Capstone Project - The Battle of Neighborhoods - SweetMilan.ipynb

{"cells": [{"metadata": {}, "cell_type": "code", "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", "execution_count": 63, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "response = requests.get('https://en.wikipedia.org/wiki/Zones_of_Milan').text\nsoup = BeautifulSoup(response,'lxml')\ntable = soup.find('table', {'class':'wikitable sortable'})", "execution_count": 64, "outputs": []}, {"metadata": {"scrolled": true}, "cell_type": "code", "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=[\"Townhall\", \"Description\", \"Area\", \"Population\",\"Density\", \"Districts\"])\ndf", "execution_count": 65, "outputs": [{"output_type": "execute_result", "execution_count": 65, "data": {"text/plain": "  Townhall                                        Description   Area  \\\n0        1                                     Centro storico   9.67   \n1        2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58   \n2        3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23   \n3        4                          Porta Vittoria, Forlanini  20.95   \n4        5                Vigentino, Chiaravalle, Gratosoglio  29.87   \n5        6                                Barona, Lorenteggio  18.28   \n6        7                        Baggio, De Angeli, San Siro  31.34   \n7        8                 Fiera, Gallaratese, Quarto Oggiaro  23.72   \n8        9                          Porta Garibaldi, Niguarda  21.12   \n\n  Population Density                                          Districts  \n0     96,315  11,074  Brera, Centro Storico, Conca del Naviglio, Gua...  \n1    153.109  13,031  Adriano, Crescenzago, Gorla, Greco, Loreto, Ma...  \n2    141,229  10,785  Casoretto, Cimiano, Citt\u00e0 Studi, Dosso, Lambra...  \n3    156.369   8,069  Acquabella, Calvairate, Castagnedo, Cavriano, ...  \n4    123,779   4,487  Basmetto, Cantalupa, Case Nuove, Chiaravalle, ...  \n5    149,000   8,998  Arzaga, Barona, Boffalora, Cascina Bianca, Con...  \n6    170,814   6,093  Assiano, Baggio, Figino, Fopponino, Forze Arma...  \n7    181,669   8,326  Boldinasco, Bullona, Cagnola, Campo dei Fiori,...  \n8    181,598   9,204  Affori, Bicocca, Bovisa, Bovisasca, Bruzzano, ...  ", "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>Townhall</th>\n      <th>Description</th>\n      <th>Area</th>\n      <th>Population</th>\n      <th>Density</th>\n      <th>Districts</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>Brera, Centro Storico, Conca del Naviglio, Gua...</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>Adriano, Crescenzago, Gorla, Greco, Loreto, Ma...</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>Casoretto, Cimiano, Citt\u00e0 Studi, Dosso, Lambra...</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>Acquabella, Calvairate, Castagnedo, Cavriano, ...</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>Basmetto, Cantalupa, Case Nuove, Chiaravalle, ...</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>Arzaga, Barona, Boffalora, Cascina Bianca, Con...</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>Assiano, Baggio, Figino, Fopponino, Forze Arma...</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>Boldinasco, Bullona, Cagnola, Campo dei Fiori,...</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>Affori, Bicocca, Bovisa, Bovisasca, Bruzzano, ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "df_Townhall = df.drop(columns=['Districts'])\ndf_Townhall", "execution_count": 66, "outputs": [{"output_type": "execute_result", "execution_count": 66, "data": {"text/plain": "  Townhall                                        Description   Area  \\\n0        1                                     Centro storico   9.67   \n1        2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58   \n2        3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23   \n3        4                          Porta Vittoria, Forlanini  20.95   \n4        5                Vigentino, Chiaravalle, Gratosoglio  29.87   \n5        6                                Barona, Lorenteggio  18.28   \n6        7                        Baggio, De Angeli, San Siro  31.34   \n7        8                 Fiera, Gallaratese, Quarto Oggiaro  23.72   \n8        9                          Porta Garibaldi, Niguarda  21.12   \n\n  Population Density  \n0     96,315  11,074  \n1    153.109  13,031  \n2    141,229  10,785  \n3    156.369   8,069  \n4    123,779   4,487  \n5    149,000   8,998  \n6    170,814   6,093  \n7    181,669   8,326  \n8    181,598   9,204  ", "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>Townhall</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>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "df_Townhall['Name'] = df_Townhall.Description.str.split(',').str[0]\ndf_Townhall['Name'] = df_Townhall.Name.str.capitalize()\ndf_Townhall", "execution_count": 67, "outputs": [{"output_type": "execute_result", "execution_count": 67, "data": {"text/plain": "  Townhall                                        Description   Area  \\\n0        1                                     Centro storico   9.67   \n1        2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58   \n2        3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23   \n3        4                          Porta Vittoria, Forlanini  20.95   \n4        5                Vigentino, Chiaravalle, Gratosoglio  29.87   \n5        6                                Barona, Lorenteggio  18.28   \n6        7                        Baggio, De Angeli, San Siro  31.34   \n7        8                 Fiera, Gallaratese, Quarto Oggiaro  23.72   \n8        9                          Porta Garibaldi, Niguarda  21.12   \n\n  Population Density               Name  \n0     96,315  11,074     Centro storico  \n1    153.109  13,031  Stazione centrale  \n2    141,229  10,785        Citt\u00e0 studi  \n3    156.369   8,069     Porta vittoria  \n4    123,779   4,487          Vigentino  \n5    149,000   8,998             Barona  \n6    170,814   6,093             Baggio  \n7    181,669   8,326              Fiera  \n8    181,598   9,204    Porta garibaldi  ", "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>Townhall</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>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "from geopy.geocoders import Nominatim\ngeolocator = Nominatim()\nlocation = geolocator.geocode(\"Milano, MI, Lom, Italia\")\naddress = []\ncoord = []\naddress = \"Municipio \"+ df['Townhall']+\", Milano, MI, Lom, Italy\"\ncoord = address.apply(geolocator.geocode).apply(lambda x: (x.latitude, x.longitude))\ndf_Townhall['Coordinates'] = coord\ndf_Townhall", "execution_count": 68, "outputs": [{"output_type": "stream", "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/ipykernel/__main__.py:2: DeprecationWarning: Using Nominatim with the default \"geopy/1.18.1\" `user_agent` is strongly discouraged, as it violates Nominatim's ToS https://operations.osmfoundation.org/policies/nominatim/ and may possibly cause 403 and 429 HTTP errors. Please specify a custom `user_agent` with `Nominatim(user_agent=\"my-application\")` or by overriding the default `user_agent`: `geopy.geocoders.options.default_user_agent = \"my-application\"`. In geopy 2.0 this will become an exception.\n  from ipykernel import kernelapp as app\n", "name": "stderr"}, {"output_type": "execute_result", "execution_count": 68, "data": {"text/plain": "  Townhall                                        Description   Area  \\\n0        1                                     Centro storico   9.67   \n1        2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58   \n2        3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23   \n3        4                          Porta Vittoria, Forlanini  20.95   \n4        5                Vigentino, Chiaravalle, Gratosoglio  29.87   \n5        6                                Barona, Lorenteggio  18.28   \n6        7                        Baggio, De Angeli, San Siro  31.34   \n7        8                 Fiera, Gallaratese, Quarto Oggiaro  23.72   \n8        9                          Porta Garibaldi, Niguarda  21.12   \n\n  Population Density               Name  \\\n0     96,315  11,074     Centro storico   \n1    153.109  13,031  Stazione centrale   \n2    141,229  10,785        Citt\u00e0 studi   \n3    156.369   8,069     Porta vittoria   \n4    123,779   4,487          Vigentino   \n5    149,000   8,998             Barona   \n6    170,814   6,093             Baggio   \n7    181,669   8,326              Fiera   \n8    181,598   9,204    Porta garibaldi   \n\n                               Coordinates  \n0  (45.467280599999995, 9.185962010120925)  \n1          (45.4999899, 9.218998626052084)  \n2  (45.489799500000004, 9.241030886135526)  \n3           (45.446205, 9.239343264566283)  \n4          (45.4198458, 9.198152549352766)  \n5         (45.43792895, 9.145648151241897)  \n6  (45.469598000000005, 9.114757320322848)  \n7         (45.50001535, 9.122774225147062)  \n8           (45.5077041, 9.17941004494827)  ", "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>Townhall</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>(45.467280599999995, 9.185962010120925)</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, 9.218998626052084)</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>(45.489799500000004, 9.241030886135526)</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, 9.239343264566283)</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, 9.198152549352766)</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, 9.145648151241897)</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>(45.469598000000005, 9.114757320322848)</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, 9.122774225147062)</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>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "df_Townhall[['Latitude', 'Longitude']] = df_Townhall['Coordinates'].apply(pd.Series)\n", "execution_count": 69, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "df_Townhall = df_Townhall.drop(columns=['Coordinates'])\n", "execution_count": 70, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "df_Townhall\n", "execution_count": 73, "outputs": [{"output_type": "execute_result", "execution_count": 73, "data": {"text/plain": "  Townhall                                        Description   Area  \\\n0        1                                     Centro storico   9.67   \n1        2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58   \n2        3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23   \n3        4                          Porta Vittoria, Forlanini  20.95   \n4        5                Vigentino, Chiaravalle, Gratosoglio  29.87   \n5        6                                Barona, Lorenteggio  18.28   \n6        7                        Baggio, De Angeli, San Siro  31.34   \n7        8                 Fiera, Gallaratese, Quarto Oggiaro  23.72   \n8        9                          Porta Garibaldi, Niguarda  21.12   \n\n  Population Density               Name   Latitude  Longitude  \n0     96,315  11,074     Centro storico  45.467281   9.185962  \n1    153.109  13,031  Stazione centrale  45.499990   9.218999  \n2    141,229  10,785        Citt\u00e0 studi  45.489800   9.241031  \n3    156.369   8,069     Porta vittoria  45.446205   9.239343  \n4    123,779   4,487          Vigentino  45.419846   9.198153  \n5    149,000   8,998             Barona  45.437929   9.145648  \n6    170,814   6,093             Baggio  45.469598   9.114757  \n7    181,669   8,326              Fiera  45.500015   9.122774  \n8    181,598   9,204    Porta garibaldi  45.507704   9.179410  ", "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>Townhall</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>45.489800</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>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "df_top_townhall = df_Townhall.loc[df_Townhall['Name'].isin([\"Centro storico\", \"Stazione centrale\", \"Citt\u00e0 studi\", \"Porta garibaldi\", \"Porta vittoria\", \"Vigentino\",\"Fiera\",\"Barona\",\"Baggio\"])]\n#df_top_townhall = df_Townhall.loc[df_Townhall['Name'].isin([\"Centro storico\", \"Stazione centrale\", \"Citt\u00e0 studi\", \"Porta garibaldi\", \"Porta vittoria\"])]\n", "execution_count": 75, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "df_top_townhall = df_top_townhall.reset_index(drop=True)\ndf_top_townhall ", "execution_count": 76, "outputs": [{"output_type": "execute_result", "execution_count": 76, "data": {"text/plain": "  Townhall                                        Description   Area  \\\n0        1                                     Centro storico   9.67   \n1        2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58   \n2        3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23   \n3        4                          Porta Vittoria, Forlanini  20.95   \n4        5                Vigentino, Chiaravalle, Gratosoglio  29.87   \n5        6                                Barona, Lorenteggio  18.28   \n6        7                        Baggio, De Angeli, San Siro  31.34   \n7        8                 Fiera, Gallaratese, Quarto Oggiaro  23.72   \n8        9                          Porta Garibaldi, Niguarda  21.12   \n\n  Population Density               Name   Latitude  Longitude  \n0     96,315  11,074     Centro storico  45.467281   9.185962  \n1    153.109  13,031  Stazione centrale  45.499990   9.218999  \n2    141,229  10,785        Citt\u00e0 studi  45.489800   9.241031  \n3    156.369   8,069     Porta vittoria  45.446205   9.239343  \n4    123,779   4,487          Vigentino  45.419846   9.198153  \n5    149,000   8,998             Barona  45.437929   9.145648  \n6    170,814   6,093             Baggio  45.469598   9.114757  \n7    181,669   8,326              Fiera  45.500015   9.122774  \n8    181,598   9,204    Porta garibaldi  45.507704   9.179410  ", "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>Townhall</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>45.489800</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>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "response2 = requests.get('https://www.mercato-immobiliare.info/lombardia/milano/milano.html').text\nsoup2 = BeautifulSoup(response2, 'html.parser')\ntable2 = soup2.find('table', {'id':'childrentable'})", "execution_count": 77, "outputs": []}, {"metadata": {}, "cell_type": "code", "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'])", "execution_count": 78, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "df_price['Name'] = df_price.Name.str.capitalize()\n", "execution_count": 79, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "df_price\n", "execution_count": 80, "outputs": [{"output_type": "execute_result", "execution_count": 80, "data": {"text/plain": "                    Name        Price\n0                Adriano  \u20ac 2.750 /m\u00b2\n1                 Affori  \u20ac 2.350 /m\u00b2\n2                 Baggio  \u20ac 2.400 /m\u00b2\n3                 Barona  \u20ac 3.200 /m\u00b2\n4                Bicocca  \u20ac 3.050 /m\u00b2\n5                Bocconi  \u20ac 5.300 /m\u00b2\n6                 Bovisa  \u20ac 2.550 /m\u00b2\n7              Bovisasca  \u20ac 2.000 /m\u00b2\n8                  Brera  \u20ac 7.600 /m\u00b2\n9               Bruzzano  \u20ac 2.100 /m\u00b2\n10               Cadorna  \u20ac 6.950 /m\u00b2\n11             Casoretto  \u20ac 3.500 /m\u00b2\n12    Centro direzionale  \u20ac 6.500 /m\u00b2\n13        Centro storico  \u20ac 7.100 /m\u00b2\n14  Chiaravalle milanese  \u20ac 2.750 /m\u00b2\n15          Chiesa rossa  \u20ac 3.100 /m\u00b2\n16       Cinque giornate  \u20ac 6.650 /m\u00b2\n17           Citt\u00e0 studi  \u20ac 4.500 /m\u00b2\n18              Comasina  \u20ac 1.800 /m\u00b2\n19              Corvetto  \u20ac 3.000 /m\u00b2\n20           Crescenzago  \u20ac 2.650 /m\u00b2\n21               Dergano  \u20ac 2.800 /m\u00b2\n22             Famagosta  \u20ac 3.350 /m\u00b2\n23                Figino  \u20ac 2.050 /m\u00b2\n24             Forlanini  \u20ac 2.950 /m\u00b2\n25          Forze armate  \u20ac 2.700 /m\u00b2\n26           Gallaratese  \u20ac 2.650 /m\u00b2\n27             Garegnano  \u20ac 2.600 /m\u00b2\n28                 Gorla  \u20ac 2.850 /m\u00b2\n29           Gratosoglio  \u20ac 2.550 /m\u00b2\n..                   ...          ...\n45          Ponte lambro  \u20ac 1.850 /m\u00b2\n46          Ponte seveso  \u20ac 4.350 /m\u00b2\n47       Porta garibaldi  \u20ac 6.800 /m\u00b2\n48          Porta genova  \u20ac 6.150 /m\u00b2\n49           Porta nuova  \u20ac 7.350 /m\u00b2\n50          Porta romana  \u20ac 5.850 /m\u00b2\n51        Porta ticinese  \u20ac 6.200 /m\u00b2\n52         Porta venezia  \u20ac 6.650 /m\u00b2\n53       Porta vigentina  \u20ac 6.200 /m\u00b2\n54        Porta vittoria  \u20ac 6.450 /m\u00b2\n55           Porta volta  \u20ac 7.150 /m\u00b2\n56              Precotto  \u20ac 3.100 /m\u00b2\n57                   Qt8  \u20ac 3.800 /m\u00b2\n58  Quartiere degli olmi  \u20ac 1.800 /m\u00b2\n59        Quarto oggiaro  \u20ac 1.750 /m\u00b2\n60         Quinto romano  \u20ac 2.300 /m\u00b2\n61             Rembrandt  \u20ac 3.900 /m\u00b2\n62              Rogoredo  \u20ac 2.550 /m\u00b2\n63        San cristoforo  \u20ac 4.350 /m\u00b2\n64              San siro  \u20ac 3.150 /m\u00b2\n65           San vittore  \u20ac 7.000 /m\u00b2\n66         Sant'ambrogio  \u20ac 7.000 /m\u00b2\n67              Sempione  \u20ac 5.800 /m\u00b2\n68                Solari  \u20ac 5.700 /m\u00b2\n69     Stazione centrale  \u20ac 5.000 /m\u00b2\n70                Trenno  \u20ac 2.400 /m\u00b2\n71                 Turro  \u20ac 2.950 /m\u00b2\n72             Vigentino  \u20ac 3.250 /m\u00b2\n73          Villapizzone  \u20ac 2.400 /m\u00b2\n74            Xxii marzo  \u20ac 5.500 /m\u00b2\n\n[75 rows x 2 columns]", "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 2.750 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Affori</td>\n      <td>\u20ac 2.350 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Baggio</td>\n      <td>\u20ac 2.400 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Barona</td>\n      <td>\u20ac 3.200 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bicocca</td>\n      <td>\u20ac 3.050 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Bocconi</td>\n      <td>\u20ac 5.300 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Bovisa</td>\n      <td>\u20ac 2.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Bovisasca</td>\n      <td>\u20ac 2.000 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Brera</td>\n      <td>\u20ac 7.600 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Bruzzano</td>\n      <td>\u20ac 2.100 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Cadorna</td>\n      <td>\u20ac 6.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Casoretto</td>\n      <td>\u20ac 3.500 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Centro direzionale</td>\n      <td>\u20ac 6.500 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Centro storico</td>\n      <td>\u20ac 7.100 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Chiaravalle milanese</td>\n      <td>\u20ac 2.750 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Chiesa rossa</td>\n      <td>\u20ac 3.100 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Cinque giornate</td>\n      <td>\u20ac 6.650 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Citt\u00e0 studi</td>\n      <td>\u20ac 4.500 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Comasina</td>\n      <td>\u20ac 1.800 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Corvetto</td>\n      <td>\u20ac 3.000 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Crescenzago</td>\n      <td>\u20ac 2.650 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Dergano</td>\n      <td>\u20ac 2.800 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Famagosta</td>\n      <td>\u20ac 3.350 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Figino</td>\n      <td>\u20ac 2.050 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Forlanini</td>\n      <td>\u20ac 2.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Forze armate</td>\n      <td>\u20ac 2.700 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Gallaratese</td>\n      <td>\u20ac 2.650 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Garegnano</td>\n      <td>\u20ac 2.600 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Gorla</td>\n      <td>\u20ac 2.850 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Gratosoglio</td>\n      <td>\u20ac 2.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>Ponte lambro</td>\n      <td>\u20ac 1.850 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>Ponte seveso</td>\n      <td>\u20ac 4.350 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>Porta garibaldi</td>\n      <td>\u20ac 6.800 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>Porta genova</td>\n      <td>\u20ac 6.150 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>Porta nuova</td>\n      <td>\u20ac 7.350 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>Porta romana</td>\n      <td>\u20ac 5.850 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>Porta ticinese</td>\n      <td>\u20ac 6.200 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>Porta venezia</td>\n      <td>\u20ac 6.650 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>Porta vigentina</td>\n      <td>\u20ac 6.200 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>Porta vittoria</td>\n      <td>\u20ac 6.450 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Porta volta</td>\n      <td>\u20ac 7.150 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>Precotto</td>\n      <td>\u20ac 3.100 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>Qt8</td>\n      <td>\u20ac 3.800 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>Quartiere degli olmi</td>\n      <td>\u20ac 1.800 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>Quarto oggiaro</td>\n      <td>\u20ac 1.750 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Quinto romano</td>\n      <td>\u20ac 2.300 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>Rembrandt</td>\n      <td>\u20ac 3.900 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>Rogoredo</td>\n      <td>\u20ac 2.550 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>San cristoforo</td>\n      <td>\u20ac 4.350 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>San siro</td>\n      <td>\u20ac 3.150 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>San vittore</td>\n      <td>\u20ac 7.000 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Sant'ambrogio</td>\n      <td>\u20ac 7.000 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>Sempione</td>\n      <td>\u20ac 5.800 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>Solari</td>\n      <td>\u20ac 5.700 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>Stazione centrale</td>\n      <td>\u20ac 5.000 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>Trenno</td>\n      <td>\u20ac 2.400 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>Turro</td>\n      <td>\u20ac 2.950 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>Vigentino</td>\n      <td>\u20ac 3.250 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>Villapizzone</td>\n      <td>\u20ac 2.400 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Xxii marzo</td>\n      <td>\u20ac 5.500 /m\u00b2</td>\n    </tr>\n  </tbody>\n</table>\n<p>75 rows \u00d7 2 columns</p>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "#df_price = df_price.loc[df_price['Name'].isin([\"Centro storico\", \"Stazione centrale\", \"Citt\u00e0 studi\", \"Porta garibaldi\", \"Porta vittoria\", \"Vigentino\"])]\ndf_price = df_price.loc[df_price['Name'].isin([\"Centro storico\", \"Stazione centrale\", \"Citt\u00e0 studi\", \"Porta garibaldi\", \"Porta vittoria\", \"Vigentino\",\"Fiera\",\"Barona\",\"Baggio\"])]\ndf_price = df_price.reset_index(drop=True)\ndf_price", "execution_count": 81, "outputs": [{"output_type": "execute_result", "execution_count": 81, "data": {"text/plain": "                Name        Price\n0             Baggio  \u20ac 2.400 /m\u00b2\n1             Barona  \u20ac 3.200 /m\u00b2\n2     Centro storico  \u20ac 7.100 /m\u00b2\n3        Citt\u00e0 studi  \u20ac 4.500 /m\u00b2\n4    Porta garibaldi  \u20ac 6.800 /m\u00b2\n5     Porta vittoria  \u20ac 6.450 /m\u00b2\n6  Stazione centrale  \u20ac 5.000 /m\u00b2\n7          Vigentino  \u20ac 3.250 /m\u00b2", "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>Baggio</td>\n      <td>\u20ac 2.400 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Barona</td>\n      <td>\u20ac 3.200 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Centro storico</td>\n      <td>\u20ac 7.100 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Citt\u00e0 studi</td>\n      <td>\u20ac 4.500 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Porta garibaldi</td>\n      <td>\u20ac 6.800 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Porta vittoria</td>\n      <td>\u20ac 6.450 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Stazione centrale</td>\n      <td>\u20ac 5.000 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Vigentino</td>\n      <td>\u20ac 3.250 /m\u00b2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "df_top_townhall = pd.merge(df_top_townhall, df_price, on='Name', how='inner')\n", "execution_count": 82, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "df_top_townhall", "execution_count": 83, "outputs": [{"output_type": "execute_result", "execution_count": 83, "data": {"text/plain": "  Townhall                                        Description   Area  \\\n0        1                                     Centro storico   9.67   \n1        2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58   \n2        3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23   \n3        4                          Porta Vittoria, Forlanini  20.95   \n4        5                Vigentino, Chiaravalle, Gratosoglio  29.87   \n5        6                                Barona, Lorenteggio  18.28   \n6        7                        Baggio, De Angeli, San Siro  31.34   \n7        9                          Porta Garibaldi, Niguarda  21.12   \n\n  Population Density               Name   Latitude  Longitude        Price  \n0     96,315  11,074     Centro storico  45.467281   9.185962  \u20ac 7.100 /m\u00b2  \n1    153.109  13,031  Stazione centrale  45.499990   9.218999  \u20ac 5.000 /m\u00b2  \n2    141,229  10,785        Citt\u00e0 studi  45.489800   9.241031  \u20ac 4.500 /m\u00b2  \n3    156.369   8,069     Porta vittoria  45.446205   9.239343  \u20ac 6.450 /m\u00b2  \n4    123,779   4,487          Vigentino  45.419846   9.198153  \u20ac 3.250 /m\u00b2  \n5    149,000   8,998             Barona  45.437929   9.145648  \u20ac 3.200 /m\u00b2  \n6    170,814   6,093             Baggio  45.469598   9.114757  \u20ac 2.400 /m\u00b2  \n7    181,598   9,204    Porta garibaldi  45.507704   9.179410  \u20ac 6.800 /m\u00b2  ", "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>Townhall</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 7.100 /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 5.000 /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>45.489800</td>\n      <td>9.241031</td>\n      <td>\u20ac 4.500 /m\u00b2</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      <td>\u20ac 6.450 /m\u00b2</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      <td>\u20ac 3.250 /m\u00b2</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      <td>\u20ac 3.200 /m\u00b2</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      <td>\u20ac 2.400 /m\u00b2</td>\n    </tr>\n    <tr>\n      <th>7</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 6.800 /m\u00b2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "CLIENT_ID = 'TM3SILTIA0FQTOSSRL2RDH5JQ4SBIURAHI0BGKGV4FWYCQ2M'\nCLIENT_SECRET = 'UDGGMOMYUBWKY5G3ILU2V5ZFWN3LXBMKICEVYBEMFQY5TUAW'\nVERSION = '20190325'", "execution_count": 84, "outputs": []}, {"metadata": {}, "cell_type": "code", "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)", "execution_count": 85, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "Milan_Venues = getNearbyVenues(names=df_top_townhall['Name'],\n                                   latitudes=df_top_townhall['Latitude'],\n                                   longitudes=df_top_townhall['Longitude']\n                                  )", "execution_count": 88, "outputs": [{"output_type": "stream", "text": "Centro storico\nStazione centrale\nCitt\u00e0 studi\nPorta vittoria\nVigentino\nBarona\nBaggio\nPorta garibaldi\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "code", "source": "print (\"Shape of the Venues Dataframe: \", Milan_Venues.shape)\nMilan_Venues.head(100)", "execution_count": 89, "outputs": [{"output_type": "stream", "text": "Shape of the Venues Dataframe:  (746, 7)\n", "name": "stdout"}, {"output_type": "execute_result", "execution_count": 89, "data": {"text/plain": "          District  Dist_Latitude  Dist_Longitude  \\\n0   Centro storico      45.467281        9.185962   \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  Centro storico      45.467281        9.185962   \n11  Centro storico      45.467281        9.185962   \n12  Centro storico      45.467281        9.185962   \n13  Centro storico      45.467281        9.185962   \n14  Centro storico      45.467281        9.185962   \n15  Centro storico      45.467281        9.185962   \n16  Centro storico      45.467281        9.185962   \n17  Centro storico      45.467281        9.185962   \n18  Centro storico      45.467281        9.185962   \n19  Centro storico      45.467281        9.185962   \n20  Centro storico      45.467281        9.185962   \n21  Centro storico      45.467281        9.185962   \n22  Centro storico      45.467281        9.185962   \n23  Centro storico      45.467281        9.185962   \n24  Centro storico      45.467281        9.185962   \n25  Centro storico      45.467281        9.185962   \n26  Centro storico      45.467281        9.185962   \n27  Centro storico      45.467281        9.185962   \n28  Centro storico      45.467281        9.185962   \n29  Centro storico      45.467281        9.185962   \n..             ...            ...             ...   \n70  Centro storico      45.467281        9.185962   \n71  Centro storico      45.467281        9.185962   \n72  Centro storico      45.467281        9.185962   \n73  Centro storico      45.467281        9.185962   \n74  Centro storico      45.467281        9.185962   \n75  Centro storico      45.467281        9.185962   \n76  Centro storico      45.467281        9.185962   \n77  Centro storico      45.467281        9.185962   \n78  Centro storico      45.467281        9.185962   \n79  Centro storico      45.467281        9.185962   \n80  Centro storico      45.467281        9.185962   \n81  Centro storico      45.467281        9.185962   \n82  Centro storico      45.467281        9.185962   \n83  Centro storico      45.467281        9.185962   \n84  Centro storico      45.467281        9.185962   \n85  Centro storico      45.467281        9.185962   \n86  Centro storico      45.467281        9.185962   \n87  Centro storico      45.467281        9.185962   \n88  Centro storico      45.467281        9.185962   \n89  Centro storico      45.467281        9.185962   \n90  Centro storico      45.467281        9.185962   \n91  Centro storico      45.467281        9.185962   \n92  Centro storico      45.467281        9.185962   \n93  Centro storico      45.467281        9.185962   \n94  Centro storico      45.467281        9.185962   \n95  Centro storico      45.467281        9.185962   \n96  Centro storico      45.467281        9.185962   \n97  Centro storico      45.467281        9.185962   \n98  Centro storico      45.467281        9.185962   \n99  Centro storico      45.467281        9.185962   \n\n                                    Venue  Venue_Lat  Venue_Long  \\\n0              Starbucks Reserve Roastery  45.464920    9.186153   \n1                       Gallerie d'Italia  45.467183    9.190056   \n2           Galleria Vittorio Emanuele II  45.465577    9.190024   \n3                              Signorvino  45.467153    9.183460   \n4                       Teatro alla Scala  45.467027    9.189686   \n5                        Park Hyatt Milan  45.465532    9.188911   \n6                      Bulgari Lounge Bar  45.470014    9.188943   \n7                  Room Mate Giulia Hotel  45.465250    9.189396   \n8                      Giovanni Cova & C.  45.468816    9.184121   \n9                         Piazza Castello  45.468965    9.181312   \n10                   BVLGARI Hotel Milano  45.470149    9.189318   \n11                     Piazza del Carmine  45.470102    9.185058   \n12                       Piazza del Duomo  45.464190    9.189527   \n13                             Risoelatte  45.466514    9.183262   \n14                     Piazza della Scala  45.466895    9.189881   \n15              Ciacco. Gelato senz'altro  45.463704    9.186796   \n16                         Bialetti Store  45.464775    9.188343   \n17                        Moleskine Store  45.467531    9.183170   \n18                               Gay Odin  45.466187    9.180801   \n19                      Mandarin Oriental  45.469461    9.190876   \n20                     Castello Sforzesco  45.469545    9.180424   \n21                 Di Viole Di Liquirizia  45.471460    9.185336   \n22                   Pasticceria Marchesi  45.465612    9.190091   \n23         Fontana del Castello Sforzesco  45.469237    9.180917   \n24                     Terrazze del Duomo  45.464207    9.191075   \n25                                    Piz  45.462163    9.185767   \n26                    Pinacoteca di Brera  45.471979    9.188128   \n27                  Lavazza Coffee Design  45.466274    9.190975   \n28                                 B Caf\u00e9  45.462640    9.183381   \n29                                  Luini  45.465707    9.191431   \n..                                    ...        ...         ...   \n70                          Dolce&Gabbana  45.468338    9.198586   \n71                   Zen Sushi Restaurant  45.459700    9.188670   \n72                      Temakinho Magenta  45.467926    9.174649   \n73            Dolce & Gabbana Martini Bar  45.468296    9.198412   \n74                           Boccondivino  45.465182    9.175372   \n75                 La Triennale di Milano  45.472243    9.173507   \n76                                Colibr\u00ec  45.461436    9.195804   \n77                 Colonne di San Lorenzo  45.458266    9.181122   \n78                                Miscusi  45.468298    9.174258   \n79                             Hamerica's  45.459639    9.181071   \n80                      Piazza San Babila  45.466667    9.197597   \n81                                  Osaka  45.476259    9.184623   \n82                                    COS  45.467595    9.198209   \n83                 Villa Necchi Campiglio  45.468181    9.201570   \n84                                 Princi  45.468877    9.199125   \n85                         Libreria Verso  45.457272    9.180974   \n86                     Drogheria Milanese  45.477477    9.188721   \n87                      Cavoli a Merenda!  45.465975    9.172271   \n88               Santa Maria delle Grazie  45.466139    9.170392   \n89               La Dogana Del Buon Gusto  45.457296    9.181781   \n90                           Caff\u00e8 Napoli  45.477952    9.184552   \n91               Giardino della Guastalla  45.459850    9.197546   \n92                Giardini di Villa Reale  45.472127    9.199721   \n93                   La Vigna di Leonardo  45.465631    9.170902   \n94                             Dictionary  45.456869    9.180853   \n95              Giardini Indro Montanelli  45.474358    9.200627   \n96                        Arco della Pace  45.475728    9.172447   \n97                Museo Cenacolo Vinciano  45.466110    9.170523   \n98  GAM Galleria d'Arte Moderna di Milano  45.472683    9.199923   \n99                       Osteria Brunello  45.479310    9.185902   \n\n                   Venue_Category  \n0                     Coffee Shop  \n1                     Art Gallery  \n2             Monument / Landmark  \n3                        Wine Bar  \n4                     Opera House  \n5                           Hotel  \n6                    Cocktail Bar  \n7                           Hotel  \n8                          Bakery  \n9                           Plaza  \n10                          Hotel  \n11                          Plaza  \n12                          Plaza  \n13             Italian Restaurant  \n14                          Plaza  \n15                 Ice Cream Shop  \n16           Kitchen Supply Store  \n17  Paper / Office Supplies Store  \n18                 Chocolate Shop  \n19                          Hotel  \n20                         Castle  \n21                   Cupcake Shop  \n22                    Pastry Shop  \n23                       Fountain  \n24                 Scenic Lookout  \n25                    Pizza Place  \n26                     Art Museum  \n27                    Coffee Shop  \n28                           Caf\u00e9  \n29                         Bakery  \n..                            ...  \n70                       Boutique  \n71            Japanese Restaurant  \n72               Sushi Restaurant  \n73                   Cocktail Bar  \n74                       Wine Bar  \n75                     Art Museum  \n76                            Bar  \n77                  Historic Site  \n78             Italian Restaurant  \n79                   Burger Joint  \n80                          Plaza  \n81            Japanese Restaurant  \n82                 Clothing Store  \n83                         Museum  \n84                         Bakery  \n85                      Bookstore  \n86                     Restaurant  \n87             Italian Restaurant  \n88                         Church  \n89             Italian Restaurant  \n90                           Caf\u00e9  \n91                           Park  \n92                           Park  \n93                         Museum  \n94                       Boutique  \n95                           Park  \n96            Monument / Landmark  \n97                     Art Museum  \n98                    Art Gallery  \n99             Italian Restaurant  \n\n[100 rows x 7 columns]", "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>0</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>1</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Gallerie d'Italia</td>\n      <td>45.467183</td>\n      <td>9.190056</td>\n      <td>Art Gallery</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>Galleria Vittorio Emanuele II</td>\n      <td>45.465577</td>\n      <td>9.190024</td>\n      <td>Monument / Landmark</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>Signorvino</td>\n      <td>45.467153</td>\n      <td>9.183460</td>\n      <td>Wine 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>Teatro alla Scala</td>\n      <td>45.467027</td>\n      <td>9.189686</td>\n      <td>Opera House</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>Park Hyatt Milan</td>\n      <td>45.465532</td>\n      <td>9.188911</td>\n      <td>Hotel</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>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>7</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Room Mate Giulia Hotel</td>\n      <td>45.465250</td>\n      <td>9.189396</td>\n      <td>Hotel</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>Giovanni Cova &amp; C.</td>\n      <td>45.468816</td>\n      <td>9.184121</td>\n      <td>Bakery</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>Piazza Castello</td>\n      <td>45.468965</td>\n      <td>9.181312</td>\n      <td>Plaza</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>BVLGARI Hotel Milano</td>\n      <td>45.470149</td>\n      <td>9.189318</td>\n      <td>Hotel</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Piazza del Carmine</td>\n      <td>45.470102</td>\n      <td>9.185058</td>\n      <td>Plaza</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Piazza del Duomo</td>\n      <td>45.464190</td>\n      <td>9.189527</td>\n      <td>Plaza</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Risoelatte</td>\n      <td>45.466514</td>\n      <td>9.183262</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Piazza della Scala</td>\n      <td>45.466895</td>\n      <td>9.189881</td>\n      <td>Plaza</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Ciacco. Gelato senz'altro</td>\n      <td>45.463704</td>\n      <td>9.186796</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Bialetti Store</td>\n      <td>45.464775</td>\n      <td>9.188343</td>\n      <td>Kitchen Supply Store</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Moleskine Store</td>\n      <td>45.467531</td>\n      <td>9.183170</td>\n      <td>Paper / Office Supplies Store</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Gay Odin</td>\n      <td>45.466187</td>\n      <td>9.180801</td>\n      <td>Chocolate Shop</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Mandarin Oriental</td>\n      <td>45.469461</td>\n      <td>9.190876</td>\n      <td>Hotel</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Castello Sforzesco</td>\n      <td>45.469545</td>\n      <td>9.180424</td>\n      <td>Castle</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Di Viole Di Liquirizia</td>\n      <td>45.471460</td>\n      <td>9.185336</td>\n      <td>Cupcake Shop</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Pasticceria Marchesi</td>\n      <td>45.465612</td>\n      <td>9.190091</td>\n      <td>Pastry Shop</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Fontana del Castello Sforzesco</td>\n      <td>45.469237</td>\n      <td>9.180917</td>\n      <td>Fountain</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Terrazze del Duomo</td>\n      <td>45.464207</td>\n      <td>9.191075</td>\n      <td>Scenic Lookout</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Piz</td>\n      <td>45.462163</td>\n      <td>9.185767</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Pinacoteca di Brera</td>\n      <td>45.471979</td>\n      <td>9.188128</td>\n      <td>Art Museum</td>\n    </tr>\n    <tr>\n      <th>27</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>28</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>29</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Luini</td>\n      <td>45.465707</td>\n      <td>9.191431</td>\n      <td>Bakery</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>70</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Dolce&amp;Gabbana</td>\n      <td>45.468338</td>\n      <td>9.198586</td>\n      <td>Boutique</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Zen Sushi Restaurant</td>\n      <td>45.459700</td>\n      <td>9.188670</td>\n      <td>Japanese Restaurant</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Temakinho Magenta</td>\n      <td>45.467926</td>\n      <td>9.174649</td>\n      <td>Sushi Restaurant</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Dolce &amp; Gabbana Martini Bar</td>\n      <td>45.468296</td>\n      <td>9.198412</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Boccondivino</td>\n      <td>45.465182</td>\n      <td>9.175372</td>\n      <td>Wine Bar</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>La Triennale di Milano</td>\n      <td>45.472243</td>\n      <td>9.173507</td>\n      <td>Art Museum</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Colibr\u00ec</td>\n      <td>45.461436</td>\n      <td>9.195804</td>\n      <td>Bar</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Colonne di San Lorenzo</td>\n      <td>45.458266</td>\n      <td>9.181122</td>\n      <td>Historic Site</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Miscusi</td>\n      <td>45.468298</td>\n      <td>9.174258</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Hamerica's</td>\n      <td>45.459639</td>\n      <td>9.181071</td>\n      <td>Burger Joint</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Piazza San Babila</td>\n      <td>45.466667</td>\n      <td>9.197597</td>\n      <td>Plaza</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Osaka</td>\n      <td>45.476259</td>\n      <td>9.184623</td>\n      <td>Japanese Restaurant</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>COS</td>\n      <td>45.467595</td>\n      <td>9.198209</td>\n      <td>Clothing Store</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Villa Necchi Campiglio</td>\n      <td>45.468181</td>\n      <td>9.201570</td>\n      <td>Museum</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Princi</td>\n      <td>45.468877</td>\n      <td>9.199125</td>\n      <td>Bakery</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Libreria Verso</td>\n      <td>45.457272</td>\n      <td>9.180974</td>\n      <td>Bookstore</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Drogheria Milanese</td>\n      <td>45.477477</td>\n      <td>9.188721</td>\n      <td>Restaurant</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Cavoli a Merenda!