Code_OPENING-A-RESTAURANT-TARGETING-TOURISTS-IN-LONDON_CourseraCapstoneProject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>OPENING A RESTAURANT TARGETING TOURISTS IN LONDON - Code - Coursera Capstone Project</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the needed libraries\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "import json\n",
    "\n",
    "import requests\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if needed\n",
    "import folium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Getting the hotels data and performing needed analysis</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we will call the Foursquare API and search for hotels around London city center. Then we will cluster the resulting venues using K-Means clustering to find areas were groups of hotels are located. Finally, we will find the centers of these areas and organize them in a data frame for later analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting the needed information to call Foursquare API\n",
    "\n",
    "CLIENT_ID = '****' # your Foursquare ID\n",
    "CLIENT_SECRET = '****' # your Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting the location to be searched, search word, and limits on results\n",
    "\n",
    "london_lat = 51.5074\n",
    "london_lon = -0.1278\n",
    "search_query = 'Hotel'\n",
    "radius = 500\n",
    "limit_hotels = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://api.foursquare.com/v2/venues/search?client_id=N1DP4XUUYDNLDR5HOEYF3YS3C1RH30KVPX2HUSQVQMAIC05A&client_secret=0DEAMLOJ2WZIMLYT0R334JKWVTQOJ1ALZMR23ZUMUHOJDCGI&ll=51.5074,-0.1278&v=20180605&query=Hotel&radius=500&limit=50'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#url to be used forr calling the API\n",
    "\n",
    "url = 'https://api.foursquare.com/v2/venues/search?client_id={}&client_secret={}&ll={},{}&v={}&query={}&radius={}&limit={}'.format(CLIENT_ID, CLIENT_SECRET, london_lat, london_lon, VERSION, search_query, radius, limit_hotels)\n",
    "url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making the call and holding saving the resulting json file\n",
    "\n",
    "results = requests.get(url).json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>categories</th>\n",
       "      <th>hasPerk</th>\n",
       "      <th>id</th>\n",
       "      <th>location.address</th>\n",
       "      <th>location.cc</th>\n",
       "      <th>location.city</th>\n",
       "      <th>location.country</th>\n",
       "      <th>location.crossStreet</th>\n",
       "      <th>location.distance</th>\n",
       "      <th>location.formattedAddress</th>\n",
       "      <th>location.labeledLatLngs</th>\n",
       "      <th>location.lat</th>\n",
       "      <th>location.lng</th>\n",
       "      <th>location.neighborhood</th>\n",
       "      <th>location.postalCode</th>\n",
       "      <th>location.state</th>\n",
       "      <th>name</th>\n",
       "      <th>referralId</th>\n",
       "      <th>venuePage.id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...</td>\n",
       "      <td>False</td>\n",
       "      <td>4d274945d86aa09024d41dc0</td>\n",
       "      <td>Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247</td>\n",
       "      <td>[Whitehall Pl, London, Greater London, SW1A 2B...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50660672347560...</td>\n",
       "      <td>51.506607</td>\n",
       "      <td>-0.124460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2BD</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>Corinthia Hotel</td>\n",
       "      <td>v-1586617993</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...</td>\n",
       "      <td>False</td>\n",
       "      <td>4bc54daa6a3e9c7496c8f548</td>\n",
       "      <td>8 Northumberland Avenue</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>[8 Northumberland Avenue, London, Greater Lond...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50698547522388...</td>\n",
       "      <td>51.506985</td>\n",
       "      <td>-0.126020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5BY</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>Club Quarters Hotel</td>\n",
       "      <td>v-1586617993</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...</td>\n",
       "      <td>False</td>\n",
       "      <td>5aa19ea5c666663fb5d39a6c</td>\n",
       "      <td>1 Leicester Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428</td>\n",
       "      <td>[1 Leicester Square, London, Greater London, W...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5107204, 'lng'...</td>\n",
       "      <td>51.510720</td>\n",
       "      <td>-0.130932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7NA</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>Hotel Indigo</td>\n",
       "      <td>v-1586617993</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...</td>\n",
       "      <td>False</td>\n",
       "      <td>4ba51d68f964a52030df38e3</td>\n",
       "      <td>45 St. Martin'S Lane</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>356</td>\n",
       "      <td>[45 St. Martin'S Lane, London, Greater London,...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51052632268574...</td>\n",
       "      <td>51.510526</td>\n",
       "      <td>-0.126705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>St. Martin's Lane Hotel</td>\n",
       "      <td>v-1586617993</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...</td>\n",
       "      <td>False</td>\n",
       "      <td>4bc78b3c93bdeee1947f37ae</td>\n",
       "      <td>The Strand, Ste 2</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241</td>\n",
       "      <td>[The Strand, Ste 2, London, Greater London, WC...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50821644805786...</td>\n",
       "      <td>51.508216</td>\n",
       "      <td>-0.124564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>Amba Charing Cross Hotel</td>\n",
       "      <td>v-1586617993</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                          categories  hasPerk  \\\n",
       "0  [{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...    False   \n",
       "1  [{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...    False   \n",
       "2  [{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...    False   \n",
       "3  [{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...    False   \n",
       "4  [{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...    False   \n",
       "\n",
       "                         id         location.address location.cc  \\\n",
       "0  4d274945d86aa09024d41dc0             Whitehall Pl          GB   \n",
       "1  4bc54daa6a3e9c7496c8f548  8 Northumberland Avenue          GB   \n",
       "2  5aa19ea5c666663fb5d39a6c       1 Leicester Square          GB   \n",
       "3  4ba51d68f964a52030df38e3     45 St. Martin'S Lane          GB   \n",
       "4  4bc78b3c93bdeee1947f37ae        The Strand, Ste 2          GB   \n",
       "\n",
       "  location.city location.country location.crossStreet  location.distance  \\\n",
       "0        London   United Kingdom                  NaN                247   \n",
       "1        London   United Kingdom                  NaN                131   \n",
       "2        London   United Kingdom                  NaN                428   \n",
       "3        London   United Kingdom                  NaN                356   \n",
       "4        London   United Kingdom                  NaN                241   \n",
       "\n",
       "                           location.formattedAddress  \\\n",
       "0  [Whitehall Pl, London, Greater London, SW1A 2B...   \n",
       "1  [8 Northumberland Avenue, London, Greater Lond...   \n",
       "2  [1 Leicester Square, London, Greater London, W...   \n",
       "3  [45 St. Martin'S Lane, London, Greater London,...   \n",
       "4  [The Strand, Ste 2, London, Greater London, WC...   \n",
       "\n",
       "                             location.labeledLatLngs  location.lat  \\\n",
       "0  [{'label': 'display', 'lat': 51.50660672347560...     51.506607   \n",
       "1  [{'label': 'display', 'lat': 51.50698547522388...     51.506985   \n",
       "2  [{'label': 'display', 'lat': 51.5107204, 'lng'...     51.510720   \n",
       "3  [{'label': 'display', 'lat': 51.51052632268574...     51.510526   \n",
       "4  [{'label': 'display', 'lat': 51.50821644805786...     51.508216   \n",
       "\n",
       "   location.lng location.neighborhood location.postalCode  location.state  \\\n",
       "0     -0.124460                   NaN            SW1A 2BD  Greater London   \n",
       "1     -0.126020                   NaN            WC2N 5BY  Greater London   \n",
       "2     -0.130932                   NaN            WC2H 7NA  Greater London   \n",
       "3     -0.126705                   NaN            WC2N 4HX  Greater London   \n",
       "4     -0.124564                   NaN            WC2N 5HX  Greater London   \n",
       "\n",
       "                       name    referralId venuePage.id  \n",
       "0           Corinthia Hotel  v-1586617993          NaN  \n",
       "1       Club Quarters Hotel  v-1586617993          NaN  \n",
       "2              Hotel Indigo  v-1586617993          NaN  \n",
       "3   St. Martin's Lane Hotel  v-1586617993          NaN  \n",
       "4  Amba Charing Cross Hotel  v-1586617993          NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign relevant part of JSON to venues\n",
    "hotels = results['response']['venues']\n",
    "\n",
    "# tranform venues into a dataframe\n",
    "hotels_df = json_normalize(hotels)\n",
    "hotels_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>lng</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>postalCode</th>\n",
       "      <th>state</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Corinthia Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247</td>\n",
       "      <td>[Whitehall Pl, London, Greater London, SW1A 2B...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50660672347560...</td>\n",
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       "      <td>-0.124460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2BD</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4d274945d86aa09024d41dc0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Club Quarters Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>8 Northumberland Avenue</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>[8 Northumberland Avenue, London, Greater Lond...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50698547522388...</td>\n",
       "      <td>51.506985</td>\n",
       "      <td>-0.126020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5BY</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4bc54daa6a3e9c7496c8f548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hotel Indigo</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1 Leicester Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428</td>\n",
       "      <td>[1 Leicester Square, London, Greater London, W...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5107204, 'lng'...</td>\n",
       "      <td>51.510720</td>\n",
       "      <td>-0.130932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7NA</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5aa19ea5c666663fb5d39a6c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>St. Martin's Lane Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>45 St. Martin'S Lane</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>356</td>\n",
       "      <td>[45 St. Martin'S Lane, London, Greater London,...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51052632268574...</td>\n",
       "      <td>51.510526</td>\n",
       "      <td>-0.126705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ba51d68f964a52030df38e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amba Charing Cross Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>The Strand, Ste 2</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241</td>\n",
       "      <td>[The Strand, Ste 2, London, Greater London, WC...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50821644805786...</td>\n",
       "      <td>51.508216</td>\n",
       "      <td>-0.124564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4bc78b3c93bdeee1947f37ae</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Radisson Blu Edwardian Hampshire Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>31-36 Leicester Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>306</td>\n",
       "      <td>[31-36 Leicester Square, London, Greater Londo...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5098669, 'lng'...</td>\n",
       "      <td>51.509867</td>\n",
       "      <td>-0.129767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7LH</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ac518b5f964a5209fa020e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Great Scotland Yard Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>3-5 Great Scotland Yard</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>[3-5 Great Scotland Yard, London, SW1A 2HN, Un...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5064253, 'lng'...</td>\n",
       "      <td>51.506425</td>\n",
       "      <td>-0.126146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2HN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5d53299e9293ba0007f03f86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afternoon Tea at the Corinthia Hotel</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>10 Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>256</td>\n",
       "      <td>[10 Whitehall Pl, London, Greater London, SW1A...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50657230583427...</td>\n",
       "      <td>51.506572</td>\n",
       "      <td>-0.124350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2BD</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>52764ec8498e83fe786a0595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FIT (W Hotel Gym)</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>384</td>\n",
       "      <td>[London, Greater London, United Kingdom]</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51002945140006...</td>\n",
       "      <td>51.510029</td>\n",
       "      <td>-0.131398</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>56fe22b0498ef2d2226413fb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>W London Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Leicester Square, 10 Wardour Street</td>\n",
