Week5.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Capstone project - provide use the comparison of 2 cities based on the data available #"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "from bs4 import BeautifulSoup\nimport requests\nimport numpy as np\nfrom geopy.geocoders import Nominatim # convert an address into latitude and longitude values\nfrom pandas.io.json import json_normalize  # tranform JSON file into a pandas dataframe\nimport pandas as pd\n# import k-means from clustering stage\nfrom sklearn.cluster import KMeans"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Read the Wikipedia page for City1 ##"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Population:  246376\nDensity:  1,080.0/km2 (2,797/sq\u00a0mi)\n"
                }
            ],
            "source": "city1_address = 'Saskatoon, Saskatchewan, Canada'\ntable=pd.read_html(\"https://en.wikipedia.org/wiki/Saskatoon\",attrs={\"class\":\"infobox geography vcard\"})[0]\nloc=table[table[table.columns[0]].str.match('^Population.*')==True].index[0]\nprint(\"Population: \",int(table.loc[[loc+1]][table.columns[1]].to_list()[0]))\nprint(\"Density: \",(table.loc[[loc+2]][table.columns[1]].to_list()[0]))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Read the Wikipedia page for City2 ##"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Population:  214089\nDensity:  2,400/sq\u00a0mi (940/km2)\n"
                }
            ],
            "source": "city2_address = 'Fremont, California, USA'\ntable=pd.read_html(\"https://en.wikipedia.org/wiki/Fremont,_California\",attrs={\"class\":\"infobox geography vcard\"})[0]\nloc=table[table[table.columns[0]].str.match('^Population.*')==True].index[0]\nprint(\"Population: \",int(table.loc[[loc+1]][table.columns[1]].to_list()[0]))\nprint(\"Density: \",(table.loc[[loc+4]][table.columns[1]].to_list()[0]))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Get the Neighborhoods for City1 ##"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "[<h2>Evergreen</h2>,\n <h2>Adelaide or Churchill</h2>,\n <h2>Mayfair</h2>,\n <h2>Lakeview</h2>]"
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "source = requests.get(\"https://canadianvisa.org/blog/cities-and-places/saskatoon/neighborhoods\").text\nsoup = BeautifulSoup(source, 'lxml')\ntable = soup.find_all(\"h2\")\nfor i in table:\n    str(i).replace(\"<h2>\",\"\")\ntable[1:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Creating function to get venues from FourSquare API"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [],
            "source": "def getNearbyVenues(names, latitudes, longitudes, radius=10000):\n    \n    venues_list=[]\n    CLIENT_ID=\"\"\n    CLIENT_SECRET=\"\"\n    VERSION='20180323'\n    LIMIT=100\n    url = 'https://api.foursquare.com/v2/venues/explore'\n\n    for name, lat, lng in zip(names, latitudes, longitudes):\n        print(name)\n\n        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n            CLIENT_ID, CLIENT_SECRET, VERSION, lat, lng, radius, LIMIT)\n\n        #print (url)\n        # make the GET request\n        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n        #print(results)\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 = ['Neighborhood', \n                  'Neighborhood Latitude', \n                  'Neighborhood Longitude', \n                  'Venue', \n                  'Venue Latitude', \n                  'Venue Longitude', \n                  'Venue Category']\n    \n    return(nearby_venues)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Define cities for comparison"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [],
            "source": "def return_most_common_venues(row, num_top_venues):\n    row_categories = row.iloc[1:]\n    row_categories_sorted = row_categories.sort_values(ascending=False)\n    \n    return row_categories_sorted.index.values[0:num_top_venues]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Define the neighborhoods for both cities (it was hard to get this publicly in a nice format)"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "['Evergreen,Saskatoon, Saskatchewan, Canada',\n 'Adelaide,Saskatoon, Saskatchewan, Canada',\n 'Mayfair,Saskatoon, Saskatchewan, Canada',\n 'Lakeview,Saskatoon, Saskatchewan, Canada',\n 'Centerville,Fremont, California, USA',\n 'Niles,Fremont, California, USA',\n 'Irvington,Fremont, California, USA',\n 'Warm Springs,Fremont, California, USA']"
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "city1_dist=['Evergreen','Adelaide','Mayfair','Lakeview']\ncity2_dist=['Centerville', 'Niles', 'Irvington', 'Warm Springs']\ncity_df = []\nfor dist in city1_dist:\n    city_df.append(dist+\",\"+city1_address)\n\nfor dist in city2_dist:\n    city_df.append(dist+\",\"+city2_address)\n\ncity_df"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Get the Coordinates for all the neighborhoods using GeoLocator API"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Centerville 37.559645599999996 -122.0067569\nNiles 37.5781679 -121.9809177\nIrvington 37.5322543 -121.9538481\nWarm Springs 37.5019913 -121.9392084\n"
                },
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Evergreen,Saskatoon, Saskatchewan, Canada</td>\n      <td>52.169376</td>\n      <td>-106.569080</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Adelaide,Saskatoon, Saskatchewan, Canada</td>\n      <td>52.098655</td>\n      <td>-106.640908</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Mayfair,Saskatoon, Saskatchewan, Canada</td>\n      <td>52.141689</td>\n      <td>-106.678728</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Lakeview,Saskatoon, Saskatchewan, Canada</td>\n      <td>52.097643</td>\n      <td>-106.595409</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Centerville,Fremont, California, USA</td>\n      <td>37.559646</td>\n      <td>-122.006757</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Niles,Fremont, California, USA</td>\n      <td>37.578168</td>\n      <td>-121.980918</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Irvington,Fremont, California, USA</td>\n      <td>37.532254</td>\n      <td>-121.953848</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Warm Springs,Fremont, California, USA</td>\n      <td>37.501991</td>\n      <td>-121.939208</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Mission San Jose,Fremont, California, USA</td>\n      <td>37.534721</td>\n      <td>-121.920418</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                                Neighborhood   Latitude   Longitude\n0  Evergreen,Saskatoon, Saskatchewan, Canada  52.169376 -106.569080\n1   Adelaide,Saskatoon, Saskatchewan, Canada  52.098655 -106.640908\n2    Mayfair,Saskatoon, Saskatchewan, Canada  52.141689 -106.678728\n3   Lakeview,Saskatoon, Saskatchewan, Canada  52.097643 -106.595409\n0       Centerville,Fremont, California, USA  37.559646 -122.006757\n1             Niles,Fremont, California, USA  37.578168 -121.980918\n2         Irvington,Fremont, California, USA  37.532254 -121.953848\n3      Warm Springs,Fremont, California, USA  37.501991 -121.939208\n0  Mission San Jose,Fremont, California, USA  37.534721 -121.920418"
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "#city1_df=pd.DataFrame(columns=['Neighborhood','Latitude','Longitude'])\ncity_df=[]\nfor dist in city1_dist:\n    geolocator = Nominatim(user_agent=\"ny_explorer\")\n    location = geolocator.geocode(dist+\",\"+city1_address)\n    latitude = location.latitude\n    longitude = location.longitude\n    city_df.append([dist+\",\"+city1_address,latitude,longitude])\n\ncity_df=pd.DataFrame(city_df,columns=['Neighborhood','Latitude','Longitude'])\n\ncity_df2=[]\nfor dist in city2_dist:\n    geolocator = Nominatim(user_agent=\"ny_explorer\")\n    location = geolocator.geocode(dist+\",\"+city2_address)\n    latitude = location.latitude\n    longitude = location.longitude\n    print(dist,latitude,longitude)\n    city_df2.append([dist+\",\"+city2_address,latitude,longitude])\n\ncity_df=city_df.append(pd.DataFrame(city_df2,columns=['Neighborhood','Latitude','Longitude']))\n\n## Adding San Jose coordinates manually, as GeoLocator was giving error for this\ncity_df=city_df.append(pd.DataFrame([['Mission San Jose,Fremont, California, USA',37.534721, -121.920418]],columns=['Neighborhood','Latitude','Longitude']))\n\ncity_df"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Evergreen,Saskatoon, Saskatchewan, Canada\nAdelaide,Saskatoon, Saskatchewan, Canada\nMayfair,Saskatoon, Saskatchewan, Canada\nLakeview,Saskatoon, Saskatchewan, Canada\nCenterville,Fremont, California, USA\nNiles,Fremont, California, USA\nIrvington,Fremont, California, USA\nWarm Springs,Fremont, California, USA\nMission San Jose,Fremont, California, USA\nThere are 130 uniques categories.\n                                Neighborhood  Afghan Restaurant  \\\n0   Adelaide,Saskatoon, Saskatchewan, Canada               0.00   \n1       Centerville,Fremont, California, USA               0.01   \n2  Evergreen,Saskatoon, Saskatchewan, Canada               0.00   \n3         Irvington,Fremont, California, USA               0.00   \n4   Lakeview,Saskatoon, Saskatchewan, Canada               0.00   \n5    Mayfair,Saskatoon, Saskatchewan, Canada               0.00   \n6  Mission San Jose,Fremont, California, USA               0.00   \n7             Niles,Fremont, California, USA               0.01   \n8      Warm Springs,Fremont, California, USA               0.00   \n\n   American Restaurant  Asian Restaurant  BBQ Joint  Bagel Shop    Bakery  \\\n0             0.027397          0.013699       0.00        0.00  0.027397   \n1             0.010000          0.000000       0.00        0.01  0.040000   \n2             0.030000          0.040000       0.00        0.00  0.040000   \n3             0.010000          0.000000       0.01        0.01  0.030000   \n4             0.035714          0.017857       0.00        0.00  0.035714   \n5             0.030000          0.030000       0.00        0.00  0.040000   \n6             0.010000          0.000000       0.01        0.00  0.040000   \n7             0.010000          0.000000       0.00        0.01  0.040000   \n8             0.010000          0.020000       0.01        0.00  0.030000   \n\n       Bank       Bar  Beach  ...  