The Battle of Neighborhoods.ipynb

{
    "nbformat_minor": 1, 
    "cells": [
        {
            "source": "# The Battle of Neighboroughs - Part 1", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "## Background of the Business Problem", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "Milan, a metropolis in Italy's northern Lombardy region, is a global capital of fashion and design. Home to the national stock exchange, it\u2019s a financial hub also known for its high-end restaurants and shops. Milan is definitely one of the best places to start up a new business.\nDuring the daytime, specially in the morning and lunch hours, office areas provide huge opportunities for coffee shops. Reasonably priced shops are usually always full during the lunch hours (11 am\u200a\u2014\u200a2 pm) and, given this scenario, we will go through the benefits and pitfalls of opening a breakfast cum lunch coffee shop in highly densed office places. \nThe core of Milano is made of 9 municipalities but, I will later concentrate on 4 most busiest business boroughs of Milan: Centro Storico, Stazione Centrale, Citt\u00e0 Studi and Porta Garibaldi to target daily office workers.\nWe will go through each step of this project and address them separately. I first outline the initial data preparation and describe future steps to start the battle of neighborhoods in Milan\n", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "## Preparation for Data (Data Section)", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "Importing Libraries", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "import requests\nimport json\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\n\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport numpy as np", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 102
        }, 
        {
            "source": "### Get table of municipalities of Milan from Wikipedia page", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "We use BeautifulSoup library for scraping the web page", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "response = requests.get('https://en.wikipedia.org/wiki/Zones_of_Milan').text\nsoup = BeautifulSoup(response,'lxml')\ntable = soup.find('table', {'class':'wikitable sortable'})", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 103
        }, 
        {
            "source": "table_rows = table.find_all('tr')\n\nres = []\nfor tr in table_rows:\n    td = tr.find_all('td')\n    row = [tr.text.strip() for tr in td if tr.text.strip()]\n    if row:\n        res.append(row)\n\n\ndf = pd.DataFrame(res, columns=[\"Id\", \"Description\", \"Area\", \"Population\",\"Density\", \"Districts\"])\n#remove column Districts\ndf = df.drop(columns=['Districts'])\ndf", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>Description</th>\n      <th>Area</th>\n      <th>Population</th>\n      <th>Density</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Centro storico</td>\n      <td>9.67</td>\n      <td>96,315</td>\n      <td>11,074</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Stazione Centrale, Gorla, Turro, Greco, Cresce...</td>\n      <td>12.58</td>\n      <td>153.109</td>\n      <td>13,031</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Citt\u00e0 Studi, Lambrate, Porta Venezia</td>\n      <td>14.23</td>\n      <td>141,229</td>\n      <td>10,785</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Porta Vittoria, Forlanini</td>\n      <td>20.95</td>\n      <td>156.369</td>\n      <td>8,069</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Vigentino, Chiaravalle, Gratosoglio</td>\n      <td>29.87</td>\n      <td>123,779</td>\n      <td>4,487</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>Barona, Lorenteggio</td>\n      <td>18.28</td>\n      <td>149,000</td>\n      <td>8,998</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>Baggio, De Angeli, San Siro</td>\n      <td>31.34</td>\n      <td>170,814</td>\n      <td>6,093</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>Fiera, Gallaratese, Quarto Oggiaro</td>\n      <td>23.72</td>\n      <td>181,669</td>\n      <td>8,326</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>Porta Garibaldi, Niguarda</td>\n      <td>21.12</td>\n      <td>181,598</td>\n      <td>9,204</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Id                                        Description   Area Population  \\\n0  1                                     Centro storico   9.67     96,315   \n1  2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58    153.109   \n2  3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23    141,229   \n3  4                          Porta Vittoria, Forlanini  20.95    156.369   \n4  5                Vigentino, Chiaravalle, Gratosoglio  29.87    123,779   \n5  6                                Barona, Lorenteggio  18.28    149,000   \n6  7                        Baggio, De Angeli, San Siro  31.34    170,814   \n7  8                 Fiera, Gallaratese, Quarto Oggiaro  23.72    181,669   \n8  9                          Porta Garibaldi, Niguarda  21.12    181,598   \n\n  Density  \n0  11,074  \n1  13,031  \n2  10,785  \n3   8,069  \n4   4,487  \n5   8,998  \n6   6,093  \n7   8,326  \n8   9,204  "
                    }, 
                    "execution_count": 104, 
                    "metadata": {}
                }
            ], 
            "execution_count": 104
        }, 
        {
            "source": "Remove columns not used", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "#we assume the first name found in Description as name of Municipality\ndf['Name'] = df.Description.str.split(',').str[0]\n# convert Shortname in Capital letters  \ndf['Name'] = df.Name.str.capitalize() ", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 105
        }, 
        {
            "source": "df", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>Description</th>\n      <th>Area</th>\n      <th>Population</th>\n      <th>Density</th>\n      <th>Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Centro storico</td>\n      <td>9.67</td>\n      <td>96,315</td>\n      <td>11,074</td>\n      <td>Centro storico</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Stazione Centrale, Gorla, Turro, Greco, Cresce...</td>\n      <td>12.58</td>\n      <td>153.109</td>\n      <td>13,031</td>\n      <td>Stazione centrale</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Citt\u00e0 Studi, Lambrate, Porta Venezia</td>\n      <td>14.23</td>\n      <td>141,229</td>\n      <td>10,785</td>\n      <td>Citt\u00e0 studi</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Porta Vittoria, Forlanini</td>\n      <td>20.95</td>\n      <td>156.369</td>\n      <td>8,069</td>\n      <td>Porta vittoria</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Vigentino, Chiaravalle, Gratosoglio</td>\n      <td>29.87</td>\n      <td>123,779</td>\n      <td>4,487</td>\n      <td>Vigentino</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>Barona, Lorenteggio</td>\n      <td>18.28</td>\n      <td>149,000</td>\n      <td>8,998</td>\n      <td>Barona</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>Baggio, De Angeli, San Siro</td>\n      <td>31.34</td>\n      <td>170,814</td>\n      <td>6,093</td>\n      <td>Baggio</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>Fiera, Gallaratese, Quarto Oggiaro</td>\n      <td>23.72</td>\n      <td>181,669</td>\n      <td>8,326</td>\n      <td>Fiera</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>Porta Garibaldi, Niguarda</td>\n      <td>21.12</td>\n      <td>181,598</td>\n      <td>9,204</td>\n      <td>Porta garibaldi</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Id                                        Description   Area Population  \\\n0  1                                     Centro storico   9.67     96,315   \n1  2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58    153.109   \n2  3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23    141,229   \n3  4                          Porta Vittoria, Forlanini  20.95    156.369   \n4  5                Vigentino, Chiaravalle, Gratosoglio  29.87    123,779   \n5  6                                Barona, Lorenteggio  18.28    149,000   \n6  7                        Baggio, De Angeli, San Siro  31.34    170,814   \n7  8                 Fiera, Gallaratese, Quarto Oggiaro  23.72    181,669   \n8  9                          Porta Garibaldi, Niguarda  21.12    181,598   \n\n  Density               Name  \n0  11,074     Centro storico  \n1  13,031  Stazione centrale  \n2  10,785        Citt\u00e0 studi  \n3   8,069     Porta vittoria  \n4   4,487          Vigentino  \n5   8,998             Barona  \n6   6,093             Baggio  \n7   8,326              Fiera  \n8   9,204    Porta garibaldi  "
                    }, 
                    "execution_count": 106, 
                    "metadata": {}
                }
            ], 
            "execution_count": 106
        }, 
        {
            "source": "### Get geo coordinates of boroughs", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "We use Geopy web service", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "from geopy.geocoders import Nominatim\ngeolocator = Nominatim()\nlocation = geolocator.geocode(\"Milano, MI, Lom, Italia\")\naddress = []\ncoord = []\naddress = \"Municipio \"+ df['Id']+\", Milano, MI, Lom, Italy\"\ncoord = address.apply(geolocator.geocode).apply(lambda x: (x.latitude, x.longitude))\ndf['Coordinates'] = coord\ndf", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>Description</th>\n      <th>Area</th>\n      <th>Population</th>\n      <th>Density</th>\n      <th>Name</th>\n      <th>Coordinates</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Centro storico</td>\n      <td>9.67</td>\n      <td>96,315</td>\n      <td>11,074</td>\n      <td>Centro storico</td>\n      <td>(45.4672806, 9.18596201012092)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Stazione Centrale, Gorla, Turro, Greco, Cresce...</td>\n      <td>12.58</td>\n      <td>153.109</td>\n      <td>13,031</td>\n      <td>Stazione centrale</td>\n      <td>(45.4999899, 9.21899862605208)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Citt\u00e0 Studi, Lambrate, Porta Venezia</td>\n      <td>14.23</td>\n      <td>141,229</td>\n      <td>10,785</td>\n      <td>Citt\u00e0 studi</td>\n      <td>(45.4897995, 9.24103088613553)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Porta Vittoria, Forlanini</td>\n      <td>20.95</td>\n      <td>156.369</td>\n      <td>8,069</td>\n      <td>Porta vittoria</td>\n      <td>(45.446205, 9.23934326456628)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Vigentino, Chiaravalle, Gratosoglio</td>\n      <td>29.87</td>\n      <td>123,779</td>\n      <td>4,487</td>\n      <td>Vigentino</td>\n      <td>(45.4198458, 9.19815254935277)</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>Barona, Lorenteggio</td>\n      <td>18.28</td>\n      <td>149,000</td>\n      <td>8,998</td>\n      <td>Barona</td>\n      <td>(45.43792895, 9.1456481512419)</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>Baggio, De Angeli, San Siro</td>\n      <td>31.34</td>\n      <td>170,814</td>\n      <td>6,093</td>\n      <td>Baggio</td>\n      <td>(45.469598, 9.11475732032285)</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>Fiera, Gallaratese, Quarto Oggiaro</td>\n      <td>23.72</td>\n      <td>181,669</td>\n      <td>8,326</td>\n      <td>Fiera</td>\n      <td>(45.50001535, 9.12277422514706)</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>Porta Garibaldi, Niguarda</td>\n      <td>21.12</td>\n      <td>181,598</td>\n      <td>9,204</td>\n      <td>Porta garibaldi</td>\n      <td>(45.5077041, 9.17941004494827)</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Id                                        Description   Area Population  \\\n0  1                                     Centro storico   9.67     96,315   \n1  2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58    153.109   \n2  3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23    141,229   \n3  4                          Porta Vittoria, Forlanini  20.95    156.369   \n4  5                Vigentino, Chiaravalle, Gratosoglio  29.87    123,779   \n5  6                                Barona, Lorenteggio  18.28    149,000   \n6  7                        Baggio, De Angeli, San Siro  31.34    170,814   \n7  8                 Fiera, Gallaratese, Quarto Oggiaro  23.72    181,669   \n8  9                          Porta Garibaldi, Niguarda  21.12    181,598   \n\n  Density               Name                      Coordinates  \n0  11,074     Centro storico   (45.4672806, 9.18596201012092)  \n1  13,031  Stazione centrale   (45.4999899, 9.21899862605208)  \n2  10,785        Citt\u00e0 studi   (45.4897995, 9.24103088613553)  \n3   8,069     Porta vittoria    (45.446205, 9.23934326456628)  \n4   4,487          Vigentino   (45.4198458, 9.19815254935277)  \n5   8,998             Barona   (45.43792895, 9.1456481512419)  \n6   6,093             Baggio    (45.469598, 9.11475732032285)  \n7   8,326              Fiera  (45.50001535, 9.12277422514706)  \n8   9,204    Porta garibaldi   (45.5077041, 9.17941004494827)  "
