The Battle of Neighborhoods.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "from geopy.extra.rate_limiter import RateLimiter\n",
    "\n",
    "import requests # library to handle requests\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import folium # map rendering library\n",
    "from credentials import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the dataset and select needed columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "london_population = pd.read_csv('london_population.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24960, 9)\n",
      "Index(['Code', 'Borough', 'Ward_Name', 'Year', 'Population', 'Hectares',\n",
      "       'Square_Kilometres', 'Population_per_hectare',\n",
      "       'Population_per_square_kilometre'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(london_population.shape)\n",
    "print(london_population.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(624, 9)\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>Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Year</th>\n",
       "      <th>Population</th>\n",
       "      <th>Hectares</th>\n",
       "      <th>Square_Kilometres</th>\n",
       "      <th>Population_per_hectare</th>\n",
       "      <th>Population_per_square_kilometre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E05000026</td>\n",
       "      <td>Barking and Dagenham</td>\n",
       "      <td>Abbey</td>\n",
       "      <td>2020</td>\n",
       "      <td>16246</td>\n",
       "      <td>127.9</td>\n",
       "      <td>1.279</td>\n",
       "      <td>127.021110</td>\n",
       "      <td>12702.111020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E05000455</td>\n",
       "      <td>Merton</td>\n",
       "      <td>Abbey</td>\n",
       "      <td>2020</td>\n",
       "      <td>10933</td>\n",
       "      <td>139.3</td>\n",
       "      <td>1.393</td>\n",
       "      <td>78.485284</td>\n",
       "      <td>7848.528356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E05000630</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>Abbey Road</td>\n",
       "      <td>2020</td>\n",
       "      <td>13331</td>\n",
       "      <td>109.5</td>\n",
       "      <td>1.095</td>\n",
       "      <td>121.744292</td>\n",
       "      <td>12174.429220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E05000214</td>\n",
       "      <td>Greenwich</td>\n",
       "      <td>Abbey Wood</td>\n",
       "      <td>2020</td>\n",
       "      <td>16376</td>\n",
       "      <td>265.3</td>\n",
       "      <td>2.653</td>\n",
       "      <td>61.726348</td>\n",
       "      <td>6172.634753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E05000382</td>\n",
       "      <td>Kensington and Chelsea</td>\n",
       "      <td>Addington</td>\n",
       "      <td>2020</td>\n",
       "      <td>11203</td>\n",
       "      <td>62.9</td>\n",
       "      <td>0.629</td>\n",
       "      <td>178.108108</td>\n",
       "      <td>17810.810810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Code                 Borough Neighborhood  Year  Population  Hectares  \\\n",
       "0  E05000026    Barking and Dagenham        Abbey  2020       16246     127.9   \n",
       "1  E05000455                  Merton        Abbey  2020       10933     139.3   \n",
       "2  E05000630             Westminster   Abbey Road  2020       13331     109.5   \n",
       "3  E05000214               Greenwich   Abbey Wood  2020       16376     265.3   \n",
       "4  E05000382  Kensington and Chelsea    Addington  2020       11203      62.9   \n",
       "\n",
       "   Square_Kilometres  Population_per_hectare  Population_per_square_kilometre  \n",
       "0              1.279              127.021110                     12702.111020  \n",
       "1              1.393               78.485284                      7848.528356  \n",
       "2              1.095              121.744292                     12174.429220  \n",
       "3              2.653               61.726348                      6172.634753  \n",
       "4              0.629              178.108108                     17810.810810  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london_population = london_population[london_population.Year == 2020].reset_index(drop=True)\n",
    "print(london_population.shape)\n",
    "london_population.columns = ['Code', 'Borough', 'Neighborhood', 'Year', 'Population', 'Hectares',\n",
    "       'Square_Kilometres', 'Population_per_hectare',\n",
    "       'Population_per_square_kilometre']\n",
    "london_population.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For each address (Neighborhood, Borough) get the Latitude and Longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "geolocator = Nominatim(user_agent=\"londo_explorer\")\n",
    "def get_lat_long(df):\n",
    "    list_lat_long = []\n",
    "    for index, row in df.iterrows():\n",
    "        latitude, longitude = 0, 0\n",
    "#         print(row['address'])       \n",
    "        geocode = RateLimiter(geolocator.geocode, min_delay_seconds=4)\n",
    "        location = geocode(row['address'])\n",
    "        if location:\n",
    "            latitude = location.latitude\n",
    "            longitude = location.longitude\n",
    "        list_lat_long.append([latitude, longitude])\n",
    "    return list_lat_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "london_population['address'] = london_population['Neighborhood'] + \", \" + london_population['Borough'] + \", England\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lat_long\n",
    "df_lat_long = pd.DataFrame.from_records(lat_long)\n",
    "london_population[['Latitude', 'Longitude']] = df_lat_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "london_population = london_population[london_population.Latitude != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For each Netighborhood get the nearby venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "LIMIT = 100\n",
    "VERSION = '20200415'\n",
    "\n",
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "london_venues = getNearbyVenues(names=london_population['Neighborhood'],\n",
    "                                   latitudes=london_population['Latitude'],\n",
    "                                   longitudes=london_population['Longitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of neighborhood for each borough in London"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "london_population.groupby('Borough')['Neighborhood'].count().plot(kind='bar')\n",
    "plt.title('Number of neighborhood around London')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### London's most crowded boroughs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "london_population.groupby('Borough')['Population'].mean().plot(kind='bar')\n",
    "plt.title('Average of population each borough in London')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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 Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abbey</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbey Road</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbey Wood</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acton Central</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addiscombe</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addison</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aldborough</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexandra</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alibon</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alperton</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ashburton</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Askew</th>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balham</th>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barkingside</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barnehurst</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barnes</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barnhill</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barnsbury</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bayswater</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beckton</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Becontree</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beddington North</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beddington South</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedfont</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedford</th>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellingham</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belmont</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belsize</th>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belvedere</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bensham Manor</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Ham</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Hampstead</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Harrow</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Hendon</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Hill</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Putney</th>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Ruislip</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Thornton</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Twickenham</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Wickham</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Westbourne</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whalebone</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White