Tennis Court  Thai Restaurant  Theater  \\\n0  0.013699  0.013699   0.00  ...          0.00         0.013699     0.00   \n1  0.000000  0.000000   0.00  ...          0.00         0.040000     0.00   \n2  0.000000  0.010000   0.00  ...          0.00         0.010000     0.01   \n3  0.000000  0.000000   0.00  ...          0.01         0.020000     0.00   \n4  0.000000  0.017857   0.00  ...          0.00         0.017857     0.00   \n5  0.000000  0.010000   0.00  ...          0.00         0.010000     0.01   \n6  0.000000  0.000000   0.00  ...          0.01         0.020000     0.00   \n7  0.000000  0.000000   0.01  ...          0.01         0.030000     0.00   \n8  0.000000  0.000000   0.00  ...          0.00         0.030000     0.00   \n\n   Trail  Train Station  Vietnamese Restaurant  Warehouse Store  Wine Shop  \\\n0   0.00       0.013699                   0.00         0.013699       0.00   \n1   0.02       0.000000                   0.00         0.000000       0.00   \n2   0.01       0.000000                   0.01         0.010000       0.00   \n3   0.03       0.000000                   0.00         0.000000       0.01   \n4   0.00       0.017857                   0.00         0.017857       0.00   \n5   0.01       0.000000                   0.02         0.000000       0.00   \n6   0.05       0.000000                   0.00         0.000000       0.01   \n7   0.02       0.000000                   0.01         0.000000       0.00   \n8   0.03       0.000000                   0.00         0.010000       0.01   \n\n   Yoga Studio       Zoo  \n0         0.00  0.013699  \n1         0.00  0.000000  \n2         0.00  0.010000  \n3         0.01  0.000000  \n4         0.00  0.017857  \n5         0.00  0.010000  \n6         0.00  0.000000  \n7         0.00  0.000000  \n8         0.01  0.000000  \n\n[9 rows x 131 columns]\n                 venue  freq\n0          Coffee Shop  0.05\n1                  Pub  0.05\n2        Grocery Store  0.04\n3          Pizza Place  0.04\n4                 Caf\u00e9  0.04\n5       Sandwich Place  0.03\n6           Restaurant  0.03\n7                Hotel  0.03\n8  American Restaurant  0.03\n9   Mexican Restaurant  0.03\n\n\n                venue  freq\n0         Coffee Shop  0.07\n1       Grocery Store  0.06\n2      Ice Cream Shop  0.04\n3     Thai Restaurant  0.04\n4              Bakery  0.04\n5    Sushi Restaurant  0.04\n6                Park  0.04\n7      Breakfast Spot  0.03\n8  Falafel Restaurant  0.02\n9  Chinese Restaurant  0.02\n\n\n                 venue  freq\n0          Coffee Shop  0.07\n1           Restaurant  0.05\n2                Hotel  0.05\n3          Pizza Place  0.04\n4     Asian Restaurant  0.04\n5                  Pub  0.04\n6               Bakery  0.04\n7                 Caf\u00e9  0.04\n8           Steakhouse  0.03\n9  American Restaurant  0.03\n\n\n                  venue  freq\n0           Coffee Shop  0.06\n1         Grocery Store  0.05\n2        Ice Cream Shop  0.05\n3  Fast Food Restaurant  0.04\n4    Falafel Restaurant  0.03\n5           Pizza Place  0.03\n6                Bakery  0.03\n7                  Park  0.03\n8                   Gym  0.03\n9                 Trail  0.03\n\n\n                 venue  freq\n0                  Pub  0.07\n1          Coffee Shop  0.05\n2                 Caf\u00e9  0.05\n3   Mexican Restaurant  0.04\n4                Hotel  0.04\n5       Breakfast Spot  0.04\n6  American Restaurant  0.04\n7            Bookstore  0.04\n8          Pizza Place  0.04\n9           Restaurant  0.04\n\n\n                 venue  freq\n0           Restaurant  0.05\n1                Hotel  0.05\n2                 Caf\u00e9  0.04\n3               Bakery  0.04\n4          Coffee Shop  0.04\n5                  Pub  0.04\n6          Pizza Place  0.03\n7     Asian Restaurant  0.03\n8           Steakhouse  0.03\n9  American Restaurant  0.03\n\n\n                  venue  freq\n0           Coffee Shop  0.07\n1                 Trail  0.05\n2        Ice Cream Shop  0.05\n3  Fast Food Restaurant  0.04\n4                Bakery  0.04\n5         Grocery Store  0.04\n6    Mexican Restaurant  0.03\n7                  Park  0.03\n8    Falafel Restaurant  0.03\n9           Pizza Place  0.03\n\n\n                  venue  freq\n0                  Park  0.07\n1           Coffee Shop  0.07\n2         Grocery Store  0.05\n3                Bakery  0.04\n4      Sushi Restaurant  0.04\n5        Breakfast Spot  0.04\n6       Thai Restaurant  0.03\n7    Mexican Restaurant  0.03\n8  Fast Food Restaurant  0.03\n9        Ice Cream Shop  0.03\n\n\n                  venue  freq\n0           Coffee Shop  0.07\n1  Fast Food Restaurant  0.04\n2                   Gym  0.04\n3         Grocery Store  0.04\n4             Juice Bar  0.03\n5    Mexican Restaurant  0.03\n6                Bakery  0.03\n7      Sushi Restaurant  0.03\n8       Thai Restaurant  0.03\n9                 Trail  0.03\n\n\n                                Neighborhood 1st Most Common Venue  \\\n0   Adelaide,Saskatoon, Saskatchewan, Canada                   Pub   \n1       Centerville,Fremont, California, USA           Coffee Shop   \n2  Evergreen,Saskatoon, Saskatchewan, Canada           Coffee Shop   \n3         Irvington,Fremont, California, USA           Coffee Shop   \n4   Lakeview,Saskatoon, Saskatchewan, Canada                   Pub   \n5    Mayfair,Saskatoon, Saskatchewan, Canada                 Hotel   \n6  Mission San Jose,Fremont, California, USA           Coffee Shop   \n7             Niles,Fremont, California, USA                  Park   \n8      Warm Springs,Fremont, California, USA           Coffee Shop   \n\n  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n0           Coffee Shop                  Caf\u00e9         Grocery Store   \n1         Grocery Store      Sushi Restaurant                Bakery   \n2                 Hotel            Restaurant                Bakery   \n3         Grocery Store        Ice Cream Shop  Fast Food Restaurant   \n4           Coffee Shop                  Caf\u00e9           Pizza Place   \n5            Restaurant           Coffee Shop                   Pub   \n6        Ice Cream Shop                 Trail                Bakery   \n7           Coffee Shop         Grocery Store        Breakfast Spot   \n8  Fast Food Restaurant                   Gym         Grocery Store   \n\n  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n0           Pizza Place            Restaurant    Mexican Restaurant   \n1        Ice Cream Shop                  Park       Thai Restaurant   \n2                   Pub      Asian Restaurant                  Caf\u00e9   \n3                   Gym           Pizza Place                 Trail   \n4    Mexican Restaurant            Restaurant   American Restaurant   \n5                  Caf\u00e9                Bakery            Steakhouse   \n6  Fast Food Restaurant         Grocery Store                   Gym   \n7      Sushi Restaurant                Bakery  Fast Food Restaurant   \n8      Sushi Restaurant             Juice Bar                 Trail   \n\n  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n0        Sandwich Place        Breakfast Spot              Bookstore  \n1        Breakfast Spot                 Trail     Mexican Restaurant  \n2           Pizza Place   American Restaurant          Grocery Store  \n3                Bakery    Falafel Restaurant                   Park  \n4                Bakery                 Hotel         Breakfast Spot  \n5           Pizza Place      Asian Restaurant    American Restaurant  \n6    Mexican Restaurant           Pizza Place     Falafel Restaurant  \n7    Chinese Restaurant    Mexican Restaurant        Thai Restaurant  \n8       Thai Restaurant                Bakery     Mexican Restaurant  \n"
                },
                {
                    "data": {
                        "text/plain": "array([3, 2, 0, 4, 3], dtype=int32)"
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "city_list=city_df\n\n#for city in city_list:\ncity_venues = getNearbyVenues(names = city_list['Neighborhood'], latitudes = city_list['Latitude'],longitudes = city_list['Longitude'])\nprint('There are {} uniques categories.'.format(len(city_venues['Venue Category'].unique())))\n# one hot encoding\ncity_onehot = pd.get_dummies(city_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n\n# add neighborhood column back to dataframe\ncity_onehot['Neighborhood'] = city_venues['Neighborhood'] \n\n# move neighborhood column to the first column\nfixed_columns = [city_onehot.columns[-1]] + list(city_onehot.columns[:-1])\ncity_onehot = city_onehot[fixed_columns]\n\n#city_onehot.head()\ncity_grouped = city_onehot.groupby('Neighborhood').mean().reset_index()\nprint(city_grouped)\nnum_top_venues = 10\n\nfor hood in city_grouped['Neighborhood']:\n    #print(\"----\"+hood+\"----\")\n    temp = city_grouped[city_grouped['Neighborhood'] == hood].T.reset_index()\n    temp.columns = ['venue','freq']\n    temp = temp.iloc[1:]\n    temp['freq'] = temp['freq'].astype(float)\n    temp = temp.round({'freq': 2})\n    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n    print('\\n')\n\nnum_top_venues = 10\nindicators = ['st', 'nd', 'rd']\n\n# create columns according to number of top venues\ncolumns = ['Neighborhood']\nfor ind in np.arange(num_top_venues):\n    try:\n        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n    except:\n        columns.append('{}th Most Common Venue'.format(ind+1))\n\n# create a new dataframe\nneighborhoods_venues_sorted = pd.DataFrame(columns=columns)\nneighborhoods_venues_sorted['Neighborhood'] = city_grouped['Neighborhood']\n\nfor ind in np.arange(city_grouped.shape[0]):\n    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(city_grouped.iloc[ind, :], num_top_venues)\n\nprint(neighborhoods_venues_sorted)\n\n# set number of clusters\nkclusters = 5\ncity_grouped_clustering = city_grouped.drop('Neighborhood', 1)\n\n# run k-means clustering\nkmeans = KMeans(n_clusters=kclusters, random_state=0).fit(city_grouped_clustering)\n# check cluster labels generated for each row in the dataframe\nkmeans.labels_[0:5] "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Process the data for Cosine Similarity (remove Neighborhood, group on cities)"