</td>\n      <td>45.465975</td>\n      <td>9.172271</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Santa Maria delle Grazie</td>\n      <td>45.466139</td>\n      <td>9.170392</td>\n      <td>Church</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>La Dogana Del Buon Gusto</td>\n      <td>45.457296</td>\n      <td>9.181781</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Caff\u00e8 Napoli</td>\n      <td>45.477952</td>\n      <td>9.184552</td>\n      <td>Caf\u00e9</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Giardino della Guastalla</td>\n      <td>45.459850</td>\n      <td>9.197546</td>\n      <td>Park</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Giardini di Villa Reale</td>\n      <td>45.472127</td>\n      <td>9.199721</td>\n      <td>Park</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>La Vigna di Leonardo</td>\n      <td>45.465631</td>\n      <td>9.170902</td>\n      <td>Museum</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Dictionary</td>\n      <td>45.456869</td>\n      <td>9.180853</td>\n      <td>Boutique</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Giardini Indro Montanelli</td>\n      <td>45.474358</td>\n      <td>9.200627</td>\n      <td>Park</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Arco della Pace</td>\n      <td>45.475728</td>\n      <td>9.172447</td>\n      <td>Monument / Landmark</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Museo Cenacolo Vinciano</td>\n      <td>45.466110</td>\n      <td>9.170523</td>\n      <td>Art Museum</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>GAM Galleria d'Arte Moderna di Milano</td>\n      <td>45.472683</td>\n      <td>9.199923</td>\n      <td>Art Gallery</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Osteria Brunello</td>\n      <td>45.479310</td>\n      <td>9.185902</td>\n      <td>Italian Restaurant</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows \u00d7 7 columns</p>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "print('unique categories: {}'.format(len(Milan_Venues['Venue_Category'].unique())))\nprint (Milan_Venues['Venue_Category'].value_counts())", "execution_count": 90, "outputs": [{"output_type": "stream", "text": "unique categories: 159\nItalian Restaurant               78\nPizza Place                      52\nCaf\u00e9                             31\nSupermarket                      29\nHotel                            24\nIce Cream Shop                   22\nPark                             22\nRestaurant                       20\nPlaza                            17\nChinese Restaurant               17\nBakery                           17\nDessert Shop                     16\nJapanese Restaurant              13\nCocktail Bar                     13\nPub                              12\nTram Station                     11\nSeafood Restaurant               11\nTrattoria/Osteria                11\nSushi Restaurant                 11\nArt Gallery                      10\nGym                              10\nWine Bar                          9\nTheater                           9\nSoccer Field                      8\nBoutique                          7\nAsian Restaurant                  6\nGym / Fitness Center              6\nNightclub                         6\nWine Shop                         6\nBar                               6\n                                 ..\nEastern European Restaurant       1\nRoad                              1\nHarbor / Marina                   1\nGovernment Building               1\nSports Club                       1\nOffice                            1\nMarket                            1\nRadio Station                     1\nMexican Restaurant                1\nRock Club                         1\nKitchen Supply Store              1\nChurch                            1\nStables                           1\nArcade                            1\nBus Stop                          1\nPool Hall                         1\nGolf Course                       1\nBelgian Restaurant                1\nPedestrian Plaza                  1\nSports Bar                        1\nClimbing Gym                      1\nCampanian Restaurant              1\nOther Nightlife                   1\nOpera House                       1\nBowling Alley                     1\nComic Shop                        1\nCultural Center                   1\nChocolate Shop                    1\nSri Lankan Restaurant             1\nVegetarian / Vegan Restaurant     1\nName: Venue_Category, Length: 159, dtype: int64\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "code", "source": "# Create a Data-Frame out of it to Concentrate Only on sweet Place\nMilan_sweet = Milan_Venues[Milan_Venues['Venue_Category'].str.contains('Coffee|Ice Cream|Chocolate|Cupcake|Pastry|Dessert|Pastry')].reset_index(drop=True)\nMilan_sweet.index = np.arange(1, len(Milan_sweet)+1)\nprint ('Shape of the Data-Frame with Venue Category Cafe: ', Milan_sweet.shape)\nMilan_sweet.head(1000)", "execution_count": 91, "outputs": [{"output_type": "stream", "text": "Shape of the Data-Frame with Venue Category Cafe:  (49, 7)\n", "name": "stdout"}, {"output_type": "execute_result", "execution_count": 91, "data": {"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     Centro storico      45.467281        9.185962   \n11     Centro storico      45.467281        9.185962   \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        Citt\u00e0 studi      45.489800        9.241031   \n24        Citt\u00e0 studi      45.489800        9.241031   \n25        Citt\u00e0 studi      45.489800        9.241031   \n26        Citt\u00e0 studi      45.489800        9.241031   \n27        Citt\u00e0 studi      45.489800        9.241031   \n28        Citt\u00e0 studi      45.489800        9.241031   \n29        Citt\u00e0 studi      45.489800        9.241031   \n30        Citt\u00e0 studi      45.489800        9.241031   \n31        Citt\u00e0 studi      45.489800        9.241031   \n32        Citt\u00e0 studi      45.489800        9.241031   \n33     Porta vittoria      45.446205        9.239343   \n34     Porta vittoria      45.446205        9.239343   \n35          Vigentino      45.419846        9.198153   \n36          Vigentino      45.419846        9.198153   \n37          Vigentino      45.419846        9.198153   \n38             Barona      45.437929        9.145648   \n39             Barona      45.437929        9.145648   \n40             Baggio      45.469598        9.114757   \n41             Baggio      45.469598        9.114757   \n42             Baggio      45.469598        9.114757   \n43             Baggio      45.469598        9.114757   \n44             Baggio      45.469598        9.114757   \n45    Porta garibaldi      45.507704        9.179410   \n46    Porta garibaldi      45.507704        9.179410   \n47    Porta garibaldi      45.507704        9.179410   \n48    Porta garibaldi      45.507704        9.179410   \n49    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           Ciacco. Gelato senz'altro  45.463704    9.186796  Ice Cream Shop  \n3                            Gay Odin  45.466187    9.180801  Chocolate Shop  \n4              Di Viole Di Liquirizia  45.471460    9.185336    Cupcake Shop  \n5                Pasticceria Marchesi  45.465612    9.190091     Pastry Shop  \n6               Lavazza Coffee Design  45.466274    9.190975     Coffee Shop  \n7                Pasticceria Marchesi  45.468299    9.195018    Dessert Shop  \n8                                Cova  45.468170    9.195347    Dessert Shop  \n9                     Garibaldi Cr\u00e8me  45.474355    9.183466  Ice Cream Shop  \n10                           Chocolat  45.467660    9.174160  Ice Cream Shop  \n11  Ammu - Cannoli espressi siciliani  45.466059    9.176548    Dessert Shop  \n12              Pasticceria Martesana  45.495824    9.203095    Dessert Shop  \n13                   Gelateria Etnica  45.489586    9.217686  Ice Cream Shop  \n14           Pasticceria La Siciliana  45.488775    9.229817    Cupcake Shop  \n15                  Gelateria Sartori  45.486618    9.206877  Ice Cream Shop  \n16  MAG - Mastri Artigiani del Gelato  45.481940    9.221057  Ice Cream Shop  \n17                              Terra  45.482487    9.206623  Ice Cream Shop  \n18                 Pasticceria Grossi  45.491280    9.237180    Dessert Shop  \n19                 Pasticceria Ungaro  45.489943    9.237042    Dessert Shop  \n20                Pasticceria Valente  45.481174    9.220150    Dessert Shop  \n21                Torrefazione Pacini  45.483298    9.231442     Coffee Shop  \n22                     Antica Sicilia  45.481146    9.216545    Dessert Shop  \n23                          Ice Dream  45.489197    9.242528  Ice Cream Shop  \n24                 Pasticceria Ungaro  45.489943    9.237042    Dessert Shop  \n25                 Pasticceria Grossi  45.491280    9.237180    Dessert Shop  \n26           Pasticceria La Siciliana  45.488775    9.229817    Cupcake Shop  \n27                Torrefazione Pacini  45.483298    9.231442     Coffee Shop  \n28  MAG - Mastri Artigiani del Gelato  45.481940    9.221057  Ice Cream Shop  \n29                Pasticceria Valente  45.481174    9.220150    Dessert Shop  \n30                   Gelateria Etnica  45.489586    9.217686  Ice Cream Shop  \n31                Pasticceria Eoliana  45.471034    9.242228    Dessert Shop  \n32                     Antica Sicilia  45.481146    9.216545    Dessert Shop  \n33               Gelateria Mela Verde  45.456911    9.222148  Ice Cream Shop  \n34                    Fonte Del Dolce  45.438798    9.216180    Dessert Shop  \n35    Gelateria Merelli - Caffetteria  45.433500    9.183310  Ice Cream Shop  \n36                         Bar Chopin  45.425268    9.204760     Coffee Shop  \n37                       Cream Garden  45.436100    9.200950  Ice Cream Shop  \n38          La Gelateria della Musica  45.446640    9.155163  Ice Cream Shop  \n39            Pasticceria Castelnuovo  45.449784    9.137536    Dessert Shop  \n40                 Gelateria Floriana  45.474316    9.116486  Ice Cream Shop  \n41                     Baci Sottozero  45.464690    9.136550  Ice Cream Shop  \n42                Gelateria Rembrandt  45.467426    9.139209  Ice Cream Shop  \n43                 Gelateria Naturale  45.461954    9.135743  Ice Cream Shop  \n44                  Pasticceria Romeo  45.461923    9.137271    Cupcake Shop  \n45                          MiSciolgo  45.499214    9.165308  Ice Cream Shop  \n46            La Gelateria di Dergano  45.502240    9.176052  Ice Cream Shop  \n47      Artico Gelateria Tradizionale  45.489179    9.186669  Ice Cream Shop  \n48            Le Botteghe di Leonardo  45.487135    9.188765  Ice Cream Shop  \n49              Pasticceria Martesana  45.495824    9.203095    Dessert Shop  ", "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>Ciacco. Gelato senz'altro</td>\n      <td>45.463704</td>\n      <td>9.186796</td>\n      <td>Ice Cream Shop</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>Gay Odin</td>\n      <td>45.466187</td>\n      <td>9.180801</td>\n      <td>Chocolate Shop</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>Di Viole Di Liquirizia</td>\n      <td>45.471460</td>\n      <td>9.185336</td>\n      <td>Cupcake 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>Pasticceria Marchesi</td>\n      <td>45.465612</td>\n      <td>9.190091</td>\n      <td>Pastry Shop</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>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>7</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Pasticceria Marchesi</td>\n      <td>45.468299</td>\n      <td>9.195018</td>\n      <td>Dessert Shop</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>Cova</td>\n      <td>45.468170</td>\n      <td>9.195347</td>\n      <td>Dessert Shop</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>Garibaldi Cr\u00e8me</td>\n      <td>45.474355</td>\n      <td>9.183466</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Chocolat</td>\n      <td>45.467660</td>\n      <td>9.174160</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Centro storico</td>\n      <td>45.467281</td>\n      <td>9.185962</td>\n      <td>Ammu - Cannoli espressi siciliani</td>\n      <td>45.466059</td>\n      <td>9.176548</td>\n      <td>Dessert Shop</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>Pasticceria Martesana</td>\n      <td>45.495824</td>\n      <td>9.203095</td>\n      <td>Dessert Shop</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>Gelateria Etnica</td>\n      <td>45.489586</td>\n      <td>9.217686</td>\n      <td>Ice Cream Shop</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>Pasticceria La Siciliana</td>\n      <td>45.488775</td>\n      <td>9.229817</td>\n      <td>Cupcake Shop</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>Gelateria Sartori</td>\n      <td>45.486618</td>\n      <td>9.206877</td>\n      <td>Ice Cream Shop</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>MAG - Mastri Artigiani del Gelato</td>\n      <td>45.481940</td>\n      <td>9.221057</td>\n      <td>Ice Cream Shop</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>Terra</td>\n      <td>45.482487</td>\n      <td>9.206623</td>\n      <td>Ice Cream Shop</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>Pasticceria Grossi</td>\n      <td>45.491280</td>\n      <td>9.237180</td>\n      <td>Dessert Shop</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>Pasticceria Ungaro</td>\n      <td>45.489943</td>\n      <td>9.237042</td>\n      <td>Dessert Shop</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>Pasticceria Valente</td>\n      <td>45.481174</td>\n      <td>9.220150</td>\n      <td>Dessert Shop</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>Torrefazione Pacini</td>\n      <td>45.483298</td>\n      <td>9.231442</td>\n      <td>Coffee Shop</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>Antica Sicilia</td>\n      <td>45.481146</td>\n      <td>9.216545</td>\n      <td>Dessert Shop</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489800</td>\n      <td>9.241031</td>\n      <td>Ice Dream</td>\n      <td>45.489197</td>\n      <td>9.242528</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489800</td>\n      <td>9.241031</td>\n      <td>Pasticceria Ungaro</td>\n      <td>45.489943</td>\n      <td>9.237042</td>\n      <td>Dessert Shop</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489800</td>\n      <td>9.241031</td>\n      <td>Pasticceria Grossi</td>\n      <td>45.491280</td>\n      <td>9.237180</td>\n      <td>Dessert Shop</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489800</td>\n      <td>9.241031</td>\n      <td>Pasticceria La Siciliana</td>\n      <td>45.488775</td>\n      <td>9.229817</td>\n      <td>Cupcake Shop</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489800</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>28</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489800</td>\n      <td>9.241031</td>\n      <td>MAG - Mastri Artigiani del Gelato</td>\n      <td>45.481940</td>\n      <td>9.221057</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489800</td>\n      <td>9.241031</td>\n      <td>Pasticceria Valente</td>\n      <td>45.481174</td>\n      <td>9.220150</td>\n      <td>Dessert Shop</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489800</td>\n      <td>9.241031</td>\n      <td>Gelateria Etnica</td>\n      <td>45.489586</td>\n      <td>9.217686</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489800</td>\n      <td>9.241031</td>\n      <td>Pasticceria Eoliana</td>\n      <td>45.471034</td>\n      <td>9.242228</td>\n      <td>Dessert Shop</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Citt\u00e0 studi</td>\n      <td>45.489800</td>\n      <td>9.241031</td>\n      <td>Antica Sicilia</td>\n      <td>45.481146</td>\n      <td>9.216545</td>\n      <td>Dessert Shop</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Porta vittoria</td>\n      <td>45.446205</td>\n      <td>9.239343</td>\n      <td>Gelateria Mela Verde</td>\n      <td>45.456911</td>\n      <td>9.222148</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Porta vittoria</td>\n      <td>45.446205</td>\n      <td>9.239343</td>\n      <td>Fonte Del Dolce</td>\n      <td>45.438798</td>\n      <td>9.216180</td>\n      <td>Dessert Shop</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Vigentino</td>\n      <td>45.419846</td>\n      <td>9.198153</td>\n      <td>Gelateria Merelli - Caffetteria</td>\n      <td>45.433500</td>\n      <td>9.183310</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>Vigentino</td>\n      <td>45.419846</td>\n      <td>9.198153</td>\n      <td>Bar Chopin</td>\n      <td>45.425268</td>\n      <td>9.204760</td>\n      <td>Coffee Shop</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Vigentino</td>\n      <td>45.419846</td>\n      <td>9.198153</td>\n      <td>Cream Garden</td>\n      <td>45.436100</td>\n      <td>9.200950</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Barona</td>\n      <td>45.437929</td>\n      <td>9.145648</td>\n      <td>La Gelateria della Musica</td>\n      <td>45.446640</td>\n      <td>9.155163</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Barona</td>\n      <td>45.437929</td>\n      <td>9.145648</td>\n      <td>Pasticceria Castelnuovo</td>\n      <td>45.449784</td>\n      <td>9.137536</td>\n      <td>Dessert Shop</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Baggio</td>\n      <td>45.469598</td>\n      <td>9.114757</td>\n      <td>Gelateria Floriana</td>\n      <td>45.474316</td>\n      <td>9.116486</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Baggio</td>\n      <td>45.469598</td>\n      <td>9.114757</td>\n      <td>Baci Sottozero</td>\n      <td>45.464690</td>\n      <td>9.136550</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>Baggio</td>\n      <td>45.469598</td>\n      <td>9.114757</td>\n      <td>Gelateria Rembrandt</td>\n      <td>45.467426</td>\n      <td>9.139209</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>Baggio</td>\n      <td>45.469598</td>\n      <td>9.114757</td>\n      <td>Gelateria Naturale</td>\n      <td>45.461954</td>\n      <td>9.135743</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>Baggio</td>\n      <td>45.469598</td>\n      <td>9.114757</td>\n      <td>Pasticceria Romeo</td>\n      <td>45.461923</td>\n      <td>9.137271</td>\n      <td>Cupcake Shop</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>MiSciolgo</td>\n      <td>45.499214</td>\n      <td>9.165308</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</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>47</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</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>48</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>Le Botteghe di Leonardo</td>\n      <td>45.487135</td>\n      <td>9.188765</td>\n      <td>Ice Cream Shop</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>Porta garibaldi</td>\n      <td>45.507704</td>\n      <td>9.179410</td>\n      <td>Pasticceria Martesana</td>\n      <td>45.495824</td>\n      <td>9.203095</td>\n      <td>Dessert Shop</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "\n!pip install folium\n\nimport folium", "execution_count": 92, "outputs": [{"output_type": "stream", "text": "Requirement already satisfied: folium in /opt/conda/envs/Python36/lib/python3.6/site-packages (0.11.0)\r\nRequirement already satisfied: branca>=0.3.0 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (0.4.1)\r\nRequirement already satisfied: jinja2>=2.9 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.10)\r\nRequirement already satisfied: numpy in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (1.15.4)\r\nRequirement already satisfied: requests in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.21.0)\r\nRequirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from jinja2>=2.9->folium) (1.1.0)\r\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2020.6.20)\r\nRequirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2.8)\r\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (3.0.4)\r\nRequirement already satisfied: urllib3<1.25,>=1.21.1 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (1.24.1)\r\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "code", "source": "#get coordinates of Milan\nfrom geopy.geocoders import Nominatim\ngeolocator = Nominatim(user_agent=\"milan_locator\")\nlocation = geolocator.geocode(\"Milano, MI, Lom, Italia\")\nprint(location.address)\n                              \nlatitude = location.latitude\nlongitude = location.longitude\n                              \nprint(latitude)\nprint(longitude)", "execution_count": 93, "outputs": [{"output_type": "stream", "text": "Milano, Lombardia, Italia\n45.4668\n9.1905\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "code", "source": "Townhall = df_top_townhall.Name\nTownhall", "execution_count": 94, "outputs": [{"output_type": "execute_result", "execution_count": 94, "data": {"text/plain": "0       Centro storico\n1    Stazione centrale\n2          Citt\u00e0 studi\n3       Porta vittoria\n4            Vigentino\n5               Barona\n6               Baggio\n7      Porta garibaldi\nName: Name, dtype: object"}, "metadata": {}}]}, {"metadata": {"scrolled": false}, "cell_type": "code", "source": "## Show in Map the Top Rated Cafe in the Top 4 Municipalties\n\nmap_sweet = 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\n#Townhall = ['Centro storico', 'Stazione centrale', 'Porta garibaldi', 'Citt\u00e0 studi', 'Porta vittoria','Vigentino']\n\nTownhall = [\"Centro storico\", \"Stazione centrale\", \"Citt\u00e0 studi\", \"Porta garibaldi\", \"Porta vittoria\", \"Vigentino\",\"Fiera\",\"Barona\",\"Baggio\"]\n\nx = np.arange(len(Townhall))\n\n#rainbow = ['#0000FF', '#FF0080','#FF8040','#FFFF00' ,'#FF0000', '#00FF00']\nrainbow = ['#0000FF', '#FF0080','#FF8040','#FFFF00' ,'#FF0000', '#00FF00','#800080','#408080','#800000']\n\n# add markers to the map\nmarkers_colors = []\nfor lat, lon, poi, distr in zip(Milan_sweet['Venue_Lat'], \n                                  Milan_sweet['Venue_Long'], \n                                  Milan_sweet['Venue_Category'], \n                                  Milan_sweet['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[Townhall.index(distr)-1],\n        fill=True,\n        fill_color=rainbow[Townhall.index(distr)-1],\n        fill_opacity=0.3).add_to(map_sweet)\n       \nmap_sweet", "execution_count": 96, "outputs": [{"output_type": "execute_result", "execution_count": 96, "data": {"text/plain": "<folium.folium.Map at 0x7f0e3600b0b8>", "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
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        <script>
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "# create a dataframe to calculate the number of cafes for each district\nMilan_sweet_by_district = Milan_sweet['District'].value_counts()[0:10].to_frame(name='frequency')\nMilan_sweet_by_district=Milan_sweet_by_district.reset_index()\n\nMilan_sweet_by_district.rename(index=str, columns={\"index\": \"Name\", \"frequency\": \"Count\"}, inplace=True)\nMilan_sweet_by_district", "execution_count": 97, "outputs": [{"output_type": "execute_result", "execution_count": 97, "data": {"text/plain": "                Name  Count\n0  Stazione centrale     11\n1     Centro storico     11\n2        Citt\u00e0 studi     10\n3             Baggio      5\n4    Porta garibaldi      5\n5          Vigentino      3\n6             Barona      2\n7     Porta vittoria      2", "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>11</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Centro storico</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Citt\u00e0 studi</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Baggio</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Porta garibaldi</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Vigentino</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Barona</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Porta vittoria</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "df_top_townhall = pd.merge(df_top_townhall, Milan_sweet_by_district, on='Name', how='inner')\n", "execution_count": 98, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "df_top_townhall", "execution_count": 99, "outputs": [{"output_type": "execute_result", "execution_count": 99, "data": {"text/plain": "  Townhall                                        Description   Area  \\\n0        1                                     Centro storico   9.67   \n1        2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58   \n2        3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23   \n3        4                          Porta Vittoria, Forlanini  20.95   \n4        5                Vigentino, Chiaravalle, Gratosoglio  29.87   \n5        6                                Barona, Lorenteggio  18.28   \n6        7                        Baggio, De Angeli, San Siro  31.34   \n7        9                          Porta Garibaldi, Niguarda  21.12   \n\n  Population Density               Name   Latitude  Longitude        Price  \\\n0     96,315  11,074     Centro storico  45.467281   9.185962  \u20ac 7.100 /m\u00b2   \n1    153.109  13,031  Stazione centrale  45.499990   9.218999  \u20ac 5.000 /m\u00b2   \n2    141,229  10,785        Citt\u00e0 studi  45.489800   9.241031  \u20ac 4.500 /m\u00b2   \n3    156.369   8,069     Porta vittoria  45.446205   9.239343  \u20ac 6.450 /m\u00b2   \n4    123,779   4,487          Vigentino  45.419846   9.198153  \u20ac 3.250 /m\u00b2   \n5    149,000   8,998             Barona  45.437929   9.145648  \u20ac 3.200 /m\u00b2   \n6    170,814   6,093             Baggio  45.469598   9.114757  \u20ac 2.400 /m\u00b2   \n7    181,598   9,204    Porta garibaldi  45.507704   9.179410  \u20ac 6.800 /m\u00b2   \n\n   Count  \n0     11  \n1     11  \n2     10  \n3      2  \n4      3  \n5      2  \n6      5  \n7      5  ", "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>Townhall</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 7.100 /m\u00b2</td>\n      <td>11</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 5.000 /m\u00b2</td>\n      <td>11</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>45.489800</td>\n      <td>9.241031</td>\n      <td>\u20ac 4.500 /m\u00b2</td>\n      <td>10</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      <td>\u20ac 6.450 /m\u00b2</td>\n      <td>2</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      <td>\u20ac 3.250 /m\u00b2</td>\n      <td>3</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      <td>\u20ac 3.200 /m\u00b2</td>\n      <td>2</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      <td>\u20ac 2.400 /m\u00b2</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>7</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 6.800 /m\u00b2</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "df_top_townhall['Population'] = df_top_townhall['Population'].str.replace(',', '')\ndf_top_townhall['Population'] = df_top_townhall['Population'].str.replace('.', '')\ndf_top_townhall['Density'] = df_top_townhall['Density'].str.replace(',', '')", "execution_count": 100, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "df_top_townhall['Area'] = df_top_townhall['Area'].astype(float)\ndf_top_townhall['Population'] = df_top_townhall['Population'].astype(float)\ndf_top_townhall['Density'] = df_top_townhall['Density'].astype(float)\n\ndf_top_townhall", "execution_count": 101, "outputs": [{"output_type": "execute_result", "execution_count": 101, "data": {"text/plain": "  Townhall                                        Description   Area  \\\n0        1                                     Centro storico   9.67   \n1        2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58   \n2        3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23   \n3        4                          Porta Vittoria, Forlanini  20.95   \n4        5                Vigentino, Chiaravalle, Gratosoglio  29.87   \n5        6                                Barona, Lorenteggio  18.28   \n6        7                        Baggio, De Angeli, San Siro  31.34   \n7        9                          Porta Garibaldi, Niguarda  21.12   \n\n   Population  Density               Name   Latitude  Longitude        Price  \\\n0     96315.0  11074.0     Centro storico  45.467281   9.185962  \u20ac 7.100 /m\u00b2   \n1    153109.0  13031.0  Stazione centrale  45.499990   9.218999  \u20ac 5.000 /m\u00b2   \n2    141229.0  10785.0        Citt\u00e0 studi  45.489800   9.241031  \u20ac 4.500 /m\u00b2   \n3    156369.0   8069.0     Porta vittoria  45.446205   9.239343  \u20ac 6.450 /m\u00b2   \n4    123779.0   4487.0          Vigentino  45.419846   9.198153  \u20ac 3.250 /m\u00b2   \n5    149000.0   8998.0             Barona  45.437929   9.145648  \u20ac 3.200 /m\u00b2   \n6    170814.0   6093.0             Baggio  45.469598   9.114757  \u20ac 2.400 /m\u00b2   \n7    181598.0   9204.0    Porta garibaldi  45.507704   9.179410  \u20ac 6.800 /m\u00b2   \n\n   Count  \n0     11  \n1     11  \n2     10  \n3      2  \n4      3  \n5      2  \n6      5  \n7      5  ", "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>Townhall</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>\u20ac 7.100 /m\u00b2</td>\n      <td>11</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>\u20ac 5.000 /m\u00b2</td>\n      <td>11</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>45.489800</td>\n      <td>9.241031</td>\n      <td>\u20ac 4.500 /m\u00b2</td>\n      <td>10</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>156369.0</td>\n      <td>8069.0</td>\n      <td>Porta vittoria</td>\n      <td>45.446205</td>\n      <td>9.239343</td>\n      <td>\u20ac 6.450 /m\u00b2</td>\n      <td>2</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>123779.0</td>\n      <td>4487.0</td>\n      <td>Vigentino</td>\n      <td>45.419846</td>\n      <td>9.198153</td>\n      <td>\u20ac 3.250 /m\u00b2</td>\n      <td>3</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>149000.0</td>\n      <td>8998.0</td>\n      <td>Barona</td>\n      <td>45.437929</td>\n      <td>9.145648</td>\n      <td>\u20ac 3.200 /m\u00b2</td>\n      <td>2</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>170814.0</td>\n      <td>6093.0</td>\n      <td>Baggio</td>\n      <td>45.469598</td>\n      <td>9.114757</td>\n      <td>\u20ac 2.400 /m\u00b2</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>7</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>\u20ac 6.800 /m\u00b2</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "df_top_townhall['PopulationByVenue'] = df_top_townhall['Population']/df_top_townhall['Count']\n", "execution_count": 102, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "df_top_townhall\n", "execution_count": 103, "outputs": [{"output_type": "execute_result", "execution_count": 103, "data": {"text/plain": "  Townhall                                        Description   Area  \\\n0        1                                     Centro storico   9.67   \n1        2  Stazione Centrale, Gorla, Turro, Greco, Cresce...  