Hart Lane</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitechapel</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefoot</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitton</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wick</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Willesden Green</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William Morris</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wimbledon Park</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winchmore Hill</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wood Street</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woodhouse</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woodside</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woolwich Common</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woolwich Riverside</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Worcester Park</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wormholt and White City</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yeading</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yiewsley</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>558 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Neighborhood Latitude  Neighborhood Longitude  Venue  \\\n",
       "Neighborhood                                                                    \n",
       "Abbey                                       50                      50     50   \n",
       "Abbey Road                                  21                      21     21   \n",
       "Abbey Wood                                   4                       4      4   \n",
       "Acton Central                               18                      18     18   \n",
       "Addiscombe                                  11                      11     11   \n",
       "Addison                                     47                      47     47   \n",
       "Aldborough                                   4                       4      4   \n",
       "Alexandra                                   14                      14     14   \n",
       "Alibon                                       4                       4      4   \n",
       "Alperton                                     8                       8      8   \n",
       "Ashburton                                    8                       8      8   \n",
       "Askew                                       36                      36     36   \n",
       "Balham                                      54                      54     54   \n",
       "Barkingside                                  4                       4      4   \n",
       "Barnehurst                                   4                       4      4   \n",
       "Barnes                                      21                      21     21   \n",
       "Barnhill                                     7                       7      7   \n",
       "Barnsbury                                   22                      22     22   \n",
       "Bayswater                                  100                     100    100   \n",
       "Beckton                                     13                      13     13   \n",
       "Becontree                                    4                       4      4   \n",
       "Beddington North                             4                       4      4   \n",
       "Beddington South                             4                       4      4   \n",
       "Bedfont                                      6                       6      6   \n",
       "Bedford                                     56                      56     56   \n",
       "Bellingham                                   6                       6      6   \n",
       "Belmont                                     12                      12     12   \n",
       "Belsize                                     52                      52     52   \n",
       "Belvedere                                    3                       3      3   \n",
       "Bensham Manor                                4                       4      4   \n",
       "...                                        ...                     ...    ...   \n",
       "West Ham                                    12                      12     12   \n",
       "West Hampstead                              42                      42     42   \n",
       "West Harrow                                  4                       4      4   \n",
       "West Hendon                                 12                      12     12   \n",
       "West Hill                                   11                      11     11   \n",
       "West Putney                                 81                      81     81   \n",
       "West Ruislip                                10                      10     10   \n",
       "West Thornton                               10                      10     10   \n",
       "West Twickenham                             47                      47     47   \n",
       "West Wickham                                13                      13     13   \n",
       "Westbourne                                  30                      30     30   \n",
       "Whalebone                                    4                       4      4   \n",
       "White Hart Lane                             17                      17     17   \n",
       "Whitechapel                                 35                      35     35   \n",
       "Whitefoot                                    4                       4      4   \n",
       "Whitton                                     24                      24     24   \n",
       "Wick                                        35                      35     35   \n",
       "Willesden Green                             28                      28     28   \n",
       "William Morris                              14                      14     14   \n",
       "Wimbledon Park                              16                      16     16   \n",
       "Winchmore Hill                               8                       8      8   \n",
       "Wood Street                                  9                       9      9   \n",
       "Woodhouse                                    4                       4      4   \n",
       "Woodside                                    13                      13     13   \n",
       "Woolwich Common                              4                       4      4   \n",
       "Woolwich Riverside                           4                       4      4   \n",
       "Worcester Park                              19                      19     19   \n",
       "Wormholt and White City                     23                      23     23   \n",
       "Yeading                                      4                       4      4   \n",
       "Yiewsley                                    23                      23     23   \n",
       "\n",
       "                         Venue Latitude  Venue Longitude  Venue Category  \n",
       "Neighborhood                                                              \n",
       "Abbey                                50               50              50  \n",
       "Abbey Road                           21               21              21  \n",
       "Abbey Wood                            4                4               4  \n",
       "Acton Central                        18               18              18  \n",
       "Addiscombe                           11               11              11  \n",
       "Addison                              47               47              47  \n",
       "Aldborough                            4                4               4  \n",
       "Alexandra                            14               14              14  \n",
       "Alibon                                4                4               4  \n",
       "Alperton                              8                8               8  \n",
       "Ashburton                             8                8               8  \n",
       "Askew                                36               36              36  \n",
       "Balham                               54               54              54  \n",
       "Barkingside                           4                4               4  \n",
       "Barnehurst                            4                4               4  \n",
       "Barnes                               21               21              21  \n",
       "Barnhill                              7                7               7  \n",
       "Barnsbury                            22               22              22  \n",
       "Bayswater                           100              100             100  \n",
       "Beckton                              13               13              13  \n",
       "Becontree                             4                4               4  \n",
       "Beddington North                      4                4               4  \n",