                    }, 
                    "execution_count": 107, 
                    "metadata": {}
                }
            ], 
            "execution_count": 107
        }, 
        {
            "source": "df[['Latitude', 'Longitude']] = df['Coordinates'].apply(pd.Series)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 108
        }, 
        {
            "source": "df = df.drop(columns=['Coordinates'])", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 109
        }, 
        {
            "source": "df", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>Description</th>\n      <th>Area</th>\n      <th>Population</th>\n      <th>Density</th>\n      <th>Name</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Centro storico</td>\n      <td>9.67</td>\n      <td>96,315</td>\n      <td>11,074</td>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Stazione Centrale, Gorla, Turro, Greco, Cresce...</td>\n      <td>12.58</td>\n      <td>153.109</td>\n      <td>13,031</td>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Citt\u00e0 Studi, Lambrate, Porta Venezia</td>\n      <td>14.23</td>\n      <td>141,229</td>\n      <td>10,785</td>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Porta Vittoria, Forlanini</td>\n      <td>20.95</td>\n      <td>156.369</td>\n      <td>8,069</td>\n      <td>Porta vittoria</td>\n      <td>45.446205</td>\n      <td>9.239343</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Vigentino, Chiaravalle, Gratosoglio</td>\n      <td>29.87</td>\n      <td>123,779</td>\n      <td>4,487</td>\n      <td>Vigentino</td>\n      <td>45.419846</td>\n      <td>9.198153</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>Barona, Lorenteggio</td>\n      <td>18.28</td>\n      <td>149,000</td>\n      <td>8,998</td>\n      <td>Barona</td>\n      <td>45.437929</td>\n      <td>9.145648</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>Baggio, De Angeli, San Siro</td>\n      <td>31.34</td>\n      <td>170,814</td>\n      <td>6,093</td>\n      <td>Baggio</td>\n      <td>45.469598</td>\n      <td>9.114757</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>Fiera, Gallaratese, Quarto Oggiaro</td>\n      <td>23.72</td>\n      <td>181,669</td>\n      <td>8,326</td>\n      <td>Fiera</td>\n      <td>45.500015</td>\n      <td>9.122774</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>Porta Garibaldi, Niguarda</td>\n      <td>21.12</td>\n      <td>181,598</td>\n      <td>9,204</td>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Id                                        Description   Area Population  \\\n0  1                                     Centro storico   9.67     96,315   \n1  2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58    153.109   \n2  3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23    141,229   \n3  4                          Porta Vittoria, Forlanini  20.95    156.369   \n4  5                Vigentino, Chiaravalle, Gratosoglio  29.87    123,779   \n5  6                                Barona, Lorenteggio  18.28    149,000   \n6  7                        Baggio, De Angeli, San Siro  31.34    170,814   \n7  8                 Fiera, Gallaratese, Quarto Oggiaro  23.72    181,669   \n8  9                          Porta Garibaldi, Niguarda  21.12    181,598   \n\n  Density               Name   Latitude  Longitude  \n0  11,074     Centro storico  45.467281   9.185962  \n1  13,031  Stazione centrale  45.499990   9.218999  \n2  10,785        Citt\u00e0 studi  45.489799   9.241031  \n3   8,069     Porta vittoria  45.446205   9.239343  \n4   4,487          Vigentino  45.419846   9.198153  \n5   8,998             Barona  45.437929   9.145648  \n6   6,093             Baggio  45.469598   9.114757  \n7   8,326              Fiera  45.500015   9.122774  \n8   9,204    Porta garibaldi  45.507704   9.179410  "
                    }, 
                    "execution_count": 110, 
                    "metadata": {}
                }
            ], 
            "execution_count": 110
        }, 
        {
            "source": "df2 = df.loc[df['Name'].isin([\"Centro storico\", \"Stazione centrale\", \"Citt\u00e0 studi\", \"Porta garibaldi\"])]", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 111
        }, 
        {
            "source": "df2 = df2.reset_index(drop=True)\ndf2", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>Description</th>\n      <th>Area</th>\n      <th>Population</th>\n      <th>Density</th>\n      <th>Name</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Centro storico</td>\n      <td>9.67</td>\n      <td>96,315</td>\n      <td>11,074</td>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Stazione Centrale, Gorla, Turro, Greco, Cresce...</td>\n      <td>12.58</td>\n      <td>153.109</td>\n      <td>13,031</td>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Citt\u00e0 Studi, Lambrate, Porta Venezia</td>\n      <td>14.23</td>\n      <td>141,229</td>\n      <td>10,785</td>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9</td>\n      <td>Porta Garibaldi, Niguarda</td>\n      <td>21.12</td>\n      <td>181,598</td>\n      <td>9,204</td>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Id                                        Description   Area Population  \\\n0  1                                     Centro storico   9.67     96,315   \n1  2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58    153.109   \n2  3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23    141,229   \n3  9                          Porta Garibaldi, Niguarda  21.12    181,598   \n\n  Density               Name   Latitude  Longitude  \n0  11,074     Centro storico  45.467281   9.185962  \n1  13,031  Stazione centrale  45.499990   9.218999  \n2  10,785        Citt\u00e0 studi  45.489799   9.241031  \n3   9,204    Porta garibaldi  45.507704   9.179410  "
                    }, 
                    "execution_count": 112, 
                    "metadata": {}
                }
            ], 
            "execution_count": 112
        }, 
        {
            "source": "### Get average of property price in major municipalities", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "We get data from page \"Mercato Immobiliare a Milano\" by using web scraping technique.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "response2 = requests.get('https://www.mercato-immobiliare.info/lombardia/milano/milano.html').text\n#soup2 = BeautifulSoup(response2,'lxml')\nsoup2 = BeautifulSoup(response2, 'html.parser')\ntable2 = soup2.find('table', {'id':'childrentable'})", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 113
        }, 
        {
            "source": "table_rows2 = table2.find_all('tr')\n\nres = []\nfor tr in table_rows2:\n    td = tr.find_all('td')\n    row = [tr.text.strip() for tr in td if tr.text.strip()]\n    if row:\n        res.append(row)\n\n\ndf_price = pd.DataFrame(res, columns=[\"Name\", \"Price\",\"Link\"])\ndf_price = df_price.drop(columns=['Link'])", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 114
        }, 
        {
            "source": "df_price['Name'] = df_price.Name.str.capitalize() ", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 115
        }, 
        {
            "source": "df_price", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Name</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adriano</td>\n      <td>\u20ac 2.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Affori</td>\n      <td>\u20ac 2.250 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Baggio</td>\n      <td>\u20ac 2.250 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Barona</td>\n      <td>\u20ac 2.850 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bicocca</td>\n      <td>\u20ac 2.700 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Bocconi</td>\n      <td>\u20ac 4.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Bovisa</td>\n      <td>\u20ac 2.350 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Bovisasca</td>\n      <td>\u20ac 1.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Brera</td>\n      <td>\u20ac 7.000 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Bruzzano</td>\n      <td>\u20ac 2.000 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Cadorna</td>\n      <td>\u20ac 6.800 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Casoretto</td>\n      <td>\u20ac 3.000 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Centro direzionale</td>\n      <td>\u20ac 5.500 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Centro storico</td>\n      <td>\u20ac 6.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Chiaravalle milanese</td>\n      <td>\u20ac 2.150 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Chiesa rossa</td>\n      <td>\u20ac 2.650 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Cinque giornate</td>\n      <td>\u20ac 5.800 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Citt\u00e0 studi</td>\n      <td>\u20ac 3.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Comasina</td>\n      <td>\u20ac 1.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Corvetto</td>\n      <td>\u20ac 2.800 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Crescenzago</td>\n      <td>\u20ac 2.300 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Dergano</td>\n      <td>\u20ac 2.500 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Famagosta</td>\n      <td>\u20ac 3.100 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Feltre</td>\n      <td>\u20ac 2.900 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Figino</td>\n      <td>\u20ac 2.150 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Forlanini</td>\n      <td>\u20ac 2.750 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Forze armate</td>\n      <td>\u20ac 2.500 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Gallaratese</td>\n      <td>\u20ac 2.300 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Garegnano</td>\n      <td>\u20ac 2.150 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Gorla</td>\n      <td>\u20ac 2.400 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>Porta garibaldi</td>\n      <td>\u20ac 5.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>Porta genova</td>\n      <td>\u20ac 5.650 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>Porta nuova</td>\n      <td>\u20ac 6.100 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>Porta romana</td>\n      <td>\u20ac 5.250 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>Porta ticinese</td>\n      <td>\u20ac 5.600 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>Porta venezia</td>\n      <td>\u20ac 6.000 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>Porta vigentina</td>\n      <td>\u20ac 5.500 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Porta vittoria</td>\n      <td>\u20ac 5.850 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>Porta volta</td>\n      <td>\u20ac 5.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>Precotto</td>\n      <td>\u20ac 2.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>Qt8</td>\n      <td>\u20ac 3.200 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>Quartiere degli olmi</td>\n      <td>\u20ac 1.850 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Quarto cagnino</td>\n      <td>\u20ac 2.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>Quarto oggiaro</td>\n      <td>\u20ac 1.600 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>Quinto romano</td>\n      <td>\u20ac 2.150 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>Quintosole</td>\n      <td>\u20ac 1.900 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>Rembrandt</td>\n      <td>\u20ac 3.450 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>Rogoredo</td>\n      <td>\u20ac 2.500 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>San cristoforo</td>\n      <td>\u20ac 3.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>San siro</td>\n      <td>\u20ac 3.150 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>San vittore</td>\n      <td>\u20ac 6.300 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>Sant'ambrogio</td>\n      <td>\u20ac 6.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>Sempione</td>\n      <td>\u20ac 5.700 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>Solari</td>\n      <td>\u20ac 5.100 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>Stazione centrale</td>\n      <td>\u20ac 4.600 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>Trenno</td>\n      <td>\u20ac 2.750 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Turro</td>\n      <td>\u20ac 2.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>Vigentino</td>\n      <td>\u20ac 2.850 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>Villapizzone</td>\n      <td>\u20ac 2.300 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>Xxii marzo</td>\n      <td>\u20ac 5.000 /m\u00b2</td>\n    </tr>\n  </tbody>\n</table>\n<p>78 rows \u00d7 2 columns</p>\n</div>", 
