaurant Tapas Restaurant Plaza \n",
"\n",
" 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue \\\n",
"0 Hostel Ice Cream Shop Bookstore \n",
"1 Hostel Ice Cream Shop Bookstore \n",
"2 Hostel Ice Cream Shop Bookstore \n",
"3 Hostel Ice Cream Shop Bookstore \n",
"4 Hostel Ice Cream Shop Bookstore \n",
"\n",
" 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue \\\n",
"0 Hotel Pastry Shop Cocktail Bar \n",
"1 Hotel Pastry Shop Cocktail Bar \n",
"2 Hotel Pastry Shop Cocktail Bar \n",
"3 Hotel Pastry Shop Cocktail Bar \n",
"4 Hotel Pastry Shop Cocktail Bar \n",
"\n",
" 10th Most Common Venue \n",
"0 Gym / Fitness Center \n",
"1 Gym / Fitness Center \n",
"2 Gym / Fitness Center \n",
"3 Gym / Fitness Center \n",
"4 Gym / Fitness Center "
]
},
"execution_count": 469,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cafeteria_districts = madrid_merged.loc[madrid_merged['Cluster Labels'] == 0, madrid_merged.columns[[0, 1, 2, 3] + list(range(5, madrid_merged.shape[1]))]]\n",
"cafeteria_districts.head()"
]
},
{
"cell_type": "code",
"execution_count": 470,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Make this Notebook Trusted to load map: File -> Trust Notebook
"
],
"text/plain": [
""
]
},
"execution_count": 470,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cafe_locations = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
"tooltip = \"Click me!\"\n",
"\n",
"\n",
"for lat, lng, location, neighborhood in zip(cafeteria_districts['District_Latitude'], cafeteria_districts['District_Longitude'],\n",
" cafeteria_districts['District'], cafeteria_districts['Neighborhood']):\n",
" label = '{}, {}'.format(neighborhood, location)\n",
" folium.Marker([lat, lng], popup='{} has geographical coordinates ({:.4f}, {:.4f})'.format(label, lat, lng),\n",
" icon=folium.Icon(color='lightred')).add_to(cafe_locations)\n",
" \n",
" label = folium.Popup(label, parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lng],\n",
" radius=5,\n",
" popup=label,\n",
" color='yellow',\n",
" fill=True,\n",
" fill_color='#3186cc',\n",
" fill_opacity=0.7,\n",
" parse_html=False).add_to(cafe_locations) \n",
"\n",
"cafe_locations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This exercise shows on the map where most coffee is consumed in the most touristic area of Madrid, but at the same time there are areas where there are many confirmed cases of COVID, for example Puente de Vallecas with more than 30000 confirmed cases is a good district due to the venues in cafeteria but is well above the average of infections, Moratalaz can be interesting as it does not have as many infections as other districts and Barajas also has the lowest number of infections and has a fourth place in venus of cafeteria.\n",
"\n",
"\n",
"Villa de Vallecas and Hortaleza have in first place the Breakfast Spot venue which can be a good idea for a stakeholder as it can be related to cafeteria but at the same time they are districts with high confirmed cases.\n",
"Therefore cluster number 1 is indicated for a new coffee shop or cafeteria due to its touristic nature and the amount of venues related to groceries, restaurants, breakfast, etc, that this cluster has but without neglecting the COVID issue."
]
}
],
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