       "Beddington South                      4                4               4  \n",
       "Bedfont                               6                6               6  \n",
       "Bedford                              56               56              56  \n",
       "Bellingham                            6                6               6  \n",
       "Belmont                              12               12              12  \n",
       "Belsize                              52               52              52  \n",
       "Belvedere                             3                3               3  \n",
       "Bensham Manor                         4                4               4  \n",
       "...                                 ...              ...             ...  \n",
       "West Ham                             12               12              12  \n",
       "West Hampstead                       42               42              42  \n",
       "West Harrow                           4                4               4  \n",
       "West Hendon                          12               12              12  \n",
       "West Hill                            11               11              11  \n",
       "West Putney                          81               81              81  \n",
       "West Ruislip                         10               10              10  \n",
       "West Thornton                        10               10              10  \n",
       "West Twickenham                      47               47              47  \n",
       "West Wickham                         13               13              13  \n",
       "Westbourne                           30               30              30  \n",
       "Whalebone                             4                4               4  \n",
       "White Hart Lane                      17               17              17  \n",
       "Whitechapel                          35               35              35  \n",
       "Whitefoot                             4                4               4  \n",
       "Whitton                              24               24              24  \n",
       "Wick                                 35               35              35  \n",
       "Willesden Green                      28               28              28  \n",
       "William Morris                       14               14              14  \n",
       "Wimbledon Park                       16               16              16  \n",
       "Winchmore Hill                        8                8               8  \n",
       "Wood Street                           9                9               9  \n",
       "Woodhouse                             4                4               4  \n",
       "Woodside                             13               13              13  \n",
       "Woolwich Common                       4                4               4  \n",
       "Woolwich Riverside                    4                4               4  \n",
       "Worcester Park                       19               19              19  \n",
       "Wormholt and White City              23               23              23  \n",
       "Yeading                               4                4               4  \n",
       "Yiewsley                             23               23              23  \n",
       "\n",
       "[558 rows x 6 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london_venues.groupby('Neighborhood').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 412 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} uniques categories.'.format(len(london_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neighborhoods where exists Brazilian Restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighborhoods_brazilian_restaurants = london_venues[london_venues['Venue Category'].str.contains('Brazil', case=False)]\n",
    "plt.figure(figsize=(12,8))\n",
    "neighborhoods_brazilian_restaurants.groupby('Neighborhood')['Neighborhood'].count().plot(kind='bar')\n",
    "plt.title('Neighborhood that contains Brazilian Restaurants')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get only neighborhoods where exists Brazilian Restaurants to cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "london_venues_brazilian_restaurants = london_venues[london_venues['Neighborhood'].isin(neighborhoods_brazilian_restaurants['Neighborhood'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1184, 7)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london_venues_brazilian_restaurants.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# london_venues_brazilian_restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>African Restaurant</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Argentinian Restaurant</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Arts &amp; Crafts Store</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Austrian Restaurant</th>\n",
       "      <th>BBQ Joint</th>\n",
       "      <th>...</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Warehouse Store</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Wine Shop</th>\n",
       "      <th>Wings Joint</th>\n",
       "      <th>Women's Store</th>\n",
       "      <th>Yoga Studio</th>\n",
       "      <th>Zoo Exhibit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbey</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbey</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbey</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbey</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbey</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  African Restaurant  American Restaurant  \\\n",
       "0        Abbey                   0                    0   \n",
       "1        Abbey                   0                    0   \n",
       "2        Abbey                   0                    0   \n",
       "3        Abbey                   0                    0   \n",
       "4        Abbey                   0                    0   \n",
       "\n",
       "   Argentinian Restaurant  Art Gallery  Arts & Crafts Store  \\\n",
       "0                       0            0                    0   \n",
       "1                       0            0                    0   \n",
       "2                       0            0                    0   \n",
       "3                       0            0                    0   \n",
       "4                       0            0                    0   \n",
       "\n",
       "   Arts & Entertainment  Asian Restaurant  Austrian Restaurant  BBQ Joint  \\\n",
       "0                     0                 0                    0          0   \n",
       "1                     0                 0                    0          0   \n",
       "2                     0                 0                    0          0   \n",
       "3                     0                 0                    0          0   \n",
       "4                     0                 0                    0          0   \n",
       "\n",
       "   ...  Vegetarian / Vegan Restaurant  Video Game Store  \\\n",
       "0  ...                              0                 0   \n",
       "1  ...                              0                 0   \n",
       "2  ...                              0                 0   \n",
       "3  ...                              0                 0   \n",
       "4  ...                              0                 0   \n",
       "\n",
       "   Vietnamese Restaurant  Warehouse Store  Wine Bar  Wine Shop  Wings Joint  \\\n",
       "0                      0                0         0          0            0   \n",
       "1                      0                0         0          0            0   \n",
       "2                      0                0         0          0            0   \n",
       "3                      0                0         0          0            0   \n",
       "4                      0                0         0          0            0   \n",
       "\n",
       "   Women's Store  Yoga Studio  Zoo Exhibit  \n",
       "0              0            0            0  \n",
       "1              0            0            0  \n",
       "2              0            0            0  \n",
       "3              0            0            0  \n",
       "4              0            0            0  \n",
       "\n",
       "[5 rows x 178 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london_onehot = pd.get_dummies(london_venues_brazilian_restaurants[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "london_onehot['Neighborhood'] = london_venues_brazilian_restaurants['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [london_onehot.columns[-1]] + list(london_onehot.columns[:-1])\n",
    "london_onehot = london_onehot[fixed_columns]\n",
    "\n",
    "london_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.020000\n",
       "1     0.010000\n",
       "2     0.017857\n",
       "3     0.011494\n",
       "4     0.011364\n",
       "5     0.020408\n",
       "6     0.012987\n",
       "7     0.038462\n",
       "8     0.020833\n",
       "9     0.013333\n",
       "10    0.043478\n",
       "11    0.006849\n",
       "12    0.015152\n",
       "13    0.015152\n",
       "14    0.008333\n",
       "15    0.012346\n",