       "      <td>GB</td>\n",
       "      <td>City of London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>458</td>\n",
       "      <td>[Leicester Square, 10 Wardour Street, City of ...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51084300795154...</td>\n",
       "      <td>51.510843</td>\n",
       "      <td>-0.131429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W1D 6QF</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ad7ed82f964a520241021e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>The Piccadilly London West End Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>65 Shaftsbury Ave</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>596</td>\n",
       "      <td>[65 Shaftsbury Ave, London, Greater London, W1...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51215571165802...</td>\n",
       "      <td>51.512156</td>\n",
       "      <td>-0.131767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W1D 6EX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>564c9b99498ec148d934dede</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hotel Chocolat</td>\n",
       "      <td>Chocolate Shop</td>\n",
       "      <td>Charing Cross Station</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Strand</td>\n",
       "      <td>247</td>\n",
       "      <td>[Charing Cross Station (Strand), London, Great...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50808137396048...</td>\n",
       "      <td>51.508081</td>\n",
       "      <td>-0.124401</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5HS</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>58e4ff8ec45ee3187f52bbd6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>St George Inn Hotel</td>\n",
       "      <td>Housing Development</td>\n",
       "      <td>Belgrave Street 25</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Near Victoria Station</td>\n",
       "      <td>123</td>\n",
       "      <td>[Belgrave Street 25 (Near Victoria Station), L...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.507716, 'lng':...</td>\n",
       "      <td>51.507716</td>\n",
       "      <td>-0.129509</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>52acd5a711d293ec4f5f4d18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Hotel Chocolat</td>\n",
       "      <td>Chocolate Shop</td>\n",
       "      <td>78 The Strand</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>485</td>\n",
       "      <td>[78 The Strand, London, Greater London, WC2R 0...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51009906249482...</td>\n",
       "      <td>51.510099</td>\n",
       "      <td>-0.122293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2R 0DE</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4bcc5decb6c49c748c8e9391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Haymarket Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1 Suffolk Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>261</td>\n",
       "      <td>[1 Suffolk Pl, London, Greater London, SW1Y 4H...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50826687319245...</td>\n",
       "      <td>51.508267</td>\n",
       "      <td>-0.131315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1Y 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4abcec53f964a520b98720e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>The Z Hotel Piccadilly</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>2 Orange St</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>316</td>\n",
       "      <td>[2 Orange St, London, Greater London, WC2H 7DF...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50900946456780...</td>\n",
       "      <td>51.509009</td>\n",
       "      <td>-0.131564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7DF</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>53a47f8b498ea84d1e81a90e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Assembly Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>27-31 Charring Cross Road</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>354</td>\n",
       "      <td>[27-31 Charring Cross Road, London, Greater Lo...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51054918955679...</td>\n",
       "      <td>51.510549</td>\n",
       "      <td>-0.128521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 0LS</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5ad474e725fb7b4523d48bf5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Amba Hotel Gym</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>239</td>\n",
       "      <td>[United Kingdom]</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50831237866142...</td>\n",
       "      <td>51.508312</td>\n",
       "      <td>-0.124669</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5601b040498e5def6d164bc6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Hotel Cecil</td>\n",
       "      <td>Historic Site</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512</td>\n",
       "      <td>[London, Greater London, WC2R 0BN, United King...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.510167, 'lng':...</td>\n",
       "      <td>51.510167</td>\n",
       "      <td>-0.121888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2R 0BN</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5dd7f228caa13c000808171c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Haymarket Hotel Spa</td>\n",
       "      <td>Spa</td>\n",
       "      <td>1 Suffolk Place</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>273</td>\n",
       "      <td>[1 Suffolk Place, London, Greater London, SW1Y...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50823877036787...</td>\n",
       "      <td>51.508239</td>\n",
       "      <td>-0.131518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1Y 4BP</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ac518d1f964a520e9a620e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>library hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>290</td>\n",
       "      <td>[United Kingdom]</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51000814331115...</td>\n",
       "      <td>51.510008</td>\n",
       "      <td>-0.127645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5650d8af498e63f6c44aec00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Z Hotel Trafalgar</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>53-59 Chandos Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>[53-59 Chandos Pl, London, Greater London, WC2...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.509965, 'lng':...</td>\n",
       "      <td>51.509965</td>\n",
       "      <td>-0.125388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 4HS</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5dfcdcad867d0c000889b709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>The 99 Club@St Martins Lane Hotel</td>\n",
       "      <td>Comedy Club</td>\n",
       "      <td>At Martin's Lane</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349</td>\n",
       "      <td>[At Martin's Lane, London, Greater London, WC2...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51043, 'lng': ...</td>\n",
       "      <td>51.510430</td>\n",
       "      <td>-0.126499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5c43911ea0215b003969bd89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Gym @ Every Hotel Piccadilly</td>\n",
       "      <td>Gym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>407</td>\n",
       "      <td>[London, Greater London, W1D, United Kingdom]</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.510223, 'lng':...</td>\n",
       "      <td>51.510223</td>\n",
       "      <td>-0.131556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W1D</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5a0eea7964c8e11593989ebd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HotelPartner Yield Management</td>\n",
       "      <td>Office</td>\n",
       "      <td>1 Northumberland Avenue, Trafalgar Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56</td>\n",
       "      <td>[1 Northumberland Avenue, Trafalgar Square, Lo...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.507214, 'lng':...</td>\n",
       "      <td>51.507214</td>\n",
       "      <td>-0.127035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5BW</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>57a087c9498e4de77af68d8a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Henrietta Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>14 Henrietta St</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>518</td>\n",
       "      <td>[14 Henrietta St, London, Greater London, WC2E...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51114060333864...</td>\n",
       "      <td>51.511141</td>\n",
       "      <td>-0.123330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2E 8QH</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ace3d9df964a52057cf20e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>The Z Hotel Covent Garden</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>31-32 Bedford St</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>472</td>\n",
       "      <td>[31-32 Bedford St, London, Greater London, WC2...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.511105, 'lng':...</td>\n",
       "      <td>51.511105</td>\n",
       "      <td>-0.124478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2E 9ED</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5be85bdba30619002c2931fe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Global Great Hotels - Investments Real Estate</td>\n",
       "      <td>Business Center</td>\n",
       "      <td>133 Cockfosters Rd</td>\n",
       "      <td>GB</td>\n",
       "      <td>Hertfordshire</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>[133 Cockfosters Rd, Hertfordshire, EN4 0AA, U...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50708638980144...</td>\n",
       "      <td>51.507086</td>\n",
       "      <td>-0.127909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EN4 0AA</td>\n",
       "      <td>Hertfordshire</td>\n",
       "      <td>5afb9f1b65211f002c89d942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>W Hotel Screening Room</td>\n",
       "      <td>Movie Theater</td>\n",
       "      <td>10 Wardour St.</td>\n",
       "      <td>GB</td>\n",
       "      <td>Soho</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>at Leicester Sq.</td>\n",
       "      <td>503</td>\n",
       "      <td>[10 Wardour St. (at Leicester Sq.), Soho, Grea...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51093257635912...</td>\n",
       "      <td>51.510933</td>\n",
       "      <td>-0.132350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W1D 6QF</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5051b834e4b08416d819eb27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>The Trafalgar St. James London, Curio Collecti...</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>2 Spring Gardens, Trafalgar Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>[2 Spring Gardens, Trafalgar Square, London, G...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50733680732381...</td>\n",
       "      <td>51.507337</td>\n",
       "      <td>-0.129073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2TS</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4af290c7f964a520eee721e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>The Grand at Trafalgar Square</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>8 Northumberland Ave</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118</td>\n",
       "      <td>[8 Northumberland Ave, London, Greater London,...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50718170797866...</td>\n",
       "      <td>51.507182</td>\n",
       "      <td>-0.126127</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5BY</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4aec417bf964a520b2c521e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ESPA Life at Corinthia</td>\n",
       "      <td>Spa</td>\n",
       "      <td>10 Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216</td>\n",
       "      <td>[10 Whitehall Pl, London, Greater London, SW1A...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50640190821809...</td>\n",
       "      <td>51.506402</td>\n",
       "      <td>-0.125114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2BD</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>509e51b6e4b03f3cc1f3f56c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Sofitel London St James</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>6 Waterloo Place</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>317</td>\n",
       "      <td>[6 Waterloo Place, St. James's, Greater London...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50763553208714...</td>\n",
       "      <td>51.507636</td>\n",
       "      <td>-0.132367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1Y 4AN</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ae982def964a520f7b421e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Wates</td>\n",
       "      <td>None</td>\n",
       "      <td>17 Cockspur Street</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186</td>\n",
       "      <td>[17 Cockspur Street, London, Greater London, S...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5074672, 'lng'...</td>\n",
       "      <td>51.507467</td>\n",
       "      <td>-0.130492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1Y 5BL</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5970d1a7829b0c1777e51eb3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>The Royal Horseguards</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>2 Whitehall Ct.</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>285</td>\n",
       "      <td>[2 Whitehall Ct., London, Greater London, SW1A...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50612671032344...</td>\n",
       "      <td>51.506127</td>\n",
       "      <td>-0.124224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2EJ</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ac518b4f964a52083a020e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Citadines Trafalgar Square London</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>18/21 Northumberland Ave</td>\n",
       "      <td>GB</td>\n",
       "      <td>Charing Cross</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219</td>\n",