12.58   \n2        3               Citt\u00e0 Studi, Lambrate, Porta Venezia  14.23   \n3        4                          Porta Vittoria, Forlanini  20.95   \n4        5                Vigentino, Chiaravalle, Gratosoglio  29.87   \n5        6                                Barona, Lorenteggio  18.28   \n6        7                        Baggio, De Angeli, San Siro  31.34   \n7        9                          Porta Garibaldi, Niguarda  21.12   \n\n   Population  Density               Name   Latitude  Longitude        Price  \\\n0     96315.0  11074.0     Centro storico  45.467281   9.185962  \u20ac 7.100 /m\u00b2   \n1    153109.0  13031.0  Stazione centrale  45.499990   9.218999  \u20ac 5.000 /m\u00b2   \n2    141229.0  10785.0        Citt\u00e0 studi  45.489800   9.241031  \u20ac 4.500 /m\u00b2   \n3    156369.0   8069.0     Porta vittoria  45.446205   9.239343  \u20ac 6.450 /m\u00b2   \n4    123779.0   4487.0          Vigentino  45.419846   9.198153  \u20ac 3.250 /m\u00b2   \n5    149000.0   8998.0             Barona  45.437929   9.145648  \u20ac 3.200 /m\u00b2   \n6    170814.0   6093.0             Baggio  45.469598   9.114757  \u20ac 2.400 /m\u00b2   \n7    181598.0   9204.0    Porta garibaldi  45.507704   9.179410  \u20ac 6.800 /m\u00b2   \n\n   Count  PopulationByVenue  \n0     11        8755.909091  \n1     11       13919.000000  \n2     10       14122.900000  \n3      2       78184.500000  \n4      3       41259.666667  \n5      2       74500.000000  \n6      5       34162.800000  \n7      5       36319.600000  ", "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>Townhall</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      <th>PopulationByVenue</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>\u20ac 7.100 /m\u00b2</td>\n      <td>11</td>\n      <td>8755.909091</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>\u20ac 5.000 /m\u00b2</td>\n      <td>11</td>\n      <td>13919.000000</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>45.489800</td>\n      <td>9.241031</td>\n      <td>\u20ac 4.500 /m\u00b2</td>\n      <td>10</td>\n      <td>14122.900000</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>156369.0</td>\n      <td>8069.0</td>\n      <td>Porta vittoria</td>\n      <td>45.446205</td>\n      <td>9.239343</td>\n      <td>\u20ac 6.450 /m\u00b2</td>\n      <td>2</td>\n      <td>78184.500000</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>123779.0</td>\n      <td>4487.0</td>\n      <td>Vigentino</td>\n      <td>45.419846</td>\n      <td>9.198153</td>\n      <td>\u20ac 3.250 /m\u00b2</td>\n      <td>3</td>\n      <td>41259.666667</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>149000.0</td>\n      <td>8998.0</td>\n      <td>Barona</td>\n      <td>45.437929</td>\n      <td>9.145648</td>\n      <td>\u20ac 3.200 /m\u00b2</td>\n      <td>2</td>\n      <td>74500.000000</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>170814.0</td>\n      <td>6093.0</td>\n      <td>Baggio</td>\n      <td>45.469598</td>\n      <td>9.114757</td>\n      <td>\u20ac 2.400 /m\u00b2</td>\n      <td>5</td>\n      <td>34162.800000</td>\n    </tr>\n    <tr>\n      <th>7</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>\u20ac 6.800 /m\u00b2</td>\n      <td>5</td>\n      <td>36319.600000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "import seaborn as sns\nfig = plt.figure(figsize=(18,7))\ns=sns.barplot(x=\"Name\", y=\"Count\", data=df_top_townhall)\ns.set_xticklabels(s.get_xticklabels(), rotation=30)\nplt.title('Distribution of Sweet place related venues in the main districts of Milan', fontsize=15)\nplt.xlabel('Townhall')\nplt.ylabel ('Frequency', fontsize=15)\nplt.savefig(\"Most_Freq_Venues.png\", dpi=300)\nplt.show()", "execution_count": 105, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 1296x504 with 1 Axes>", "image/png": 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\n"}, "metadata": {"needs_background": "light"}}]}, {"metadata": {}, "cell_type": "code", "source": "import seaborn as sns\nfig = plt.figure(figsize=(18,7))\ns=sns.barplot(x=\"Name\", y=\"PopulationByVenue\", data=df_top_townhall)\ns.set_xticklabels(s.get_xticklabels(), rotation=30)\nplt.title('Distribution of Sweet place related venues in the main districts of Milan', fontsize=15)\nplt.xlabel('Townhall')\nplt.ylabel ('in habitants per venue', fontsize=15)\nplt.savefig(\"Most_Freq_Venues.png\", dpi=300)\nplt.show()", "execution_count": 106, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 1296x504 with 1 Axes>", "image/png": 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\n"}, "metadata": {"needs_background": "light"}}]}, {"metadata": {}, "cell_type": "code", "source": "# create a dataframe of top 10 categories\nMilan_Venues_Top = Milan_Pizza[['District','Venue_Category']]\nMilan_Venues_Top = Milan_Venues_Top.groupby(['District','Venue_Category']).size().to_frame('count').reset_index()\n\n\nMilan_Venues_Top", "execution_count": 107, "outputs": [{"output_type": "execute_result", "execution_count": 107, "data": {"text/plain": "             District  Venue_Category  count\n0      Centro storico  Chocolate Shop      1\n1      Centro storico     Coffee Shop      2\n2      Centro storico    Cupcake Shop      1\n3      Centro storico    Dessert Shop      3\n4      Centro storico  Ice Cream Shop      3\n5      Centro storico     Pastry Shop      1\n6         Citt\u00e0 studi     Coffee Shop      1\n7         Citt\u00e0 studi    Cupcake Shop      1\n8         Citt\u00e0 studi    Dessert Shop      5\n9         Citt\u00e0 studi  Ice Cream Shop      3\n10    Porta garibaldi    Dessert Shop      1\n11    Porta garibaldi  Ice Cream Shop      4\n12     Porta vittoria    Dessert Shop      1\n13     Porta vittoria  Ice Cream Shop      1\n14  Stazione centrale     Coffee Shop      1\n15  Stazione centrale    Cupcake Shop      1\n16  Stazione centrale    Dessert Shop      5\n17  Stazione centrale  Ice Cream Shop      4\n18          Vigentino     Coffee Shop      1\n19          Vigentino  Ice Cream Shop      2", "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>Venue_Category</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Centro storico</td>\n      <td>Chocolate Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Centro storico</td>\n      <td>Coffee Shop</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Centro storico</td>\n      <td>Cupcake Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Centro storico</td>\n      <td>Dessert Shop</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Centro storico</td>\n      <td>Ice Cream Shop</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Centro storico</td>\n      <td>Pastry Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Citt\u00e0 studi</td>\n      <td>Coffee Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Citt\u00e0 studi</td>\n      <td>Cupcake Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Citt\u00e0 studi</td>\n      <td>Dessert Shop</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Citt\u00e0 studi</td>\n      <td>Ice Cream Shop</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Porta garibaldi</td>\n      <td>Dessert Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Porta garibaldi</td>\n      <td>Ice Cream Shop</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Porta vittoria</td>\n      <td>Dessert Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Porta vittoria</td>\n      <td>Ice Cream Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Stazione centrale</td>\n      <td>Coffee Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Stazione centrale</td>\n      <td>Cupcake Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Stazione centrale</td>\n      <td>Dessert Shop</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Stazione centrale</td>\n      <td>Ice Cream Shop</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Vigentino</td>\n      <td>Coffee Shop</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Vigentino</td>\n      <td>Ice Cream Shop</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "code", "source": "\nimport seaborn as sns\nfig = plt.figure(figsize=(18,7))\ns=sns.barplot(x='Venue_Category', y='count', hue='District', data=Milan_Venues_Top)\ns.set_xticklabels(s.get_xticklabels(), rotation=30)\nplt.title('6 Most Frequently Occuring Venues in Major Districts of Milan', fontsize=15)\nplt.xlabel(\"Townhall Category\", fontsize=15)\nplt.ylabel (\"Frequency\", fontsize=15)\nplt.savefig(\"Most_Freq_Venues.png\", dpi=300)\nplt.show()", "execution_count": 108, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 1296x504 with 1 Axes>", "image/png": 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nSv6cFbftdmCIPzlkC7zE2Etm1tQ5t6mITUI5todjBt5TlL5yITyxKEh7vOTi+c657LyFZThvREQqhHo8iMjxaB4Q709umKcD3njXT0OoZzbehcHoEu4OfgRcbAVn42+Fd9d3aXBh59zXzrkxeGOM83pfVMTdprV4F54xRfSWSPd/iIM3N8HlQd3dC9wJ9Luu/5eA7sX+BV3wMJb3gOZ4vU+C2yvLo+YClbkHhP+j+z285E4byjbMYjze+1jUUJyH8CbBm+K//gDvorF/Me3/jPcovOtKGDawBahnBZ9+0rmYsuXlS7zeDX8MWh7KEw2mA4l4iR2H9xkJ9B5ed+2finjPQ3085Rb/38Bu7B0oeB6swtunRsWc1z8FVuicO+ic+xBvjo26VE7X8jfx5iYY4/eAKEpNCj9B59qiCgb4Au9Y9ApafjVesmxdoS0Okz/h7ji8oTzB50Ahznvc5ivAcxTsdXZYPZsCvAf0zZvItwglfrc6TybeZLwRFB6SkCfcx3Ym3v9rjuSxtHmTpeafN/732W+LLi4iUrHU40FEjkeT8WYrf93MRuNdYDyMN1a+UDKgOM57SsIVpRR7DO+JBu+Y2cN43YrH4F0QzQEws3/j3RFbjjfHQUe8cdp3+3V85v97s5nNAPY654rqlXDY/LuDfwWeNbNT8OZeOACchTfj/B/9cdIP4z19YbqZpQLxeL0Ggs0FBpnZp3gXtAPxn+gQYCze0w3eN7OJeJOm/RbvqR9pzrmXQtiF9UBDM+uH9yP/+6AxysH+g3eR/CXeeP/SjMObvO81M3sEr4v7yXj71RW41jn3LXh3S/3z6gEzq4H35I+aeJOE/t05tw3vvZ2P9zSHp/Em8GyLN2HeW/42OUCqmT0O/A/erPxh45w7YGZjgdFm9iPeneoe/HphX5YeCa/jTQh4C97cJsHDcObh9Q56z/88rMN7MkGSH8P9IYS8FG+YzxNm9gDe3ea/4D09JG+fcs3sLuBpM4vCO+a5eMezB97kstF4n8UX8IYW1QLuwktsbKSC+Z+zq0op9i7enBR/w+s1dDm/zhlTXL0HzOwfwP+Z2U5gIV7X/euBO4uZo6asWpvZQbx5M+LxztXTgSuKS574Qxf64D39YQvesJcbKPh5XI+X6FyI976uw+tRVVb34yV/08xsHN73bDKwxTn3gl8/eL0b5gC7nXNrzOwjvF4Ga/B6wt2C1wsro6hGwnxscc5tpfT/15TmA7wE6X/875QYvOPzbUkbiYhUFPV4EJHjjn/HuRPeXA8z8B5z9h5Fd/8+0ra+x0sk5OBd6D6B91i/zgETAi7Du1P7LN7dzh54M6q/4tfxJd7j4nri/Xgsrvv1kcb6ot92S7xJ0ubgzZT+MV4SAr+b7zV4Y4NfwbuYLupZ9Pfjjfkf7e9XOt7dzMD2vsPrcbAJ78J+Pl5ioy5FD/coyXTgebxEzwrgvlLKv453IZ1algke/QuHy/Ge4HEVXmIgFe//kx38CUoDy/8Db0z2xXgTBk7C2689/vqFQBe8MejT8O5otsOfJ8JPTvTCmzviVbxjXNod7fIwFvgX3hCJOXiJsof9dcHzhBTinNuLt79GEWPR/Z5Bl+Ht81147/lTeBeDH4QSqHMuB+98rebHehve0zt2B5WbijfRZWu/3By8MfzL8c6B3XjJvTvxEiNT8L4bujjnDoQSUwWagDfM6694+3Mq3hMPipJ/fjvnJvjb9Mbb157AMOfcuCOMZyleQnIuXiLyDSDOOfdeCdtswLvB9TDeefBPvHMnMMF2J16i4S28z3VcKEE551bjDTHYhfe+voz3Of7KX78Bb36dPn78c/xNl+E9xvVlvPO4Lt5TPoqdVyKMx7ZcOOe24MXWGO8434qX6CkpQSsiUmGsDL/HRETkBOb3kPgJ6OffRTzqmdnleImT//Enb5Ni+D1b2jvn/qeyY5GyM7MGeD1Cejrn5lZ2PCIiIiXRUAsRETlumFlDvHHao/EeC6qkQwAza4F3pzavN8CleHfS76zMuCQ0ZvZ7vLvZh/AeDSkiInJUU+JBRESOJ0PwJopLx5vnQwragzfHxjC88fpfAn89WrqLS5k9ijcs4XYl10RE5FigoRYiIiIiIiIiEjaaXFJEREREREREwuaoH2oRHR3tYmJiKjsMEREREREREQmQkZHxg3OuQWnljvrEQ0xMDOnp6ZUdhoiIiIiIiIgEMLMyPbZXQy1EREREREREJGyUeBARERERERGRsFHiQURERERERETC5qif40FEROR4dODAAbZs2UJOTk5lhyJhVKNGDc444wyqVq1a2aGIiIhUGiUeREREKsGWLVuoW7cuMTExmFllhyNh4Jzjxx9/ZMuWLTRu3LiywxEREak0GmohIiJSCXJycqhfv76SDscxM6N+/frq1SIiIic8JR5EREQqiZIOxz+9xyIiIko8iIiIiIiIiEgYKfEgIiIiZRIREUFCQgLNmzenRYsWPPbYYxw6dAiA9PR0hg0bVuy22dnZTJs2rdj1W7du5aqrriqx/XHjxrF3797DC15EREQqjRIPIiIiUiY1a9YkMzOTNWvW8O677/Lmm2/ywAMPAJCcnMz48eOL3bakxENubi4NGzZk9uzZJbavxIOIiMixSU+1EBERkZCdeuqpTJ48mVatWjFy5EgWLVrE2LFjmTdvHosWLeLPf/4z4M1xsHjxYoYPH866detISEigf//+1KtXjzfeeIOcnBz27NnDlClTuOyyy1i9ejUHDx7k7rvv5p133sHMGDhwIM45tm7dSseOHYmOjmbhwoWVfARERESkrJR4EBERkcNy1llncejQIb777rsCy8eOHcsTTzxB27Zt2b17NzVq1GDMmDH5iQmA1NRUli1bRlZWFlFRUWRnZ+dvP3nyZL744gs++eQTIiMj2b59O1FRUTz22GMsXLiQ6OjoitxNEREROUIaaiEiIiKHzTlXaFnbtm258847GT9+PDt27CAysuj7HJ07dyYqKqrQ8gULFjB48OD87YoqIyIiIseOCk88mFm2ma0ys0wzS6/o9kVERKR8fP7550RERHDqqacWWD58+HCeeeYZ9u3bR5s2bVi/fn2R29euXbvI5c45PYZSRETkOFJZPR46OucSnHPJldS+iIiIHIHvv/+ewYMHM3To0EJJgs2bNxMXF8fdd99NcnIy69evp27duuzatatMdXfp0oVJkyaRm5sLwPbt2wFCqkNERESOHprjQURERMpk3759JCQkcODAASIjI+nXrx933nlnoXLjxo1j4cKFRERE8Pvf/55LLrmEKlWqEBkZSYsWLRgwYAD16tUrtp2bbrqJDRs2EB8fT9WqVRk4cCBDhw5l0KBBXHLJJZx22mmaXFJEROQYYkWNzQxrg2ZfAD8BDvi3c25yEWUGAYMAzjzzzJZffvllhcYoIiISbuvWraNZs2aVHYYE2L91TVjq/XxnlWPuvZ74l9fDUu/QR7uHpV4REakcZpZRlpEMlTHUoq1zLgm4BLjVzDoEF3DOTXbOJTvnkhs0aFDxEYqIiIiIiIhIuajwxINzbqv/73fAXOC8io5BRERERERERCpGhSYezKy2mdXN+2+gC7C6ImMQERERERERkYpT0ZNL/gaY689+HQlMc869XcExiIiIiIiIiEgFqdDEg3Puc6BFRbYpIiIiIiIiIpWnMiaXFBEREREREZETREUPtRAREZEitLzruXKtL+OR60ot8+2333L77bezYsUKqlevTkxMDOPGjePcc88Nub3U1FS6dOlCw4YNDyfcYmVmZrJ161a6desW0nbp6ek899xzjB8/vlzjERERkdCpx4OIiMgJyDlHjx49SElJYfPmzaxdu5bRo0ezbdu2w6ovNTWVrVu3Frnu4MGDhx1nZmYmb775Zkjb5ObmkpycrKSDiIjIUUKJBxERkRPQwoULqVq1KoMHD85flpCQQPv27QF45JFHaNWqFfHx8YwYMQKA7OxsmjVrxsCBA2nevDldunRh3759zJ49m/T0dK699loSEhLYt28fMTExjBo1inbt2jFr1iwyMzNp06YN8fHx9OjRg59++qlQTLNmzSI2NpYWLVrQoUMHfvnlF+6//35mzpxJQkICM2fOZPv27VxxxRXEx8fTpk0bsrKyABg5ciSDBg2iS5cuXHfddaSlpXHZZZcBsHv3bq6//nri4uKIj49nzpw5AEyfPp24uDhiY2O5++67w3q8RURETmRKPIiIiJyAVq9eTcuWLYtcN3/+fDZu3MjHH39MZmYmGRkZLF68GICNGzdy6623smbNGk455RTmzJnDVVddRXJyMi+++CKZmZnUrFkTgBo1arB06VL69OnDddddx8MPP0xWVhZxcXE88MADhdodNWoU77zzDp9++imvvfYa1apVY9SoUfTu3ZvMzEx69+7NiBEjSExMJCsri9GjR3Pddb8OKcnIyODVV19l2rRpBer9xz/+wcknn8yqVavIysqiU6dObN26lbvvvpv333+fzMxMVqxYwWtvv1deh1dEREQCKPEgIiIiBcyfP5/58+eTmJhIUlIS69evZ+PGjQA0btyYhIQEAFq2bEl2dnax9fTu3RuAnTt3smPHDi688EIA+vfvn5/ICNS2bVsGDBjA008/XezwjKVLl9KvXz8AOnXqxI8//sjOnTsBuPzyy/OTHoEWLFjArbfemv+6Xr16rFixgpSUFBo0aEBkZCTXXnstS5dnlHZoRERE5DBockkREZETUPPmzZk9e3aR65xz3HPPPdx8880FlmdnZ1O9evX81xEREezbt6/YNmrXrh1STJMmTeKjjz7ijTfeICEhgczMzCJjC2ZmJbbnnMsvU1I9IiIiEh7q8SAiInIC6tSpE/v37+fpp5/OX7ZixQoWLVrExRdfzJQpU9i9ezcA//3vf/nuu+9KrK9u3brs2rWryHUnn3wy9erVY8mSJQA8//zz+b0fAm3evJnWrVszatQooqOj+frrrwvV26FDB1588UUA0tLSiI6O5qSTTioxti5dujBx4sT81z/99BOtW7dm0aJF/PDDDxw8eJDp06fT/vzkEusRERGRw6MeDyIiIkeBsjz+sjyZGXPnzuX2229nzJgx1KhRI/9xmueccw7r1q3j/PPPB6BOnTq88MILREREFFvfgAEDGDx4MDVr1mTZsmWF1k+dOpXBgwezd+9ezjrrLJ599tlCZe666y42btyIc44//OEPtGjRgjPPPJMxY8aQkJDAPffcw8iRI7n++uuJj4+nVq1aTJ06tdR9/d///V9uvfVWYmNjiYiIYMSIEfTs2ZN//vOfdOzYEecc3bp1o/vFnUI4giIiIlJWdrR3NUxOTnbp6emVHYaIiEi5WrduHc2aNavsMCTA/q1rwlLv5zurHHPv9cS/vB6Weoc+2j0s9YqISOUwswznXKldBjXUQkRERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREROWy//PIL48eP52h/SpaIiIhUnsjKDkBERETgq1Fx5VrfmfevKrXMt99+y+23386KFSuoXr06MTExjBs3jjp16jBs2DBmz55NZmYmW7dupVu3bgCkpaVRrVo1LrjgAgCqVauGmTFp0iRuueWWMse3Y8cOpk2bxpAhQ0Lar7S0NMaOHcu8efN47bXXWLt2LcOHDw+pDhEREalY6vEgIiJyAnLO0aNHD1JSUti8eTNr165l9OjRbNu2jYYNGzJ79mwAMjMzefPNN/O3S0tL48MPPyxQ12233Ubt2rVD6vWwY8cOnnzyySPah8svv1xJBxERkWOAEg8iIiInoIULF1K1alUGDx6cvywhIYH27duTnZ1NbGwsv/zyC/fffz8zZ84kISGBhx9+mEmTJvH444+TkJDAkiVLeP3112ndujWPP/44nTt3Ztu2bYXaWrNmDeeddx4JCQnEx8ezceNGhg8fzubNm0lISOCuu+4iLS2Nyy67LH+boUOHkpqaCsDbb79N06ZNadeuHS+//HJ+mdTUVIYOHRq+gyQiIiLlQkMtRERETkCrV6+mZcuWJZapVq0ao0aNIj09nYkTJwKwb98+6tSpw1//+lcAfvrpJ5aQQwL2AAAgAElEQVQvX46Z8cwzz/Cvf/2LRx99tEA9kyZN4s9//jPXXnstv/zyCwcPHmTMmDGsXr2azMxMwOtJUZScnBwGDhzI+++/z9lnn03v3r2PcM9FRESkoinxICIiIodty5Yt9O7dm2+++YZffvmFxo0bFypz/vnn89BDD7FlyxZ69uzJOeecU+b6169fT+PGjfO3+dOf/sTkyZPLLX4REREJPw21EBEROQE1b96cjIyMI67ntttuY+jQoaxatYp///vf5OTkFCpzzTXX8Nprr1GzZk0uvvhi3n///UJlIiMjOXToUP7rwHrM7IjjFBERkcqjxIOIiMgJqFOnTuzfv5+nn346f9mKFStYtGhRgXJ169Zl165dxb7euXMnp59+OgBTp04tsq3PP/+cs846i2HDhnH55ZeTlZVVqJ5GjRqxdu1a9u/fz86dO3nvvfcAaNq0KV988QWbN28GYPr06Ue45yIiIlLRNNRCRETkKFCWx1+WJzNj7ty53H777YwZM4YaNWrkP04zUMeOHRkzZgwJCQncc889dO/enauuuopXX32VCRMmMHLkSHr16sXpp59OmzZt+OKLLwq1NXPmTF544QWqVq3Kb3/7W+6//36ioqJo27YtsbGxXHLJJTzyyCNcffXVxMfHc84555CYmAhAjRo1mDx5MpdeeinR0dG0a9eO1atXV8gxEhERkfJhoTz6qjIkJye79PT0yg5DRESkXK1bt45mzZpVdhgSYP/WNWGp9/OdVY6593riX14PS71DH+0elnpFRKRymFmGcy65tHIaaiEiIiIiIiIiYaPEg4iIiIiIiIiEjRIPIiIiIiIiIhI2SjyIiIiIiIiISNgo8SAiIiIiIiIiYaPEg4iIiIiIiIiETWRlByAiIiLQdkLbcq3vg9s+KLVMREQEcXFx5Obm0qxZM6ZOnUqtWrXK3Mbo0aO59957jyTMkG3dupVhw4Yxe/ZsUlNTSU9PZ+LEiWXePiYmhvT0dKKjowssHzlyJDUO7eaOwdfzwCMTade6JX/ocH55hy8iInJCUo8HERGRE1TNmjXJzMxk9erVVKtWjUmTJpVpO+cchw4dYvTo0WGOsKDc3FwaNmzI7Nmzw9rOiLuGKukgIiJSjpR4EBEREdq3b8+mTZsAeOyxx4iNjSU2NpZx48YBkJ2dTbNmzRgyZAhJSUnceOON7Nu3j4SEBK699loArrjiClq2bEnz5s2ZPHlyke28+eabNG3alHbt2jFs2DAuu+wyAD7++GMuuOACEhMTueCCC/jss88ASE1NpVevXnTv3p0uXbqQnZ1NbGxsfn1ff/01Xbt2pUmTJjzwwAP5y8sSy0MPPUSTJk246KKL8tsDuOn2v/PyvPmHeyhFREQkiIZaiIiInOByc3N566236Nq1KxkZGTz77LN89NFHOOdo3bo1F154IfXq1eOzzz7j2Wef5cknnwRg1qxZZGZm5tczZcoUoqKi2LdvH61ateLKK6+kfv36+etzcnK4+eabWbx4MY0bN6Zv377565o2bcrixYuJjIxkwYIF3HvvvcyZMweAZcuWkZWVRVRUFNnZ2QVi//jjj1m9ejW1atWiVatWXHrppSQnJ5caS0ZGBjNmzOCTTz4hNzeXpKQkWpxzejgOr4iIyAlPPR5EREROUHk9FpKTkznzzDO58cYbWbp0KT169KB27drUqVOHnj17smTJEgAaNWpEmzZtiq1v/PjxtGjRgjZt2vD111+zcePGAuvXr1/PWWedRePGjQEKJB527txJr169iI2N5Y477mDNmjX56zp37kxUVFSRbXbu3Jn69etTs2ZNevbsydKlS8sUy5IlS+jRowe1atXipJNO4vLLLw/hyImIiEgo1ONBRETkBJU3x0Mg51yx5WvXrl3surS0NBYsWMCyZcuoVasWKSkp5OTklLnu++67j44dOzJ37lyys7NJSUkpU7tmVuh1WWIpalsREREJD/V4EBERkXwdOnTglVdeYe/evezZs4e5c+fSvn37IstWrVqVAwcOAF6PhXr16lGrVi3Wr1/P8uXLC5Vv2rQpn3/+ef5wiZkzZ+av27lzJ6ef7g11SE1NLXO87777Ltu3b2ffvn288sortG3btkyxdOjQgblz57Jv3z527drF66+/XuY2RUREJDTq8SAiInIUKMvjLytCUlISAwYM4LzzzgPgpptuIjExsdDcCgCDBg0iPj6epKQkpkyZwqRJk4iPj6dJkyZFDsmoWbMmTz75JF27diU6Ojq/DYC//e1v9O/fn8cee4xOnTqVOd527drRr18/Nm3axDXXXENycjJxcXGlxpKUlETv3r1JSEigUaNGxSZXRERE5MhZSd0ejwbJyckuPT29ssMQEREpV+vWraNZs2aVHUaF2717N3Xq1ME5x6233so555zDHXfcUdlhAbB/65rSCx2Gz3dWOebe64l/CU8PkKGPdg9LvSIiUjnMLMM5l1xaOQ21EBERkQrz9NNPk5CQQPPmzdm5cyc333xzZYckIiIiYaahFiIiIlJh7rjjjqOmh4OIiIhUDPV4EBEREREREZGwUeJBRERERERERMJGiQcRERERERERCRslHkREREREREQkbDS5pIiIyFFgUYcLy7W+CxcvKrVMREQEcXFx5Obm0qxZM6ZOnUqtWrXK3Mbo0aO59957jyTMfN26dWPatGkATJs2jSFDhgCQnZ3Nhx9+yDXXXBNSfVu3bmXYsGHMnj27XOITERGRw6ceDyIiIieomjVrkpmZyerVq6lWrRqTJk0q03bOOQ4dOsTo0aPLLZY333yTU045hR07dvDkk0/mL8/Ozs5PSJRVbm4uDRs2VNJBRETkKKHEg4iIiNC+fXs2bdoEwGOPPUZsbCyxsbGMGzcO8BIAzZo1Y8iQISQlJXHjjTeyb98+EhISuPbaawG44ooraNmyJc2bN2fy5MmF2njrrbe4+uqr81+npaXRvXt3AGJiYvjhhx8YPnw4mzdvJiEhgbvuuovhw4ezZMkSEhISePzxx8nJyeH6668nLi6OxMREFi5cCEBqaiq9evWie/fudOnShezsbGJjY/Njb9++PUlJSSQlJfHhhx+G70CKiIhIIRpqISIicoLLzc3lrbfeomvXrmRkZPDss8/y0Ucf4ZyjdevWXHjhhdSrV4/PPvuMZ599Nr9HwqxZs8jMzMyvZ8qUKURFRbFv3z5atWrFlVdeSf369fPXd+7cmZtvvpk9e/ZQu3ZtZs6cSe/evQvEMmbMGFavXp1fb1paGmPHjmXevHkAPProowCsWrWK9evX06VLFzZs2ADAsmXLyMrKIioqiuzs7Pw6Tz31VN59911q1KjBxo0b6du3L+np6eV/IEVERKRI6vEgIiJygsrrsZCcnMyZZ57JjTfeyNKlS+nRowe1a9emTp069OzZkyVLlgDQqFEj2rRpU2x948ePp0WLFrRp04avv/6ajRs3FlgfGRlJ165def3118nNzeWNN97gj3/8Y0gxL126lH79+gHQtGlTGjVqlJ946Ny5M1FRUYW2OXDgAAMHDiQuLo5evXqxdu3akNoUERGRI6MeDyIiIieovDkeAjnnii1fu3btYtelpaWxYMECli1bRq1atUhJSSEnJ6dQud69e/PEE08QFRVFq1atqFu3bkgxH058jz/+OL/5zW/49NNPOXToEDVq1AipTRERETky6vEgIiIi+Tp06MArr7zC3r172bNnD3PnzqV9+/ZFlq1atSoHDhwAYOfOndSrV49atWqxfv16li9fXuQ2KSkprFy5kqeffrrQMAuAunXrsmvXrmJfd+jQgRdffBGADRs28NVXX9GkSZMS92nnzp2cdtppVKlSheeff56DBw+WfBBERESkXKnHg4iIyFGgLI+/rAhJSUkMGDCA8847D4CbbrqJxMTEAnMm5Bk0aBDx8fEkJSUxZcoUJk2aRHx8PE2aNCl2SEZERASXXXYZqampTJ06tdD6+vXr07ZtW2JjY7nkkksYPXo0kZGRtGjRggEDBjBkyBAGDx5MXFwckZGRpKamUr169RL3aciQIVx55ZXMmjWLjh07lthzQ0RERMqfldRl8WiQnJzsNAGUiIgcb9atW0ezZs0qOwwJsH/rmrDU+/nOKsfcez3xL6+Hpd6hj3YPS70iIlI5zCzDOZdcWjkNtRARERERERGRsFHiQURERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCJrKyAxAREZHyf3xhWR5b+NBDDzFt2jQiIiKoUqUK//73v2ndujXjxo1j0KBB1KpVK+R277//fjp06MBFF110OGFXmOzsbD788EOuueaakLetf04rfty4IgxRiYiIHJ/U40FEROQEtGzZMubNm8fKlSvJyspiwYIF/O53vwNg3Lhx7N2797DqHTVq1FGfdAAv8TBt2rQi1+Xm5lZwNCIiIsc3JR5EREROQN988w3R0dFUr14dgOjoaBo2bMj48ePZunUrHTt2pGPHjgDccsstJCcn07x5c0aMGAFAeno6CQkJJCQkEBcXh5kBMGDAAGbPng3Ae++9R2JiInFxcdxwww3s378fgJiYGEaMGEFSUhJxcXGsX78egD179nDDDTfQqlUrEhMTefXVV4uM/V//+hdxcXG0aNGC4cOHA7B582a6du1Ky5Ytad++fX6dAwYMYNiwYVxwwQWcddZZ+bENHz6cJUuWkJCQwOOPP05qairXDLqTnv1v5dK+g9i9Zy9dr76RNhf3ouUfevD6O+8XGctjT02hbbfeJF/Ug1FjJx7ZmyIiInKcqpTEg5lFmNknZjavMtoXERE50XXp0oWvv/6ac889lyFDhrBo0SIAhg0bRsOGDVm4cCELFy4EvCEZ6enpZGVlsWjRIrKyskhOTiYzM5PMzEy6du3KX//61wL15+TkMGDAAGbOnMmqVavIzc3lqaeeyl8fHR3NypUrueWWWxg7dmx+O506dWLFihUsXLiQu+66iz179hSo96233uKVV17ho48+4tNPP+Vvf/sbAIMGDWLChAlkZGQwduxYhgwZkr/NN998w9KlS5k3b15+omLMmDG0b9+ezMxM7rjjDgA+yviUZ8aN5p1ZU6hRvRov/ef/WP7OLN6ZNYW7Rz2Cc65ALO8u+oBNX3zF0jdm8PH8OXyStZYly9OP+L0RERE53lRWj4c/A+sqqW0REZETXp06dcjIyGDy5Mk0aNCA3r17k5qaWmTZl156iaSkJBITE1mzZg1r164tsG7lypWMGTOmwDafffYZjRs35txzzwWgf//+LF68OH99z549AWjZsiXZ2dkAzJ8/nzFjxpCQkEBKSgo5OTl89dVXBepdsGAB119/ff78E1FRUezevZsPP/yQXr16kZCQwM0338w333yTv80VV1xBlSpV+P3vf8+2bduKPSadOpxPVL2TAXDOcf+Y/yP5oh50630TW7/9jm3f/1ig/HuLPmTBog9p3eUq2lzci882f8GmL74stn4REZETVYVPLmlmZwCXAg8Bd1Z0+yIiIuKJiIggJSWFlJQU4uLimDp1KgMGDChQ5osvvmDs2LGsWLGCevXqMWDAAHJycgBYs2YNI0aMYPHixURERBTYLrh3QLC8IR4RERH5cyo455gzZw5NmjQpdjvnXP6wjjyHDh3ilFNOITMzs8S2Sourdq2a+f89/eU3+OHH7Sx76yWqVq3Kua27kOMPFfm1Lrhr6E0M7Hd1sXWKiIhI5TzVYhzwN6BucQXMbBAwCODMM8+soLBEREROHJ999hlVqlThnHPOASAzM5NGjRoBULduXXbt2kV0dDQ///wztWvX5uSTT2bbtm289dZbpKSksHPnTvr06cNzzz1HgwYNCtXftGlTsrOz2bRpE2effTbPP/88F154YYkxXXzxxUyYMIEJEyZgZnzyySckJiYWKNOlSxdGjRrFNddcQ61atdi+fTtRUVE0btyYWbNm0atXL5xzZGVl0aJFi2LbytvH4vy8axcNoutTtWpV0j74mK+2bC1U5qKUCxj1yET69ryMOrVr8d9vtlG1aiSnRtcvcT9FjiZtJ7QNS70f3PZBWOot7ycABSrL04BE5PBUaOLBzC4DvnPOZZhZSnHlnHOTgckAycnJJd8yEREROQ5U9A/e3bt3c9ttt7Fjxw4iIyM5++yzmTx5MuDNl3DJJZdw2mmnsXDhQhITE2nevDlnnXUWbdt6FymvvPIKX375JQMHDsyvM7DHQY0aNXj22Wfp1asXubm5tGrVisGDB5cY03333cftt99OfHw8zjliYmKYN6/gdFBdu3YlMzOT5ORkqlWrRrdu3Rg9ejQvvvgit9xyCw8++CAHDhygT58+JSYe4uPjiYyMpEWLFgwYMIB69eoVWN+n52Vc2X8oF1xyNfHNm9Lk7MaF6uh8YVs+2/g5F15+LQB1atViyoR/KvEgIiISxErrClmujZn9E+gH5AI1gJOAl51zfypum+TkZJeeromaRETk+LJu3TqaNWtW2WFIgP1b14Sl3s93Vjnm3utw3VXWHeWjj3o8/Ernp0jozCzDOZdcWrkKnVzSOXePc+4M51wM0Ad4v6Skg4iIiIiIiIgc2yrrqRYiIiIiIiIicgKojMklAXDOpQFpldW+iIiIiIiIiISfejyIiIiIiIiISNgo8SAiIiIiIiIiYaPEg4iIiIiIiIiETaXN8SAiIiK/euhPV5VrfX9/YXaJ61NSUrjnnnu4+OKL85eNGzeOrKwsfv75Z2bPLnn7UKWlpVGtWjUuuOACACZNmkStWrW47rrryrUdEREROfoo8SAiInIC6tu3LzNmzCiQeJgxYwaPPPII7du3L/f20tLSqFOnTn7iYfDgweXehoiIiBydNNRCRETkBHTVVVcxb9489u/fD0B2djZbt27ljDPOIDY2FoC9e/dy9dVXEx8fT+/evWndujXp6ekAzJ8/n/PPP5+kpCR69erF7t27AYiJiWHEiBEkJSURFxfH+vXryc7OZtKkSTz++OMkJCSwZMkSRo4cydixYwGv98Xdd9/Neeedx7nnnsuSJUsAyMnJ4frrrycuLo7ExEQWLlxY0YdJREREyoESDyIiIieg+vXrc9555/H2228DXm+H3r17Y2b5ZZ588knq1atHVlYW9913HxkZGQD88MMPPPjggyxYsICVK1eSnJzMY489lr9ddHQ0K1eu5JZbbmHs2LHExMQwePBg7rjjDjIzM4vsUZGbm8vHH3/MuHHjeOCBBwB44oknAFi1ahXTp0+nf//+5OTkhO2YiIiISHgo8SAiInKCyhtuAV7ioW/fvgXWL126lD59+gAQGxtLfHw8AMuXL2ft2rW0bduWhIQEpk6dypdffpm/Xc+ePQFo2bIl2dnZZYqlqG2WLl1Kv379AGjatCmNGjViw4YNh7ezIiIiUmk0x4OIiMgJ6oorruDOO+9k5cqV7Nu3j6SkpAKJAudckds55+jcuTPTp08vcn316tUBiIiIIDc3t0yxFLVNce2LiIjIsUU9HkRERE5QderUISUlhRtuuKFQbweAdu3a8dJLLwGwdu1aVq1aBUCbNm344IMP2LRpE+DNBVFaT4S6deuya9eukOLr0KEDL774IgAbNmzgq6++okmTJiHVISIiIpVPPR5ERESOAqU9/jJc+vbtS8+ePfOHXAQaMmQI/fv3Jz4+nsTEROLj4zn55JNp0KABqamp9O3bN39yygcffJBzzz232Ha6d+/OVVddxauvvsqECRPKFNuQIUMYPHgwcXFxREZGkpqamt8zQkRERI4ddrR3Y0xOTnZ5M2iLiIgcL9atW0ezZs0qO4wSHTx4kAMHDlCjRg02b97MH/7wBzZs2EC1atUqO7Sw2L91TVjq/XxnlaP+vQ428S+vh6XeoY92D0u9cvjaTmgblno/uO2DsNQbrnMTdH6KHA4zy3DOJZdWTj0eREREpEh79+6lY8eOHDhwAOccTz311HGbdBAREZHwUeJBREREilS3bl3U61BERESOlCaXFBERqSRH+3BHOXJ6j0VERJR4EBERqRQ1atTgxx9/1IXpccw5x449v1CjRo3KDkVERKRSaaiFiIhIJTjjjDPYsmUL33//fWWHIr7cHd+Wc42OiJ+/5pxOfyrnekVERI4tSjyIiIhUgqpVq9K4cePKDkMCfDXq6rDUW/Xi68NSr4iIyLFCQy1EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREREREREQkbJR5EREREREREJGyUeBARERERERGRsFHiQURERERERETCRokHEREREREREQmbkBIPZjbDzLqYmYUrIBERERERERE5foTa4+F3wNvAV2b2oJmdHYaYREREREREROQ4EVLiwTnXFmgCPA9cB3xmZovNbICZ1Q5HgCIiIiIiIiJy7Ap5jgfn3Ebn3L1AI6AbsAV4AvjGzP5jZu3KOUYREREREREROUYd9uSSzjkHLAbeAtYAdfASEYvNLMPMWpRPiCIiIiIiIiJyrDqsxIOZtTWzp4FvgQlAJnC+c+40IAH4GW84RvB2NczsYzP71MzWmNkDRxC7iIiIiIiIiBzlIkMpbGb3AAOAs4FlwO3ATOfc3rwyzrksM/tfvN4QwfYDnZxzu82sKrDUzN5yzi0/3B0QERERERERkaNXSIkH4M/Ac8B/nHOflVBuPTAoeKE/PGO3/7Kq/+dCjEFEREREREREjhGhJh7OcM7lllbIOfcj8J+i1plZBJCB12viCefcR0WUGYSfuDjzzDNDDFFERESOZy3vei4s9c6tG5Zq5QQSvnPzkbDUC0C9k8JXt4iIL9Q5HtqZ2XVFrTCzfmZ2YWkVOOcOOucSgDOA88wstogyk51zyc655AYNGoQYooiIiIiIiIgcLUJNPIwGGhaz7rf++jJxzu0A0oCuIcYgIiIiIiIiIseIUBMPsUB6MetWAs1L2tjMGpjZKf5/1wQuwpsPQkRERERERESOQ6HO8XAIqFfMuvqUnsg4DZjqz/NQBXjJOTcvxBhERERERERE5BgRauLhA+AvZvaKc+5A3kL/0Zh3AEtL2tg5lwUkhhyliIiIiIiIiByTQk083IuXXNhoZtOBb/B6MfQBooD25RueiIiIiIiIiBzLQko8OOc+NbM2wEhgIN6wi5+A94ARzjnN1yAiIiIiIiIi+ULt8YBzbg3QKwyxiIiIiIiIiMhxJtSnWoiIiIiIiIiIlFnIPR7M7AqgJ3AGUCN4vXPugnKIS0RERERERESOAyElHszsPuABYA2wFvglHEGJiIiIiIiIyPEh1B4Pg4BHnHN3hyMYERERERERETm+hDrHQ11gfjgCEREREREREZHjT6iJh5eALuEIRERERERERESOP6EOtXgbGGtmUcC7wI7gAs459YgQERERERERESD0xMNs/98b/b9gDog4oohERERERERE5LgRauLhnLBEISIiIiIiIiLHpZASD865zeEKRERERERERESOP6FOLomZVTWzgWb2bzN708zO9pdfZWZNyj9EERERERERETlWhdTjwU8yzAeigZVAe+Akf3VHoDvQvzwDFBEREREREZFjV6g9HsYD3wIxwEWABaxbhJeIEBEREREREREBQp9c8kLgaufcdjMLfnrFt8Bp5ROWiIiIiIiIiBwPQu3xsB+oXsy6hsCOIwtHRERERERERI4noSYe3gXuMbO6AcucmVUFhgJvl1tkIiIiIiIiInLMC3WoxV3Ah8Am4B3AAX8HmgO1gavLNToREREREREROaaF1OPBOfcV0AKYAjQFvsSbaPI1oKVzbmt5BygiIiIiIiIix65QezzgnPsRuCcMsYiIiIiIiIjIcSbUOR5ERERERERERMospB4PZvYN3rwOxXLONTyiiERERERERETkuBHqUIv/UDjxEAX/396dx9taz/0ff72bS5qUoSRuYyWFlHnKfJd5brhlyH3f7h8RiUwZIplnMiRDAw0yNYhSKBWSIkQpKhSiJNXn98f3uzqrbR+dfdrX2ufs83o+Hudx9rrWta79Xaera/hcn+/nw8OB1YBPz8agJEmSJEnS/DCjwENVvWa65UmWA74AXDkbg5IkSZIkSfPDrNR4qKrrgP2AF8/G9iRJkiRJ0vwwm8UlNwJWmsXtSZIkSZKkpdxMi0vuMs3ilYCNgZ2Aw2ZjUJIkSZIkaX6YaXHJj0yz7Brgt7SpFq+7ySOSJEmSJEnzxkwDDytOXVBV187SWCRJkiRJ0jwz064WBhkkSZIkSdIim2mNh2fPZP2q+vzMhiNJkiRJkuaTmU61+CxQ/eeMLV/YMgMPkiRJkiQtw2baTnNr4HxgL+AewK3732/sy7cG1u5/1pm9YUqSJEmSpKXRTDMe9gE+XFX7ji37PfCTJFcCb6+qh83a6CRJkiRJ0lJtphkP9wXOWMh7P6ZlPEiSJEmSJAEzDzxcCGqgjaMAACAASURBVDxnIe89B/jtTRmMJEmSJEmaX2Y61eI1wOeTbAIcSZtmcUvg8cBmwLNmd3iSJEmSJGlpNqPAQ1UdkuQ8YA9gZ+BWwCXAqcALq+qUWR+hJEmSJElaas0044Gq+j7w5AHGIkmSJEmS5pmZ1ngAIMmaSe6X5OlJ1urLVpzdoUmSJEmSpKXdjAIPSZZLsjetiOR3gAOB/+hvH5nk9bM8PkmSJEmStBSbacbDW4AXAS8F7gJk7L0jaEUmJUmSJEmSgJnXePgvYI+q2i/J8lPeOxe44+wMS5IkSZIkzQczzXhYG/jFQt5bEZgajJAkSZIkScuwmQYezgK2W8h7jwZ+eNOGI0mSJEmS5pOZTrXYGzgkycrAF4AC7p5kO+B/gCfO8vgkSZIkSdJSbEYZD1V1GLAT8J/AsbTikvsDLwR2rqqvz/YAJUmSJEnS0mumGQ9U1eeTHAhsDKwLXAacXVXXzfbgJEmSJEnS0m2RAw9JVgF+ALy0qo4Gzh5sVJIkSZIkaV5Y5KkWVXUVLcOhhhuOJEmSJEmaT2ba1eJAWo0HSZIkSZKkGzXTGg/nAk9NcjLwNeASbpgBUVW132wNTpIkSZIkLd1mGnh4T//7NsBW07xfgIEHSZIkSZIEzDzwsOIgo5AkSZIkSfPSjdZ4SHJMkrsCVNW1VXUt8BBgldHr8T9DD1iSJEmSJC09FqW45COANUcvkiwPHAvcdahBSZIkSZKk+WGmXS1GMqujkCRJkiRJ89LiBh4kSZIkSZJu1KIGHmoRl0mSJEmSJF1vUbtaHJ3kminLjptmGVV1y5s+LEmSJEmSNB8sSuBhr8FHIUmSJEmS5qUbDTxUlYEHSZIkSZK0WCZaXDLJhkm+leSnSc5K8pJJ/n5JkiRJkjRZi1rjYbZcA+xWVT9IcnPg9CTHVtXZEx6HJEmSJEmagIlmPFTVRVX1g/7zX4GfAhtMcgySJEmSJGlyJp3xcL0ktwfuCZwyzXu7ALsA3O52t5vouKRJuvcrDhhku6fvu9Mg25UkSZqv3rLDUwfZ7p6f/eIg25WWJhPNeBhJsjpwKLBrVV0+9f2q+lhVbVlVW6633nqTH6AkSZIkSZoVEw88JFmRFnT4XFUdNunfL0mSJEmSJmfSXS0CfAL4aVW9a5K/W5IkSZIkTd6kMx4eAOwIPDzJj/qfx014DJIkSZIkaUImWlyyqk4CMsnfKUmSJEmS5s6cFJeUJEmSJEnLBgMPkiRJkiRpMAYeJEmSJEnSYAw8SJIkSZKkwRh4kCRJkiRJgzHwIEmSJEmSBmPgQZIkSZIkDcbAgyRJkiRJGoyBB0mSJEmSNBgDD5IkSZIkaTAGHiRJkiRJ0mAMPEiSJEmSpMEYeJAkSZIkSYMx8CBJkiRJkgZj4EGSJEmSJA3GwIMkSZIkSRqMgQdJkiRJkjQYAw+SJEmSJGkwBh4kSZIkSdJgDDxIkiRJkqTBGHiQJEmSJEmDMfAgSZIkSZIGY+BBkiRJkiQNxsCDJEmSJEkajIEHSZIkSZI0GAMPkiRJkiRpMAYeJEmSJEnSYAw8SJIkSZKkwRh4kCRJkiRJgzHwIEmSJEmSBmPgQZIkSZIkDcbAgyRJkiRJGoyBB0mSJEmSNBgDD5IkSZIkaTAGHiRJkiRJ0mAMPEiSJEmSpMEYeJAkSZIkSYMx8CBJkiRJkgZj4EGSJEmSJA3GwIMkSZIkSRqMgQdJkiRJkjQYAw+SJEmSJGkwBh4kSZIkSdJgDDxIkiRJkqTBGHiQJEmSJEmDMfAgSZIkSZIGY+BBkiRJkiQNxsCDJEmSJEkajIEHSZIkSZI0GAMPkiRJkiRpMAYeJEmSJEnSYAw8SJIkSZKkwRh4kCRJkiRJgzHwIEmSJEmSBmPgQZIkSZIkDcbAgyRJkiRJGoyBB0mSJEmSNBgDD5IkSZIkaTAGHiRJkiRJ0mAMPEiSJEmSpMEYeJAkSZIkSYMx8CBJkiRJkgZj4EGSJEmSJA1mooGHJJ9M8vskP5nk75UkSZIkSXNj0hkP+wOPmfDvlCRJkiRJc2SigYeq+jZw2SR/pyRJkiRJmjsrzPUAppNkF2AXgNvd7nY3aVv3fsUBszGkaZ2+706DbVu6KX7zxs0G2e7tXnfmINsd0gd2+/Ig2/3LJZ8eZLsAe372i4Nte1ENdew8/Ob7DrJdWPr2z6H2TYD/e+d2g21bM/eA9z9gkO3u/YUBL+Pu8/Lhtq1lwgkPfsgwG3bfXCzeE2muLZHFJavqY1W1ZVVtud566831cCRJkiRJ0mJaIgMPkiRJkiRpfjDwIEmSJEmSBjPpdpoHAt8D7prkwiTPm+TvlyRJkiRJkzXR4pJV9axJ/j5JkiRJkjS3nGohSZIkSZIGY+BBkiRJkiQNxsCDJEmSJEkajIEHSZIkSZI0GAMPkiRJkiRpMAYeJEmSJEnSYAw8SJIkSZKkwRh4kCRJkiRJgzHwIEmSJEmSBmPgQZIkSZIkDcbAgyRJkiRJGoyBB0mSJEmSNBgDD5IkSZIkaTAGHiRJkiRJ0mAMPEiSJEmSpMEYeJAkSZIkSYMx8CBJkiRJkgZj4EGSJEmSJA3GwIMkSZIkSRqMgQdJkiRJkjQYAw+SJEmSJGkwBh4kSZIkSdJgDDxIkiRJkqTBGHiQJEmSJEmDMfAgSZIkSZIGY+BBkiRJkiQNxsCDJEmSJEkajIEHSZIkSZI0GAMPkiRJkiRpMAYeJEmSJEnSYAw8SJIkSZKkwRh4kCRJkiRJgzHwIEmSJEmSBmPgQZIkSZIkDcbAgyRJkiRJGoyBB0mSJEmSNBgDD5IkSZIkaTAGHiRJkiRJ0mAMPEiSJEmSpMEYeJAkSZIkSYMx8CBJkiRJkgZj4EGSJEmSJA3GwIMkSZIkSRqMgQdJkiRJkjQYAw+SJEmSJGkwBh4kSZIkSdJgDDxIkiRJkqTBGHiQJEmSJEmDMfAgSZIkSZIGY+BBkiRJkiQNxsCDJEmSJEkajIEHSZIkSZI0GAMPkiRJkiRpMAYeJEmSJEnSYAw8SJIkSZKkwRh4kCRJkiRJgzHwIEmSJEmSBmPgQZIkSZIkDcbAgyRJkiRJGoyBB0mSJEmSNBgDD5IkSZIkaTAGHiRJkiRJ0mAMPEiSJEmSpMEYeJAkSZIkSYOZeOAhyWOSnJPkl0n2mPTvlyRJkiRJkzPRwEOS5YEPAo8FNgGelWSTSY5BkiRJkiRNzqQzHrYCfllVv6qqq4GDgCdMeAySJEmSJGlCUlWT+2XJU4HHVNXz++sdga2r6v+mrLcLsEt/eVfgnIkNcn5bF/jjXA9CWgj3Ty2p3De1pHLf1JLM/VNLKvfN2bVRVa13YyutMImRjMk0y/4l8lFVHwM+Nvxwli1JTquqLed6HNJ03D+1pHLf1JLKfVNLMvdPLancN+fGpKdaXAhsOPb6tsDvJjwGSZIkSZI0IZMOPJwK3DnJHZKsBDwTOHLCY5AkSZIkSRMy0akWVXVNkv8DjgaWBz5ZVWdNcgzLOKevaEnm/qkllfumllTum1qSuX9qSeW+OQcmWlxSkiRJkiQtWyY91UKSJEmSJC1DDDxIkiRJkqTBGHjQoJJM10JVkiRJkuYF73lunIEHDSLJcgBlERHNkdE+KC0Nkiw/12OQxqXxOKqlhsdRzZUky43f8xiEmJ4nFM26JKmq6/rPz07yliRPmOtxadnQL5bH98FbzPWYpIUZXZxU1bX99QOT3GZuR6Vl3egiuqquS7LuXI9H+ndGAYex4+jqY+95A6jBjD1ovS7JbZPslGRtwP1uGgYeNOuqqpJskGQ3YAfg18AHkjwryURbuGrZ0y+WK8naST4LHJ7kpUn+Y67HJk01ekKS5D5Jvgm8CTg0ycM8Xmqu9Ivo5ZO8CTgpyTtHDxDMgtCSZizg8NAkZwIfTvK6/p6ZtxrM+INW4CDgmcB7gZ3nclxLKi9qdJMlWX500B+9BvYC7gXsUlWnJfkjsCNwInDh3IxU81V/Onfd2N+vBO4OnAD8kHYieDdg5o3m3Pgxsx8vHw/sAbyrqg7uQdvH0c7Rx87dSLWsGB07x14/Dtge+DnwSOChwIeSHF1VV/WsMm/oNGfGzvfLA2vQbvb+ALwIuBT4bJJLq+qDU/dvaXH1wGtNmVbxJuD5wFZVdUGS7YD/TvK9qjp7rsa6JDJqrZts7AL6eUkeC6wK7AtcAmyUZMWqOgJYh3aBLc2a8WkVLDimrQg8Cziiqk4DXgWs2U8G0pyqqmuTrJRknX78vABYGdiwr/IZ4FrAKRca3PhN2ViK+p+B7YALq+qCqvoMLZD79jkapgT8S2r7SlV1bVX9CdgY2KSqvl1VZwH/DbzSoINmU1Vd17Nq75zkrn3xwcDNgdv2198BfgI8eC7GuCQz8KAZmzpfLsntk5wAPBF4NPAN4Hzg27Ssh037qqfSLmakWdNPAA9I8hngbUnuXlVvBs6j7Y9U1T9pJ4Jbzd1IpSbJTsBZtHTgTwJnAB8Fbp9k/ar6PfBPFhw7pcH0G7jbJfk48PEkz6yq7wIfB7YeW3U/YK1+s2e2g+bEWJDs5cBhSV6VZCPgecCdk9y8r3cK8APgnnM2WM07SVbt5+0vAPsl2R34HfAW2kMuquoyYG3g4jkb6BLKwIMW2VjxnqkXHBsBZ1TVdlW1K3A2LeXtA8BmwN5J9gMeC3x/gkPWPDR13nuSLWj725eBAl6e5IG0tLcPJ9k0yZq0yPN5Ex6ulmHTdQVIcjfa3M9tadPP7gI8HTgNWB14T9+nH047lkqzKsmK/e/0v28GfAz4GXAo8MgkewGvBR6R5EX9Zu6/gF9V1dVzM3Iti5IsN80Dr/9Hm/6zDy2b9p20emLfogXP7tRrkqxDmy4kLZZpipM+DFitqrYAXgasD+wEfArYrNfD2ZF2zenD1ikMPGiRJHkq8JX+82pJXp5k8/723YFbj62+G+1/uBWBI2hTLo6vqs2q6pcTHLbmkSSbAVTVNf316GTwYODMqjqEVpjva8Azq+p44Lu0Yj+vAb4HfHPCw9YyKMk6SR471hVgvSRr9LfXAi4CLu43cHvRAhE/Ab4KrAu8AnhrVX16Lsav+SnJVnB9Bhi06T0AdwPWqqp3VNUXgLcC9wBuQZtasXv/80/ggxMdtJZZSW6TZKOx1Pab9eUr0GrgvK2qTqQFH34M7EKrlbM18HJaFu47quqvc/MNtLTqD6x2huuzarfoWTXQplSs1d87jXZteSfgj8CbgSfRAl7b9OtQjTHwoH8ryWP705EvAfdL8nzgw7Tshd2SvIgW5dsmySgteDngZOAvwJG0p9CrJ1ll4l9A80KSpwN79hu6/0xyKLBnf/tHtPTfW1fV5bSiUhv09/6XNuduv6ravd8E2uJIg+kXxZsAb+4XL7vTplK8L8kzaEGHVWg3dVTVsbSLmE1oGWHfAH5eVaNAr+dp3WT9ovntSR6d5A5Jvgp8NMntaTdt1yTZpq9+KXA5cPuq+gjtgvrcqnpBVf3BfVIT8mLg9QBJ3gEckeSp/eHDabTip1TVH2lFJderqkuB/YHbVdXOVXXknIxcS6Uk6/dz+N1p9zxPSfJF4BPAV5LciZZZ8+Mko/oN36YFu1YHDqBNK1++qn6bZKXJf4slmycPTSvJQ5IcR4seb9KfkOxG6wxwWFVtAxwI3Jd2k7cHrWXmK2lFVq4FVugnhBNoFYfd37TIkmyW5N39gvkHwLnAO4Dn0IJdT0/yPODvtPnyL+kf/Vv//OpVdS4t62a/vmw55yZrCEm2T3Ii8NiqOgk4iXZcXJc2x/hAWnbD74FfAC9L8vAkT6Td6P2iqs6nTa+401iGj0XRtFh6ps2Hkjyl71sH0IKxb6PVb7iCliq8Ge0Y+cr+0X/Q0oev6K/fAbwpycrJDYr5SrMqyQuSfKC/PBhYKckRtJT1/YHnJXk8cBitePm2fd07Alf2n/cC7t2nWkg3Ksnqfb/7LHBvWvbhb2kFdk+rqnvTsr7fQKvn8EfgNUnuS8uuOQP4Z79X+gTwtCSrOS3tX3kjqH+R5H7A3rTWbg+pqjMAquoTtKd19+qrnkG7IXxRVe1HC0ysCny4qp5fVf/on/t0Ve1bVVdO/V3SVGnV/t9Fq+x/MZA+ReeHtGJ7R/anwXvQ2rxBu6l7dJKDaFMrDqyqv/X3XgB8GryJ0+zrFyyH0AJiewHH9LfeDdwBuKaqLqmqo2lPRvYGXk2bWvES2v75pp6tA3A8sHtVnTmxL6F5J8n/0VqxXgX8qi8+nvZQ4LqqOpxWDA1gS1qA9i9JDqbtm6NWxFTVgbQL7qsN3GoISe6Z5Fha57Pje4bsL2gB3K1p0yo+R8sIuy/tgcP+wKt6wHcT2k3jqA7Zf2GNHC2CnsVwEnA1bf87s18/Hk8rSL4qQFW9Crgz7Xj5buBwWueU1YAXV9UVfb1jgAd7zzO9eA7RSH+SUUkeAexcVdv35bcGru0plg8Dvgis29fdihbt+3JvtzW+vet71UuLKskDaCmWLxi7GSPJLWnBhuWBXfv+937gT7R5dTejFeo7c3TAj220NLBe6+aNVfWE/vr6416SXWnZDi+tqsuS3Io2nWKHqjoxyS17B4vrj79z9DU0j/RCkPvTbtZOHVse4MnAS4FnVes3/2zggcD+VfX9JLejBSYu7J/xPK7BJXkNsGJVvX7K8g2ATwJfqqoP9QzI3YEzaZ2AVgfuWFU/6ut7zteMJHkycI+qekN/fbNREKGfw9cFPltVPxtN+wUeWlV/SrJKVV3V1/VYuQjMeND1xi56VwT+keTjSQ6nFZc6PcmrqupbwNHAh/q65wDvo9WAABYU/fN/QC2mBwLnV9XlSTZP8qgkj6RNoTgcWAEYpVB+lFb9f9Oq+lNVnVJVV2ZBBxYvQDS039MqWW+dZHvgpUn2TSvI+z5gPVo2zgpVdQmtON8mAGNBh+UNOmgWbUp7end+r+fwtJ6efg/gOFrmzf/r6x5KO+ffPcmKVfWbqrowvZOA53ENLcmqtOyw09NaFW7ba5E8pap+S68rllbH6Xxa0OHuwAZV9dexoMPynvO1GFYAXpBWQHJv2rSyg3uQ4VBaMckHAlQrYn4KC2o0XZVmOY+Vi8aMh2XY1CdsYxkPqwFb0KLK36Slaa5N6x5wDHAibY7Thv2kMO32pJkY2//uTptvvBItLfgC2py7k2iFph5Lu+jYs6ouTXKnsluK5khaIaoX0ubK/56Wnnlb2oX0e2j1G14PPLeqfrWQzUizpu+TnwI2BG4PfAfYmDYH/s20ws+vAfatquOTbDB+LpcmZey8/zLalIpH0s711wD3o3WsOJrW+WfFqnppWsG+lcamU0qLZGx/m3r/8w5ae9afAr8ErqNlhm0GPAh4FPCxqjp58qOeXww8LKMWlhL074IHST4JfLWqDk1yn6o61WCDFtdoHxzfh0Zpkn3O3ea0auvL9xS3c2nz4S+jzd/8VFX9eM6+gJYpU4+ZU1N6k2xIC8iuVlV/TbILcLeqelm/qHlvVV0wtr7HTt0kSW4DrF1VZ/fX48fSNWgPEH5HO4aek1Y87QrgjcCuwHl93jxTPy/Ntp4Nu9yU4+j1N4K0ApHrA7+hdam4B20u/RNpWTzb0tLcr+rXCe6vWiyjaY5JVqqqq3uwdoWewbBK//sLtIetR9KCD1+uXrvOKT2Lb4W5HoDmxtgc5NfQUthPrarvTD2Ij04UwItYUPma0bxRD/paHFNSeNegPYEDGE3T+SUt6jzuO/29HyU5qxb0opcG14NkN6M9lfteVf0dFlyAjAUV/tovYh4NfLl/9uXTbM9jpxbL2A3XxsBWSR5O6y71XlpBXqrVx/n2lI+eB6xeVVckefvUY6j7pIbU969re1Dsb/24WWM3cTc47/eHDZfRCvQeR5smNHV70r81zUOCZwNPB55YvetEtRat1/R1r0prlbkeraPFJbTadtcz6LD4rPGwjEiy1tjPSbJhWh/vdWh1Gr6e3r5tbL11gCfRiqHdF3h6VX1vgsPWPNUvNh6a5Bhg7yQvnzpHLq27xR2TPDit2vUtaJ1UqKp/9qCYNIgky015/VxaF59nAO9OsjUsuADp++vGSd5M6zH/K1q3ldHn3V91k+WGLYEDPJ+WvfD9qrp4yrrpx9DN0rpVPJNej2kUdHC/1JCmOY6+kJbO/vYsaIU5nvJ+q359+mraVN9vVmvLPu32pIUZ7Ss9O2bNsbcup9VpuMH+1I+Fz0zyRWBf4KNV9cMJDnmZYMbDMiCtK8U2SX5Em4O8Nu2C5Uu0uXO70U4EV4x/rloV9p/Qugv8oG/L9CLN2NRpFUluD7yClja5Oq2n/HmMRZV7+tt2tODXR6vq8+Pb9GmHhjTlCcmtaHVGNqFNAToKODnJqWPr/ZPWcWUN4NlT09/dXzUb+kX0crQaTPei1Vy6jPYA4Qbn6H6svQPwv8ApVfWMabbnfqlB9GPfKDC7FbAWsCatjsNWwPOS/LCqfju234bW1WpDYNuqOm98m15/6saMnXNH+962wOuTfKyq9qPd6zwJeOv4/tSPlxcB366q903d3oS/xrxljYd5bOwmbzVa4bOn0w7qO9Lmfv5Pf31QVb2rf2bVqvp7pqkBYdBBsyXJE2i9kH9Ca5F5cFW9bez9UaBirar689Tlkx+xlgVp7VkvrKp9+rSKdwIH0NLX96EVPF0HeFdVHZ7WBWDaKT/95tCAg26SadKE7wnsQKvt8Ny+n70B+Afwkaq6dMrnr5+73F97DNXEpLW/fBWtrfAawLFV9eIk69GCZ/+sqldP+czaVfWn/rPHUS2SJCvSpuWM6tzch1aU9BfA2cD2tEzEDwDvAN43eqi6kO15rByAKUvz0Fh6UfW/r6RVtl4Z+EC11kNfAVYDXj0WdNibFqCYthWmQQctqiRPSLL52Osdk3wzyZ5JtqG1w3oprS3mw6rqbUlWTnJvWLD/jYIOWdAe05OAZt1YuuURwCvSCk5dQZvecwqtu8pKwK+r6kE96LAhrcPKtNsbzV+exPg1P40HHZJs0BdfCtwNWCkL2geeAtwS2DLJPXpwAmhzl/ucZdtjalCj8/TY67sAnwMur6qtaW3Yb5Vk3ar6A3AYsHmSB/b1RzWerg86eBzVokjyAOA+/WHrLZNsTMsG+31VvbuqjqZl2K5Ga8t+C+DP/2Z7HisHYuBhHhq7UHlskt2S/AetG8CrgXskuVufM/cRYJck+yc5kXYxc/CcDVxLvT6neG/aAf6XfdnTgO1oLQdXBvaipVt+HLikqv7cI9NfpV04Lz91u54ANISxm7Hr+kXuccB3gbemtXW9uKqurdY7/vvAakl2TrIj8DVgi+nmyBuk1Wzo++Vtknwa+FSSdwKrAB+mpQtv0lc9htYBaDfaRfVK023LGzgNqRYULb9tX3Qe7WnzqH7YB2k3fk/sr8+kFY2+Xf/8DfZPj6O6MWPXi1cB+yQ5iJZV81PgUFpba5KsUFUXVNXuffnWtE4V09a58Vg5HKdazAM9vejlwM/6k7h1aRcma9CKm+1I69l9Li2t/cKqeleSmwPr0v4H/GNVfaNvz/lMmpEka9MCW1+gtb3aBLgE+Bgts+FnwK1paW9vqaov9FTLw4ALgbvQ0oT3m4Phaxk0fpzrT0f+WVW/TGtR+HPaNKADaC2Ef9PXewzwn7SWb3tX1elzM3rNRwuZ4rgfcBJwCC3A8CtgF9rT41OAQ3rwNsCdquoXEx62BLSHXbQHCz8GVqqqnZJsCryFdn4/qs+334NWO+yn/266mrQw00xB24gWULgaeHxV/THJXYGvA4/q5/bRFN7lgP+mTVd7y5x8gWWYGQ/zwwrAlbRqrMsBq9LqNjyadrN3R+DtVfV74HjaU+Uv0eY4XVZVB40FHZYz6KCZSKsW/AFaGvr3gfsAzwUeUq2l2xW07IZbAvfuQYfbA38HHgy8Eth6FHSYLvoszZaxdN5KcuskBwCfAN6Z5OlVdRHwWlpAdgPg40nel+TFtBvAl1TVU6rq9J7h43lUN9l4am9aJ5+7JrkFrWDpZbQL6F8Ae1TrJX8o8ChavaY2Cb4HHXpdB2kw00yr2IJWFPKFwOuBJyd5aVWdRetOsSNAVX2FllV2TX9tdxXN2Fhm99OSfAK4Ey2z+3LgDkluVlXn0Iro79U/c+3YZzenHVc1YV4wLaVGKcIA1frJH0lLNXpBtX7yxyQ5kNbne3Ng+SQvqKojgU+z4ALmL+PbNbVNM9X3oY2AjZPsBVwAfBIYPQ0+kXbw/36PNj+VdqO3ab9Y/k1VXTNWx8HAl2ZdptS+6V5G2y/vTwve7plk06p6D+3J8rHAtrRpFv+oqr+NXfAs1/dfj5laLOM3Wz0QtmmSE2gZjCtVKxR5D+BdwPOr6rlVdVGSbarqa7Rj679kOFTrSS8Npp/LV0hyu5618CPgqbTrzS8B7wPeltaW/SDgFkme3z+7+9TMHM/7ujGj42UP+K+YZB9a5sJnquq4aq0vv8uCwvnQCps+tteAGG1nE1qgwlaZc8CpFkuhKSnCGwG/7TduTwKeR0vD/Dsto2HXqvprkiOAhwAb9afQo23ZqUIzNpaytmJV/TPJcbT5ck+qqq8m2Zo2reLoPv1nW1q65QW0+g57V9XX5+4baFmVZCfavM9RF5Xb0gJlPwduRpt29uIkzwXeUVXrzM1INZ+NT6sYndOT7A98Z3zKWZJH0+bG34u2f74RuDnw3zXW8Uca0jSp7TvRplceB6xRVTsmWYN2LH1TVZ2R5Ezg3Kp6YpItgZ9WK9rrlF4tsumOlf3n/WnTdU+iBRKuBc6g1V/6IvAI2nTfX1fVuXMwdE3DdLylUL9AuQXwXtrc+D8leQfwLeD+tHSjdwJ3Bp6T1j/5FOC9VXX52EWOQQctlh50WJU2feJ8YFdaYOGRtCKRvwB+BDw8yclV9ZUk3wU2rKoz5mrcWnZMvbBNsjrtBm4DWurldT1otg3w5xRpPQAAE4dJREFU46r6f0keAhyd5Kiq+mSSU/tnl6tW6M+LZc2KsQvpVwN/TvIpWjG+Zye5Je3Yug7wClqW4vto2Q/HAv87nqLuPqmh9KfMGcv0ujmwFrAN8ADgNsCPk3yGlrp+Na3jyn2BbwN3SbJKVZ022l7PFHOf1Y2aMgVtN+A2SU6sqi/RpvB8jBZ8uCWtQP6raHXFdqB1rfiewa4li4GHpUCm7yX7IuCvVbVVT1/blZaeeQStcM96tHSjHWjFJN86+uDofzyDDlpU/Ybsx7WgzdXOwOuAM5JcU1VP7U+If5XkQ1X1837Tdk/gmcC7q+oy+py6hezT0qxYSFB1Ddr0widU1RVjtRmuBO6U5M7Aw4GjgYsBqurMfrFyXX/tRYsWyzSBsLvRppydS3tC/Pck76ZV/f8Z8FNaluL/VNXrk6xEK4Z2Sf+8rQY1mCRrAStWa3tZPZvhLbQ59N+nPXDYmza1YseqOqZPlzwLeBOwIW160PfGt+v+qhuT5OG0VuuHAD9Lch0tg+HXtGPmoUmuqqoDkhxTVRf3z+1Gy745K8nrpgZn3feWDNZ4WAqMRfu2T/JfffGqtJQiqurjwO+B7arqO7SKwm+pqrOq6lVV9ar+ef97a0aSPDqt1erLgAcmWbNP73km8NCqeiJwbZLdq7VofT+t2jq0/fCg/ucGDDpoSGNP516cZNskt6ZlOtyxBx1WBUYXIT8ETgO+3F8/uap+MLYtL1Z0k2SsaHMWFOW7BfA94CXA79I6q1zX578fStsvNwR+AFBVV1fVJRlrATv5b6JlQZI30jqo/E9//VjgzcDfq2pPWseqHWkp7Petqs+ltR/eolqXgBdX1aajoEOmaZEtTZVkgySH0QqO/5N2bHxBf+D1JmB34Em0+g3PTXIH4A9JNk8rEr097Vw+XrTUgvlLGDMelgJ9WsUBtP7de/fFKwCrJlmzWnG/g2k9vN9OiwiuOPb5URFKL1S0yJI8hTaH8zVV9fUkK1fVP/pN3HUsuHF7L/CKJOtU1Z5JrksrfnYcbfqPNKhpniZvCTyN1j/+F7R2wvcD1kzrXHFIX++RtIvn1yZ5bw+eWftGs6pP07kZ7ebt0iSHAyvRgg8n0IpEPhD4dpIPANvR6jV9tKcU32BbEx28lhn93H4Y8EvgcbSuaNA6UGxOmzpBVZ2c5AzaNeh9aO2F30Brp316Vf28b2/5qrrWBw1aRA8EnlhVy8H116BP6A+7Tgb2A86qqtsk+QGtmOlnaPvqOVW109QNerxc8vgEfAmzkMjwxsAvq2p0MwfweVoxv1161O8ZwFcAqupX1drI0F+bYqRFNgpU0doJ7tODDqnWwg1aUOs8WoE+quq7tAuP+/T37zG2n0qDG3uavEJaq9aTgFWr6nFV9RJaWvBbaE9EXpHkLf3mb2969etqfb99mqybrKepj7/ejHZDdx6twO4htALQrwK2raoXAf9Le8p3EfAN4D7VuqvYalCTshXtxm6nHoQ9C6CqjqU9RLiuX29Ce9D1F9pT6BfSshz2Ht+YAQfNRFUdDPwgycv7ol/TipFfRGsrfEsWZCZeSJsauTLtOvUtYHbN0sCMhyXM2LSKJwA/6wGEjWlFpUiyCnB1Vf0wyUeAhwH709KLPjgng9a8Mhak2gL4Sf95OVrFYKrqJ0kuALZPsj6tjetfx9Y9Cyzko2FlQcHH0d9PpXXteWeSDwF3HVv9JbQny2+lpQg/gBbM/dT4Ng046KZI8ghaltjFSS4Gjqmqo2j1Rb4GHEULdv2CVu3/Dz1Y9nBaevFvgSurtSYcf2LscVSTsBYtmxG4vpD5ij1t/RBaoGzzJL+t1iXgXT3TcVS76QaFKKXF8H/AV5KcRpvi+wvaPrkyrUj+23q9m3OA91fV+aMP9mtOg11LOAMPS5gk96RNqzifNnf+h8DnaH1oH1JVJ/T1HlpVxwDHJFmrelstU4Q1i44GtkhyUFVdnWQF2hzk64DjaRXXn01r8fbaqvot3KB4qRfLmnVJHgyc2ed9Qmst+BfahckmfZrFm4DTkzywqk6iPUn+PkBV/YxWvG+0PQud6iZJsgHwAdqx8P20rIZHAwcluQet8v9OwLOAd1bVF/rnbk57yvxiYL+q+uL4dt0vNWHr07qkjYINsOCBw9lJTqDNsf85cHZfPrVgtOd9LbY+jecrtDatj+nZNgCXJdkHeA7wh7HpkvGac+kS/zvNjekCBD1avCvticdH09oR7UBLKfolre/87rSbvfWBpwCXjJ760WdVTPJ7aP5K8gDavnZsVR0xtvyRtDT2I5OsUVWX9+VmOGgwSR5Nq9VwKa2OzenAk4Etq+o5/fi5N60A2huT7Ep7evIe4Im0ThU7j11QS7MiyTOAA8fmJo9aVn+cVgj6VbSWmG8eTUNL8klabYfPju+TPjzQkMZqfo3XxBlljW0MHEjrRnHa2Pujjj+fAZ4PfLKq/jbZkWtZ0aeqnQ/cqWeFrQL8Y+r1pcfKpZM1HuZAv0jZvf988yTPSLJ2/59qa+BOfdUzaVG/jfqTkD1pBX6Or6r7V9VFtaDNm221NNtOpk2feFlaZ4B1krwSeAf92DEWdFje/U9D6UWm9gb2rtZJ5aiq+h1wKrBGL2ZatPmf90jyyD4//g/AnYHdqmoHgw4awtjc5F37opX73y8Gtu2vPwq8MMlBSX5EK9h3RI1VX+/b8kJag+g3atWDYrccLe9Bh+Wr6qfAV4EXJXla/8zGwL7A3WjTfN9n0EFD6hnc76Zl1lJVV00JlFkwfylmxsMEJXkQLcX3vrQ2RYfT0oauA/4I7AGsS3s6sktV/S7JM4H7V9WLp9meKcIaVD/Av4gWELst7UZut6q6YE4HpmXC2JPjtwOnVdUh45k1SVYHdqZlPfxXX/Yt2vF0F1qV7DdV1Rb9PY+ZGkTPUPwasH5VXZVklf73AbR9931pHaoeAvxqrI6DmWKamCSr0bqr3J9WxPT0qjo8yUp9SuUqtAyxV9CmVNwT+EgP5I624T6rwSU5ilYQ+jL3t/nDwMMEjKUI/wn4MC2FaDvg6cCzq+qcJKN5oV+lTa/YmhZlfiXw9araZ2x7HvQ1cUk2GNVxMMVNk5TkGFpK+gFTgwdJ7gi8i5b9cBotqPsl4HPV2r+eBLy9qo6ci7Fr2ZHki8DlVfXcsWVHAW+rquOnrGshPg1qIVN630yrifN+2vXoZlW1ZX9vPKi7FrABcF5VXbGw7UnSTDjVYmBTUoQfD3yjqs4GjqW1hhlVXj8YuCNwe1pV7INpNRw+NB50AAuoaG4YdNAcGhU6Xamqru2FTkmyMq2Y2atp09BeAryhqj5ZC9q/PsKggyZkF+ApSdYDSPI62pSKc6au2DPePY5qVo3S0HsQ4br+832TPKy/tz5tCuXBtC4WO44+O35tWVV/rqqzquqK9BaF7q+atNgec94x42Egi5AivCrtImXLqtqxL9sDuAOwb1X9csr2vNmTtEy6kUKnN6uqI6YWOgWDtJq8JHvRajucBlwC7FFVF87tqLQsSHJbYL1q7daXowUWPgbcijbl7JielbMNrWPAKf1z2wAnjzIbJGkoZjwMZOyCdwtglf7zcmPv/502H/TaJDv3xYcB3wV+M1rPglOS9G8LnY5aad2g0KlBB82Fqno9cAIty3GHqrpwdB6XBvZo4Jtw/TXjE2idzx5Urf06wF7AlcAfk6yZ5K3AbrRMCEkalBkPA0uyG22e3B69cM8KVXVNkpVo0yw2obV8+8/RhbMk6YYsdKqlkdmKmqQkpwJfqKq3J3ktsE5VvbRfc17bp6rtQetSsQnwQ2DPqvrjHA5b0jLCwMPAbiRFeAXgB8Btq+r0sekZFo+UpIWw0KmWdO6Xmgtj3VVuTbv2XB84YDTdJ8ndqupn/eeNqur8/rP7q6TBmf43vIWlCL8TWKGqLqmq02HB9AyDDpK0cAYdtKRzv9RcqKqTaVN99qF199kQeGuSzZJ8Anh1klv3dc9P43FU0kSY8TABpghLkiRpaEnWAc6j1Rj7I7ArsCnw66raYw6HJmkZZ+BhwkwRliRJ0lB6d5UnV9Vm/fUqVXVV/9lrT0lzwsDDHPHAL0mSpCEkOQrYAbisqq6zzbCkuWbgQZIkSZIkDcbikpIkSdI8k2T5uR6DJI2Y8SBJkiRJkgZjxoMkSZIkSRqMgQdJkiRJkjQYAw+SJEmSJGkwBh4kSZplSWoR/jx0jsf4mD6OO83Ctv67b2uF/vpu/fUjFuGz6yf5QJJfJflHksuSfCXJNjMcw/2TvGZxv4MkSRrOCnM9AEmS5qH7jf28KvBN4M3AV8eWnz3RES2BkmxK+7e5DNgH+BmwJrAt8PUkm1XVOYu4ufsDr6H9O0uSpCWIgQdJkmZZVZ08+jnJ6v3Hc8eXL+uSLAccBPwWeHBV/W3s7SOTfBS4fE4GN8uSrFJVV831OCRJmitOtZAkaQ4l2TLJ8UmuTHJpkk8nWXfs/e8ned/Y6yf0aQx7jy3bPsnfk6zUX1+c5M1Jdk/yuz594TNJbj7NEG6V5PAkVyQ5N8nzp4zvwX3qw0VJ/pbkB0meNgtf/RHA3YFXTgk6AFBVp1fVRYsyhiT/DewLrDw2leWosfe3SHJUkr8m+UuSA5OsN+V73ivJKUmuSnJmkkcm+UmSj0xZb/skZ/VpIb9J8oYky4+Ppf/+eyU5Mcnfgf+X5MdJPjz1eyY5OMl3F/tfUZKkpYCBB0mS5kiS2wDfApYHngm8DHg0cNSoXgLwbeBBYx97MHDVNMtOqaqrx5btRJvy8TxgT+ApwF7TDOOTwCnAE4HvAfsl2Xzs/Y2A44HnAk8AvgwcmORJM/y6Uz0E+Aft+9+YGxvDYcD7gatp3/l+wK4ASTYGTuzrbQ88H7g3cPho4z0gczQQ2n+HfYAPArceH0SS7YDP0v6dHg98hPZv+85pxnwwcCjwOOAY4BPAM5OsMra9tfv3+dQi/BtIkrTUcqqFJElz55W0m+/HVNUVAEl+DZwAbEe7OT4ReGmSNavqL7SAwyeA5ydZuar+0ZcdOmXbVwBPqarr+nY3B55MC26M+3RVva2vcyKtvsKTgDMAquozoxX79IgTgNsDL2Ds5n0xbABcVFXX3NiKNzaGqvp9kt+0Vf9lOssbgfOAbUe/K8nZwJlJtqmq44BdgJsD/1lVf+jr/Kb/nnFvAo6qqlFWyNE9QPTaJHtX1e/H1n1HVX10bNwX0gIaTwIO7IufBVxHC1JIkjRvmfEgSdLc2Qr42ijoAFBV3wYuBh7YF51EexJ//14v4p60p/F/B7bq0zLuxoKn+iPHjYIO3dnABkkyZb1jxn73VcCvgNuOliW5RZIP9hvxq4F/0rIp7rJ4X/kGalFWuoljeAQtI4IkK/RAwTnARcCWfZ37AN8bBR3g+v8Ofx4bw8rAPYAvTNn+wbQHOVtPWT5eSJSquhT4EvCcscXPAQ6tqnlRy0KSpIUx40GSpLlzG+A70yy/BFgH2g1rf0L/IOAa4LKq+mmS7/Rl69Kemn9vyjb+POX11bTz/vJ9O/9uvVXGXn+eVovhzbSuE38FXgI8bBG+37/zW+A2SVZYhKyHxRpDr72wFvC6/meqDfvftwZ+P837fxz7+da0ANAlU9YZvV5nIcvHfYLWreO2wBq0gMcrFzZ+SZLmCwMPkiTNnYuAW06z/Fa0FpMjJ9KCDNeyILPhROChwHrAj6rqr7M9uCRrAI8Cdq6q/ceWz8b1w/HAq2m1Ho4bYgxVdW2Sy4H9gc9Ms8oo2HAx7d98qvEClBfTMjSm/vcafe6yKcuny+b4BnABLVtjbdoUkOOnHbwkSfOIUy0kSZo7pwCPS7LaaEGSB9Gerp80tt6JtKfjj6QVm6T/fX9a8GHqNIvZsmr/+x9j41ubVjDxpjoO+Amwz/j3H/s99+zFNxd1DFcDK/YaEFN/z6ZVddo0f37T1zkVuN94p4skDwbWHL3utTTOAKZ29Hg6LYPklBv7wn3qy6eAnYEdgP2rapGmm0iStDQz8CBJ0tzZlzat4etJtkuyE61mwOm0zg0j3wZWptURGAUeTgNWBLZgoMBDVV0CnAm8McmTkjyF9tT+0lnY9nW0DhIbAqcm2aW3zdwuyYeAk4E1ZjCGn9Gua16S5D5J7tyXvxbYOsmRSZ6c5KFJdujtRe/f19kP+Bvw1bR2pdvTpkVcSpvGMvI64LFJPpbkUUn26Nv/4JTCkv/Op4D/oGVKfHoRPyNJ0lLNwIMkSXOkqn4HPLy/PAR4D+2m+jHjdQ+q6kJaWv5fgB/3Zf9kwVP28eyI2fZ04HfA54B30dpJHjIbG66qs4B7sWDaxTdoN+N3AJ5WVefMYAzfAN5Lq5lwCq295uh33Jc2TeUTwNeB19PqRPy6r3M5rY0pfbuvodWQuBK4vvBjVX0Z2JFW+PMrwIuAvYHdZvCdfwP8EPhmVZ23qJ+TJGlpFjP8JEmSbijJXWhZFNtX1YE3tv4MtnsrWp2Hnavqc7O1XUmSlmQGHiRJ0jIvyWtpWSUXALcH9gRWAjauqitnYftrAJsALwfuB/xHrxshSdK8Z1cLSZKk1irzjcD6wN+BE4DdZiPo0N2fNs3j18COBh0kScsSMx4kSZIkSdJgLC4pSZIkSZIGY+BBkiRJkiQNxsCDJEmSJEkajIEHSZIkSZI0GAMPkiRJkiRpMP8fnxV8nmqM0ewAAAAASUVORK5CYII=\n"}, 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