       "Name: Brazilian Restaurant, dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london_grouped = london_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "london_grouped['Brazilian Restaurant']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 178)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london_grouped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Abbey----\n",
      "                  venue  freq\n",
      "0                 Hotel  0.12\n",
      "1           Coffee Shop  0.08\n",
      "2           Supermarket  0.08\n",
      "3  Fast Food Restaurant  0.04\n",
      "4        Discount Store  0.02\n",
      "\n",
      "\n",
      "----Bayswater----\n",
      "                venue  freq\n",
      "0               Hotel  0.09\n",
      "1                 Pub  0.07\n",
      "2  Chinese Restaurant  0.05\n",
      "3    Greek Restaurant  0.04\n",
      "4         Coffee Shop  0.04\n",
      "\n",
      "\n",
      "----Chase----\n",
      "            venue  freq\n",
      "0  Clothing Store  0.07\n",
      "1     Coffee Shop  0.07\n",
      "2    Optical Shop  0.05\n",
      "3             Pub  0.05\n",
      "4            Café  0.04\n",
      "\n",
      "\n",
      "----Clapham Common----\n",
      "          venue  freq\n",
      "0           Pub  0.09\n",
      "1          Café  0.05\n",
      "2  Burger Joint  0.05\n",
      "3   Coffee Shop  0.05\n",
      "4    Restaurant  0.03\n",
      "\n",
      "\n",
      "----Clapham Town----\n",
      "          venue  freq\n",
      "0           Pub  0.09\n",
      "1          Café  0.05\n",
      "2  Burger Joint  0.05\n",
      "3           Bar  0.05\n",
      "4   Coffee Shop  0.05\n",
      "\n",
      "\n",
      "----Fulham Broadway----\n",
      "            venue  freq\n",
      "0     Coffee Shop  0.14\n",
      "1             Pub  0.08\n",
      "2            Café  0.08\n",
      "3  Soccer Stadium  0.06\n",
      "4       Gastropub  0.04\n",
      "\n",
      "\n",
      "----Haverstock----\n",
      "         venue  freq\n",
      "0         Café  0.09\n",
      "1          Bar  0.05\n",
      "2          Pub  0.04\n",
      "3  Pizza Place  0.04\n",
      "4  Coffee Shop  0.04\n",
      "\n",
      "\n",
      "----Peninsula----\n",
      "                  venue  freq\n",
      "0                Lounge  0.10\n",
      "1           Coffee Shop  0.08\n",
      "2          Burger Joint  0.06\n",
      "3                   Bar  0.06\n",
      "4  Brazilian Restaurant  0.04\n",
      "\n",
      "\n",
      "----Regent's Park----\n",
      "         venue  freq\n",
      "0  Zoo Exhibit  0.12\n",
      "1         Café  0.12\n",
      "2       Garden  0.08\n",
      "3         Park  0.06\n",
      "4  Coffee Shop  0.06\n",
      "\n",
      "\n",
      "----Romford Town----\n",
      "                  venue  freq\n",
      "0        Clothing Store  0.09\n",
      "1           Coffee Shop  0.09\n",
      "2                   Pub  0.07\n",
      "3  Fast Food Restaurant  0.05\n",
      "4         Shopping Mall  0.05\n",
      "\n",
      "\n",
      "----Stockwell----\n",
      "                  venue  freq\n",
      "0         Grocery Store  0.13\n",
      "1           Coffee Shop  0.09\n",
      "2              Platform  0.09\n",
      "3  Fast Food Restaurant  0.09\n",
      "4             Nightclub  0.04\n",
      "\n",
      "\n",
      "----Town----\n",
      "            venue  freq\n",
      "0             Pub  0.08\n",
      "1            Café  0.06\n",
      "2     Coffee Shop  0.06\n",
      "3  Clothing Store  0.05\n",
      "4        Pharmacy  0.03\n",
      "\n",
      "\n",
      "----Uxbridge North----\n",
      "                venue  freq\n",
      "0         Coffee Shop  0.12\n",
      "1      Clothing Store  0.09\n",
      "2  Italian Restaurant  0.08\n",
      "3      Sandwich Place  0.05\n",
      "4                 Pub  0.05\n",
      "\n",
      "\n",
      "----Uxbridge South----\n",
      "                venue  freq\n",
      "0         Coffee Shop  0.12\n",
      "1      Clothing Store  0.09\n",
      "2  Italian Restaurant  0.08\n",
      "3      Sandwich Place  0.05\n",
      "4                 Pub  0.05\n",
      "\n",
      "\n",
      "----Village----\n",
      "                  venue  freq\n",
      "0                 Hotel  0.08\n",
      "1                   Pub  0.08\n",
      "2           Coffee Shop  0.06\n",
      "3  Gym / Fitness Center  0.06\n",
      "4                   Bar  0.03\n",
      "\n",
      "\n",
      "----West Putney----\n",
      "                 venue  freq\n",
      "0          Coffee Shop  0.09\n",
      "1                 Café  0.05\n",
      "2       Clothing Store  0.05\n",
      "3  Japanese Restaurant  0.05\n",
      "4                  Pub  0.05\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "for hood in london_grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = london_grouped[london_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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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": "code",
   "execution_count": 166,
   "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>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>Abbey</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Harbor / Marina</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Lebanese Restaurant</td>\n",
       "      <td>Brazilian Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Shopping Plaza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bayswater</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Persian Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Garden</td>\n",
       "      <td>Hostel</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chase</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Optical Shop</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Bookstore</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clapham Common</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clapham Town</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Café</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Bus Stop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fulham Broadway</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Soccer Stadium</td>\n",
       "      <td>Park</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>Sports Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Haverstock</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Park</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Peninsula</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Bar</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Music Venue</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Hotel Bar</td>\n",
       "      <td>Brazilian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Regent's Park</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Café</td>\n",
       "      <td>Garden</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Fountain</td>\n",
       "      <td>Diner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Romford Town</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Stockwell</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Portuguese Restaurant</td>\n",
       "      <td>Market</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Post Office</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Nightclub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Town</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Uxbridge North</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Supermarket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Uxbridge South</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Supermarket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Village</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Tram Station</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>West Putney</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Café</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0             Abbey                 Hotel           Supermarket   \n",
       "1         Bayswater                 Hotel                   Pub   \n",
       "2             Chase           Coffee Shop        Clothing Store   \n",
       "3    Clapham Common                   Pub           Coffee Shop   \n",
       "4      Clapham Town                   Pub           Coffee Shop   \n",
       "5   Fulham Broadway           Coffee Shop                  Café   \n",
       "6        Haverstock                  Café                   Bar   \n",
       "7         Peninsula                Lounge           Coffee Shop   \n",
       "8     Regent's Park           Zoo Exhibit                  Café   \n",
       "9      Romford Town           Coffee Shop        Clothing Store   \n",
       "10        Stockwell         Grocery Store              Platform   \n",
       "11             Town                   Pub           Coffee Shop   \n",
       "12   Uxbridge North           Coffee Shop        Clothing Store   \n",
       "13   Uxbridge South           Coffee Shop        Clothing Store   \n",
       "14          Village                 Hotel                   Pub   \n",
       "15      West Putney           Coffee Shop                   Pub   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue  5th Most Common Venue  \\\n",
       "0            Coffee Shop  Fast Food Restaurant        Harbor / Marina   \n",
       "1     Chinese Restaurant                  Café       Greek Restaurant   \n",