       "      <td>[18/21 Northumberland Ave, Charing Cross, Grea...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50702046674641...</td>\n",
       "      <td>51.507020</td>\n",
       "      <td>-0.124685</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5EA</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4b5f733bf964a5200fbb29e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Northumberland House</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>8 Northumberland Ave.</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163</td>\n",
       "      <td>[8 Northumberland Ave., London, Greater London...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50680059043122...</td>\n",
       "      <td>51.506801</td>\n",
       "      <td>-0.125639</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E12 5HF</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4b1a4ffef964a520d3e823e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Daniel Galvin</td>\n",
       "      <td>Salon / Barbershop</td>\n",
       "      <td>10 Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>246</td>\n",
       "      <td>[10 Whitehall Pl, London, Greater London, WC2N...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.506609, 'lng':...</td>\n",
       "      <td>51.506609</td>\n",
       "      <td>-0.124471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5AE</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5b81715a75dcb7002c755002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Court Room</td>\n",
       "      <td>Event Space</td>\n",
       "      <td>10 Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>252</td>\n",
       "      <td>[10 Whitehall Pl, London, Greater London, SW1A...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50628280639648...</td>\n",
       "      <td>51.506283</td>\n",
       "      <td>-0.124621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2BD</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>50c5ebe2e4b04a2d9e87a68b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>The Northall</td>\n",
       "      <td>English Restaurant</td>\n",
       "      <td>10A Northumberland Ave</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Whitehall Pl</td>\n",
       "      <td>289</td>\n",
       "      <td>[10A Northumberland Ave (Whitehall Pl), London...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50659890641054...</td>\n",
       "      <td>51.506599</td>\n",
       "      <td>-0.123821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5AE</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4e13812ecc3f2ee5189898dc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>hub by Premier Inn London Covent Garden</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>110 St Martin's Ln</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>297</td>\n",
       "      <td>[110 St Martin's Ln, London, WC2N 4BA, United ...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51003622082837...</td>\n",
       "      <td>51.510036</td>\n",
       "      <td>-0.127111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 4BA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54514efd498e59da3cda9ea8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Lobby Lounge</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>1 Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268</td>\n",
       "      <td>[1 Whitehall Pl, London, Greater London, SW1 A...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50637874867045...</td>\n",
       "      <td>51.506379</td>\n",
       "      <td>-0.124293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1 A 2</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4e3e8a721495bf24a5e8f9ff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ESPA Life Gym</td>\n",
       "      <td>Gym</td>\n",
       "      <td>10 Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>249</td>\n",
       "      <td>[10 Whitehall Pl, London, Greater London, Unit...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50631843415083...</td>\n",
       "      <td>51.506318</td>\n",
       "      <td>-0.124648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>503a6e8be4b0bd2ec5157d9b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Blind Spot</td>\n",
       "      <td>Speakeasy</td>\n",
       "      <td>45 St. Martin's Ln</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345</td>\n",
       "      <td>[45 St. Martin's Ln, London, Greater London, W...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51042996262026...</td>\n",
       "      <td>51.510430</td>\n",
       "      <td>-0.126747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ac518bef964a52022a320e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Radisson Blu Gym</td>\n",
       "      <td>Gym</td>\n",
       "      <td>31-36 Leicester Sq</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>318</td>\n",
       "      <td>[31-36 Leicester Sq, London, Greater London, W...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50995, 'lng': ...</td>\n",
       "      <td>51.509950</td>\n",
       "      <td>-0.129873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7LH</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5881e9efcc5b6a08df5a8768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>AWAY Spa</td>\n",
       "      <td>Spa</td>\n",
       "      <td>10 Wardour St</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>444</td>\n",
       "      <td>[10 Wardour St, London, Greater London, W1D 6Q...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51070562391806...</td>\n",
       "      <td>51.510706</td>\n",
       "      <td>-0.131396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W1D 6QF</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>51d2e8c9011c45a7b6364284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Premier Inn London Leicester Square</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Queens House, 1 Leicester Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>City of London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>440</td>\n",
       "      <td>[Queens House, 1 Leicester Square, City of Lon...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51103781780964...</td>\n",
       "      <td>51.511038</td>\n",
       "      <td>-0.130300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7BP</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4faea95de4b0604807fb2a4a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>HotelTonight European HQ</td>\n",
       "      <td>Tech Startup</td>\n",
       "      <td>146 -148 Clerkenwell Road</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>520</td>\n",
       "      <td>[146 -148 Clerkenwell Road, EC1R 5DG, United K...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51123793552026...</td>\n",
       "      <td>51.511238</td>\n",
       "      <td>-0.123505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EC1R 5DG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>518a0ca1498eb20d63bb9b84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>W Lounge</td>\n",
       "      <td>Hotel Bar</td>\n",
       "      <td>10 Wardour St</td>\n",
       "      <td>GB</td>\n",
       "      <td>Leicester Square</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>443</td>\n",
       "      <td>[10 Wardour St, Leicester Square, Greater Lond...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51069429260831...</td>\n",
       "      <td>51.510694</td>\n",
       "      <td>-0.131406</td>\n",
       "      <td>Leicester Square, London, Greater London</td>\n",
       "      <td>W1D 6QF</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4d601b5d1939a35d959b1dee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Thistle Piccadilly</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Coventry St</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>406</td>\n",
       "      <td>[Coventry St, London, Greater London, W1D 6BZ,...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51028793352039...</td>\n",
       "      <td>51.510288</td>\n",
       "      <td>-0.131396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W1D 6BZ</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4adf93a5f964a520c17b21e3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 name            categories  \\\n",
       "0                                     Corinthia Hotel                 Hotel   \n",
       "1                                 Club Quarters Hotel                 Hotel   \n",
       "2                                        Hotel Indigo                 Hotel   \n",
       "3                             St. Martin's Lane Hotel                 Hotel   \n",
       "4                            Amba Charing Cross Hotel                 Hotel   \n",
       "5              Radisson Blu Edwardian Hampshire Hotel                 Hotel   \n",
       "6                           Great Scotland Yard Hotel                 Hotel   \n",
       "7                Afternoon Tea at the Corinthia Hotel              Tea Room   \n",
       "8                                   FIT (W Hotel Gym)  Gym / Fitness Center   \n",
       "9                                      W London Hotel                 Hotel   \n",
       "10               The Piccadilly London West End Hotel                 Hotel   \n",
       "11                                     Hotel Chocolat        Chocolate Shop   \n",
       "12                                St George Inn Hotel   Housing Development   \n",
       "13                                     Hotel Chocolat        Chocolate Shop   \n",
       "14                                    Haymarket Hotel                 Hotel   \n",
       "15                             The Z Hotel Piccadilly                 Hotel   \n",
       "16                                     Assembly Hotel                 Hotel   \n",
       "17                                     Amba Hotel Gym  Gym / Fitness Center   \n",
       "18                                        Hotel Cecil         Historic Site   \n",
       "19                                Haymarket Hotel Spa                   Spa   \n",
       "20                                      library hotel                 Hotel   \n",
       "21                                  Z Hotel Trafalgar                 Hotel   \n",
       "22                  The 99 Club@St Martins Lane Hotel           Comedy Club   \n",
       "23                       Gym @ Every Hotel Piccadilly                   Gym   \n",
       "24                      HotelPartner Yield Management                Office   \n",
       "25                                    Henrietta Hotel                 Hotel   \n",
       "26                          The Z Hotel Covent Garden                 Hotel   \n",
       "27      Global Great Hotels - Investments Real Estate       Business Center   \n",
       "28                             W Hotel Screening Room         Movie Theater   \n",
       "29  The Trafalgar St. James London, Curio Collecti...                 Hotel   \n",
       "30                      The Grand at Trafalgar Square                 Hotel   \n",
       "31                             ESPA Life at Corinthia                   Spa   \n",
       "32                            Sofitel London St James                 Hotel   \n",
       "33                                              Wates                  None   \n",
       "34                              The Royal Horseguards                 Hotel   \n",
       "35                  Citadines Trafalgar Square London                 Hotel   \n",
       "36                               Northumberland House                 Hotel   \n",
       "37                                      Daniel Galvin    Salon / Barbershop   \n",
       "38                                         Court Room           Event Space   \n",
       "39                                       The Northall    English Restaurant   \n",
       "40            hub by Premier Inn London Covent Garden                 Hotel   \n",
       "41                                       Lobby Lounge                Lounge   \n",
       "42                                      ESPA Life Gym                   Gym   \n",
       "43                                         Blind Spot             Speakeasy   \n",
       "44                                   Radisson Blu Gym                   Gym   \n",
       "45                                           AWAY Spa                   Spa   \n",
       "46                Premier Inn London Leicester Square                 Hotel   \n",
       "47                           HotelTonight European HQ          Tech Startup   \n",
       "48                                           W Lounge             Hotel Bar   \n",
       "49                                 Thistle Piccadilly                 Hotel   \n",
       "\n",
       "                                      address  cc              city  \\\n",
       "0                                Whitehall Pl  GB            London   \n",
       "1                     8 Northumberland Avenue  GB            London   \n",
       "2                          1 Leicester Square  GB            London   \n",
       "3                        45 St. Martin'S Lane  GB            London   \n",
       "4                           The Strand, Ste 2  GB            London   \n",
       "5                      31-36 Leicester Square  GB            London   \n",
       "6                     3-5 Great Scotland Yard  GB            London   \n",
       "7                             10 Whitehall Pl  GB            London   \n",
       "8                                         NaN  GB            London   \n",
       "9         Leicester Square, 10 Wardour Street  GB    City of London   \n",
       "10                          65 Shaftsbury Ave  GB            London   \n",
       "11                      Charing Cross Station  GB            London   \n",
       "12                         Belgrave Street 25  GB            London   \n",
       "13                              78 The Strand  GB            London   \n",
       "14                               1 Suffolk Pl  GB            London   \n",
       "15                                2 Orange St  GB            London   \n",
       "16                  27-31 Charring Cross Road  GB            London   \n",
       "17                                        NaN  GB               NaN   \n",
       "18                                        NaN  GB            London   \n",