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [],
            "source": "def get_city(neighborhood):\n    return neighborhood.split(\",\")[1]+\",\"+neighborhood.split(\",\")[2]+\",\"+neighborhood.split(\",\")[3] \n\ncity_group2 = city_onehot.groupby('Neighborhood').sum().reset_index()\n#city_group2\ncity_group2['City']=city_group2.apply(lambda x: get_city(x['Neighborhood']),axis=1)\ncity_group2_new = city_group2.drop(['Neighborhood'],axis=1).groupby('City').sum()\n#city_group2_new = city_group2.drop(['Neighborhood'],axis=1).sort_values('City', ascending=False)\n#city_group2_new.set_index('City',inplace=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Calculate Similarity between the cities"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.5692370799616825"
                    },
                    "execution_count": 20,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "## METHOD 1\n\nfrom sklearn.metrics import pairwise_distances\nimport sklearn.metrics.pairwise as pairwise\n\npairwise.cosine_similarity(city_group2_new)[0][1]"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.343105388838693"
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "## METHOD 2\n\nimport scipy.spatial as spatial\nspatial.distance.cosine(city_group2_new.transpose().iloc[:,0],city_group2_new.transpose().iloc[:,1])"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Find top 10 venues for these cities"
        },
        {
            "cell_type": "code",
            "execution_count": 43,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>City</th>\n      <th>Fremont, California, USA</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Coffee Shop</th>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>Grocery Store</th>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>Park</th>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>Ice Cream Shop</th>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>Bakery</th>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>Fast Food Restaurant</th>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>Trail</th>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>Sushi Restaurant</th>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>Thai Restaurant</th>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>Mexican Restaurant</th>\n      <td>13</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "City                  Fremont, California, USA\nCoffee Shop                                 34\nGrocery Store                               24\nPark                                        19\nIce Cream Shop                              19\nBakery                                      18\nFast Food Restaurant                        17\nTrail                                       15\nSushi Restaurant                            15\nThai Restaurant                             14\nMexican Restaurant                          13"
                    },
                    "execution_count": 43,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "city_vens=city_group2_new.transpose()\ncity1_ven=city_vens[[city_vens.columns[0]]].sort_values(city_vens.columns[0],ascending=False)\ncity1_ven.head(10)"
        },
        {
            "cell_type": "code",
            "execution_count": 41,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>City</th>\n      <th>Saskatoon, Saskatchewan, Canada</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Coffee Shop</th>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>Pub</th>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>Hotel</th>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>Caf\u00e9</th>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>Restaurant</th>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>Bakery</th>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>Pizza Place</th>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>American Restaurant</th>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>Grocery Store</th>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>Asian Restaurant</th>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "City                 Saskatoon, Saskatchewan, Canada\nCoffee Shop                                       18\nPub                                               16\nHotel                                             14\nCaf\u00e9                                              14\nRestaurant                                        14\nBakery                                            12\nPizza Place                                       12\nAmerican Restaurant                               10\nGrocery Store                                      9\nAsian Restaurant                                   9"
                    },
                    "execution_count": 41,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "city2_ven=city_vens[[city_vens.columns[1]]].sort_values(city_vens.columns[1],ascending=False)\ncity2_ven.head(10)"
        },
        {
            "cell_type": "code",
            "execution_count": 49,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>City</th>\n      <th>Fremont, California, USA</th>\n      <th>Saskatoon, Saskatchewan, Canada</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Afghan Restaurant</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>American Restaurant</th>\n      <td>5</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>Asian Restaurant</th>\n      <td>2</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>BBQ Joint</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Bagel Shop</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Bakery</th>\n      <td>18</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>Bank</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Bar</th>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Beach</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Beer Garden</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Big Box Store</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Bookstore</th>\n      <td>5</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>Bowling Alley</th>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Brazilian Restaurant</th>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Breakfast Spot</th>\n      <td>11</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>Brewery</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Bubble Tea Shop</th>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Burger Joint</th>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Burmese Restaurant</th>\n      <td>5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Caf\u00e9</th>\n      <td>6</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>Cajun / Creole Restaurant</th>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Camera Store</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Caribbean Restaurant</th>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Cheese Shop</th>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Chinese Restaurant</th>\n      <td>8</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Church</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Clothing Store</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Coffee Shop</th>\n      <td>34</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>Comedy Club</th>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Convenience Store</th>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Cosmetics Shop</th>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Creperie</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Cupcake Shop</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Deli / Bodega</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Dessert Shop</th>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Dim Sum Restaurant</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Diner</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Discount Store</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Dive Bar</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Dog Run</th>\n      <td>5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Donut Shop</th>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Dumpling Restaurant</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Falafel Restaurant</th>\n      <td>12</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Farm</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Farmers Market</th>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Fast Food Restaurant</th>\n      <td>17</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Food Court</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Food Truck</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>French Restaurant</th>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Fried Chicken Joint</th>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Frozen Yogurt Shop</th>\n      <td>7</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Furniture / Home Store</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Gas Station</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Gastropub</th>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>German Restaurant</th>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Gift Shop</th>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Gluten-free Restaurant</th>\n      <td>0</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>Golf Course</th>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Greek Restaurant</th>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Grocery Store</th>\n      <td>24</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>Gym</th>\n      <td>12</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>Gym / Fitness Center</th>\n      <td>7</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Gymnastics Gym</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Hardware