       "2                    Pub          Optical Shop       Department Store   \n",
       "3                   Café          Burger Joint           Cocktail Bar   \n",
       "4                    Bar                  Café           Burger Joint   \n",
       "5                    Pub        Soccer Stadium                   Park   \n",
       "6     Italian Restaurant                   Pub            Coffee Shop   \n",
       "7           Burger Joint                   Bar    American Restaurant   \n",
       "8                 Garden           Coffee Shop                   Park   \n",
       "9                    Pub  Fast Food Restaurant                   Café   \n",
       "10           Coffee Shop  Fast Food Restaurant  Portuguese Restaurant   \n",
       "11                  Café        Clothing Store   Gym / Fitness Center   \n",
       "12    Italian Restaurant              Pharmacy         Sandwich Place   \n",
       "13    Italian Restaurant              Pharmacy         Sandwich Place   \n",
       "14  Gym / Fitness Center           Coffee Shop                    Bar   \n",
       "15                  Café   Japanese Restaurant         Clothing Store   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0                    Pub   Lebanese Restaurant  Brazilian Restaurant   \n",
       "1     Persian Restaurant           Coffee Shop                Garden   \n",
       "2              Bookstore           Supermarket    Turkish Restaurant   \n",
       "3                    Bar    Italian Restaurant            Restaurant   \n",
       "4   Gym / Fitness Center    Italian Restaurant            Restaurant   \n",
       "5   Gym / Fitness Center           Pizza Place             Gastropub   \n",
       "6            Pizza Place                  Park            Restaurant   \n",
       "7            Music Venue    Italian Restaurant            Restaurant   \n",
       "8   Gym / Fitness Center           Art Gallery                 Plaza   \n",
       "9          Shopping Mall        Sandwich Place           Supermarket   \n",
       "10                Market         Deli / Bodega           Post Office   \n",
       "11                 Hotel        Sandwich Place              Pharmacy   \n",
       "12                   Pub                   Gym         Grocery Store   \n",
       "13                   Pub                   Gym         Grocery Store   \n",
       "14    Italian Restaurant          Burger Joint          Tram Station   \n",
       "15           Pizza Place                Bakery         Grocery Store   \n",
       "\n",
       "     9th Most Common Venue 10th Most Common Venue  \n",
       "0             Liquor Store         Shopping Plaza  \n",
       "1                   Hostel            Pizza Place  \n",
       "2                     Café      Fish & Chips Shop  \n",
       "3            Grocery Store            Yoga Studio  \n",
       "4            Grocery Store               Bus Stop  \n",
       "5                   Lounge             Sports Bar  \n",
       "6                   Bakery         Breakfast Spot  \n",
       "7                Hotel Bar   Brazilian Restaurant  \n",
       "8                 Fountain                  Diner  \n",
       "9   Furniture / Home Store     Italian Restaurant  \n",
       "10       Convenience Store              Nightclub  \n",
       "11    Fast Food Restaurant     Turkish Restaurant  \n",
       "12            Burger Joint            Supermarket  \n",
       "13            Burger Joint            Supermarket  \n",
       "14                 Theater           Cocktail Bar  \n",
       "15          Sandwich Place     Italian Restaurant  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = london_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(london_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(london_grouped.iloc[ind, :], \n",
    "                                                                          num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Choosing the right number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_max = 10\n",
    "london_grouped_clustering = london_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "wcss = []\n",
    "for i in range(1, k_max+1):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "    kmeans.fit(london_grouped_clustering)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(range(1, k_max+1), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the number of clusters where the change in WCSS begins to level off is 3;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. K Clusters = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 2, 0, 0, 0, 0, 0, 0, 2], dtype=int32)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "kmeans.fit(london_grouped_clustering)\n",
    "kmeans.labels_[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Year</th>\n",
       "      <th>Population</th>\n",
       "      <th>Hectares</th>\n",
       "      <th>Square_Kilometres</th>\n",
       "      <th>Population_per_hectare</th>\n",
       "      <th>Population_per_square_kilometre</th>\n",
       "      <th>address</th>\n",
       "      <th>...</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>E05000026</td>\n",
       "      <td>Barking and Dagenham</td>\n",
       "      <td>Abbey</td>\n",
       "      <td>2020</td>\n",
       "      <td>16246</td>\n",
       "      <td>127.9</td>\n",
       "      <td>1.279</td>\n",
       "      <td>127.021110</td>\n",
       "      <td>12702.111020</td>\n",
       "      <td>Abbey, Barking and Dagenham, England</td>\n",
       "      <td>...</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Harbor / Marina</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Lebanese Restaurant</td>\n",
       "      <td>Brazilian Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Shopping Plaza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E05000455</td>\n",
       "      <td>Merton</td>\n",
       "      <td>Abbey</td>\n",
       "      <td>2020</td>\n",
       "      <td>10933</td>\n",
       "      <td>139.3</td>\n",
       "      <td>1.393</td>\n",
       "      <td>78.485284</td>\n",
       "      <td>7848.528356</td>\n",
       "      <td>Abbey, Merton, England</td>\n",
       "      <td>...</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Harbor / Marina</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Lebanese Restaurant</td>\n",
       "      <td>Brazilian Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Shopping Plaza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>E05000631</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>Bayswater</td>\n",
       "      <td>2020</td>\n",
       "      <td>11660</td>\n",
       "      <td>51.8</td>\n",
       "      <td>0.518</td>\n",
       "      <td>225.096525</td>\n",
       "      <td>22509.652510</td>\n",
       "      <td>Bayswater, Westminster, England</td>\n",
       "      <td>...</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Persian Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Garden</td>\n",
       "      <td>Hostel</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>E05000195</td>\n",
       "      <td>Enfield</td>\n",
       "      <td>Chase</td>\n",
       "      <td>2020</td>\n",
       "      <td>14663</td>\n",
       "      <td>1694.4</td>\n",
       "      <td>16.944</td>\n",
       "      <td>8.653801</td>\n",
       "      <td>865.380076</td>\n",
       "      <td>Chase, Enfield, England</td>\n",
       "      <td>...</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Optical Shop</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Bookstore</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>E05000418</td>\n",
       "      <td>Lambeth</td>\n",
       "      <td>Clapham Common</td>\n",
       "      <td>2020</td>\n",
       "      <td>14376</td>\n",
       "      <td>134.3</td>\n",
       "      <td>1.343</td>\n",
       "      <td>107.043931</td>\n",
       "      <td>10704.393150</td>\n",
       "      <td>Clapham Common, Lambeth, England</td>\n",
       "      <td>...</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Code               Borough    Neighborhood  Year  Population  \\\n",
       "0    E05000026  Barking and Dagenham           Abbey  2020       16246   \n",
       "1    E05000455                Merton           Abbey  2020       10933   \n",
       "23   E05000631           Westminster       Bayswater  2020       11660   \n",
       "100  E05000195               Enfield           Chase  2020       14663   \n",
       "116  E05000418               Lambeth  Clapham Common  2020       14376   \n",
       "\n",
       "     Hectares  Square_Kilometres  Population_per_hectare  \\\n",
       "0       127.9              1.279              127.021110   \n",
       "1       139.3              1.393               78.485284   \n",
       "23       51.8              0.518              225.096525   \n",
       "100    1694.4             16.944                8.653801   \n",
       "116     134.3              1.343              107.043931   \n",
       "\n",
       "     Population_per_square_kilometre                               address  \\\n",
       "0                       12702.111020  Abbey, Barking and Dagenham, England   \n",
       "1                        7848.528356                Abbey, Merton, England   \n",
       "23                      22509.652510       Bayswater, Westminster, England   \n",