       "19                            1 Suffolk Place  GB            London   \n",
       "20                                        NaN  GB               NaN   \n",
       "21                           53-59 Chandos Pl  GB            London   \n",
       "22                           At Martin's Lane  GB            London   \n",
       "23                                        NaN  GB            London   \n",
       "24  1 Northumberland Avenue, Trafalgar Square  GB            London   \n",
       "25                            14 Henrietta St  GB            London   \n",
       "26                           31-32 Bedford St  GB            London   \n",
       "27                         133 Cockfosters Rd  GB     Hertfordshire   \n",
       "28                             10 Wardour St.  GB              Soho   \n",
       "29         2 Spring Gardens, Trafalgar Square  GB            London   \n",
       "30                       8 Northumberland Ave  GB            London   \n",
       "31                            10 Whitehall Pl  GB            London   \n",
       "32                           6 Waterloo Place  GB            London   \n",
       "33                         17 Cockspur Street  GB            London   \n",
       "34                            2 Whitehall Ct.  GB            London   \n",
       "35                   18/21 Northumberland Ave  GB     Charing Cross   \n",
       "36                      8 Northumberland Ave.  GB            London   \n",
       "37                            10 Whitehall Pl  GB            London   \n",
       "38                            10 Whitehall Pl  GB            London   \n",
       "39                     10A Northumberland Ave  GB            London   \n",
       "40                         110 St Martin's Ln  GB            London   \n",
       "41                             1 Whitehall Pl  GB            London   \n",
       "42                            10 Whitehall Pl  GB            London   \n",
       "43                         45 St. Martin's Ln  GB            London   \n",
       "44                         31-36 Leicester Sq  GB            London   \n",
       "45                              10 Wardour St  GB            London   \n",
       "46           Queens House, 1 Leicester Square  GB    City of London   \n",
       "47                  146 -148 Clerkenwell Road  GB               NaN   \n",
       "48                              10 Wardour St  GB  Leicester Square   \n",
       "49                                Coventry St  GB            London   \n",
       "\n",
       "           country            crossStreet  distance  \\\n",
       "0   United Kingdom                    NaN       247   \n",
       "1   United Kingdom                    NaN       131   \n",
       "2   United Kingdom                    NaN       428   \n",
       "3   United Kingdom                    NaN       356   \n",
       "4   United Kingdom                    NaN       241   \n",
       "5   United Kingdom                    NaN       306   \n",
       "6   United Kingdom                    NaN       157   \n",
       "7   United Kingdom                    NaN       256   \n",
       "8   United Kingdom                    NaN       384   \n",
       "9   United Kingdom                    NaN       458   \n",
       "10  United Kingdom                    NaN       596   \n",
       "11  United Kingdom                 Strand       247   \n",
       "12  United Kingdom  Near Victoria Station       123   \n",
       "13  United Kingdom                    NaN       485   \n",
       "14  United Kingdom                    NaN       261   \n",
       "15  United Kingdom                    NaN       316   \n",
       "16  United Kingdom                    NaN       354   \n",
       "17  United Kingdom                    NaN       239   \n",
       "18  United Kingdom                    NaN       512   \n",
       "19  United Kingdom                    NaN       273   \n",
       "20  United Kingdom                    NaN       290   \n",
       "21  United Kingdom                    NaN       330   \n",
       "22  United Kingdom                    NaN       349   \n",
       "23  United Kingdom                    NaN       407   \n",
       "24  United Kingdom                    NaN        56   \n",
       "25  United Kingdom                    NaN       518   \n",
       "26  United Kingdom                    NaN       472   \n",
       "27  United Kingdom                    NaN        35   \n",
       "28  United Kingdom       at Leicester Sq.       503   \n",
       "29  United Kingdom                    NaN        88   \n",
       "30  United Kingdom                    NaN       118   \n",
       "31  United Kingdom                    NaN       216   \n",
       "32  United Kingdom                    NaN       317   \n",
       "33  United Kingdom                    NaN       186   \n",
       "34  United Kingdom                    NaN       285   \n",
       "35  United Kingdom                    NaN       219   \n",
       "36  United Kingdom                    NaN       163   \n",
       "37  United Kingdom                    NaN       246   \n",
       "38  United Kingdom                    NaN       252   \n",
       "39  United Kingdom           Whitehall Pl       289   \n",
       "40  United Kingdom                    NaN       297   \n",
       "41  United Kingdom                    NaN       268   \n",
       "42  United Kingdom                    NaN       249   \n",
       "43  United Kingdom                    NaN       345   \n",
       "44  United Kingdom                    NaN       318   \n",
       "45  United Kingdom                    NaN       444   \n",
       "46  United Kingdom                    NaN       440   \n",
       "47  United Kingdom                    NaN       520   \n",
       "48  United Kingdom                    NaN       443   \n",
       "49  United Kingdom                    NaN       406   \n",
       "\n",
       "                                     formattedAddress  \\\n",
       "0   [Whitehall Pl, London, Greater London, SW1A 2B...   \n",
       "1   [8 Northumberland Avenue, London, Greater Lond...   \n",
       "2   [1 Leicester Square, London, Greater London, W...   \n",
       "3   [45 St. Martin'S Lane, London, Greater London,...   \n",
       "4   [The Strand, Ste 2, London, Greater London, WC...   \n",
       "5   [31-36 Leicester Square, London, Greater Londo...   \n",
       "6   [3-5 Great Scotland Yard, London, SW1A 2HN, Un...   \n",
       "7   [10 Whitehall Pl, London, Greater London, SW1A...   \n",
       "8            [London, Greater London, United Kingdom]   \n",
       "9   [Leicester Square, 10 Wardour Street, City of ...   \n",
       "10  [65 Shaftsbury Ave, London, Greater London, W1...   \n",
       "11  [Charing Cross Station (Strand), London, Great...   \n",
       "12  [Belgrave Street 25 (Near Victoria Station), L...   \n",
       "13  [78 The Strand, London, Greater London, WC2R 0...   \n",
       "14  [1 Suffolk Pl, London, Greater London, SW1Y 4H...   \n",
       "15  [2 Orange St, London, Greater London, WC2H 7DF...   \n",
       "16  [27-31 Charring Cross Road, London, Greater Lo...   \n",
       "17                                   [United Kingdom]   \n",
       "18  [London, Greater London, WC2R 0BN, United King...   \n",
       "19  [1 Suffolk Place, London, Greater London, SW1Y...   \n",
       "20                                   [United Kingdom]   \n",
       "21  [53-59 Chandos Pl, London, Greater London, WC2...   \n",
       "22  [At Martin's Lane, London, Greater London, WC2...   \n",
       "23      [London, Greater London, W1D, United Kingdom]   \n",
       "24  [1 Northumberland Avenue, Trafalgar Square, Lo...   \n",
       "25  [14 Henrietta St, London, Greater London, WC2E...   \n",
       "26  [31-32 Bedford St, London, Greater London, WC2...   \n",
       "27  [133 Cockfosters Rd, Hertfordshire, EN4 0AA, U...   \n",
       "28  [10 Wardour St. (at Leicester Sq.), Soho, Grea...   \n",
       "29  [2 Spring Gardens, Trafalgar Square, London, G...   \n",
       "30  [8 Northumberland Ave, London, Greater London,...   \n",
       "31  [10 Whitehall Pl, London, Greater London, SW1A...   \n",
       "32  [6 Waterloo Place, St. James's, Greater London...   \n",
       "33  [17 Cockspur Street, London, Greater London, S...   \n",
       "34  [2 Whitehall Ct., London, Greater London, SW1A...   \n",
       "35  [18/21 Northumberland Ave, Charing Cross, Grea...   \n",
       "36  [8 Northumberland Ave., London, Greater London...   \n",
       "37  [10 Whitehall Pl, London, Greater London, WC2N...   \n",
       "38  [10 Whitehall Pl, London, Greater London, SW1A...   \n",
       "39  [10A Northumberland Ave (Whitehall Pl), London...   \n",
       "40  [110 St Martin's Ln, London, WC2N 4BA, United ...   \n",
       "41  [1 Whitehall Pl, London, Greater London, SW1 A...   \n",
       "42  [10 Whitehall Pl, London, Greater London, Unit...   \n",
       "43  [45 St. Martin's Ln, London, Greater London, W...   \n",
       "44  [31-36 Leicester Sq, London, Greater London, W...   \n",
       "45  [10 Wardour St, London, Greater London, W1D 6Q...   \n",
       "46  [Queens House, 1 Leicester Square, City of Lon...   \n",
       "47  [146 -148 Clerkenwell Road, EC1R 5DG, United K...   \n",
       "48  [10 Wardour St, Leicester Square, Greater Lond...   \n",
       "49  [Coventry St, London, Greater London, W1D 6BZ,...   \n",
       "\n",
       "                                       labeledLatLngs        lat       lng  \\\n",
       "0   [{'label': 'display', 'lat': 51.50660672347560...  51.506607 -0.124460   \n",
       "1   [{'label': 'display', 'lat': 51.50698547522388...  51.506985 -0.126020   \n",
       "2   [{'label': 'display', 'lat': 51.5107204, 'lng'...  51.510720 -0.130932   \n",
       "3   [{'label': 'display', 'lat': 51.51052632268574...  51.510526 -0.126705   \n",
       "4   [{'label': 'display', 'lat': 51.50821644805786...  51.508216 -0.124564   \n",
       "5   [{'label': 'display', 'lat': 51.5098669, 'lng'...  51.509867 -0.129767   \n",
       "6   [{'label': 'display', 'lat': 51.5064253, 'lng'...  51.506425 -0.126146   \n",
       "7   [{'label': 'display', 'lat': 51.50657230583427...  51.506572 -0.124350   \n",
       "8   [{'label': 'display', 'lat': 51.51002945140006...  51.510029 -0.131398   \n",
       "9   [{'label': 'display', 'lat': 51.51084300795154...  51.510843 -0.131429   \n",
       "10  [{'label': 'display', 'lat': 51.51215571165802...  51.512156 -0.131767   \n",
       "11  [{'label': 'display', 'lat': 51.50808137396048...  51.508081 -0.124401   \n",
       "12  [{'label': 'display', 'lat': 51.507716, 'lng':...  51.507716 -0.129509   \n",
       "13  [{'label': 'display', 'lat': 51.51009906249482...  51.510099 -0.122293   \n",
       "14  [{'label': 'display', 'lat': 51.50826687319245...  51.508267 -0.131315   \n",
       "15  [{'label': 'display', 'lat': 51.50900946456780...  51.509009 -0.131564   \n",
       "16  [{'label': 'display', 'lat': 51.51054918955679...  51.510549 -0.128521   \n",
       "17  [{'label': 'display', 'lat': 51.50831237866142...  51.508312 -0.124669   \n",
       "18  [{'label': 'display', 'lat': 51.510167, 'lng':...  51.510167 -0.121888   \n",
       "19  [{'label': 'display', 'lat': 51.50823877036787...  51.508239 -0.131518   \n",
       "20  [{'label': 'display', 'lat': 51.51000814331115...  51.510008 -0.127645   \n",
       "21  [{'label': 'display', 'lat': 51.509965, 'lng':...  51.509965 -0.125388   \n",
       "22  [{'label': 'display', 'lat': 51.51043, 'lng': ...  51.510430 -0.126499   \n",
       "23  [{'label': 'display', 'lat': 51.510223, 'lng':...  51.510223 -0.131556   \n",
       "24  [{'label': 'display', 'lat': 51.507214, 'lng':...  51.507214 -0.127035   \n",
       "25  [{'label': 'display', 'lat': 51.51114060333864...  51.511141 -0.123330   \n",
       "26  [{'label': 'display', 'lat': 51.511105, 'lng':...  51.511105 -0.124478   \n",
       "27  [{'label': 'display', 'lat': 51.50708638980144...  51.507086 -0.127909   \n",
       "28  [{'label': 'display', 'lat': 51.51093257635912...  51.510933 -0.132350   \n",
       "29  [{'label': 'display', 'lat': 51.50733680732381...  51.507337 -0.129073   \n",
       "30  [{'label': 'display', 'lat': 51.50718170797866...  51.507182 -0.126127   \n",
       "31  [{'label': 'display', 'lat': 51.50640190821809...  51.506402 -0.125114   \n",
       "32  [{'label': 'display', 'lat': 51.50763553208714...  51.507636 -0.132367   \n",
       "33  [{'label': 'display', 'lat': 51.5074672, 'lng'...  51.507467 -0.130492   \n",
       "34  [{'label': 'display', 'lat': 51.50612671032344...  51.506127 -0.124224   \n",
       "35  [{'label': 'display', 'lat': 51.50702046674641...  51.507020 -0.124685   \n",
       "36  [{'label': 'display', 'lat': 51.50680059043122...  51.506801 -0.125639   \n",
       "37  [{'label': 'display', 'lat': 51.506609, 'lng':...  51.506609 -0.124471   \n",
       "38  [{'label': 'display', 'lat': 51.50628280639648...  51.506283 -0.124621   \n",
       "39  [{'label': 'display', 'lat': 51.50659890641054...  51.506599 -0.123821   \n",
       "40  [{'label': 'display', 'lat': 51.51003622082837...  51.510036 -0.127111   \n",
       "41  [{'label': 'display', 'lat': 51.50637874867045...  51.506379 -0.124293   \n",
       "42  [{'label': 'display', 'lat': 51.50631843415083...  51.506318 -0.124648   \n",
       "43  [{'label': 'display', 'lat': 51.51042996262026...  51.510430 -0.126747   \n",
       "44  [{'label': 'display', 'lat': 51.50995, 'lng': ...  51.509950 -0.129873   \n",
       "45  [{'label': 'display', 'lat': 51.51070562391806...  51.510706 -0.131396   \n",
       "46  [{'label': 'display', 'lat': 51.51103781780964...  51.511038 -0.130300   \n",
       "47  [{'label': 'display', 'lat': 51.51123793552026...  51.511238 -0.123505   \n",
       "48  [{'label': 'display', 'lat': 51.51069429260831...  51.510694 -0.131406   \n",
       "49  [{'label': 'display', 'lat': 51.51028793352039...  51.510288 -0.131396   \n",
       "\n",
       "                                neighborhood postalCode           state  \\\n",
       "0                                        NaN   SW1A 2BD  Greater London   \n",
       "1                                        NaN   WC2N 5BY  Greater London   \n",
       "2                                        NaN   WC2H 7NA  Greater London   \n",
       "3                                        NaN   WC2N 4HX  Greater London   \n",
       "4                                        NaN   WC2N 5HX  Greater London   \n",