                        "text/plain": "                    Name        Price\n0                Adriano  \u20ac 2.550 /m\u00b2\n1                 Affori  \u20ac 2.250 /m\u00b2\n2                 Baggio  \u20ac 2.250 /m\u00b2\n3                 Barona  \u20ac 2.850 /m\u00b2\n4                Bicocca  \u20ac 2.700 /m\u00b2\n5                Bocconi  \u20ac 4.950 /m\u00b2\n6                 Bovisa  \u20ac 2.350 /m\u00b2\n7              Bovisasca  \u20ac 1.950 /m\u00b2\n8                  Brera  \u20ac 7.000 /m\u00b2\n9               Bruzzano  \u20ac 2.000 /m\u00b2\n10               Cadorna  \u20ac 6.800 /m\u00b2\n11             Casoretto  \u20ac 3.000 /m\u00b2\n12    Centro direzionale  \u20ac 5.500 /m\u00b2\n13        Centro storico  \u20ac 6.550 /m\u00b2\n14  Chiaravalle milanese  \u20ac 2.150 /m\u00b2\n15          Chiesa rossa  \u20ac 2.650 /m\u00b2\n16       Cinque giornate  \u20ac 5.800 /m\u00b2\n17           Citt\u00e0 studi  \u20ac 3.950 /m\u00b2\n18              Comasina  \u20ac 1.950 /m\u00b2\n19              Corvetto  \u20ac 2.800 /m\u00b2\n20           Crescenzago  \u20ac 2.300 /m\u00b2\n21               Dergano  \u20ac 2.500 /m\u00b2\n22             Famagosta  \u20ac 3.100 /m\u00b2\n23                Feltre  \u20ac 2.900 /m\u00b2\n24                Figino  \u20ac 2.150 /m\u00b2\n25             Forlanini  \u20ac 2.750 /m\u00b2\n26          Forze armate  \u20ac 2.500 /m\u00b2\n27           Gallaratese  \u20ac 2.300 /m\u00b2\n28             Garegnano  \u20ac 2.150 /m\u00b2\n29                 Gorla  \u20ac 2.400 /m\u00b2\n..                   ...          ...\n48       Porta garibaldi  \u20ac 5.550 /m\u00b2\n49          Porta genova  \u20ac 5.650 /m\u00b2\n50           Porta nuova  \u20ac 6.100 /m\u00b2\n51          Porta romana  \u20ac 5.250 /m\u00b2\n52        Porta ticinese  \u20ac 5.600 /m\u00b2\n53         Porta venezia  \u20ac 6.000 /m\u00b2\n54       Porta vigentina  \u20ac 5.500 /m\u00b2\n55        Porta vittoria  \u20ac 5.850 /m\u00b2\n56           Porta volta  \u20ac 5.950 /m\u00b2\n57              Precotto  \u20ac 2.550 /m\u00b2\n58                   Qt8  \u20ac 3.200 /m\u00b2\n59  Quartiere degli olmi  \u20ac 1.850 /m\u00b2\n60        Quarto cagnino  \u20ac 2.950 /m\u00b2\n61        Quarto oggiaro  \u20ac 1.600 /m\u00b2\n62         Quinto romano  \u20ac 2.150 /m\u00b2\n63            Quintosole  \u20ac 1.900 /m\u00b2\n64             Rembrandt  \u20ac 3.450 /m\u00b2\n65              Rogoredo  \u20ac 2.500 /m\u00b2\n66        San cristoforo  \u20ac 3.950 /m\u00b2\n67              San siro  \u20ac 3.150 /m\u00b2\n68           San vittore  \u20ac 6.300 /m\u00b2\n69         Sant'ambrogio  \u20ac 6.550 /m\u00b2\n70              Sempione  \u20ac 5.700 /m\u00b2\n71                Solari  \u20ac 5.100 /m\u00b2\n72     Stazione centrale  \u20ac 4.600 /m\u00b2\n73                Trenno  \u20ac 2.750 /m\u00b2\n74                 Turro  \u20ac 2.550 /m\u00b2\n75             Vigentino  \u20ac 2.850 /m\u00b2\n76          Villapizzone  \u20ac 2.300 /m\u00b2\n77            Xxii marzo  \u20ac 5.000 /m\u00b2\n\n[78 rows x 2 columns]"
                    }, 
                    "execution_count": 116, 
                    "metadata": {}
                }
            ], 
            "execution_count": 116
        }, 
        {
            "source": "We select only data for the major municipalities: Centro Storico, Stazione Centrale, Citt\u00e0 Studi and Porta Garibaldi.\n", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "df_price = df_price.loc[df_price['Name'].isin([\"Centro storico\", \"Stazione centrale\", \"Citt\u00e0 studi\", \"Porta garibaldi\"])]\ndf_price = df_price.reset_index(drop=True)\ndf_price", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Name</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Centro storico</td>\n      <td>\u20ac 6.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Citt\u00e0 studi</td>\n      <td>\u20ac 3.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Porta garibaldi</td>\n      <td>\u20ac 5.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Stazione centrale</td>\n      <td>\u20ac 4.600 /m\u00b2</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                Name        Price\n0     Centro storico  \u20ac 6.550 /m\u00b2\n1        Citt\u00e0 studi  \u20ac 3.950 /m\u00b2\n2    Porta garibaldi  \u20ac 5.550 /m\u00b2\n3  Stazione centrale  \u20ac 4.600 /m\u00b2"
                    }, 
                    "execution_count": 117, 
                    "metadata": {}
                }
            ], 
            "execution_count": 117
        }, 
        {
            "source": "### Join the datasets", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "df_top4 = pd.merge(df2, df_price, on='Name', how='inner')", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 118
        }, 
        {
            "source": "df_top4", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>Description</th>\n      <th>Area</th>\n      <th>Population</th>\n      <th>Density</th>\n      <th>Name</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Centro storico</td>\n      <td>9.67</td>\n      <td>96,315</td>\n      <td>11,074</td>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>\u20ac 6.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Stazione Centrale, Gorla, Turro, Greco, Cresce...</td>\n      <td>12.58</td>\n      <td>153.109</td>\n      <td>13,031</td>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>\u20ac 4.600 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Citt\u00e0 Studi, Lambrate, Porta Venezia</td>\n      <td>14.23</td>\n      <td>141,229</td>\n      <td>10,785</td>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>\u20ac 3.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9</td>\n      <td>Porta Garibaldi, Niguarda</td>\n      <td>21.12</td>\n      <td>181,598</td>\n      <td>9,204</td>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>\u20ac 5.550 /m\u00b2</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Id                                        Description   Area Population  \\\n0  1                                     Centro storico   9.67     96,315   \n1  2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58    153.109   \n2  3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23    141,229   \n3  9                          Porta Garibaldi, Niguarda  21.12    181,598   \n\n  Density               Name   Latitude  Longitude        Price  \n0  11,074     Centro storico  45.467281   9.185962  \u20ac 6.550 /m\u00b2  \n1  13,031  Stazione centrale  45.499990   9.218999  \u20ac 4.600 /m\u00b2  \n2  10,785        Citt\u00e0 studi  45.489799   9.241031  \u20ac 3.950 /m\u00b2  \n3   9,204    Porta garibaldi  45.507704   9.179410  \u20ac 5.550 /m\u00b2  "
                    }, 
                    "execution_count": 119, 
                    "metadata": {}
                }
            ], 
            "execution_count": 119
        }, 
        {
            "source": "## Conclusion\n### Part 1: Description of Problem and Data Preparation\n\nWe get the Initial Data-Frame with Names of Major Municipalities, and corresponding coordinates of those major districts and average proerty price. Before comparing all the municipalities, since we want to concentrate only on lunch coffee shops targeting the office workers, we need to get the idea about the best business areas in Milan. \nHere we want to concentrate on the best four boroughs:\n\n    Centro storico\n    Stazione Centrale\n    Citt\u00e0 Studi\n    Porta Garibaldi\n    \nSo as the next step we will use Foursquare data and obtain information on coffee shops. With these, we can start with our battle of neighborhoods for opening a coffee shop in Milan.\n", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "# get Milan Latitude and Longitude \n\nfrom geopy.geocoders import Nominatim # convert an address into latitude and longitude values\naddress = 'Milan, MI, Lom, Italy'\n\ngeolocator = Nominatim(user_agent=\"Milan_explorer\")\nlocation = geolocator.geocode(address)\nlatitude = location.latitude\nlongitude = location.longitude\nprint('The geograpical coordinates of Milan are {}, {}.'.format(latitude, longitude))", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "The geograpical coordinates of Milan are 45.4667971, 9.1904984.\n"
                }
            ], 
            "execution_count": 120
        }, 
        {
            "source": "!pip install folium", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Requirement already satisfied: folium in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (0.9.1)\nRequirement already satisfied: numpy in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium) (1.13.3)\nRequirement already satisfied: branca>=0.3.0 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium) (0.3.1)\nRequirement already satisfied: jinja2>=2.9 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium) (2.9.6)\nRequirement already satisfied: requests in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium) (2.18.4)\nRequirement already satisfied: six in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from branca>=0.3.0->folium) (1.11.0)\nRequirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from jinja2>=2.9->folium) (1.0)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium) (3.0.4)\nRequirement already satisfied: idna<2.7,>=2.5 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium) (2.6)\nRequirement already satisfied: urllib3<1.23,>=1.21.1 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium) (1.22)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium) (2019.3.9)\n\u001b[31mtensorflow 1.3.0 requires tensorflow-tensorboard<0.2.0,>=0.1.0, which is not installed.\u001b[0m\n"
                }
            ], 
            "execution_count": 121
        }, 
        {
            "source": "import folium\n# create map of busiest office areas in Milan using latitude and longitude values\n# default map using Milan coordinates\nMilanTop4 = folium.Map(location=[latitude, longitude], zoom_start=12)\n\n# add markers to map for each municipality\nfor lat, lng, label in zip(df3['Latitude'], df3['Longitude'], df3['Shortname']):\n    label = folium.Popup(label, parse_html=True)\n    folium.CircleMarker(\n        [lat, lng],\n        radius=9,\n        popup=label,\n        color='magenta',\n        fill=True,\n        fill_color='#3186cc',\n        fill_opacity=0.7).add_to(MilanTop4)  \n\nMilanTop4\n", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_044b80b1f0274e2a9a35b1598e4eb550 {
                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_044b80b1f0274e2a9a35b1598e4eb550" ></div>
        