Store</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Hawaiian Restaurant</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Hockey Arena</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Hot Dog Joint</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Hotel</th>\n      <td>1</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>Hunan Restaurant</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Ice Cream Shop</th>\n      <td>19</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>Indian Restaurant</th>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Indie Movie Theater</th>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Italian Restaurant</th>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Japanese Restaurant</th>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Juice Bar</th>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Karaoke Bar</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Lake</th>\n      <td>7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Latin American Restaurant</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Library</th>\n      <td>5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Liquor Store</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Lounge</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Mediterranean Restaurant</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Mexican Restaurant</th>\n      <td>13</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>Middle Eastern Restaurant</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Mountain</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Movie Theater</th>\n      <td>8</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Museum</th>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Nature Preserve</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>New American Restaurant</th>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Outdoor Supply Store</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Paper / Office Supplies Store</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Park</th>\n      <td>19</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Pet Store</th>\n      <td>10</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Pharmacy</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Pizza Place</th>\n      <td>10</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>Playground</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Pool</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Pool Hall</th>\n      <td>5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Pub</th>\n      <td>1</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>Racetrack</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Restaurant</th>\n      <td>4</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>Salon / Barbershop</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Sandwich Place</th>\n      <td>8</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>Scenic Lookout</th>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Seafood Restaurant</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Shanghai Restaurant</th>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Shipping Store</th>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Shoe Store</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Shopping Mall</th>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Smoke Shop</th>\n      <td>5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Smoothie Shop</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Soccer Stadium</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Spa</th>\n      <td>7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Sporting Goods Shop</th>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Steakhouse</th>\n      <td>0</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>Supermarket</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Sushi Restaurant</th>\n      <td>15</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Taco Place</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Taiwanese Restaurant</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Tea Room</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Tennis Court</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Thai Restaurant</th>\n      <td>14</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Theater</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Trail</th>\n      <td>15</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Train Station</th>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Vietnamese Restaurant</th>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Warehouse Store</th>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Wine Shop</th>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Yoga Studio</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Zoo</th>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "City                           Fremont, California, USA  \\\nAfghan Restaurant                                     2   \nAmerican Restaurant                                   5   \nAsian Restaurant                                      2   \nBBQ Joint                                             3   \nBagel Shop                                            3   \nBakery                                               18   \nBank                                                  0   \nBar                                                   0   \nBeach                                                 1   \nBeer Garden                                           2   \nBig Box Store                                         1   \nBookstore                                             5   \nBowling Alley                                         4   \nBrazilian Restaurant                                  0   \nBreakfast Spot                                       11   \nBrewery                                               2   \nBubble Tea Shop                                       4   \nBurger Joint                                          2   \nBurmese Restaurant                                    5   \nCaf\u00e9                                                  6   \nCajun / Creole Restaurant                             0   \nCamera Store                                          0   \nCaribbean Restaurant                                  0   \nCheese Shop                                           0   \nChinese Restaurant                                    8   \nChurch                                                3   \nClothing Store                                        0   \nCoffee Shop                                          34   \nComedy Club                                           3   \nConvenience Store                                     4   \nCosmetics Shop                                        3   \nCreperie                                              0   \nCupcake Shop                                          0   \nDeli / Bodega                                         1   \nDessert Shop                                          6   \nDim Sum Restaurant                                    0   \nDiner                                                 0   \nDiscount Store                                        2   \nDive Bar                                              0   \nDog Run                                               5   \nDonut Shop                                            6   \nDumpling Restaurant                                   2   \nFalafel Restaurant                                   12   \nFarm                                                  2   \nFarmers Market                                        3   \nFast Food Restaurant                                 17   \nFood Court                                            0   \nFood Truck                                            2   \nFrench Restaurant                                     3   \nFried Chicken Joint                                   3   \nFrozen Yogurt Shop                                    7   \nFurniture / Home Store                                1   \nGas Station                                           0   \nGastropub                                             0   \nGerman Restaurant                                     4   \nGift Shop                                             0   \nGluten-free Restaurant                                0   \nGolf Course                                           0   \nGreek Restaurant                                      3   \nGrocery Store                                        24   \nGym                                                  12   \nGym / Fitness Center                                  7   \nGymnastics Gym                                        2   \nHardware Store                                        0   \nHawaiian Restaurant                                   2   \nHockey Arena                                          0   \nHot Dog Joint                                         3   \nHotel                                                 1   \nHunan Restaurant                                      2   \nIce Cream Shop                                       19   \nIndian Restaurant                                     4   \nIndie Movie Theater                                   0   \nItalian Restaurant                                    0   \nJapanese Restaurant                                   6   \nJuice Bar                                             6   \nKaraoke Bar                                           0   \nLake                                                  7   \nLatin American Restaurant                             0   \nLibrary                                               5   \nLiquor Store                                          0   \nLounge                                                0   \nMediterranean Restaurant                              3   \nMexican Restaurant                                   13   \nMiddle Eastern Restaurant                             1   \nMountain                                              3   \nMovie