       "5                                        NaN   WC2H 7LH  Greater London   \n",
       "6                                        NaN   SW1A 2HN             NaN   \n",
       "7                                        NaN   SW1A 2BD  Greater London   \n",
       "8                                        NaN        NaN  Greater London   \n",
       "9                                        NaN    W1D 6QF  Greater London   \n",
       "10                                       NaN    W1D 6EX  Greater London   \n",
       "11                                       NaN   WC2N 5HS  Greater London   \n",
       "12                                       NaN        NaN  Greater London   \n",
       "13                                       NaN   WC2R 0DE  Greater London   \n",
       "14                                       NaN   SW1Y 4HX  Greater London   \n",
       "15                                       NaN   WC2H 7DF  Greater London   \n",
       "16                                       NaN   WC2H 0LS  Greater London   \n",
       "17                                       NaN        NaN             NaN   \n",
       "18                                       NaN   WC2R 0BN  Greater London   \n",
       "19                                       NaN   SW1Y 4BP  Greater London   \n",
       "20                                       NaN        NaN             NaN   \n",
       "21                                       NaN   WC2N 4HS  Greater London   \n",
       "22                                       NaN   WC2N 4HX  Greater London   \n",
       "23                                       NaN        W1D  Greater London   \n",
       "24                                       NaN   WC2N 5BW  Greater London   \n",
       "25                                       NaN   WC2E 8QH  Greater London   \n",
       "26                                       NaN   WC2E 9ED  Greater London   \n",
       "27                                       NaN    EN4 0AA   Hertfordshire   \n",
       "28                                       NaN    W1D 6QF  Greater London   \n",
       "29                                       NaN   SW1A 2TS  Greater London   \n",
       "30                                       NaN   WC2N 5BY  Greater London   \n",
       "31                                       NaN   SW1A 2BD  Greater London   \n",
       "32                                       NaN   SW1Y 4AN  Greater London   \n",
       "33                                       NaN   SW1Y 5BL  Greater London   \n",
       "34                                       NaN   SW1A 2EJ  Greater London   \n",
       "35                                       NaN   WC2N 5EA  Greater London   \n",
       "36                                       NaN    E12 5HF  Greater London   \n",
       "37                                       NaN   WC2N 5AE  Greater London   \n",
       "38                                       NaN   SW1A 2BD  Greater London   \n",
       "39                                       NaN   WC2N 5AE  Greater London   \n",
       "40                                       NaN   WC2N 4BA             NaN   \n",
       "41                                       NaN    SW1 A 2  Greater London   \n",
       "42                                       NaN        NaN  Greater London   \n",
       "43                                       NaN   WC2N 4HX  Greater London   \n",
       "44                                       NaN   WC2H 7LH  Greater London   \n",
       "45                                       NaN    W1D 6QF  Greater London   \n",
       "46                                       NaN   WC2H 7BP  Greater London   \n",
       "47                                       NaN   EC1R 5DG             NaN   \n",
       "48  Leicester Square, London, Greater London    W1D 6QF  Greater London   \n",
       "49                                       NaN    W1D 6BZ  Greater London   \n",
       "\n",
       "                          id  \n",
       "0   4d274945d86aa09024d41dc0  \n",
       "1   4bc54daa6a3e9c7496c8f548  \n",
       "2   5aa19ea5c666663fb5d39a6c  \n",
       "3   4ba51d68f964a52030df38e3  \n",
       "4   4bc78b3c93bdeee1947f37ae  \n",
       "5   4ac518b5f964a5209fa020e3  \n",
       "6   5d53299e9293ba0007f03f86  \n",
       "7   52764ec8498e83fe786a0595  \n",
       "8   56fe22b0498ef2d2226413fb  \n",
       "9   4ad7ed82f964a520241021e3  \n",
       "10  564c9b99498ec148d934dede  \n",
       "11  58e4ff8ec45ee3187f52bbd6  \n",
       "12  52acd5a711d293ec4f5f4d18  \n",
       "13  4bcc5decb6c49c748c8e9391  \n",
       "14  4abcec53f964a520b98720e3  \n",
       "15  53a47f8b498ea84d1e81a90e  \n",
       "16  5ad474e725fb7b4523d48bf5  \n",
       "17  5601b040498e5def6d164bc6  \n",
       "18  5dd7f228caa13c000808171c  \n",
       "19  4ac518d1f964a520e9a620e3  \n",
       "20  5650d8af498e63f6c44aec00  \n",
       "21  5dfcdcad867d0c000889b709  \n",
       "22  5c43911ea0215b003969bd89  \n",
       "23  5a0eea7964c8e11593989ebd  \n",
       "24  57a087c9498e4de77af68d8a  \n",
       "25  4ace3d9df964a52057cf20e3  \n",
       "26  5be85bdba30619002c2931fe  \n",
       "27  5afb9f1b65211f002c89d942  \n",
       "28  5051b834e4b08416d819eb27  \n",
       "29  4af290c7f964a520eee721e3  \n",
       "30  4aec417bf964a520b2c521e3  \n",
       "31  509e51b6e4b03f3cc1f3f56c  \n",
       "32  4ae982def964a520f7b421e3  \n",
       "33  5970d1a7829b0c1777e51eb3  \n",
       "34  4ac518b4f964a52083a020e3  \n",
       "35  4b5f733bf964a5200fbb29e3  \n",
       "36  4b1a4ffef964a520d3e823e3  \n",
       "37  5b81715a75dcb7002c755002  \n",
       "38  50c5ebe2e4b04a2d9e87a68b  \n",
       "39  4e13812ecc3f2ee5189898dc  \n",
       "40  54514efd498e59da3cda9ea8  \n",
       "41  4e3e8a721495bf24a5e8f9ff  \n",
       "42  503a6e8be4b0bd2ec5157d9b  \n",
       "43  4ac518bef964a52022a320e3  \n",
       "44  5881e9efcc5b6a08df5a8768  \n",
       "45  51d2e8c9011c45a7b6364284  \n",
       "46  4faea95de4b0604807fb2a4a  \n",
       "47  518a0ca1498eb20d63bb9b84  \n",
       "48  4d601b5d1939a35d959b1dee  \n",
       "49  4adf93a5f964a520c17b21e3  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keep only columns that include venue name, and anything that is associated with location\n",
    "filtered_columns = ['name', 'categories'] + [col for col in hotels_df.columns if col.startswith('location.')] + ['id']\n",
    "hotels_df_filtered = hotels_df.loc[:, filtered_columns]\n",
    "\n",
    "# function that extracts the category of the venue\n",
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']\n",
    "\n",
    "# filter the category for each row\n",
    "hotels_df_filtered['categories'] = hotels_df_filtered.apply(get_category_type, axis=1)\n",
    "\n",
    "# clean column names by keeping only last term\n",
    "hotels_df_filtered.columns = [column.split('.')[-1] for column in hotels_df_filtered.columns]\n",
    "\n",
    "hotels_df_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>address</th>\n",
       "      <th>cc</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>crossStreet</th>\n",
       "      <th>distance</th>\n",
       "      <th>formattedAddress</th>\n",
       "      <th>labeledLatLngs</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>postalCode</th>\n",
       "      <th>state</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Corinthia Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247</td>\n",
       "      <td>[Whitehall Pl, London, Greater London, SW1A 2B...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50660672347560...</td>\n",
       "      <td>51.506607</td>\n",
       "      <td>-0.124460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2BD</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4d274945d86aa09024d41dc0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Club Quarters Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>8 Northumberland Avenue</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>[8 Northumberland Avenue, London, Greater Lond...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50698547522388...</td>\n",
       "      <td>51.506985</td>\n",
       "      <td>-0.126020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5BY</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4bc54daa6a3e9c7496c8f548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hotel Indigo</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1 Leicester Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428</td>\n",
       "      <td>[1 Leicester Square, London, Greater London, W...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5107204, 'lng'...</td>\n",
       "      <td>51.510720</td>\n",
       "      <td>-0.130932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7NA</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5aa19ea5c666663fb5d39a6c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>St. Martin's Lane Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>45 St. Martin'S Lane</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>356</td>\n",
       "      <td>[45 St. Martin'S Lane, London, Greater London,...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51052632268574...</td>\n",
       "      <td>51.510526</td>\n",
       "      <td>-0.126705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ba51d68f964a52030df38e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amba Charing Cross Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>The Strand, Ste 2</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241</td>\n",
       "      <td>[The Strand, Ste 2, London, Greater London, WC...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50821644805786...</td>\n",
       "      <td>51.508216</td>\n",
       "      <td>-0.124564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4bc78b3c93bdeee1947f37ae</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Radisson Blu Edwardian Hampshire Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>31-36 Leicester Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>306</td>\n",
       "      <td>[31-36 Leicester Square, London, Greater Londo...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5098669, 'lng'...</td>\n",
       "      <td>51.509867</td>\n",
       "      <td>-0.129767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7LH</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ac518b5f964a5209fa020e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Great Scotland Yard Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>3-5 Great Scotland Yard</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>[3-5 Great Scotland Yard, London, SW1A 2HN, Un...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5064253, 'lng'...</td>\n",
       "      <td>51.506425</td>\n",
       "      <td>-0.126146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2HN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5d53299e9293ba0007f03f86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>W London Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Leicester Square, 10 Wardour Street</td>\n",
       "      <td>GB</td>\n",
       "      <td>City of London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>458</td>\n",
       "      <td>[Leicester Square, 10 Wardour Street, City of ...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51084300795154...</td>\n",
       "      <td>51.510843</td>\n",
       "      <td>-0.131429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W1D 6QF</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ad7ed82f964a520241021e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>The Piccadilly London West End Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>65 Shaftsbury Ave</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>596</td>\n",
       "      <td>[65 Shaftsbury Ave, London, Greater London, W1...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51215571165802...</td>\n",
       "      <td>51.512156</td>\n",
       "      <td>-0.131767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W1D 6EX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>564c9b99498ec148d934dede</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Haymarket Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1 Suffolk Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>261</td>\n",
       "      <td>[1 Suffolk Pl, London, Greater London, SW1Y 4H...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50826687319245...</td>\n",
       "      <td>51.508267</td>\n",
       "      <td>-0.131315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1Y 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4abcec53f964a520b98720e3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      name categories  \\\n",
       "0                          Corinthia Hotel      Hotel   \n",
       "1                      Club Quarters Hotel      Hotel   \n",
       "2                             Hotel Indigo      Hotel   \n",
       "3                  St. Martin's Lane Hotel      Hotel   \n",
       "4                 Amba Charing Cross Hotel      Hotel   \n",
       "5   Radisson Blu Edwardian Hampshire Hotel      Hotel   \n",
       "6                Great Scotland Yard Hotel      Hotel   \n",
       "9                           W London Hotel      Hotel   \n",
       "10    The Piccadilly London West End Hotel      Hotel   \n",
       "14                         Haymarket Hotel      Hotel   \n",
       "\n",
       "                                address  cc            city         country  \\\n",
       "0                          Whitehall Pl  GB          London  United Kingdom   \n",
       "1               8 Northumberland Avenue  GB          London  United Kingdom   \n",
       "2                    1 Leicester Square  GB          London  United Kingdom   \n",
       "3                  45 St. Martin'S Lane  GB          London  United Kingdom   \n",
       "4                     The Strand, Ste 2  GB          London  United Kingdom   \n",
       "5                31-36 Leicester Square  GB          London  United Kingdom   \n",
       "6               3-5 Great Scotland Yard  GB          London  United Kingdom   \n",
       "9   Leicester Square, 10 Wardour Street  GB  City of London  United Kingdom   \n",
       "10                    65 Shaftsbury Ave  GB          London  United Kingdom   \n",
       "14                         1 Suffolk Pl  GB          London  United Kingdom   \n",
       "\n",
       "   crossStreet  distance                                   formattedAddress  \\\n",