       "100                       865.380076               Chase, Enfield, England   \n",
       "116                     10704.393150      Clapham Common, Lambeth, England   \n",
       "\n",
       "     ...  1st Most Common Venue  2nd Most Common Venue  3rd Most Common Venue  \\\n",
       "0    ...                  Hotel            Supermarket            Coffee Shop   \n",
       "1    ...                  Hotel            Supermarket            Coffee Shop   \n",
       "23   ...                  Hotel                    Pub     Chinese Restaurant   \n",
       "100  ...            Coffee Shop         Clothing Store                    Pub   \n",
       "116  ...                    Pub            Coffee Shop                   Café   \n",
       "\n",
       "    4th Most Common Venue 5th Most Common Venue 6th Most Common Venue  \\\n",
       "0    Fast Food Restaurant       Harbor / Marina                   Pub   \n",
       "1    Fast Food Restaurant       Harbor / Marina                   Pub   \n",
       "23                   Café      Greek Restaurant    Persian Restaurant   \n",
       "100          Optical Shop      Department Store             Bookstore   \n",
       "116          Burger Joint          Cocktail Bar                   Bar   \n",
       "\n",
       "    7th Most Common Venue 8th Most Common Venue 9th Most Common Venue  \\\n",
       "0     Lebanese Restaurant  Brazilian Restaurant          Liquor Store   \n",
       "1     Lebanese Restaurant  Brazilian Restaurant          Liquor Store   \n",
       "23            Coffee Shop                Garden                Hostel   \n",
       "100           Supermarket    Turkish Restaurant                  Café   \n",
       "116    Italian Restaurant            Restaurant         Grocery Store   \n",
       "\n",
       "    10th Most Common Venue  \n",
       "0           Shopping Plaza  \n",
       "1           Shopping Plaza  \n",
       "23             Pizza Place  \n",
       "100      Fish & Chips Shop  \n",
       "116            Yoga Studio  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "\n",
    "london_merged = london_population[london_population.Neighborhood.isin(neighborhoods_brazilian_restaurants['Neighborhood'])]\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "london_merged = london_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "london_merged.head() # check the last columns!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Code', 'Borough', 'Neighborhood', 'Year', 'Population', 'Hectares',\n",
       "       'Square_Kilometres', 'Population_per_hectare',\n",
       "       'Population_per_square_kilometre', 'address', 'Latitude', 'Longitude',\n",
       "       'Cluster Labels', '1st Most Common Venue', '2nd Most Common Venue',\n",
       "       '3rd Most Common Venue', '4th Most Common Venue',\n",
       "       '5th Most Common Venue', '6th Most Common Venue',\n",
       "       '7th Most Common Venue', '8th Most Common Venue',\n",
       "       '9th Most Common Venue', '10th Most Common Venue'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london_merged = london_merged[london_merged['Cluster Labels'].isnull()==False]\n",
    "london_merged.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For each neighborhood get the venue category most commum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "london_frequent_venue = []#pd.DataFrame({'Borough' : [], 'Neighborhood' : [], 'Venue Category'})\n",
    "cols = ['1st Most Common Venue', '2nd Most Common Venue',\n",
    "       '3rd Most Common Venue', '4th Most Common Venue',\n",
    "       '5th Most Common Venue', '6th Most Common Venue',\n",
    "       '7th Most Common Venue', '8th Most Common Venue',\n",
    "       '9th Most Common Venue', '10th Most Common Venue']\n",
    "\n",
    "for i, row in london_merged.iterrows():\n",
    "    for col in cols:\n",
    "        #if london_frequent_venue.shape[0] > 0:\n",
    "        london_frequent_venue.append([row['Borough'], row['Neighborhood'], row['Population'], row['Square_Kilometres'], row[col]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_london_frequent_venue = pd.DataFrame.from_records(london_frequent_venue)\n",
    "df_london_frequent_venue.columns = ['Borough', 'Neighborhood', 'Population', 'Square_Kilometres', 'Venue Category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Population</th>\n",
       "      <th>Square_Kilometres</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barking and Dagenham</td>\n",
       "      <td>Abbey</td>\n",
       "      <td>16246</td>\n",
       "      <td>1.279</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Barking and Dagenham</td>\n",
       "      <td>Abbey</td>\n",
       "      <td>16246</td>\n",
       "      <td>1.279</td>\n",
       "      <td>Lebanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Barking and Dagenham</td>\n",
       "      <td>Abbey</td>\n",
       "      <td>16246</td>\n",
       "      <td>1.279</td>\n",
       "      <td>Brazilian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Merton</td>\n",
       "      <td>Abbey</td>\n",
       "      <td>10933</td>\n",
       "      <td>1.393</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Merton</td>\n",
       "      <td>Abbey</td>\n",
       "      <td>10933</td>\n",
       "      <td>1.393</td>\n",
       "      <td>Lebanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Merton</td>\n",
       "      <td>Abbey</td>\n",
       "      <td>10933</td>\n",
       "      <td>1.393</td>\n",
       "      <td>Brazilian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Westminster</td>\n",
       "      <td>Bayswater</td>\n",
       "      <td>11660</td>\n",
       "      <td>0.518</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Westminster</td>\n",
       "      <td>Bayswater</td>\n",
       "      <td>11660</td>\n",
       "      <td>0.518</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Westminster</td>\n",
       "      <td>Bayswater</td>\n",
       "      <td>11660</td>\n",
       "      <td>0.518</td>\n",
       "      <td>Persian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Enfield</td>\n",
       "      <td>Chase</td>\n",
       "      <td>14663</td>\n",
       "      <td>16.944</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Lambeth</td>\n",
       "      <td>Clapham Common</td>\n",
       "      <td>14376</td>\n",
       "      <td>1.343</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Lambeth</td>\n",
       "      <td>Clapham Common</td>\n",
       "      <td>14376</td>\n",
       "      <td>1.343</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Lambeth</td>\n",
       "      <td>Clapham Town</td>\n",
       "      <td>15507</td>\n",
       "      <td>1.120</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Lambeth</td>\n",
       "      <td>Clapham Town</td>\n",
       "      <td>15507</td>\n",
       "      <td>1.120</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Camden</td>\n",
       "      <td>Haverstock</td>\n",
       "      <td>14179</td>\n",
       "      <td>0.732</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Camden</td>\n",
       "      <td>Haverstock</td>\n",
       "      <td>14179</td>\n",
       "      <td>0.732</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Greenwich</td>\n",
       "      <td>Peninsula</td>\n",
       "      <td>23434</td>\n",
       "      <td>4.482</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Greenwich</td>\n",
       "      <td>Peninsula</td>\n",
       "      <td>23434</td>\n",
       "      <td>4.482</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Greenwich</td>\n",
       "      <td>Peninsula</td>\n",
       "      <td>23434</td>\n",
       "      <td>4.482</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Greenwich</td>\n",
       "      <td>Peninsula</td>\n",
       "      <td>23434</td>\n",
       "      <td>4.482</td>\n",
       "      <td>Brazilian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Havering</td>\n",
       "      <td>Romford Town</td>\n",
       "      <td>18554</td>\n",
       "      <td>2.866</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Havering</td>\n",
       "      <td>Romford Town</td>\n",
       "      <td>18554</td>\n",
       "      <td>2.866</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Lambeth</td>\n",
       "      <td>Stockwell</td>\n",
       "      <td>16075</td>\n",
       "      <td>0.869</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Lambeth</td>\n",
       "      <td>Stockwell</td>\n",
       "      <td>16075</td>\n",
       "      <td>0.869</td>\n",
       "      <td>Portuguese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Enfield</td>\n",
       "      <td>Town</td>\n",
       "      <td>15988</td>\n",
       "      <td>2.233</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Enfield</td>\n",
       "      <td>Town</td>\n",
       "      <td>15988</td>\n",
       "      <td>2.233</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Hammersmith and Fulham</td>\n",
       "      <td>Town</td>\n",
       "      <td>11058</td>\n",
       "      <td>0.650</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Hammersmith and Fulham</td>\n",
       "      <td>Town</td>\n",
       "      <td>11058</td>\n",
       "      <td>0.650</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Hillingdon</td>\n",
       "      <td>Uxbridge North</td>\n",
       "      <td>16260</td>\n",