Theater                                         8   \nMuseum                                                0   \nNature Preserve                                       2   \nNew American Restaurant                               0   \nOutdoor Supply Store                                  1   \nPaper / Office Supplies Store                         0   \nPark                                                 19   \nPet Store                                            10   \nPharmacy                                              0   \nPizza Place                                          10   \nPlayground                                            3   \nPool                                                  0   \nPool Hall                                             5   \nPub                                                   1   \nRacetrack                                             1   \nRestaurant                                            4   \nSalon / Barbershop                                    1   \nSandwich Place                                        8   \nScenic Lookout                                        3   \nSeafood Restaurant                                    3   \nShanghai Restaurant                                   6   \nShipping Store                                        4   \nShoe Store                                            0   \nShopping Mall                                         3   \nSmoke Shop                                            5   \nSmoothie Shop                                         0   \nSoccer Stadium                                        0   \nSpa                                                   7   \nSporting Goods Shop                                   4   \nSteakhouse                                            0   \nSupermarket                                           0   \nSushi Restaurant                                     15   \nTaco Place                                            3   \nTaiwanese Restaurant                                  2   \nTea Room                                              3   \nTennis Court                                          3   \nThai Restaurant                                      14   \nTheater                                               0   \nTrail                                                15   \nTrain Station                                         0   \nVietnamese Restaurant                                 1   \nWarehouse Store                                       1   \nWine Shop                                             3   \nYoga Studio                                           2   \nZoo                                                   0   \n\nCity                           Saskatoon, Saskatchewan, Canada  \nAfghan Restaurant                                            0  \nAmerican Restaurant                                         10  \nAsian Restaurant                                             9  \nBBQ Joint                                                    0  \nBagel Shop                                                   0  \nBakery                                                      12  \nBank                                                         1  \nBar                                                          4  \nBeach                                                        0  \nBeer Garden                                                  0  \nBig Box Store                                                0  \nBookstore                                                    8  \nBowling Alley                                                0  \nBrazilian Restaurant                                         4  \nBreakfast Spot                                               8  \nBrewery                                                      0  \nBubble Tea Shop                                              0  \nBurger Joint                                                 3  \nBurmese Restaurant                                           0  \nCaf\u00e9                                                        14  \nCajun / Creole Restaurant                                    4  \nCamera Store                                                 2  \nCaribbean Restaurant                                         4  \nCheese Shop                                                  4  \nChinese Restaurant                                           4  \nChurch                                                       0  \nClothing Store                                               2  \nCoffee Shop                                                 18  \nComedy Club                                                  4  \nConvenience Store                                            0  \nCosmetics Shop                                               1  \nCreperie                                                     2  \nCupcake Shop                                                 2  \nDeli / Bodega                                                0  \nDessert Shop                                                 0  \nDim Sum Restaurant                                           2  \nDiner                                                        2  \nDiscount Store                                               0  \nDive Bar                                                     1  \nDog Run                                                      0  \nDonut Shop                                                   0  \nDumpling Restaurant                                          0  \nFalafel Restaurant                                           0  \nFarm                                                         0  \nFarmers Market                                               4  \nFast Food Restaurant                                         2  \nFood Court                                                   2  \nFood Truck                                                   0  \nFrench Restaurant                                            2  \nFried Chicken Joint                                          1  \nFrozen Yogurt Shop                                           1  \nFurniture / Home Store                                       0  \nGas Station                                                  2  \nGastropub                                                    3  \nGerman Restaurant                                            0  \nGift Shop                                                    4  \nGluten-free Restaurant                                       5  \nGolf Course                                                  3  \nGreek Restaurant                                             2  \nGrocery Store                                                9  \nGym                                                          5  \nGym / Fitness Center                                         1  \nGymnastics Gym                                               0  \nHardware Store                                               1  \nHawaiian Restaurant                                          0  \nHockey Arena                                                 2  \nHot Dog Joint                                                0  \nHotel                                                       14  \nHunan Restaurant                                             0  \nIce Cream Shop                                               6  \nIndian Restaurant                                            4  \nIndie Movie Theater                                          4  \nItalian Restaurant                                           4  \nJapanese Restaurant                                          0  \nJuice Bar                                                    0  \nKaraoke Bar                                                  2  \nLake                                                         0  \nLatin American Restaurant                                    2  \nLibrary                                                      0  \nLiquor Store                                                 2  \nLounge                                                       2  \nMediterranean Restaurant                                     0  \nMexican Restaurant                                           8  \nMiddle Eastern Restaurant                                    0  \nMountain                                                     0  \nMovie Theater                                                4  \nMuseum                                                       3  \nNature Preserve                                              0  \nNew American Restaurant                                      4  \nOutdoor Supply Store                                         0  \nPaper / Office Supplies Store                                1  \nPark                                                         0  \nPet Store                                                    0  \nPharmacy                                                     1  \nPizza Place                                                 12  \nPlayground                                                   0  \nPool                                                         2  \nPool Hall                                                    0  \nPub                                                         16  \nRacetrack                                                    0  \nRestaurant                                                  14  \nSalon / Barbershop                                           0  \nSandwich Place                                               5  \nScenic Lookout                                               4  \nSeafood Restaurant                                           0  \nShanghai Restaurant                                          0  \nShipping Store                                               0  \nShoe Store                                                   1  \nShopping Mall                                                4  \nSmoke Shop                                                   0  \nSmoothie Shop                                                1  \nSoccer Stadium                                               1  \nSpa                                                          0  \nSporting Goods Shop                                          4  \nSteakhouse                                                   8  \nSupermarket                                                  2  \nSushi Restaurant                                             0  \nTaco Place                                                   0  \nTaiwanese Restaurant                                         0  \nTea Room                                                     0  \nTennis Court                                                 0  \nThai Restaurant                                              4  \nTheater                                                      2  \nTrail                                                        2  \nTrain Station                                                2  \nVietnamese Restaurant                                        3  \nWarehouse Store                                              3  \nWine Shop                                                    0  \nYoga Studio                                                  0  \nZoo                                                          4  "