       "0          NaN       247  [Whitehall Pl, London, Greater London, SW1A 2B...   \n",
       "1          NaN       131  [8 Northumberland Avenue, London, Greater Lond...   \n",
       "2          NaN       428  [1 Leicester Square, London, Greater London, W...   \n",
       "3          NaN       356  [45 St. Martin'S Lane, London, Greater London,...   \n",
       "4          NaN       241  [The Strand, Ste 2, London, Greater London, WC...   \n",
       "5          NaN       306  [31-36 Leicester Square, London, Greater Londo...   \n",
       "6          NaN       157  [3-5 Great Scotland Yard, London, SW1A 2HN, Un...   \n",
       "9          NaN       458  [Leicester Square, 10 Wardour Street, City of ...   \n",
       "10         NaN       596  [65 Shaftsbury Ave, London, Greater London, W1...   \n",
       "14         NaN       261  [1 Suffolk Pl, London, Greater London, SW1Y 4H...   \n",
       "\n",
       "                                       labeledLatLngs        lat       lng  \\\n",
       "0   [{'label': 'display', 'lat': 51.50660672347560...  51.506607 -0.124460   \n",
       "1   [{'label': 'display', 'lat': 51.50698547522388...  51.506985 -0.126020   \n",
       "2   [{'label': 'display', 'lat': 51.5107204, 'lng'...  51.510720 -0.130932   \n",
       "3   [{'label': 'display', 'lat': 51.51052632268574...  51.510526 -0.126705   \n",
       "4   [{'label': 'display', 'lat': 51.50821644805786...  51.508216 -0.124564   \n",
       "5   [{'label': 'display', 'lat': 51.5098669, 'lng'...  51.509867 -0.129767   \n",
       "6   [{'label': 'display', 'lat': 51.5064253, 'lng'...  51.506425 -0.126146   \n",
       "9   [{'label': 'display', 'lat': 51.51084300795154...  51.510843 -0.131429   \n",
       "10  [{'label': 'display', 'lat': 51.51215571165802...  51.512156 -0.131767   \n",
       "14  [{'label': 'display', 'lat': 51.50826687319245...  51.508267 -0.131315   \n",
       "\n",
       "   neighborhood postalCode           state                        id  \n",
       "0           NaN   SW1A 2BD  Greater London  4d274945d86aa09024d41dc0  \n",
       "1           NaN   WC2N 5BY  Greater London  4bc54daa6a3e9c7496c8f548  \n",
       "2           NaN   WC2H 7NA  Greater London  5aa19ea5c666663fb5d39a6c  \n",
       "3           NaN   WC2N 4HX  Greater London  4ba51d68f964a52030df38e3  \n",
       "4           NaN   WC2N 5HX  Greater London  4bc78b3c93bdeee1947f37ae  \n",
       "5           NaN   WC2H 7LH  Greater London  4ac518b5f964a5209fa020e3  \n",
       "6           NaN   SW1A 2HN             NaN  5d53299e9293ba0007f03f86  \n",
       "9           NaN    W1D 6QF  Greater London  4ad7ed82f964a520241021e3  \n",
       "10          NaN    W1D 6EX  Greater London  564c9b99498ec148d934dede  \n",
       "14          NaN   SW1Y 4HX  Greater London  4abcec53f964a520b98720e3  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#keeping only venues with the category \"Hotel\"\n",
    "\n",
    "hotels_df_filtered = hotels_df_filtered[hotels_df_filtered.categories == 'Hotel']\n",
    "hotels_df_filtered.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>address</th>\n",
       "      <th>cc</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>crossStreet</th>\n",
       "      <th>distance</th>\n",
       "      <th>formattedAddress</th>\n",
       "      <th>labeledLatLngs</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>postalCode</th>\n",
       "      <th>state</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Corinthia Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247</td>\n",
       "      <td>[Whitehall Pl, London, Greater London, SW1A 2B...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50660672347560...</td>\n",
       "      <td>51.506607</td>\n",
       "      <td>-0.124460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2BD</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4d274945d86aa09024d41dc0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Club Quarters Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>8 Northumberland Avenue</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>[8 Northumberland Avenue, London, Greater Lond...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50698547522388...</td>\n",
       "      <td>51.506985</td>\n",
       "      <td>-0.126020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5BY</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4bc54daa6a3e9c7496c8f548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hotel Indigo</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1 Leicester Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428</td>\n",
       "      <td>[1 Leicester Square, London, Greater London, W...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5107204, 'lng'...</td>\n",
       "      <td>51.510720</td>\n",
       "      <td>-0.130932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7NA</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5aa19ea5c666663fb5d39a6c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>St. Martin's Lane Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>45 St. Martin'S Lane</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>356</td>\n",
       "      <td>[45 St. Martin'S Lane, London, Greater London,...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51052632268574...</td>\n",
       "      <td>51.510526</td>\n",
       "      <td>-0.126705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ba51d68f964a52030df38e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amba Charing Cross Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>The Strand, Ste 2</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241</td>\n",
       "      <td>[The Strand, Ste 2, London, Greater London, WC...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50821644805786...</td>\n",
       "      <td>51.508216</td>\n",
       "      <td>-0.124564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4bc78b3c93bdeee1947f37ae</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Radisson Blu Edwardian Hampshire Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>31-36 Leicester Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>306</td>\n",
       "      <td>[31-36 Leicester Square, London, Greater Londo...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5098669, 'lng'...</td>\n",
       "      <td>51.509867</td>\n",
       "      <td>-0.129767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7LH</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ac518b5f964a5209fa020e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Great Scotland Yard Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>3-5 Great Scotland Yard</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>[3-5 Great Scotland Yard, London, SW1A 2HN, Un...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5064253, 'lng'...</td>\n",
       "      <td>51.506425</td>\n",
       "      <td>-0.126146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1A 2HN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5d53299e9293ba0007f03f86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>W London Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Leicester Square, 10 Wardour Street</td>\n",
       "      <td>GB</td>\n",
       "      <td>City of London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>458</td>\n",
       "      <td>[Leicester Square, 10 Wardour Street, City of ...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51084300795154...</td>\n",
       "      <td>51.510843</td>\n",
       "      <td>-0.131429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W1D 6QF</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ad7ed82f964a520241021e3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The Piccadilly London West End Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>65 Shaftsbury Ave</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>596</td>\n",
       "      <td>[65 Shaftsbury Ave, London, Greater London, W1...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51215571165802...</td>\n",
       "      <td>51.512156</td>\n",
       "      <td>-0.131767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W1D 6EX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>564c9b99498ec148d934dede</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Haymarket Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1 Suffolk Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>261</td>\n",
       "      <td>[1 Suffolk Pl, London, Greater London, SW1Y 4H...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50826687319245...</td>\n",
       "      <td>51.508267</td>\n",
       "      <td>-0.131315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW1Y 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4abcec53f964a520b98720e3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     name categories  \\\n",
       "0                         Corinthia Hotel      Hotel   \n",
       "1                     Club Quarters Hotel      Hotel   \n",
       "2                            Hotel Indigo      Hotel   \n",
       "3                 St. Martin's Lane Hotel      Hotel   \n",
       "4                Amba Charing Cross Hotel      Hotel   \n",
       "5  Radisson Blu Edwardian Hampshire Hotel      Hotel   \n",
       "6               Great Scotland Yard Hotel      Hotel   \n",
       "7                          W London Hotel      Hotel   \n",
       "8    The Piccadilly London West End Hotel      Hotel   \n",
       "9                         Haymarket Hotel      Hotel   \n",
       "\n",
       "                               address  cc            city         country  \\\n",
       "0                         Whitehall Pl  GB          London  United Kingdom   \n",
       "1              8 Northumberland Avenue  GB          London  United Kingdom   \n",
       "2                   1 Leicester Square  GB          London  United Kingdom   \n",
       "3                 45 St. Martin'S Lane  GB          London  United Kingdom   \n",
       "4                    The Strand, Ste 2  GB          London  United Kingdom   \n",
       "5               31-36 Leicester Square  GB          London  United Kingdom   \n",
       "6              3-5 Great Scotland Yard  GB          London  United Kingdom   \n",
       "7  Leicester Square, 10 Wardour Street  GB  City of London  United Kingdom   \n",
       "8                    65 Shaftsbury Ave  GB          London  United Kingdom   \n",
       "9                         1 Suffolk Pl  GB          London  United Kingdom   \n",
       "\n",
       "  crossStreet  distance                                   formattedAddress  \\\n",
       "0         NaN       247  [Whitehall Pl, London, Greater London, SW1A 2B...   \n",
       "1         NaN       131  [8 Northumberland Avenue, London, Greater Lond...   \n",
       "2         NaN       428  [1 Leicester Square, London, Greater London, W...   \n",
       "3         NaN       356  [45 St. Martin'S Lane, London, Greater London,...   \n",
       "4         NaN       241  [The Strand, Ste 2, London, Greater London, WC...   \n",
       "5         NaN       306  [31-36 Leicester Square, London, Greater Londo...   \n",
       "6         NaN       157  [3-5 Great Scotland Yard, London, SW1A 2HN, Un...   \n",
       "7         NaN       458  [Leicester Square, 10 Wardour Street, City of ...   \n",
       "8         NaN       596  [65 Shaftsbury Ave, London, Greater London, W1...   \n",
       "9         NaN       261  [1 Suffolk Pl, London, Greater London, SW1Y 4H...   \n",
       "\n",
       "                                      labeledLatLngs        lat       lng  \\\n",
       "0  [{'label': 'display', 'lat': 51.50660672347560...  51.506607 -0.124460   \n",
       "1  [{'label': 'display', 'lat': 51.50698547522388...  51.506985 -0.126020   \n",
       "2  [{'label': 'display', 'lat': 51.5107204, 'lng'...  51.510720 -0.130932   \n",
       "3  [{'label': 'display', 'lat': 51.51052632268574...  51.510526 -0.126705   \n",
       "4  [{'label': 'display', 'lat': 51.50821644805786...  51.508216 -0.124564   \n",
       "5  [{'label': 'display', 'lat': 51.5098669, 'lng'...  51.509867 -0.129767   \n",
       "6  [{'label': 'display', 'lat': 51.5064253, 'lng'...  51.506425 -0.126146   \n",
       "7  [{'label': 'display', 'lat': 51.51084300795154...  51.510843 -0.131429   \n",
       "8  [{'label': 'display', 'lat': 51.51215571165802...  51.512156 -0.131767   \n",
       "9  [{'label': 'display', 'lat': 51.50826687319245...  51.508267 -0.131315   \n",
       "\n",
       "  neighborhood postalCode           state                        id  \n",
       "0          NaN   SW1A 2BD  Greater London  4d274945d86aa09024d41dc0  \n",
       "1          NaN   WC2N 5BY  Greater London  4bc54daa6a3e9c7496c8f548  \n",
       "2          NaN   WC2H 7NA  Greater London  5aa19ea5c666663fb5d39a6c  \n",
       "3          NaN   WC2N 4HX  Greater London  4ba51d68f964a52030df38e3  \n",
       "4          NaN   WC2N 5HX  Greater London  4bc78b3c93bdeee1947f37ae  \n",
       "5          NaN   WC2H 7LH  Greater London  4ac518b5f964a5209fa020e3  \n",
       "6          NaN   SW1A 2HN             NaN  5d53299e9293ba0007f03f86  \n",
       "7          NaN    W1D 6QF  Greater London  4ad7ed82f964a520241021e3  \n",
       "8          NaN    W1D 6EX  Greater London  564c9b99498ec148d934dede  \n",
       "9          NaN   SW1Y 4HX  Greater London  4abcec53f964a520b98720e3  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#resetting index\n",
    "\n",
    "hotels_df_filtered.reset_index(drop=True, inplace=True)\n",
    "hotels_df_filtered.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x15b7eb90390>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a map to view the resulting hotels and their locations\n",
    "hotels_map = folium.Map(location=[london_lat, london_lon], zoom_start=15) # generate map centred around London\n",
    "\n",
    "# add the hotels as blue circle markers\n",
    "for lat, lng, label in zip(hotels_df_filtered.lat, hotels_df_filtered.lng, hotels_df_filtered.categories):\n",
    "    folium.features.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        color='blue',\n",
    "        popup=label,\n",
    "        fill = True,\n",
    "        fill_color='blue',\n",
    "        fill_opacity=0.6\n",
    "    ).add_to(hotels_map)\n",
    "\n",
    "# display map\n",
    "hotels_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 16)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many hotels are there near london city center?\n",
    "hotels_df_filtered.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