       "      <td>4.633</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Hillingdon</td>\n",
       "      <td>Uxbridge South</td>\n",
       "      <td>15930</td>\n",
       "      <td>3.117</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Barking and Dagenham</td>\n",
       "      <td>Village</td>\n",
       "      <td>11212</td>\n",
       "      <td>2.140</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Merton</td>\n",
       "      <td>Village</td>\n",
       "      <td>8594</td>\n",
       "      <td>6.218</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Southwark</td>\n",
       "      <td>Village</td>\n",
       "      <td>13029</td>\n",
       "      <td>2.788</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Wandsworth</td>\n",
       "      <td>West Putney</td>\n",
       "      <td>15454</td>\n",
       "      <td>1.846</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Wandsworth</td>\n",
       "      <td>West Putney</td>\n",
       "      <td>15454</td>\n",
       "      <td>1.846</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Borough    Neighborhood  Population  Square_Kilometres  \\\n",
       "3      Barking and Dagenham           Abbey       16246              1.279   \n",
       "6      Barking and Dagenham           Abbey       16246              1.279   \n",
       "7      Barking and Dagenham           Abbey       16246              1.279   \n",
       "13                   Merton           Abbey       10933              1.393   \n",
       "16                   Merton           Abbey       10933              1.393   \n",
       "17                   Merton           Abbey       10933              1.393   \n",
       "22              Westminster       Bayswater       11660              0.518   \n",
       "24              Westminster       Bayswater       11660              0.518   \n",
       "25              Westminster       Bayswater       11660              0.518   \n",
       "37                  Enfield           Chase       14663             16.944   \n",
       "46                  Lambeth  Clapham Common       14376              1.343   \n",
       "47                  Lambeth  Clapham Common       14376              1.343   \n",
       "56                  Lambeth    Clapham Town       15507              1.120   \n",
       "57                  Lambeth    Clapham Town       15507              1.120   \n",
       "72                   Camden      Haverstock       14179              0.732   \n",
       "77                   Camden      Haverstock       14179              0.732   \n",
       "84                Greenwich       Peninsula       23434              4.482   \n",
       "86                Greenwich       Peninsula       23434              4.482   \n",
       "87                Greenwich       Peninsula       23434              4.482   \n",
       "89                Greenwich       Peninsula       23434              4.482   \n",
       "113                Havering    Romford Town       18554              2.866   \n",
       "119                Havering    Romford Town       18554              2.866   \n",
       "123                 Lambeth       Stockwell       16075              0.869   \n",
       "124                 Lambeth       Stockwell       16075              0.869   \n",
       "138                 Enfield            Town       15988              2.233   \n",
       "139                 Enfield            Town       15988              2.233   \n",
       "148  Hammersmith and Fulham            Town       11058              0.650   \n",
       "149  Hammersmith and Fulham            Town       11058              0.650   \n",
       "152              Hillingdon  Uxbridge North       16260              4.633   \n",
       "162              Hillingdon  Uxbridge South       15930              3.117   \n",
       "175    Barking and Dagenham         Village       11212              2.140   \n",
       "185                  Merton         Village        8594              6.218   \n",
       "195               Southwark         Village       13029              2.788   \n",
       "203              Wandsworth     West Putney       15454              1.846   \n",
       "209              Wandsworth     West Putney       15454              1.846   \n",
       "\n",
       "            Venue Category  \n",
       "3     Fast Food Restaurant  \n",
       "6      Lebanese Restaurant  \n",
       "7     Brazilian Restaurant  \n",
       "13    Fast Food Restaurant  \n",
       "16     Lebanese Restaurant  \n",
       "17    Brazilian Restaurant  \n",
       "22      Chinese Restaurant  \n",
       "24        Greek Restaurant  \n",
       "25      Persian Restaurant  \n",
       "37      Turkish Restaurant  \n",
       "46      Italian Restaurant  \n",
       "47              Restaurant  \n",
       "56      Italian Restaurant  \n",
       "57              Restaurant  \n",
       "72      Italian Restaurant  \n",
       "77              Restaurant  \n",
       "84     American Restaurant  \n",
       "86      Italian Restaurant  \n",
       "87              Restaurant  \n",
       "89    Brazilian Restaurant  \n",
       "113   Fast Food Restaurant  \n",
       "119     Italian Restaurant  \n",
       "123   Fast Food Restaurant  \n",
       "124  Portuguese Restaurant  \n",
       "138   Fast Food Restaurant  \n",
       "139     Turkish Restaurant  \n",
       "148   Fast Food Restaurant  \n",
       "149     Turkish Restaurant  \n",
       "152     Italian Restaurant  \n",
       "162     Italian Restaurant  \n",
       "175     Italian Restaurant  \n",
       "185     Italian Restaurant  \n",
       "195     Italian Restaurant  \n",
       "203    Japanese Restaurant  \n",
       "209     Italian Restaurant  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_london_frequent_venue[(df_london_frequent_venue['Venue Category'].str.contains('Restaurant')) | \n",
    "                         (df_london_frequent_venue['Venue Category'].str.contains('Food', case=False))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of restaurants per neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,8)) \n",
    "ax = fig.add_subplot(111)\n",
    "ax2 = ax.twinx() \n",
    "width = 0.4          \n",
    "(df_london_frequent_venue.groupby('Neighborhood')['Population'].mean()/\n",
    "df_london_frequent_venue.groupby('Neighborhood')['Square_Kilometres'].mean()).plot(kind='bar', color= 'red', ax=ax, width=width, position=1)\n",
    "df_london_frequent_venue.groupby('Neighborhood')['Venue Category'].count().plot(kind='bar', ax=ax2, width=width, position=0)\n",
    "ax.set_ylabel('Number venue category')\n",
    "ax2.set_ylabel('Average population/ha')\n",
    "plt.title('Average of population and number of restaurants per neighborhood')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighborhood\n",
       "Abbey              20\n",
       "Bayswater          10\n",
       "Chase              10\n",
       "Clapham Common     10\n",
       "Clapham Town       10\n",
       "Fulham Broadway    10\n",
       "Haverstock         10\n",
       "Peninsula          10\n",
       "Regent's Park      20\n",
       "Romford Town       10\n",
       "Stockwell          10\n",
       "Town               20\n",
       "Uxbridge North     10\n",
       "Uxbridge South     10\n",
       "Village            30\n",
       "West Putney        10\n",
       "Name: Venue Category, dtype: int64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = df_london_frequent_venue.groupby('Neighborhood')['Population'].mean()/df_london_frequent_venue.groupby('Neighborhood')['Square_Kilometres'].mean()\n",
    "df_london_frequent_venue.groupby('Neighborhood')['Venue Category'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = (df_london_frequent_venue.groupby('Neighborhood')['Population'].mean()/df_london_frequent_venue.groupby('Neighborhood')\\\n",
    " ['Square_Kilometres'].mean()).to_frame().reset_index()\n",
    "final.columns = ['Neighborhood', 'Population_per_hectares']\n",
    "final = final.merge(df_london_frequent_venue.groupby('Neighborhood', as_index=False)['Venue Category'].count(), on='Neighborhood')\n",
    "final = final.merge(london_merged[['Borough','Neighborhood','Cluster Labels', 'Latitude', 'Longitude']], how='left', on='Neighborhood')\n",
    "# df_london_frequent_venue.groupby('Neighborhood')['Venue Category'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>Population_per_hectares</th>\n",
       "      <th>Venue Category</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbey</td>\n",
       "      <td>10171.781437</td>\n",
       "      <td>20</td>\n",
       "      <td>Barking and Dagenham</td>\n",
       "      <td>2</td>\n",
       "      <td>51.535688</td>\n",
       "      <td>0.075530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbey</td>\n",
       "      <td>10171.781437</td>\n",
       "      <td>20</td>\n",
       "      <td>Merton</td>\n",
       "      <td>2</td>\n",
       "      <td>51.413054</td>\n",
       "      <td>-0.183346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bayswater</td>\n",
       "      <td>22509.652510</td>\n",
       "      <td>10</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>0</td>\n",
       "      <td>51.512276</td>\n",
       "      <td>-0.188385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chase</td>\n",
       "      <td>865.380076</td>\n",
       "      <td>10</td>\n",