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "from IPython.display import display\npd.options.display.max_rows = None\ndisplay(city_vens)\n#print(city_vens)"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n      <th>Cluster Labels</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Evergreen,Saskatoon, Saskatchewan, Canada</td>\n      <td>52.169376</td>\n      <td>-106.569080</td>\n      <td>3</td>\n      <td>Coffee Shop</td>\n      <td>Hotel</td>\n      <td>Restaurant</td>\n      <td>Bakery</td>\n      <td>Pub</td>\n      <td>Asian Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Pizza Place</td>\n      <td>American Restaurant</td>\n      <td>Grocery Store</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Adelaide,Saskatoon, Saskatchewan, Canada</td>\n      <td>52.098655</td>\n      <td>-106.640908</td>\n      <td>2</td>\n      <td>Pub</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Grocery Store</td>\n      <td>Pizza Place</td>\n      <td>Restaurant</td>\n      <td>Mexican Restaurant</td>\n      <td>Sandwich Place</td>\n      <td>Breakfast Spot</td>\n      <td>Bookstore</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Mayfair,Saskatoon, Saskatchewan, Canada</td>\n      <td>52.141689</td>\n      <td>-106.678728</td>\n      <td>0</td>\n      <td>Hotel</td>\n      <td>Restaurant</td>\n      <td>Coffee Shop</td>\n      <td>Pub</td>\n      <td>Caf\u00e9</td>\n      <td>Bakery</td>\n      <td>Steakhouse</td>\n      <td>Pizza Place</td>\n      <td>Asian Restaurant</td>\n      <td>American Restaurant</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Lakeview,Saskatoon, Saskatchewan, Canada</td>\n      <td>52.097643</td>\n      <td>-106.595409</td>\n      <td>4</td>\n      <td>Pub</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Pizza Place</td>\n      <td>Mexican Restaurant</td>\n      <td>Restaurant</td>\n      <td>American Restaurant</td>\n      <td>Bakery</td>\n      <td>Hotel</td>\n      <td>Breakfast Spot</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Centerville,Fremont, California, USA</td>\n      <td>37.559646</td>\n      <td>-122.006757</td>\n      <td>3</td>\n      <td>Coffee Shop</td>\n      <td>Grocery Store</td>\n      <td>Sushi Restaurant</td>\n      <td>Bakery</td>\n      <td>Ice Cream Shop</td>\n      <td>Park</td>\n      <td>Thai Restaurant</td>\n      <td>Breakfast Spot</td>\n      <td>Trail</td>\n      <td>Mexican Restaurant</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Niles,Fremont, California, USA</td>\n      <td>37.578168</td>\n      <td>-121.980918</td>\n      <td>0</td>\n      <td>Park</td>\n      <td>Coffee Shop</td>\n      <td>Grocery Store</td>\n      <td>Breakfast Spot</td>\n      <td>Sushi Restaurant</td>\n      <td>Bakery</td>\n      <td>Fast Food Restaurant</td>\n      <td>Chinese Restaurant</td>\n      <td>Mexican Restaurant</td>\n      <td>Thai Restaurant</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Irvington,Fremont, California, USA</td>\n      <td>37.532254</td>\n      <td>-121.953848</td>\n      <td>4</td>\n      <td>Coffee Shop</td>\n      <td>Grocery Store</td>\n      <td>Ice Cream Shop</td>\n      <td>Fast Food Restaurant</td>\n      <td>Gym</td>\n      <td>Pizza Place</td>\n      <td>Trail</td>\n      <td>Bakery</td>\n      <td>Falafel Restaurant</td>\n      <td>Park</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Warm Springs,Fremont, California, USA</td>\n      <td>37.501991</td>\n      <td>-121.939208</td>\n      <td>2</td>\n      <td>Coffee Shop</td>\n      <td>Fast Food Restaurant</td>\n      <td>Gym</td>\n      <td>Grocery Store</td>\n      <td>Sushi Restaurant</td>\n      <td>Juice Bar</td>\n      <td>Trail</td>\n      <td>Thai Restaurant</td>\n      <td>Bakery</td>\n      <td>Mexican Restaurant</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Mission San Jose,Fremont, California, USA</td>\n      <td>37.534721</td>\n      <td>-121.920418</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Ice Cream Shop</td>\n      <td>Trail</td>\n      <td>Bakery</td>\n      <td>Fast Food Restaurant</td>\n      <td>Grocery Store</td>\n      <td>Gym</td>\n      <td>Mexican Restaurant</td>\n      <td>Pizza Place</td>\n      <td>Falafel Restaurant</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                                Neighborhood   Latitude   Longitude  \\\n0  Evergreen,Saskatoon, Saskatchewan, Canada  52.169376 -106.569080   \n1   Adelaide,Saskatoon, Saskatchewan, Canada  52.098655 -106.640908   \n2    Mayfair,Saskatoon, Saskatchewan, Canada  52.141689 -106.678728   \n3   Lakeview,Saskatoon, Saskatchewan, Canada  52.097643 -106.595409   \n0       Centerville,Fremont, California, USA  37.559646 -122.006757   \n1             Niles,Fremont, California, USA  37.578168 -121.980918   \n2         Irvington,Fremont, California, USA  37.532254 -121.953848   \n3      Warm Springs,Fremont, California, USA  37.501991 -121.939208   \n0  Mission San Jose,Fremont, California, USA  37.534721 -121.920418   \n\n   Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n0               3           Coffee Shop                 Hotel   \n1               2                   Pub           Coffee Shop   \n2               0                 Hotel            Restaurant   \n3               4                   Pub           Coffee Shop   \n0               3           Coffee Shop         Grocery Store   \n1               0                  Park           Coffee Shop   \n2               4           Coffee Shop         Grocery Store   \n3               2           Coffee Shop  Fast Food Restaurant   \n0               1           Coffee Shop        Ice Cream Shop   \n\n  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n0            Restaurant                Bakery                   Pub   \n1                  Caf\u00e9         Grocery Store           Pizza Place   \n2           Coffee Shop                   Pub                  Caf\u00e9   \n3                  Caf\u00e9           Pizza Place    Mexican Restaurant   \n0      Sushi Restaurant                Bakery        Ice Cream Shop   \n1         Grocery Store        Breakfast Spot      Sushi Restaurant   \n2        Ice Cream Shop  Fast Food Restaurant                   Gym   \n3                   Gym         Grocery Store      Sushi Restaurant   \n0                 Trail                Bakery  Fast Food Restaurant   \n\n  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n0      Asian Restaurant                  Caf\u00e9           Pizza Place   \n1            Restaurant    Mexican Restaurant        Sandwich Place   \n2                Bakery            Steakhouse           Pizza Place   \n3            Restaurant   American Restaurant                Bakery   \n0                  Park       Thai Restaurant        Breakfast Spot   \n1                Bakery  Fast Food Restaurant    Chinese Restaurant   \n2           Pizza Place                 Trail                Bakery   \n3             Juice Bar                 Trail       Thai Restaurant   \n0         Grocery Store                   Gym    Mexican Restaurant   \n\n  9th Most Common Venue 10th Most Common Venue  \n0   American Restaurant          Grocery Store  \n1        Breakfast Spot              Bookstore  \n2      Asian Restaurant    American Restaurant  \n3                 Hotel         Breakfast Spot  \n0                 Trail     Mexican Restaurant  \n1    Mexican Restaurant        Thai Restaurant  \n2    Falafel Restaurant                   Park  \n3                Bakery     Mexican Restaurant  \n0           Pizza Place     Falafel Restaurant  "
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "city_merged = city_list.copy(deep=True)\ncity_merged.reset_index()\n\n# add clustering labels\ncity_merged['Cluster Labels'] = kmeans.labels_\n\n# merge city_grouped with city_data to add latitude/longitude for each neighborhood\ncity_merged = city_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n\ncity_merged.to_csv(\"CityComparison_data.csv\")\n\ncity_merged # check the last columns!"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Put all clusters on the map"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "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: jinja2>=2.9 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.10)\r\nRequirement already satisfied: requests in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.21.0)\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: numpy in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (1.15.4)\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: 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: idna<2.9,>=2.5 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2.8)\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\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2020.6.20)\r\n"
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                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a024ec1f5d3749b0afd5be4d6d30cf67);
        