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       "      <td>Hotel</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>St. Martin's Lane Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>45 St. Martin'S Lane</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>356</td>\n",
       "      <td>[45 St. Martin'S Lane, London, Greater London,...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51052632268574...</td>\n",
       "      <td>51.510526</td>\n",
       "      <td>-0.126705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ba51d68f964a52030df38e3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amba Charing Cross Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>The Strand, Ste 2</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241</td>\n",
       "      <td>[The Strand, Ste 2, London, Greater London, WC...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50821644805786...</td>\n",
       "      <td>51.508216</td>\n",
       "      <td>-0.124564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4bc78b3c93bdeee1947f37ae</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name categories                  address  cc    city  \\\n",
       "0           Corinthia Hotel      Hotel             Whitehall Pl  GB  London   \n",
       "1       Club Quarters Hotel      Hotel  8 Northumberland Avenue  GB  London   \n",
       "2              Hotel Indigo      Hotel       1 Leicester Square  GB  London   \n",
       "3   St. Martin's Lane Hotel      Hotel     45 St. Martin'S Lane  GB  London   \n",
       "4  Amba Charing Cross Hotel      Hotel        The Strand, Ste 2  GB  London   \n",
       "\n",
       "          country crossStreet  distance  \\\n",
       "0  United Kingdom         NaN       247   \n",
       "1  United Kingdom         NaN       131   \n",
       "2  United Kingdom         NaN       428   \n",
       "3  United Kingdom         NaN       356   \n",
       "4  United Kingdom         NaN       241   \n",
       "\n",
       "                                    formattedAddress  \\\n",
       "0  [Whitehall Pl, London, Greater London, SW1A 2B...   \n",
       "1  [8 Northumberland Avenue, London, Greater Lond...   \n",
       "2  [1 Leicester Square, London, Greater London, W...   \n",
       "3  [45 St. Martin'S Lane, London, Greater London,...   \n",
       "4  [The Strand, Ste 2, London, Greater London, WC...   \n",
       "\n",
       "                                      labeledLatLngs        lat       lng  \\\n",
       "0  [{'label': 'display', 'lat': 51.50660672347560...  51.506607 -0.124460   \n",
       "1  [{'label': 'display', 'lat': 51.50698547522388...  51.506985 -0.126020   \n",
       "2  [{'label': 'display', 'lat': 51.5107204, 'lng'...  51.510720 -0.130932   \n",
       "3  [{'label': 'display', 'lat': 51.51052632268574...  51.510526 -0.126705   \n",
       "4  [{'label': 'display', 'lat': 51.50821644805786...  51.508216 -0.124564   \n",
       "\n",
       "  neighborhood postalCode           state                        id  id_n  \n",
       "0          NaN   SW1A 2BD  Greater London  4d274945d86aa09024d41dc0     1  \n",
       "1          NaN   WC2N 5BY  Greater London  4bc54daa6a3e9c7496c8f548     2  \n",
       "2          NaN   WC2H 7NA  Greater London  5aa19ea5c666663fb5d39a6c     3  \n",
       "3          NaN   WC2N 4HX  Greater London  4ba51d68f964a52030df38e3     4  \n",
       "4          NaN   WC2N 5HX  Greater London  4bc78b3c93bdeee1947f37ae     5  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prepare hotels dataframe for clustering\n",
    "\n",
    "#adding an ID number to each venue will help in re-assembling the data after clustering\n",
    "id_n = list(range(1,len(hotels_df_filtered)+1))\n",
    "hotels_df_filtered['id_n'] = id_n\n",
    "hotels_df_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   id_n        lat       lng\n",
       "0     1  51.506607 -0.124460\n",
       "1     2  51.506985 -0.126020\n",
       "2     3  51.510720 -0.130932\n",
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       "8     9  51.512156 -0.131767\n",
       "9    10  51.508267 -0.131315"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The dataset that will be used in K-Means clustering\n",
    "X=hotels_df_filtered.loc[:,['id_n','lat','lng']]\n",
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[51.50979179 -0.13076647]\n",
      " [51.51046355 -0.12577598]\n",
      " [51.50692043 -0.12523318]]\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>id_n</th>\n",
       "      <th>lat</th>\n",
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       "      <th>cluster_label</th>\n",
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      ],
      "text/plain": [
       "   id_n        lat       lng  cluster_label\n",
       "0     1  51.506607 -0.124460              2\n",
       "1     2  51.506985 -0.126020              2\n",
       "2     3  51.510720 -0.130932              0\n",
       "3     4  51.510526 -0.126705              1\n",
       "4     5  51.508216 -0.124564              2\n",
       "5     6  51.509867 -0.129767              0\n",
       "6     7  51.506425 -0.126146              2\n",
       "7     8  51.510843 -0.131429              0\n",
       "8     9  51.512156 -0.131767              0\n",
       "9    10  51.508267 -0.131315              0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clustering the hotels based on their locations \n",
    "kmeans = KMeans(n_clusters = 3, init ='k-means++') # hotels will be clustered in 3 groups\n",
    "kmeans.fit(X[X.columns[1:3]]) # Compute k-means clustering.\n",
    "X['cluster_label'] = kmeans.fit_predict(X[X.columns[1:3]])\n",
    "centers = kmeans.cluster_centers_ # Coordinates of cluster centers.\n",
    "labels = kmeans.predict(X[X.columns[1:3]]) # Labels of each point\n",
    "centers = kmeans.cluster_centers_\n",
    "print(centers) #to show coordinates of the center for each cluster \n",
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>address</th>\n",
       "      <th>cc</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>crossStreet</th>\n",
       "      <th>distance</th>\n",
       "      <th>formattedAddress</th>\n",
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       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>postalCode</th>\n",
       "      <th>state</th>\n",
       "      <th>id</th>\n",
       "      <th>id_n</th>\n",
       "      <th>cluster_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Corinthia Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Whitehall Pl</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247</td>\n",
       "      <td>[Whitehall Pl, London, Greater London, SW1A 2B...</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Club Quarters Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>8 Northumberland Avenue</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>[8 Northumberland Avenue, London, Greater Lond...</td>\n",
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       "      <td>51.506985</td>\n",
       "      <td>-0.126020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5BY</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4bc54daa6a3e9c7496c8f548</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hotel Indigo</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1 Leicester Square</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428</td>\n",
       "      <td>[1 Leicester Square, London, Greater London, W...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.5107204, 'lng'...</td>\n",
       "      <td>51.510720</td>\n",
       "      <td>-0.130932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2H 7NA</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>5aa19ea5c666663fb5d39a6c</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>St. Martin's Lane Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>45 St. Martin'S Lane</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>356</td>\n",
       "      <td>[45 St. Martin'S Lane, London, Greater London,...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.51052632268574...</td>\n",
       "      <td>51.510526</td>\n",
       "      <td>-0.126705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 4HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4ba51d68f964a52030df38e3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amba Charing Cross Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>The Strand, Ste 2</td>\n",
       "      <td>GB</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241</td>\n",
       "      <td>[The Strand, Ste 2, London, Greater London, WC...</td>\n",
       "      <td>[{'label': 'display', 'lat': 51.50821644805786...</td>\n",
       "      <td>51.508216</td>\n",
       "      <td>-0.124564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WC2N 5HX</td>\n",
       "      <td>Greater London</td>\n",
       "      <td>4bc78b3c93bdeee1947f37ae</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name categories                  address  cc    city  \\\n",
       "0           Corinthia Hotel      Hotel             Whitehall Pl  GB  London   \n",
       "1       Club Quarters Hotel      Hotel  8 Northumberland Avenue  GB  London   \n",
       "2              Hotel Indigo      Hotel       1 Leicester Square  GB  London   \n",
       "3   St. Martin's Lane Hotel      Hotel     45 St. Martin'S Lane  GB  London   \n",
       "4  Amba Charing Cross Hotel      Hotel        The Strand, Ste 2  GB  London   \n",
       "\n",
       "          country crossStreet  distance  \\\n",
       "0  United Kingdom         NaN       247   \n",
       "1  United Kingdom         NaN       131   \n",
       "2  United Kingdom         NaN       428   \n",
       "3  United Kingdom         NaN       356   \n",
       "4  United Kingdom         NaN       241   \n",
       "\n",
       "                                    formattedAddress  \\\n",
       "0  [Whitehall Pl, London, Greater London, SW1A 2B...   \n",
       "1  [8 Northumberland Avenue, London, Greater Lond...   \n",
       "2  [1 Leicester Square, London, Greater London, W...   \n",
       "3  [45 St. Martin'S Lane, London, Greater London,...   \n",
       "4  [The Strand, Ste 2, London, Greater London, WC...   \n",
       "\n",
       "                                      labeledLatLngs        lat       lng  \\\n",
       "0  [{'label': 'display', 'lat': 51.50660672347560...  51.506607 -0.124460   \n",
       "1  [{'label': 'display', 'lat': 51.50698547522388...  51.506985 -0.126020   \n",
       "2  [{'label': 'display', 'lat': 51.5107204, 'lng'...  51.510720 -0.130932   \n",
       "3  [{'label': 'display', 'lat': 51.51052632268574...  51.510526 -0.126705   \n",
       "4  [{'label': 'display', 'lat': 51.50821644805786...  51.508216 -0.124564   \n",
       "\n",
       "  neighborhood postalCode           state                        id  id_n  \\\n",
       "0          NaN   SW1A 2BD  Greater London  4d274945d86aa09024d41dc0     1   \n",
       "1          NaN   WC2N 5BY  Greater London  4bc54daa6a3e9c7496c8f548     2   \n",
       "2          NaN   WC2H 7NA  Greater London  5aa19ea5c666663fb5d39a6c     3   \n",
       "3          NaN   WC2N 4HX  Greater London  4ba51d68f964a52030df38e3     4   \n",
       "4          NaN   WC2N 5HX  Greater London  4bc78b3c93bdeee1947f37ae     5   \n",
       "\n",
       "   cluster_label  \n",
       "0              2  \n",
       "1              2  \n",
       "2              0  \n",
       "3              1  \n",
       "4              2  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adding the cluster label to each venue in the hotels data frame\n",
    "X = X[['id_n','cluster_label']]\n",
    "clustered_hotels = hotels_df_filtered.merge(X, left_on='id_n', right_on='id_n')\n",
    "clustered_hotels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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                var html_487586284c1147419a5a523dbdd94a5c = $('<div id="html_487586284c1147419a5a523dbdd94a5c" style="width: 100.0%; height: 100.0%;">Northumberland House Cluster 2</div>')[0];
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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 0x15b7ec90f98>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#map showing each hotel with different color based on the cluster\n",
    "map_clusters = folium.Map(location=[london_lat, london_lon], zoom_start=15)\n",
    "\n",
    "# add markers to the map\n",
    "marker_colors = ['red','green','blue']\n",
    "for lat, lon, poi, cluster in zip(clustered_hotels['lat'], clustered_hotels['lng'], clustered_hotels['name'], clustered_hotels['cluster_label']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=marker_colors[cluster],\n",
    "        fill=True,\n",
    "        fill_color=marker_colors[cluster],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters #show map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>c_lat</th>\n",
       "      <th>c_lon</th>\n",
       "      <th>cluster_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.509792</td>\n",
       "      <td>-0.130766</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.510464</td>\n",
       "      <td>-0.125776</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.506920</td>\n",
       "      <td>-0.125233</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       c_lat     c_lon  cluster_label\n",
       "0  51.509792 -0.130766              0\n",
       "1  51.510464 -0.125776              1\n",
       "2  51.506920 -0.125233              2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#putting the center of each cluster in a dataframe for later analysis\n",
    "centers_df = pd.DataFrame(centers, columns =['c_lat', 'c_lon'])\n",
    "cluster_label = list(range(0,3))\n",
    "centers_df['cluster_label'] = cluster_label\n",
    "centers_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x15b7f8f94e0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#map of centers\n",
    "map_centers = folium.Map(location=[london_lat, london_lon], zoom_start=15)\n",
    "\n",
    "# add markers to the map in different colors according to cluster\n",
    "marker_colors = ['red','green','blue']\n",
    "for lat, lon, poi, cluster in zip(centers_df['c_lat'], centers_df['c_lon'], centers_df['cluster_label'], centers_df['cluster_label']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=10,\n",
    "        popup=label,\n",
    "        color=marker_colors[cluster],\n",
    "        fill=True,\n",
    "        fill_color=marker_colors[cluster],\n",