</body>
<script>    
    
            var map_044b80b1f0274e2a9a35b1598e4eb550 = L.map(
                "map_044b80b1f0274e2a9a35b1598e4eb550",
                {
                    center: [45.4667971, 9.1904984],
                    crs: L.CRS.EPSG3857,
                    preferCanvas: false,
                    zoomControl: true,
                    zoom: 12,
                }
            );

            

        
    
            var tile_layer_4ee1ab47b7814638ab012ff2992f483a = L.tileLayer(
                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_044b80b1f0274e2a9a35b1598e4eb550);
        
    
            var circle_marker_2df084c3ee8a42b091aafabab30d182a = L.circleMarker(
                [45.4672806, 9.18596201012092],
                {"bubblingMouseEvents": true, "color": "magenta", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 9, "stroke": true, "weight": 3}
            ).addTo(map_044b80b1f0274e2a9a35b1598e4eb550);
        
    
        var popup_2ae0bc65956a47629e82133be43c6d30 = L.popup({"maxWidth": "100%"});

        
            var html_d5c3296243e84e558310bb9dba7b88f0 = $(`<div id="html_d5c3296243e84e558310bb9dba7b88f0" style="width: 100.0%; height: 100.0%;">Centro storico</div>`)[0];
            popup_2ae0bc65956a47629e82133be43c6d30.setContent(html_d5c3296243e84e558310bb9dba7b88f0);
        

        circle_marker_2df084c3ee8a42b091aafabab30d182a.bindPopup(popup_2ae0bc65956a47629e82133be43c6d30)
        ;

        
    
    
            var circle_marker_f53c5810056e40bc8e37a81cade8211c = L.circleMarker(
                [45.4999899, 9.21899862605208],
                {"bubblingMouseEvents": true, "color": "magenta", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 9, "stroke": true, "weight": 3}
            ).addTo(map_044b80b1f0274e2a9a35b1598e4eb550);
        
    
        var popup_ee86f9fb64dc4e27aa618adc0ff1af10 = L.popup({"maxWidth": "100%"});

        
            var html_2e47877e0aec44f7be4718a8259b02c9 = $(`<div id="html_2e47877e0aec44f7be4718a8259b02c9" style="width: 100.0%; height: 100.0%;">Stazione centrale</div>`)[0];
            popup_ee86f9fb64dc4e27aa618adc0ff1af10.setContent(html_2e47877e0aec44f7be4718a8259b02c9);
        

        circle_marker_f53c5810056e40bc8e37a81cade8211c.bindPopup(popup_ee86f9fb64dc4e27aa618adc0ff1af10)
        ;

        
    
    
            var circle_marker_57f4fb0c263a4955b312ee3b757298cc = L.circleMarker(
                [45.4897995, 9.24103088613553],
                {"bubblingMouseEvents": true, "color": "magenta", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 9, "stroke": true, "weight": 3}
            ).addTo(map_044b80b1f0274e2a9a35b1598e4eb550);
        
    
        var popup_a62c8779d5c34ebdafdc8d6d2fcc168a = L.popup({"maxWidth": "100%"});

        
            var html_e523dd4fda4f4f16b7c470f07f073b55 = $(`<div id="html_e523dd4fda4f4f16b7c470f07f073b55" style="width: 100.0%; height: 100.0%;">Città studi</div>`)[0];
            popup_a62c8779d5c34ebdafdc8d6d2fcc168a.setContent(html_e523dd4fda4f4f16b7c470f07f073b55);
        

        circle_marker_57f4fb0c263a4955b312ee3b757298cc.bindPopup(popup_a62c8779d5c34ebdafdc8d6d2fcc168a)
        ;

        
    
    
            var circle_marker_53c10a0a6fdd49d2864280223125580f = L.circleMarker(
                [45.5077041, 9.17941004494827],
                {"bubblingMouseEvents": true, "color": "magenta", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 9, "stroke": true, "weight": 3}
            ).addTo(map_044b80b1f0274e2a9a35b1598e4eb550);
        
    
        var popup_8c22a6acb7d046d6a682b1d1c808e3b2 = L.popup({"maxWidth": "100%"});

        
            var html_b3e9139af874429487f8e2bf13ca7b20 = $(`<div id="html_b3e9139af874429487f8e2bf13ca7b20" style="width: 100.0%; height: 100.0%;">Porta garibaldi</div>`)[0];
            popup_8c22a6acb7d046d6a682b1d1c808e3b2.setContent(html_b3e9139af874429487f8e2bf13ca7b20);
        

        circle_marker_53c10a0a6fdd49d2864280223125580f.bindPopup(popup_8c22a6acb7d046d6a682b1d1c808e3b2)
        ;

        
    