    
        var popup_ae7381b1b4ca427ca78f514c91e14869 = L.popup({"maxWidth": "100%"});

        
            var html_72cfee7771774a328b8b2d49df7f7249 = $(`<div id="html_72cfee7771774a328b8b2d49df7f7249" style="width: 100.0%; height: 100.0%;">Mayfair,Saskatoon, Saskatchewan, Canada Cluster 0</div>`)[0];
            popup_ae7381b1b4ca427ca78f514c91e14869.setContent(html_72cfee7771774a328b8b2d49df7f7249);
        

        circle_marker_b3e4107be4d446128e1ded852aef80f0.bindPopup(popup_ae7381b1b4ca427ca78f514c91e14869)
        ;

        
    
    
            var circle_marker_97d6d0ba1e0b4856a00625a0b7c9771c = L.circleMarker(
                [52.0976431, -106.5954091086798],
                {"bubblingMouseEvents": true, "color": "#ffb360", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ffb360", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a024ec1f5d3749b0afd5be4d6d30cf67);
        
    
        var popup_ab1aba27f9ec49f693b222baddd292b1 = L.popup({"maxWidth": "100%"});

        
            var html_3a788baa9c2f4997b56a05b78d09eb6f = $(`<div id="html_3a788baa9c2f4997b56a05b78d09eb6f" style="width: 100.0%; height: 100.0%;">Lakeview,Saskatoon, Saskatchewan, Canada Cluster 4</div>`)[0];
            popup_ab1aba27f9ec49f693b222baddd292b1.setContent(html_3a788baa9c2f4997b56a05b78d09eb6f);
        

        circle_marker_97d6d0ba1e0b4856a00625a0b7c9771c.bindPopup(popup_ab1aba27f9ec49f693b222baddd292b1)
        ;