       "      <td>Enfield</td>\n",
       "      <td>2</td>\n",
       "      <td>51.652085</td>\n",
       "      <td>-0.081017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clapham Common</td>\n",
       "      <td>10704.393150</td>\n",
       "      <td>10</td>\n",
       "      <td>Lambeth</td>\n",
       "      <td>0</td>\n",
       "      <td>51.462075</td>\n",
       "      <td>-0.137359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Neighborhood  Population_per_hectares  Venue Category  \\\n",
       "0           Abbey             10171.781437              20   \n",
       "1           Abbey             10171.781437              20   \n",
       "2       Bayswater             22509.652510              10   \n",
       "3           Chase               865.380076              10   \n",
       "4  Clapham Common             10704.393150              10   \n",
       "\n",
       "                Borough  Cluster Labels   Latitude  Longitude  \n",
       "0  Barking and Dagenham               2  51.535688   0.075530  \n",
       "1                Merton               2  51.413054  -0.183346  \n",
       "2           Westminster               0  51.512276  -0.188385  \n",
       "3               Enfield               2  51.652085  -0.081017  \n",
       "4               Lambeth               0  51.462075  -0.137359  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,8)) \n",
    "ax = fig.add_subplot(111)\n",
    "ax2 = ax.twinx() \n",
    "width = 0.4          \n",
    "final.groupby('Cluster Labels')['Venue Category'].mean().plot(kind='bar', color= 'red', ax=ax, width=width, position=1)\n",
    "final.groupby('Cluster Labels')['Population_per_hectares'].mean().plot(kind='bar', ax=ax2, width=width, position=0)\n",
    "ax.set_ylabel('Number venue category')\n",
    "ax2.set_ylabel('Average population/ha')\n",
    "plt.title('Average of population and number of restaurants per neighborhood')\n",
    "#final.groupby('Cluster Labels')['Venue Category'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>Population_per_hectares</th>\n",
       "      <th>Venue Category</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbey</td>\n",
       "      <td>10171.781437</td>\n",
       "      <td>20</td>\n",
       "      <td>51.474371</td>\n",
       "      <td>-0.053908</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bayswater</td>\n",
       "      <td>22509.652510</td>\n",
       "      <td>10</td>\n",
       "      <td>51.512276</td>\n",
       "      <td>-0.188385</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chase</td>\n",
       "      <td>865.380076</td>\n",
       "      <td>10</td>\n",
       "      <td>51.652085</td>\n",
       "      <td>-0.081017</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clapham Common</td>\n",
       "      <td>10704.393150</td>\n",
       "      <td>10</td>\n",
       "      <td>51.462075</td>\n",
       "      <td>-0.137359</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clapham Town</td>\n",
       "      <td>13845.535714</td>\n",
       "      <td>10</td>\n",
       "      <td>51.462292</td>\n",
       "      <td>-0.138856</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fulham Broadway</td>\n",
       "      <td>18371.814093</td>\n",
       "      <td>10</td>\n",
       "      <td>51.480883</td>\n",
       "      <td>-0.194349</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Haverstock</td>\n",
       "      <td>19370.218579</td>\n",
       "      <td>10</td>\n",
       "      <td>51.544868</td>\n",
       "      <td>-0.153213</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Peninsula</td>\n",
       "      <td>5228.469433</td>\n",
       "      <td>10</td>\n",
       "      <td>51.502931</td>\n",
       "      <td>0.003197</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Regent's Park</td>\n",
       "      <td>8097.615086</td>\n",
       "      <td>20</td>\n",
       "      <td>51.526930</td>\n",
       "      <td>-0.150284</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Romford Town</td>\n",
       "      <td>6473.831124</td>\n",
       "      <td>10</td>\n",
       "      <td>51.576046</td>\n",
       "      <td>0.182265</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Town</td>\n",
       "      <td>9381.200139</td>\n",
       "      <td>20</td>\n",
       "      <td>51.572105</td>\n",
       "      <td>-0.152579</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Uxbridge North</td>\n",
       "      <td>3509.605008</td>\n",
       "      <td>10</td>\n",
       "      <td>51.546655</td>\n",
       "      <td>-0.477511</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Uxbridge South</td>\n",
       "      <td>5110.683349</td>\n",
       "      <td>10</td>\n",
       "      <td>51.546655</td>\n",
       "      <td>-0.477511</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Village</td>\n",
       "      <td>2945.899874</td>\n",
       "      <td>30</td>\n",
       "      <td>51.489281</td>\n",
       "      <td>-0.047017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>West Putney</td>\n",
       "      <td>8371.614301</td>\n",
       "      <td>10</td>\n",
       "      <td>51.460934</td>\n",
       "      <td>-0.215348</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighborhood  Population_per_hectares  Venue Category   Latitude  \\\n",
       "0             Abbey             10171.781437              20  51.474371   \n",
       "1         Bayswater             22509.652510              10  51.512276   \n",
       "2             Chase               865.380076              10  51.652085   \n",
       "3    Clapham Common             10704.393150              10  51.462075   \n",
       "4      Clapham Town             13845.535714              10  51.462292   \n",
       "5   Fulham Broadway             18371.814093              10  51.480883   \n",
       "6        Haverstock             19370.218579              10  51.544868   \n",
       "7         Peninsula              5228.469433              10  51.502931   \n",
       "8     Regent's Park              8097.615086              20  51.526930   \n",
       "9      Romford Town              6473.831124              10  51.576046   \n",
       "10             Town              9381.200139              20  51.572105   \n",
       "11   Uxbridge North              3509.605008              10  51.546655   \n",
       "12   Uxbridge South              5110.683349              10  51.546655   \n",
       "13          Village              2945.899874              30  51.489281   \n",
       "14      West Putney              8371.614301              10  51.460934   \n",
       "\n",
       "    Longitude  Cluster Labels  \n",
       "0   -0.053908               2  \n",
       "1   -0.188385               0  \n",
       "2   -0.081017               2  \n",
       "3   -0.137359               0  \n",
       "4   -0.138856               0  \n",
       "5   -0.194349               0  \n",
       "6   -0.153213               0  \n",
       "7    0.003197               0  \n",
       "8   -0.150284               0  \n",
       "9    0.182265               2  \n",
       "10  -0.152579               2  \n",
       "11  -0.477511               2  \n",
       "12  -0.477511               2  \n",
       "13  -0.047017               0  \n",
       "14  -0.215348               2  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final[final['Cluster Labels'] != 1].groupby('Neighborhood', as_index=False)[['Population_per_hectares', \n",
    "                                                                            'Venue Category', 'Latitude', 'Longitude', 'Cluster Labels']].mean()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
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     "data": {
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     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
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    "geolocator = Nominatim(user_agent=\"londo_explorer\")\n",
    "location = geolocator.geocode('London, England')\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "f'{latitude}, {longitude}'"
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  },
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   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
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     "name": "stdout",
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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>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f966efe4940>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Toronto using latitude and longitude values\n",
    "map_london = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "\n",
    "x = np.arange(3)\n",
    "ys = [i+x+(i*x)**2 for i in range(3)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "print(rainbow)\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood, cluster in zip(final['Latitude'], final['Longitude'], \n",
    "                                           final['Borough'], final['Neighborhood'], \n",
    "                                                   final['Cluster Labels']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\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,\n",
    "        parse_html=False).add_to(map_london)  \n",
    "    \n",
    "map_london"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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