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", 
                        "text/plain": "<folium.folium.Map at 0x7f643f23e940>"
                    }, 
                    "execution_count": 122, 
                    "metadata": {}
                }
            ], 
            "execution_count": 122
        }, 
        {
            "source": "## Access Foursquare API", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "CLIENT_ID = 'ALFCXANCSQUMZZTOMEXEUNIX4GVF5OSWGUFQE11Y3DELBJKT'\nCLIENT_SECRET = 'HDLGHOGJN2WUAQLPU1WGVWSDQP4REHQ4RWCOZAL0YAPCLAWF'\nVERSION = '20190325'", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 123
        }, 
        {
            "source": "### Exploring the Major Districts\n\n1. Create the get request url (using Foursquare ID and Secret) and get 100 venues in the radious of 2 KM\n2. Create a json from the request object (Need requests Module) \n3. Create the lists Containing all the information \n4. From the lists create the dataframe.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "radius = 2500\nLIMIT = 500\n\ndef getNearbyVenues(names, latitudes, longitudes, radius=2500):\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                  'Dist_Latitude', \n                  'Dist_Longitude', \n                  'Venue', \n                  'Venue_Lat', \n                  'Venue_Long', \n                  'Venue_Category']\n    \n    return(nearby_venues)\n", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 124
        }, 
        {
            "source": "MilanTop4_Venues = getNearbyVenues(names=df_top4['Name'],\n                                   latitudes=df_top4['Latitude'],\n                                   longitudes=df_top4['Longitude']\n                                  )", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Centro storico\nStazione centrale\nCitt\u00e0 studi\nPorta garibaldi\n"
                }
            ], 
            "execution_count": 125
        }, 
        {
            "source": "print (\"Shape of the Venues Dataframe: \", MilanTop4_Venues.shape)\nMilanTop4_Venues.tail(100)\n", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Shape of the Venues Dataframe:  (400, 7)\n"
                }, 
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>District</th>\n      <th>Dist_Latitude</th>\n      <th>Dist_Longitude</th>\n      <th>Venue</th>\n      <th>Venue_Lat</th>\n      <th>Venue_Long</th>\n      <th>Venue_Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>300</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Virgin Active</td>\n      <td>45.502018</td>\n      <td>9.182590</td>\n      <td>Gym / Fitness Center</td>\n    </tr>\n    <tr>\n      <th>301</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Al Paradiso Della Pizza</td>\n      <td>45.511351</td>\n      <td>9.175416</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>302</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Birrificio La Ribalta</td>\n      <td>45.507038</td>\n      <td>9.173219</td>\n      <td>Brewery</td>\n    </tr>\n    <tr>\n      <th>303</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Istanbul  Kebab</td>\n      <td>45.510421</td>\n      <td>9.176128</td>\n      <td>Kebab Restaurant</td>\n    </tr>\n    <tr>\n      <th>304</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Esselunga</td>\n      <td>45.512380</td>\n      <td>9.173461</td>\n      <td>Supermarket</td>\n    </tr>\n    <tr>\n      <th>305</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Total Natural Training</td>\n      <td>45.509142</td>\n      <td>9.194221</td>\n      <td>Gym</td>\n    </tr>\n    <tr>\n      <th>306</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Spirit de Milan</td>\n      <td>45.506678</td>\n      <td>9.159744</td>\n      <td>Ballroom</td>\n    </tr>\n    <tr>\n      <th>307</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Il Bucatino con Giardino</td>\n      <td>45.502088</td>\n      <td>9.165959</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>308</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Sushi Ran</td>\n      <td>45.500348</td>\n      <td>9.170942</td>\n      <td>Japanese Restaurant</td>\n    </tr>\n    <tr>\n      <th>309</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Hotel La Residenza</td>\n      <td>45.512413</td>\n      <td>9.178770</td>\n      <td>Hotel</td>\n    </tr>\n    <tr>\n      <th>310</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Piazza Dergano</td>\n      <td>45.504034</td>\n      <td>9.175839</td>\n      <td>Plaza</td>\n    </tr>\n    <tr>\n      <th>311</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Palestra McFIT</td>\n      <td>45.504708</td>\n      <td>9.198828</td>\n      <td>Gym</td>\n    </tr>\n    <tr>\n      <th>312</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Biologic Bar &amp; Restaurant</td>\n      <td>45.512302</td>\n      <td>9.178709</td>\n      <td>Hotel Bar</td>\n    </tr>\n    <tr>\n      <th>313</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>MiSciolgo</td>\n      <td>45.499214</td>\n      <td>9.165308</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>314</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Teatro della Cooperativa</td>\n      <td>45.515042</td>\n      <td>9.190504</td>\n      <td>Theater</td>\n    </tr>\n    <tr>\n      <th>315</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>La Gelateria di Dergano</td>\n      <td>45.502240</td>\n      <td>9.176052</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>316</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Ristorante Siciliano - Franca e Lillo</td>\n      <td>45.499927</td>\n      <td>9.168322</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>317</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Da Cri</td>\n      <td>45.502836</td>\n      <td>9.176668</td>\n      <td>Food</td>\n    </tr>\n    <tr>\n      <th>318</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Parco di Villa Litta</td>\n      <td>45.516414</td>\n      <td>9.167165</td>\n      <td>Park</td>\n    </tr>\n    <tr>\n      <th>319</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>S.P.I.B. Pizza</td>\n      <td>45.499981</td>\n      <td>9.181465</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>320</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>La Caveja</td>\n      <td>45.503095</td>\n      <td>9.161968</td>\n      <td>Piadineria</td>\n    </tr>\n    <tr>\n      <th>321</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>SEF</td>\n      <td>45.503223</td>\n      <td>9.162137</td>\n      <td>Paper / Office Supplies Store</td>\n    </tr>\n    <tr>\n      <th>322</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Bricocenter</td>\n      <td>45.509665</td>\n      <td>9.176412</td>\n      <td>Hobby Shop</td>\n    </tr>\n    <tr>\n      <th>323</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Artico Gelateria Tradizionale</td>\n      <td>45.489179</td>\n      <td>9.186669</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>324</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>BB Hotels Residenza Bicocca</td>\n      <td>45.499852</td>\n      <td>9.198630</td>\n      <td>Bed &amp; Breakfast</td>\n    </tr>\n    <tr>\n      <th>325</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Pizzeria d'Asporto Da Nicola</td>\n      <td>45.497256</td>\n      <td>9.170545</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>326</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Deus Caf\u00e9</td>\n      <td>45.490973</td>\n      <td>9.188650</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>327</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Nisida</td>\n      <td>45.507636</td>\n      <td>9.203686</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>328</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>BeerShow</td>\n      <td>45.489050</td>\n      <td>9.189068</td>\n      <td>Pub</td>\n    </tr>\n    <tr>\n      <th>329</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Todo Modo</td>\n      <td>45.503457</td>\n      <td>9.161437</td>\n      <td>Creperie</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>370</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>La Coccinella</td>\n      <td>45.487925</td>\n      <td>9.189161</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>371</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Pizzeria alla Fontana</td>\n      <td>45.492984</td>\n      <td>9.188239</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>372</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Gli Artigiani Della Pizza</td>\n      <td>45.507078</td>\n      <td>9.203834</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>373</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Pizza Club No Limits</td>\n      <td>45.500981</td>\n      <td>9.182886</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>374</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Parco Nicol\u00f2 Savarino</td>\n      <td>45.499088</td>\n      <td>9.177683</td>\n      <td>Park</td>\n    </tr>\n    <tr>\n      <th>375</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Scalo Farini</td>\n      <td>45.488660</td>\n      <td>9.182737</td>\n      <td>Pub</td>\n    </tr>\n    <tr>\n      <th>376</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Frida</td>\n      <td>45.488718</td>\n      <td>9.187129</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>377</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Taj Mahal</td>\n      <td>45.489092</td>\n      <td>9.188089</td>\n      <td>Indian Restaurant</td>\n    </tr>\n    <tr>\n      <th>378</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Kome Fusion</td>\n      <td>45.489565</td>\n      <td>9.163648</td>\n      <td>Japanese Restaurant</td>\n    </tr>\n    <tr>\n      <th>379</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Garbagnati</td>\n      <td>45.499662</td>\n      <td>9.195031</td>\n      <td>Bakery</td>\n    </tr>\n    <tr>\n      <th>380</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Pizzeria Astesani</td>\n      <td>45.516140</td>\n      <td>9.172112</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>381</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Trattoria Laravaelafava</td>\n      <td>45.489979</td>\n      <td>9.164766</td>\n      <td>Trattoria/Osteria</td>\n    </tr>\n    <tr>\n      <th>382</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>GetFIT</td>\n      <td>45.488056</td>\n      <td>9.170602</td>\n      <td>Gym / Fitness Center</td>\n    </tr>\n    <tr>\n      <th>383</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Piazza Carbonari</td>\n      <td>45.495042</td>\n      <td>9.201393</td>\n      <td>Plaza</td>\n    </tr>\n    <tr>\n      <th>384</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>MIC - Museo Interattivo del Cinema</td>\n      <td>45.513470</td>\n      <td>9.203987</td>\n      <td>Museum</td>\n    </tr>\n    <tr>\n      <th>385</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Naxos</td>\n      <td>45.502123</td>\n      <td>9.162126</td>\n      <td>Sicilian Restaurant</td>\n    </tr>\n    <tr>\n      <th>386</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Prato Centenaro</td>\n      <td>45.508644</td>\n      <td>9.200241</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>387</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Trattoria Atipica</td>\n      <td>45.492714</td>\n      <td>9.159584</td>\n      <td>Trattoria/Osteria</td>\n    </tr>\n    <tr>\n      <th>388</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Golfo Di Mondello Rosticceria</td>\n      <td>45.497257</td>\n      <td>9.191769</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>389</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Il Borghetto</td>\n      <td>45.501415</td>\n      <td>9.209785</td>\n      <td>Steakhouse</td>\n    </tr>\n    <tr>\n      <th>390</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Casa Ramen Super</td>\n      <td>45.489726</td>\n      <td>9.187352</td>\n      <td>Ramen Restaurant</td>\n    </tr>\n    <tr>\n      <th>391</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>BB Hotels Residenza Arcimboldi</td>\n      <td>45.508578</td>\n      <td>9.204992</td>\n      <td>Bed &amp; Breakfast</td>\n    </tr>\n    <tr>\n      <th>392</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Effecorta</td>\n      <td>45.517989</td>\n      <td>9.169883</td>\n      <td>Health Food Store</td>\n    </tr>\n    <tr>\n      <th>393</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Testina Milano</td>\n      <td>45.489849</td>\n      <td>9.195869</td>\n      <td>Restaurant</td>\n    </tr>\n    <tr>\n      <th>394</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>TELLER - Telecomitalia -TIM</td>\n      <td>45.497189</td>\n      <td>9.182856</td>\n      <td>Mobile Phone Shop</td>\n    </tr>\n    <tr>\n      <th>395</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Osteria dei Vecchi Sapori</td>\n      <td>45.486403</td>\n      <td>9.186558</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>396</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Rock Burger</td>\n      <td>45.491131</td>\n      <td>9.197096</td>\n      <td>Burger Joint</td>\n    </tr>\n    <tr>\n      <th>397</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Stelvio</td>\n      <td>45.487690</td>\n      <td>9.191503</td>\n      <td>Lombard Restaurant</td>\n    </tr>\n    <tr>\n      <th>398</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Les Pommes Italian Bistrot</td>\n      <td>45.486482</td>\n      <td>9.187409</td>\n      <td>Bar</td>\n    </tr>\n    <tr>\n      <th>399</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.17941</td>\n      <td>Trattoria Il Marinaio</td>\n      <td>45.485999</td>\n      <td>9.187029</td>\n      <td>Puglia Restaurant</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows \u00d7 7 columns</p>\n</div>", 