        
    
    
            var circle_marker_6658870be227427bb169f7b0fd4ee933 = L.circleMarker(
                [37.559645599999996, -122.0067569],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a024ec1f5d3749b0afd5be4d6d30cf67);
        
    
        var popup_8c943e1190b448d79bf291d569fefdbb = L.popup({"maxWidth": "100%"});

        
            var html_39fef5d963664903ade1f1c2daf4d35a = $(`<div id="html_39fef5d963664903ade1f1c2daf4d35a" style="width: 100.0%; height: 100.0%;">Centerville,Fremont, California, USA Cluster 3</div>`)[0];
            popup_8c943e1190b448d79bf291d569fefdbb.setContent(html_39fef5d963664903ade1f1c2daf4d35a);
        

        circle_marker_6658870be227427bb169f7b0fd4ee933.bindPopup(popup_8c943e1190b448d79bf291d569fefdbb)
        ;

        
    
    
            var circle_marker_6680d74fa5304ca28561f0a783f6867b = L.circleMarker(
                [37.5781679, -121.9809177],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a024ec1f5d3749b0afd5be4d6d30cf67);
        
    
        var popup_815dbd52f46247d7b0b4d93308a1b4dc = L.popup({"maxWidth": "100%"});

        
            var html_fc682ea9a4af4b3e9c5e29b0bdef98d2 = $(`<div id="html_fc682ea9a4af4b3e9c5e29b0bdef98d2" style="width: 100.0%; height: 100.0%;">Niles,Fremont, California, USA Cluster 0</div>`)[0];
            popup_815dbd52f46247d7b0b4d93308a1b4dc.setContent(html_fc682ea9a4af4b3e9c5e29b0bdef98d2);
        

        circle_marker_6680d74fa5304ca28561f0a783f6867b.bindPopup(popup_815dbd52f46247d7b0b4d93308a1b4dc)
        ;

        
    
    
            var circle_marker_57a51a9ee90d44e7bdb1641f3db36791 = L.circleMarker(
                [37.5322543, -121.9538481],
                {"bubblingMouseEvents": true, "color": "#ffb360", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ffb360", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a024ec1f5d3749b0afd5be4d6d30cf67);
        
    
        var popup_f3b44bf758f841f68d58ecc9d765df67 = L.popup({"maxWidth": "100%"});

        
            var html_9efac4865a2b42c38b7cd52b11014aa9 = $(`<div id="html_9efac4865a2b42c38b7cd52b11014aa9" style="width: 100.0%; height: 100.0%;">Irvington,Fremont, California, USA Cluster 4</div>`)[0];
            popup_f3b44bf758f841f68d58ecc9d765df67.setContent(html_9efac4865a2b42c38b7cd52b11014aa9);
        

        circle_marker_57a51a9ee90d44e7bdb1641f3db36791.bindPopup(popup_f3b44bf758f841f68d58ecc9d765df67)
        ;

        
    
    
            var circle_marker_1ad8adb459cc42c6810c59db59a32c45 = L.circleMarker(
                [37.5019913, -121.9392084],
                {"bubblingMouseEvents": true, "color": "#00b5eb", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#00b5eb", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a024ec1f5d3749b0afd5be4d6d30cf67);
        
    
        var popup_4207a45c6263439e8773e82e9be3c1df = L.popup({"maxWidth": "100%"});

        
            var html_6356a3032c6d435d95966007c2509cb8 = $(`<div id="html_6356a3032c6d435d95966007c2509cb8" style="width: 100.0%; height: 100.0%;">Warm Springs,Fremont, California, USA Cluster 2</div>`)[0];
            popup_4207a45c6263439e8773e82e9be3c1df.setContent(html_6356a3032c6d435d95966007c2509cb8);
        

        circle_marker_1ad8adb459cc42c6810c59db59a32c45.bindPopup(popup_4207a45c6263439e8773e82e9be3c1df)
        ;

        
    
    
            var circle_marker_14adb552f2884e91b965b9d7220ce0b0 = L.circleMarker(
                [37.534721, -121.920418],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a024ec1f5d3749b0afd5be4d6d30cf67);
        
    
        var popup_eccbcd13baa1499f92bebdb0ac850056 = L.popup({"maxWidth": "100%"});

        
            var html_9ca5d5b195174ec1937b051ee04226f7 = $(`<div id="html_9ca5d5b195174ec1937b051ee04226f7" style="width: 100.0%; height: 100.0%;">Mission San Jose,Fremont, California, USA Cluster 1</div>`)[0];
            popup_eccbcd13baa1499f92bebdb0ac850056.setContent(html_9ca5d5b195174ec1937b051ee04226f7);
        

        circle_marker_14adb552f2884e91b965b9d7220ce0b0.bindPopup(popup_eccbcd13baa1499f92bebdb0ac850056)
        ;

        
    
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>",
                        "text/plain": "<folium.folium.Map at 0x7fd092841e80>"
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Matplotlib and associated plotting modules\n!pip install folium\nimport folium\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\n# create map\nmap_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n\n# set color scheme for the clusters\nx = np.arange(kclusters)\nys = [i+x+(i*x)**2 for i in range(kclusters)]\ncolors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\nrainbow = [colors.rgb2hex(i) for i in colors_array]\n\n# add markers to the map\nmarkers_colors = []\nfor lat, lon, poi, cluster in zip(city_merged['Latitude'], city_merged['Longitude'], city_merged['Neighborhood'],kmeans.labels_):\n    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n    folium.CircleMarker(\n        [lat, lon],\n        radius=5,\n        popup=label,\n        color=rainbow[cluster-1],\n        fill=True,\n        fill_color=rainbow[cluster-1],\n        fill_opacity=0.7).add_to(map_clusters)\n       \nmap_clusters"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.6",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.6.9"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 1
}