                        "text/plain": "            District  Dist_Latitude  Dist_Longitude  \\\n300  Porta garibaldi      45.507704         9.17941   \n301  Porta garibaldi      45.507704         9.17941   \n302  Porta garibaldi      45.507704         9.17941   \n303  Porta garibaldi      45.507704         9.17941   \n304  Porta garibaldi      45.507704         9.17941   \n305  Porta garibaldi      45.507704         9.17941   \n306  Porta garibaldi      45.507704         9.17941   \n307  Porta garibaldi      45.507704         9.17941   \n308  Porta garibaldi      45.507704         9.17941   \n309  Porta garibaldi      45.507704         9.17941   \n310  Porta garibaldi      45.507704         9.17941   \n311  Porta garibaldi      45.507704         9.17941   \n312  Porta garibaldi      45.507704         9.17941   \n313  Porta garibaldi      45.507704         9.17941   \n314  Porta garibaldi      45.507704         9.17941   \n315  Porta garibaldi      45.507704         9.17941   \n316  Porta garibaldi      45.507704         9.17941   \n317  Porta garibaldi      45.507704         9.17941   \n318  Porta garibaldi      45.507704         9.17941   \n319  Porta garibaldi      45.507704         9.17941   \n320  Porta garibaldi      45.507704         9.17941   \n321  Porta garibaldi      45.507704         9.17941   \n322  Porta garibaldi      45.507704         9.17941   \n323  Porta garibaldi      45.507704         9.17941   \n324  Porta garibaldi      45.507704         9.17941   \n325  Porta garibaldi      45.507704         9.17941   \n326  Porta garibaldi      45.507704         9.17941   \n327  Porta garibaldi      45.507704         9.17941   \n328  Porta garibaldi      45.507704         9.17941   \n329  Porta garibaldi      45.507704         9.17941   \n..               ...            ...             ...   \n370  Porta garibaldi      45.507704         9.17941   \n371  Porta garibaldi      45.507704         9.17941   \n372  Porta garibaldi      45.507704         9.17941   \n373  Porta garibaldi      45.507704         9.17941   \n374  Porta garibaldi      45.507704         9.17941   \n375  Porta garibaldi      45.507704         9.17941   \n376  Porta garibaldi      45.507704         9.17941   \n377  Porta garibaldi      45.507704         9.17941   \n378  Porta garibaldi      45.507704         9.17941   \n379  Porta garibaldi      45.507704         9.17941   \n380  Porta garibaldi      45.507704         9.17941   \n381  Porta garibaldi      45.507704         9.17941   \n382  Porta garibaldi      45.507704         9.17941   \n383  Porta garibaldi      45.507704         9.17941   \n384  Porta garibaldi      45.507704         9.17941   \n385  Porta garibaldi      45.507704         9.17941   \n386  Porta garibaldi      45.507704         9.17941   \n387  Porta garibaldi      45.507704         9.17941   \n388  Porta garibaldi      45.507704         9.17941   \n389  Porta garibaldi      45.507704         9.17941   \n390  Porta garibaldi      45.507704         9.17941   \n391  Porta garibaldi      45.507704         9.17941   \n392  Porta garibaldi      45.507704         9.17941   \n393  Porta garibaldi      45.507704         9.17941   \n394  Porta garibaldi      45.507704         9.17941   \n395  Porta garibaldi      45.507704         9.17941   \n396  Porta garibaldi      45.507704         9.17941   \n397  Porta garibaldi      45.507704         9.17941   \n398  Porta garibaldi      45.507704         9.17941   \n399  Porta garibaldi      45.507704         9.17941   \n\n                                     Venue  Venue_Lat  Venue_Long  \\\n300                          Virgin Active  45.502018    9.182590   \n301                Al Paradiso Della Pizza  45.511351    9.175416   \n302                  Birrificio La Ribalta  45.507038    9.173219   \n303                        Istanbul  Kebab  45.510421    9.176128   \n304                              Esselunga  45.512380    9.173461   \n305                 Total Natural Training  45.509142    9.194221   \n306                        Spirit de Milan  45.506678    9.159744   \n307               Il Bucatino con Giardino  45.502088    9.165959   \n308                              Sushi Ran  45.500348    9.170942   \n309                     Hotel La Residenza  45.512413    9.178770   \n310                         Piazza Dergano  45.504034    9.175839   \n311                         Palestra McFIT  45.504708    9.198828   \n312              Biologic Bar & Restaurant  45.512302    9.178709   \n313                              MiSciolgo  45.499214    9.165308   \n314               Teatro della Cooperativa  45.515042    9.190504   \n315                La Gelateria di Dergano  45.502240    9.176052   \n316  Ristorante Siciliano - Franca e Lillo  45.499927    9.168322   \n317                                 Da Cri  45.502836    9.176668   \n318                   Parco di Villa Litta  45.516414    9.167165   \n319                         S.P.I.B. Pizza  45.499981    9.181465   \n320                              La Caveja  45.503095    9.161968   \n321                                    SEF  45.503223    9.162137   \n322                            Bricocenter  45.509665    9.176412   \n323          Artico Gelateria Tradizionale  45.489179    9.186669   \n324            BB Hotels Residenza Bicocca  45.499852    9.198630   \n325           Pizzeria d'Asporto Da Nicola  45.497256    9.170545   \n326                              Deus Caf\u00e9  45.490973    9.188650   \n327                                 Nisida  45.507636    9.203686   \n328                               BeerShow  45.489050    9.189068   \n329                              Todo Modo  45.503457    9.161437   \n..                                     ...        ...         ...   \n370                          La Coccinella  45.487925    9.189161   \n371                  Pizzeria alla Fontana  45.492984    9.188239   \n372              Gli Artigiani Della Pizza  45.507078    9.203834   \n373                   Pizza Club No Limits  45.500981    9.182886   \n374                  Parco Nicol\u00f2 Savarino  45.499088    9.177683   \n375                           Scalo Farini  45.488660    9.182737   \n376                                  Frida  45.488718    9.187129   \n377                              Taj Mahal  45.489092    9.188089   \n378                            Kome Fusion  45.489565    9.163648   \n379                             Garbagnati  45.499662    9.195031   \n380                      Pizzeria Astesani  45.516140    9.172112   \n381                Trattoria Laravaelafava  45.489979    9.164766   \n382                                 GetFIT  45.488056    9.170602   \n383                       Piazza Carbonari  45.495042    9.201393   \n384     MIC - Museo Interattivo del Cinema  45.513470    9.203987   \n385                                  Naxos  45.502123    9.162126   \n386                        Prato Centenaro  45.508644    9.200241   \n387                      Trattoria Atipica  45.492714    9.159584   \n388          Golfo Di Mondello Rosticceria  45.497257    9.191769   \n389                           Il Borghetto  45.501415    9.209785   \n390                       Casa Ramen Super  45.489726    9.187352   \n391         BB Hotels Residenza Arcimboldi  45.508578    9.204992   \n392                              Effecorta  45.517989    9.169883   \n393                         Testina Milano  45.489849    9.195869   \n394            TELLER - Telecomitalia -TIM  45.497189    9.182856   \n395              Osteria dei Vecchi Sapori  45.486403    9.186558   \n396                            Rock Burger  45.491131    9.197096   \n397                                Stelvio  45.487690    9.191503   \n398             Les Pommes Italian Bistrot  45.486482    9.187409   \n399                  Trattoria Il Marinaio  45.485999    9.187029   \n\n                    Venue_Category  \n300           Gym / Fitness Center  \n301                    Pizza Place  \n302                        Brewery  \n303               Kebab Restaurant  \n304                    Supermarket  \n305                            Gym  \n306                       Ballroom  \n307             Italian Restaurant  \n308            Japanese Restaurant  \n309                          Hotel  \n310                          Plaza  \n311                            Gym  \n312                      Hotel Bar  \n313                 Ice Cream Shop  \n314                        Theater  \n315                 Ice Cream Shop  \n316             Italian Restaurant  \n317                           Food  \n318                           Park  \n319                    Pizza Place  \n320                     Piadineria  \n321  Paper / Office Supplies Store  \n322                     Hobby Shop  \n323                 Ice Cream Shop  \n324                Bed & Breakfast  \n325                    Pizza Place  \n326                           Caf\u00e9  \n327                    Pizza Place  \n328                            Pub  \n329                       Creperie  \n..                             ...  \n370                    Pizza Place  \n371             Italian Restaurant  \n372                    Pizza Place  \n373                    Pizza Place  \n374                           Park  \n375                            Pub  \n376                   Cocktail Bar  \n377              Indian Restaurant  \n378            Japanese Restaurant  \n379                         Bakery  \n380                    Pizza Place  \n381              Trattoria/Osteria  \n382           Gym / Fitness Center  \n383                          Plaza  \n384                         Museum  \n385            Sicilian Restaurant  \n386                    Pizza Place  \n387              Trattoria/Osteria  \n388             Italian Restaurant  \n389                     Steakhouse  \n390               Ramen Restaurant  \n391                Bed & Breakfast  \n392              Health Food Store  \n393                     Restaurant  \n394              Mobile Phone Shop  \n395             Italian Restaurant  \n396                   Burger Joint  \n397             Lombard Restaurant  \n398                            Bar  \n399              Puglia Restaurant  \n\n[100 rows x 7 columns]"
                    }, 
                    "execution_count": 126, 
                    "metadata": {}
                }
            ], 
            "execution_count": 126
        }, 
        {
            "source": "print('unique categories: {}'.format(len(MilanTop4_Venues['Venue_Category'].unique())))\nprint (MilanTop4_Venues['Venue_Category'].value_counts())", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "unique categories: 109\nItalian Restaurant                          43\nPizza Place                                 26\nIce Cream Shop                              20\nHotel                                       19\nCaf\u00e9                                        12\nPlaza                                       11\nDessert Shop                                11\nChinese Restaurant                          11\nJapanese Restaurant                          9\nTrattoria/Osteria                            9\nCocktail Bar                                 9\nRestaurant                                   8\nBoutique                                     8\nBakery                                       7\nTheater                                      7\nSandwich Place                               7\nPub                                          7\nPark                                         7\nWine Bar                                     6\nSushi Restaurant                             6\nBar                                          5\nGym                                          5\nArt Gallery                                  5\nGym / Fitness Center                         5\nSeafood Restaurant                           5\nAsian Restaurant                             4\nCoffee Shop                                  4\nKorean Restaurant                            4\nBed & Breakfast                              4\nBrewery                                      4\n                                            ..\nPerforming Arts Venue                        1\nScenic Lookout                               1\nFast Food Restaurant                         1\nMusic Venue                                  1\nMobile Phone Shop                            1\nChurch                                       1\nCastle                                       1\nCollege Gym                                  1\nPaper / Office Supplies Store                1\nSoccer Field                                 1\nCafeteria                                    1\nRoad                                         1\nMartial Arts Dojo                            1\nCemetery                                     1\nMediterranean Restaurant                     1\nOpera House                                  1\nWomen's Store                                1\nSports Club                                  1\nDepartment Store                             1\nCreperie                                     1\nRock Club                                    1\nEvent Space                                  1\nJewelry Store                                1\nButcher                                      1\nSports Bar                                   1\nAccessories Store                            1\nFurniture / Home Store                       1\nDiner                                        1\nPuglia Restaurant                            1\nResidential Building (Apartment / Condo)     1\nName: Venue_Category, Length: 109, dtype: int64\n"
                }
            ], 
            "execution_count": 127
        }, 
        {
            "source": "Now we select only the venies categories which are similar and related to a coffee shop. So the select only categories containing the string \"Cof\", \"Caf\", \"Bar\".", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "# Create a Data-Frame out of it to Concentrate Only on Coffee Shops \nMilanTop4_Cafe = MilanTop4_Venues[MilanTop4_Venues['Venue_Category'].str.contains('Caf|Cof|Bar')].reset_index(drop=True)\nMilanTop4_Cafe.index = np.arange(1, len(MilanTop4_Cafe)+1)\nprint ('Shape of the Data-Frame with Venue Category Cafe: ', MilanTop4_Cafe.shape)\nMilanTop4_Cafe.head(100)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Shape of the Data-Frame with Venue Category Cafe:  (45, 7)\n"
                }, 
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>District</th>\n      <th>Dist_Latitude</th>\n      <th>Dist_Longitude</th>\n      <th>Venue</th>\n      <th>Venue_Lat</th>\n      <th>Venue_Long</th>\n      <th>Venue_Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Starbucks Reserve Roastery</td>\n      <td>45.464920</td>\n      <td>9.186153</td>\n      <td>Coffee Shop</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Signorvino</td>\n      <td>45.467243</td>\n      <td>9.183567</td>\n      <td>Wine Bar</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Bulgari Lounge Bar</td>\n      <td>45.470014</td>\n      <td>9.188943</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Lavazza Coffee Design</td>\n      <td>45.466274</td>\n      <td>9.190975</td>\n      <td>Coffee Shop</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>B Caf\u00e9</td>\n      <td>45.462640</td>\n      <td>9.183381</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Signorvino</td>\n      <td>45.464552</td>\n      <td>9.192885</td>\n      <td>Wine Bar</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Armani Bamboo Bar</td>\n      <td>45.470483</td>\n      <td>9.192785</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Chiosco Mentana</td>\n      <td>45.461765</td>\n      <td>9.181841</td>\n      <td>Bar</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>N'Ombra de Vin</td>\n      <td>45.473452</td>\n      <td>9.187873</td>\n      <td>Wine Bar</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Birrificio Italiano</td>\n      <td>45.488232</td>\n      <td>9.208678</td>\n      <td>Beer Bar</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>NoLoSo</td>\n      <td>45.493018</td>\n      <td>9.217877</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Company Club</td>\n      <td>45.499426</td>\n      <td>9.236614</td>\n      <td>Gay Bar</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Upcycle - Milano Bike Caf\u00e8</td>\n      <td>45.483730</td>\n      <td>9.226041</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Caf\u00e9 Copernico</td>\n      <td>45.490663</td>\n      <td>9.205095</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Clash BAR NYX</td>\n      <td>45.487034</td>\n      <td>9.201920</td>\n      <td>Bar</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Pasticceria Bar Rovida</td>\n      <td>45.483423</td>\n      <td>9.208028</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>A\u00e7a\u00ed Frullateria</td>\n      <td>45.484267</td>\n      <td>9.215842</td>\n      <td>Juice Bar</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Alvin's Bar Pasticceria</td>\n      <td>45.494939</td>\n      <td>9.207228</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Ebony bar</td>\n      <td>45.486228</td>\n      <td>9.226442</td>\n      <td>Bar</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Moscow Mule</td>\n      <td>45.486907</td>\n      <td>9.229972</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Vino Al Vino</td>\n      <td>45.481489</td>\n      <td>9.214537</td>\n      <td>Wine Bar</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>La Gintoneria di Davide</td>\n      <td>45.504550</td>\n      <td>9.208707</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>Torrefazione Pacini</td>\n      <td>45.483298</td>\n      <td>9.231442</td>\n      <td>Coffee Shop</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Torrefazione Pacini</td>\n      <td>45.483298</td>\n      <td>9.231442</td>\n      <td>Coffee Shop</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Upcycle - Milano Bike Caf\u00e8</td>\n      <td>45.483730</td>\n      <td>9.226041</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Moscow Mule</td>\n      <td>45.486907</td>\n      <td>9.229972</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Company Club</td>\n      <td>45.499426</td>\n      <td>9.236614</td>\n      <td>Gay Bar</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Il Cappuccino</td>\n      <td>45.481074</td>\n      <td>9.235862</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Ebony bar</td>\n      <td>45.486228</td>\n      <td>9.226442</td>\n      <td>Bar</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Ligera</td>\n      <td>45.498130</td>\n      <td>9.232640</td>\n      <td>Wine Bar</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>A\u00e7a\u00ed Frullateria</td>\n      <td>45.484267</td>\n      <td>9.215842</td>\n      <td>Juice Bar</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Impronta Birraia</td>\n      <td>45.470170</td>\n      <td>9.245080</td>\n      <td>Beer Bar</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Bakery Rombon</td>\n      <td>45.486255</td>\n      <td>9.239914</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Santeria</td>\n      <td>45.469750</td>\n      <td>9.234684</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>Vino Al Vino</td>\n      <td>45.481489</td>\n      <td>9.214537</td>\n      <td>Wine Bar</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>Biologic Bar &amp; Restaurant</td>\n      <td>45.512302</td>\n      <td>9.178709</td>\n      <td>Hotel Bar</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>Deus Caf\u00e9</td>\n      <td>45.490973</td>\n      <td>9.188650</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>MOMO</td>\n      <td>45.487785</td>\n      <td>9.188689</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>7's Gi\u00f2 Farini</td>\n      <td>45.494232</td>\n      <td>9.185227</td>\n      <td>Cafeteria</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>Francesino caff\u00e8</td>\n      <td>45.501907</td>\n      <td>9.166499</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>Tre Chicchere</td>\n      <td>45.491874</td>\n      <td>9.186870</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>Offside Sports Pub</td>\n      <td>45.487450</td>\n      <td>9.165725</td>\n      <td>Sports Bar</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>Vinyl</td>\n      <td>45.487858</td>\n      <td>9.187334</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>Frida</td>\n      <td>45.488718</td>\n      <td>9.187129</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>Les Pommes Italian Bistrot</td>\n      <td>45.486482</td>\n      <td>9.187409</td>\n      <td>Bar</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "             District  Dist_Latitude  Dist_Longitude  \\\n1      Centro storico      45.467281        9.185962   \n2      Centro storico      45.467281        9.185962   \n3      Centro storico      45.467281        9.185962   \n4      Centro storico      45.467281        9.185962   \n5      Centro storico      45.467281        9.185962   \n6      Centro storico      45.467281        9.185962   \n7      Centro storico      45.467281        9.185962   \n8      Centro storico      45.467281        9.185962   \n9      Centro storico      45.467281        9.185962   \n10  Stazione centrale      45.499990        9.218999   \n11  Stazione centrale      45.499990        9.218999   \n12  Stazione centrale      45.499990        9.218999   \n13  Stazione centrale      45.499990        9.218999   \n14  Stazione centrale      45.499990        9.218999   \n15  Stazione centrale      45.499990        9.218999   \n16  Stazione centrale      45.499990        9.218999   \n17  Stazione centrale      45.499990        9.218999   \n18  Stazione centrale      45.499990        9.218999   \n19  Stazione centrale      45.499990        9.218999   \n20  Stazione centrale      45.499990        9.218999   \n21  Stazione centrale      45.499990        9.218999   \n22  Stazione centrale      45.499990        9.218999   \n23  Stazione centrale      45.499990        9.218999   \n24        Citt\u00e0 studi      45.489799        9.241031   \n25        Citt\u00e0 studi      45.489799        9.241031   \n26        Citt\u00e0 studi      45.489799        9.241031   \n27        Citt\u00e0 studi      45.489799        9.241031   \n28        Citt\u00e0 studi      45.489799        9.241031   \n29        Citt\u00e0 studi      45.489799        9.241031   \n30        Citt\u00e0 studi      45.489799        9.241031   \n31        Citt\u00e0 studi      45.489799        9.241031   \n32        Citt\u00e0 studi      45.489799        9.241031   \n33        Citt\u00e0 studi      45.489799        9.241031   \n34        Citt\u00e0 studi      45.489799        9.241031   \n35        Citt\u00e0 studi      45.489799        9.241031   \n36    Porta garibaldi      45.507704        9.179410   \n37    Porta garibaldi      45.507704        9.179410   \n38    Porta garibaldi      45.507704        9.179410   \n39    Porta garibaldi      45.507704        9.179410   \n40    Porta garibaldi      45.507704        9.179410   \n41    Porta garibaldi      45.507704        9.179410   \n42    Porta garibaldi      45.507704        9.179410   \n43    Porta garibaldi      45.507704        9.179410   \n44    Porta garibaldi      45.507704        9.179410   \n45    Porta garibaldi      45.507704        9.179410   \n\n                         Venue  Venue_Lat  Venue_Long Venue_Category  \n1   Starbucks Reserve Roastery  45.464920    9.186153    Coffee Shop  \n2                   Signorvino  45.467243    9.183567       Wine Bar  \n3           Bulgari Lounge Bar  45.470014    9.188943   Cocktail Bar  \n4        Lavazza Coffee Design  45.466274    9.190975    Coffee Shop  \n5                       B Caf\u00e9  45.462640    9.183381           Caf\u00e9  \n6                   Signorvino  45.464552    9.192885       Wine Bar  \n7            Armani Bamboo Bar  45.470483    9.192785   Cocktail Bar  \n8              Chiosco Mentana  45.461765    9.181841            Bar  \n9               N'Ombra de Vin  45.473452    9.187873       Wine Bar  \n10         Birrificio Italiano  45.488232    9.208678       Beer Bar  \n11                      NoLoSo  45.493018    9.217877   Cocktail Bar  \n12                Company Club  45.499426    9.236614        Gay Bar  \n13  Upcycle - Milano Bike Caf\u00e8  45.483730    9.226041           Caf\u00e9  \n14              Caf\u00e9 Copernico  45.490663    9.205095           Caf\u00e9  \n15               Clash BAR NYX  45.487034    9.201920            Bar  \n16      Pasticceria Bar Rovida  45.483423    9.208028           Caf\u00e9  \n17            A\u00e7a\u00ed Frullateria  45.484267    9.215842      Juice Bar  \n18     Alvin's Bar Pasticceria  45.494939    9.207228           Caf\u00e9  \n19                   Ebony bar  45.486228    9.226442            Bar  \n20                 Moscow Mule  45.486907    9.229972   Cocktail Bar  \n21                Vino Al Vino  45.481489    9.214537       Wine Bar  \n22     La Gintoneria di Davide  45.504550    9.208707   Cocktail Bar  \n23         Torrefazione Pacini  45.483298    9.231442    Coffee Shop  \n24         Torrefazione Pacini  45.483298    9.231442    Coffee Shop  \n25  Upcycle - Milano Bike Caf\u00e8  45.483730    9.226041           Caf\u00e9  \n26                 Moscow Mule  45.486907    9.229972   Cocktail Bar  \n27                Company Club  45.499426    9.236614        Gay Bar  \n28               Il Cappuccino  45.481074    9.235862           Caf\u00e9  \n29                   Ebony bar  45.486228    9.226442            Bar  \n30                      Ligera  45.498130    9.232640       Wine Bar  \n31            A\u00e7a\u00ed Frullateria  45.484267    9.215842      Juice Bar  \n32            Impronta Birraia  45.470170    9.245080       Beer Bar  \n33               Bakery Rombon  45.486255    9.239914           Caf\u00e9  \n34                    Santeria  45.469750    9.234684   Cocktail Bar  \n35                Vino Al Vino  45.481489    9.214537       Wine Bar  \n36   Biologic Bar & Restaurant  45.512302    9.178709      Hotel Bar  \n37                   Deus Caf\u00e9  45.490973    9.188650           Caf\u00e9  \n38                        MOMO  45.487785    9.188689           Caf\u00e9  \n39              7's Gi\u00f2 Farini  45.494232    9.185227      Cafeteria  \n40            Francesino caff\u00e8  45.501907    9.166499           Caf\u00e9  \n41               Tre Chicchere  45.491874    9.186870           Caf\u00e9  \n42          Offside Sports Pub  45.487450    9.165725     Sports Bar  \n43                       Vinyl  45.487858    9.187334   Cocktail Bar  \n44                       Frida  45.488718    9.187129   Cocktail Bar  \n45  Les Pommes Italian Bistrot  45.486482    9.187409            Bar  "
                    }, 
                    "execution_count": 128, 
                    "metadata": {}
                }
            ], 
            "execution_count": 128
        }, 
        {
            "source": "", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": null
        }, 
        {
            "source": "## Show in Map the Top Rated Cafe in the Top 4 Municipalties\n\nmap_cafes = folium.Map(location=[latitude, longitude], zoom_start=11, tiles=\"openstreetmap\", \n                            attr=\"<a href=https://github.com/python-visualization/folium/>Folium</a>\")\n\n# set color scheme for the Venues based on the Major Municipalities\nMunicips = ['Centro storico', 'Stazione centrale', 'Citt\u00e0 studi', 'Porta garibaldi']\n\nx = np.arange(len(Municips))\n\nrainbow = ['#00ff00', '#ff00ff','#0000ff','#ffa500' ,'#ff0000']\n\n# add markers to the map\n# markers_colors = []\nfor lat, lon, poi, distr in zip(MilanTop4_Cafe['Venue_Lat'], \n                                  MilanTop4_Cafe['Venue_Long'], \n                                  MilanTop4_Cafe['Venue_Category'], \n                                  MilanTop4_Cafe['District']):\n    label = folium.Popup(str(poi) + ' ' + str(distr), parse_html=True)\n    folium.CircleMarker(\n        [lat, lon],\n        radius=7,\n        popup=label,\n        color=rainbow[Municips.index(distr)-1],\n        fill=True,\n        fill_color=rainbow[Municips.index(distr)-1],\n        fill_opacity=0.3).add_to(map_cafes)\n       \nmap_cafes", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", 
                        "text/plain": "<folium.folium.Map at 0x7f643f1a0a90>"
                    }, 
                    "execution_count": 129, 
                    "metadata": {}
                }
            ], 
            "execution_count": 129
        }, 
        {
            "source": "# create a dataframe to calculate the number of cafes for each district\nMilan_cafe_by_top4district = MilanTop4_Cafe['District'].value_counts()[0:10].to_frame(name='frequency')\nMilan_cafe_by_top4district=Milan_cafe_by_top4district.reset_index()\n#MilanTop4_Cafe_Top10\n\nMilan_cafe_by_top4district.rename(index=str, columns={\"index\": \"Name\", \"frequency\": \"Count\"}, inplace=True)\nMilan_cafe_by_top4district", 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Name</th>\n      <th>Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Stazione centrale</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Citt\u00e0 studi</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Porta garibaldi</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Centro storico</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                Name  Count\n0  Stazione centrale     14\n1        Citt\u00e0 studi     12\n2    Porta garibaldi     10\n3     Centro storico      9"
                    }, 
                    "execution_count": 130, 
                    "metadata": {}
                }
            ], 
            "execution_count": 130
        }, 
        {
            "source": "Now we merge the counter into the main dataframe", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "df_top4 = pd.merge(df_top4, Milan_cafe_by_top4district, on='Name', how='inner')", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 131
        }, 
        {
            "source": "df_top4", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>Description</th>\n      <th>Area</th>\n      <th>Population</th>\n      <th>Density</th>\n      <th>Name</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n      <th>Price</th>\n      <th>Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Centro storico</td>\n      <td>9.67</td>\n      <td>96,315</td>\n      <td>11,074</td>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>\u20ac 6.550 /m\u00b2</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Stazione Centrale, Gorla, Turro, Greco, Cresce...</td>\n      <td>12.58</td>\n      <td>153.109</td>\n      <td>13,031</td>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>\u20ac 4.600 /m\u00b2</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Citt\u00e0 Studi, Lambrate, Porta Venezia</td>\n      <td>14.23</td>\n      <td>141,229</td>\n      <td>10,785</td>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>\u20ac 3.950 /m\u00b2</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9</td>\n      <td>Porta Garibaldi, Niguarda</td>\n      <td>21.12</td>\n      <td>181,598</td>\n      <td>9,204</td>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>\u20ac 5.550 /m\u00b2</td>\n      <td>10</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Id                                        Description   Area Population  \\\n0  1                                     Centro storico   9.67     96,315   \n1  2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58    153.109   \n2  3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23    141,229   \n3  9                          Porta Garibaldi, Niguarda  21.12    181,598   \n\n  Density               Name   Latitude  Longitude        Price  Count  \n0  11,074     Centro storico  45.467281   9.185962  \u20ac 6.550 /m\u00b2      9  \n1  13,031  Stazione centrale  45.499990   9.218999  \u20ac 4.600 /m\u00b2     14  \n2  10,785        Citt\u00e0 studi  45.489799   9.241031  \u20ac 3.950 /m\u00b2     12  \n3   9,204    Porta garibaldi  45.507704   9.179410  \u20ac 5.550 /m\u00b2     10  "
                    }, 
                    "execution_count": 132, 
                    "metadata": {}
                }
            ], 
            "execution_count": 132
        }, 
        {
            "source": "## Data cleaning", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "\ndf_top4['Population'] = df_top4['Population'].str.replace(',', '')\ndf_top4['Population'] = df_top4['Population'].str.replace('.', '')\ndf_top4['Density'] = df_top4['Density'].str.replace(',', '')\ndf_top4['Price'] = df_top4['Price'].str.replace('\u20ac ', '')\ndf_top4['Price'] = df_top4['Price'].str.replace('.', '')\ndf_top4['Price'] = df_top4['Price'].str.replace(' /m\u00b2', '')", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 133
        }, 
        {
            "source": "df_top4['Area'] = df_top4['Area'].astype(float)\ndf_top4['Population'] = df_top4['Population'].astype(float)\ndf_top4['Density'] = df_top4['Density'].astype(float)\ndf_top4['Price'] = df_top4['Price'].astype(float)\n\ndf_top4", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>Description</th>\n      <th>Area</th>\n      <th>Population</th>\n      <th>Density</th>\n      <th>Name</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n      <th>Price</th>\n      <th>Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Centro storico</td>\n      <td>9.67</td>\n      <td>96315.0</td>\n      <td>11074.0</td>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>6550.0</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Stazione Centrale, Gorla, Turro, Greco, Cresce...</td>\n      <td>12.58</td>\n      <td>153109.0</td>\n      <td>13031.0</td>\n      <td>Stazione centrale</td>\n      <td>45.499990</td>\n      <td>9.218999</td>\n      <td>4600.0</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Citt\u00e0 Studi, Lambrate, Porta Venezia</td>\n      <td>14.23</td>\n      <td>141229.0</td>\n      <td>10785.0</td>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489799</td>\n      <td>9.241031</td>\n      <td>3950.0</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9</td>\n      <td>Porta Garibaldi, Niguarda</td>\n      <td>21.12</td>\n      <td>181598.0</td>\n      <td>9204.0</td>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>5550.0</td>\n      <td>10</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Id                                        Description   Area  Population  \\\n0  1                                     Centro storico   9.67     96315.0   \n1  2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58    153109.0   \n2  3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23    141229.0   \n3  9                          Porta Garibaldi, Niguarda  21.12    181598.0   \n\n   Density               Name   Latitude  Longitude   Price  Count  \n0  11074.0     Centro storico  45.467281   9.185962  6550.0      9  \n1  13031.0  Stazione centrale  45.499990   9.218999  4600.0     14  \n2  10785.0        Citt\u00e0 studi  45.489799   9.241031  3950.0     12  \n3   9204.0    Porta garibaldi  45.507704   9.179410  5550.0     10  "
                    }, 
                    "execution_count": 134, 
                    "metadata": {}
                }
            ], 
            "execution_count": 134
        }, 
        {
            "source": "import seaborn as sns\nimport matplotlib.pyplot as plt", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 135
        }, 
        {
            "source": "fig = plt.figure(figsize=(18,7))\ns=sns.barplot(x=\"Name\", y=\"Count\", data=df_top4)\ns.set_xticklabels(s.get_xticklabels(), rotation=30)\nplt.title('Distribution of Bar and Coffee Shops in the 4 main municipalities of Milan', fontsize=15)\nplt.xlabel(\"Municipality\", fontsize=15)\nplt.ylabel (\"Count\", fontsize=15)\nplt.savefig(\"CofeeShop-by-district.png\", dpi=300)\nplt.show()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stderr", 
                    "text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/seaborn/categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n  stat_data = remove_na(group_data)\n"
                }, 
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f643f197400>"
                    }, 
                    "metadata": {}
                }
            ], 
            "execution_count": 136
        }, 
        {
            "source": "\nsns.regplot(x=\"Density\", y=\"Count\", data=df_top4)\nplt.ylim(0,)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "(0, 32.719538438999827)"
                    }, 
                    "execution_count": 139, 
                    "metadata": {}
                }, 
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f6433c17ac8>"
                    }, 
                    "metadata": {}
                }
            ], 
            "execution_count": 139
        }
    ], 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.5", 
            "name": "python3", 
            "language": "python"
        }, 
        "language_info": {
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
            "version": "3.5.5", 
            "name": "python", 
            "pygments_lexer": "ipython3", 
            "file_extension": ".py", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }
        }
    }, 
    "nbformat": 4
}