    "        fill_opacity=0.7).add_to(map_centers)\n",
    "       \n",
    "map_centers #display the map showing 3 markers, one for each cluster "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Getting the restaurants data form Foursquare and analysing it</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will call foursquare API again and call for trending venues around london city center using the \"explore\" call, then we will clean the data, keep restaurants only, and classify these restaurants to one of the clusters created earlier using KNN classification. \n",
    "\n",
    "To do this we will use the centers data created before as a training set for KNN, then we will predict the cluster to which each restaurant belongs to by finding the nearest center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://api.foursquare.com/v2/venues/explore?&client_id=N1DP4XUUYDNLDR5HOEYF3YS3C1RH30KVPX2HUSQVQMAIC05A&client_secret=0DEAMLOJ2WZIMLYT0R334JKWVTQOJ1ALZMR23ZUMUHOJDCGI&v=20180605&ll=51.5074,-0.1278&radius=500&limit=500'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating the url to call on foursquare API\n",
    "\n",
    "limit_restaurants = 500\n",
    "\n",
    "url2 = '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",
    "    london_lat, \n",
    "    london_lon, \n",
    "    radius, \n",
    "    limit_restaurants)\n",
    "url2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sving the results\n",
    "results2 = requests.get(url2).json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>National Gallery</td>\n",
       "      <td>Art Museum</td>\n",
       "      <td>51.508876</td>\n",
       "      <td>-0.128478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Trafalgar Square</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>51.507987</td>\n",
       "      <td>-0.128048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East Trafalgar Square Fountain</td>\n",
       "      <td>Fountain</td>\n",
       "      <td>51.508088</td>\n",
       "      <td>-0.127700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sainsbury Wing National Gallery</td>\n",
       "      <td>Art Museum</td>\n",
       "      <td>51.508384</td>\n",
       "      <td>-0.129001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Trafalgar Square Lions</td>\n",
       "      <td>Outdoor Sculpture</td>\n",
       "      <td>51.507641</td>\n",
       "      <td>-0.127888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nelson's Column</td>\n",
       "      <td>Monument / Landmark</td>\n",
       "      <td>51.507744</td>\n",
       "      <td>-0.127931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>St Martin-in-the-Fields</td>\n",
       "      <td>Church</td>\n",
       "      <td>51.508746</td>\n",
       "      <td>-0.126507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>National Portrait Gallery</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>51.509438</td>\n",
       "      <td>-0.128032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Corinthia Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>51.506607</td>\n",
       "      <td>-0.124460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Waterstones</td>\n",
       "      <td>Bookstore</td>\n",
       "      <td>51.507650</td>\n",
       "      <td>-0.127130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Barrafina</td>\n",
       "      <td>Spanish Restaurant</td>\n",
       "      <td>51.509427</td>\n",
       "      <td>-0.125894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Institute of Contemporary Arts (ICA)</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>51.506265</td>\n",
       "      <td>-0.130817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Haymarket Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>51.508267</td>\n",
       "      <td>-0.131315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Thai Square</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>51.507656</td>\n",
       "      <td>-0.129830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tandoor Chop House</td>\n",
       "      <td>North Indian Restaurant</td>\n",
       "      <td>51.509192</td>\n",
       "      <td>-0.125638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>English National Opera</td>\n",
       "      <td>Opera House</td>\n",
       "      <td>51.509804</td>\n",
       "      <td>-0.126945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>The London Coliseum</td>\n",
       "      <td>Theater</td>\n",
       "      <td>51.509846</td>\n",
       "      <td>-0.127005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Her Majesty's Theatre</td>\n",
       "      <td>Theater</td>\n",
       "      <td>51.508289</td>\n",
       "      <td>-0.131641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>The Harp</td>\n",
       "      <td>Pub</td>\n",
       "      <td>51.509719</td>\n",
       "      <td>-0.125943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>The Admiralty</td>\n",
       "      <td>Pub</td>\n",
       "      <td>51.507303</td>\n",
       "      <td>-0.128847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Horse Guards Parade</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>51.504847</td>\n",
       "      <td>-0.126590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mall Galleries</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>51.506370</td>\n",
       "      <td>-0.129776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Pret A Manger</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>51.508114</td>\n",
       "      <td>-0.129910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Gordon's Wine Bar</td>\n",
       "      <td>Wine Bar</td>\n",
       "      <td>51.507911</td>\n",
       "      <td>-0.123293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ICA Cinema</td>\n",
       "      <td>Indie Movie Theater</td>\n",
       "      <td>51.506018</td>\n",
       "      <td>-0.131068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Café In The Crypt</td>\n",
       "      <td>Café</td>\n",
       "      <td>51.508962</td>\n",
       "      <td>-0.126729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Terroirs</td>\n",
       "      <td>Wine Bar</td>\n",
       "      <td>51.509547</td>\n",
       "      <td>-0.125250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Sofitel London St James</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>51.507636</td>\n",
       "      <td>-0.132367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Dover Street Market</td>\n",
       "      <td>Boutique</td>\n",
       "      <td>51.508981</td>\n",
       "      <td>-0.131867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>The Royal Horseguards</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>51.506127</td>\n",
       "      <td>-0.124224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Amorino</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>51.510427</td>\n",
       "      <td>-0.128401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Five Guys</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>51.508520</td>\n",
       "      <td>-0.124363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Whitehall Gardens</td>\n",
       "      <td>Garden</td>\n",
       "      <td>51.506354</td>\n",
       "      <td>-0.122900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Japan Centre</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>51.509761</td>\n",
       "      <td>-0.131273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Brumus</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>51.508198</td>\n",
       "      <td>-0.131485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Leicester Square Garden</td>\n",
       "      <td>Garden</td>\n",
       "      <td>51.510361</td>\n",
       "      <td>-0.130127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>St. Martin's Lane Hotel</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>51.510526</td>\n",
       "      <td>-0.126705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>The London Beer House</td>\n",
       "      <td>Bar</td>\n",
       "      <td>51.508214</td>\n",
       "      <td>-0.132578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Ole &amp; Steen</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>51.509219</td>\n",
       "      <td>-0.132597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Harold Pinter Theatre</td>\n",
       "      <td>Theater</td>\n",
       "      <td>51.509443</td>\n",
       "      <td>-0.131644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>So Spa</td>\n",
       "      <td>Spa</td>\n",
       "      <td>51.507601</td>\n",
       "      <td>-0.132690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Steak &amp; Co.</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>51.509923</td>\n",
       "      <td>-0.128494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Milos</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>51.508117</td>\n",
       "      <td>-0.133341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Wahaca</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>51.510221</td>\n",
       "      <td>-0.124524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Prince of Wales Theatre</td>\n",
       "      <td>Theater</td>\n",
       "      <td>51.510132</td>\n",
       "      <td>-0.131594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Leicester Square</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>51.510519</td>\n",
       "      <td>-0.130098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>The LEGO Store</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>51.510497</td>\n",
       "      <td>-0.131001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Victoria Embankment Gardens</td>\n",
       "      <td>Park</td>\n",
       "      <td>51.508135</td>\n",
       "      <td>-0.122079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Retro Bar</td>\n",
       "      <td>Gay Bar</td>\n",
       "      <td>51.509153</td>\n",
       "      <td>-0.123533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>The Z Hotel Piccadilly</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>51.509009</td>\n",
       "      <td>-0.131564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Pall Mall Fine Wine Bar</td>\n",
       "      <td>Wine Bar</td>\n",
       "      <td>51.507660</td>\n",
       "      <td>-0.132015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Noel Coward Theatre</td>\n",
       "      <td>Theater</td>\n",
       "      <td>51.511042</td>\n",
       "      <td>-0.127619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Kanada-Ya</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>51.509359</td>\n",
       "      <td>-0.132215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>La Gelatiera</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>51.511046</td>\n",
       "      <td>-0.126828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Sandwich Centre</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>51.508174</td>\n",
       "      <td>-0.132451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Playhouse Theatre</td>\n",
       "      <td>Theater</td>\n",
       "      <td>51.506798</td>\n",
       "      <td>-0.123479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Scully</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>51.508744</td>\n",
       "      <td>-0.133083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Superdrug</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>51.509174</td>\n",
       "      <td>-0.124054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Gymbox</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>51.510468</td>\n",
       "      <td>-0.126803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Guards Memorial</td>\n",
       "      <td>Monument / Landmark</td>\n",
       "      <td>51.504595</td>\n",
       "      <td>-0.129438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>The Alchemist</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>51.511458</td>\n",
       "      <td>-0.126955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>St James Bar</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>51.507442</td>\n",
       "      <td>-0.132402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Lao Café</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>51.509987</td>\n",
       "      <td>-0.124891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Flat Iron</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>51.510841</td>\n",
       "      <td>-0.124093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Machiya</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>51.509413</td>\n",
       "      <td>-0.131994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>The Royal Trafalgar by Thistle</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>51.509087</td>\n",
       "      <td>-0.130328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Adelphi Theatre</td>\n",
       "      <td>Theater</td>\n",
       "      <td>51.510383</td>\n",
       "      <td>-0.123068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Wyndham's Theatre</td>\n",
       "      <td>Theater</td>\n",
       "      <td>51.511072</td>\n",
       "      <td>-0.127990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Rochelle Canteen at ICA</td>\n",
       "      <td>English Restaurant</td>\n",
       "      <td>51.506258</td>\n",
       "      <td>-0.130640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Amorino</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>51.511510</td>\n",
       "      <td>-0.125794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>M&amp;M's World</td>\n",
       "      <td>Candy Store</td>\n",
       "      <td>51.510599</td>\n",
       "      <td>-0.131149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>The Travellers Club</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>51.506654</td>\n",
       "      <td>-0.132939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>The Espresso Room</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>51.511074</td>\n",
       "      <td>-0.126568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Koshari Street</td>\n",
       "      <td>African Restaurant</td>\n",
       "      <td>51.510889</td>\n",
       "      <td>-0.127005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>wagamama</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>51.510792</td>\n",
       "      <td>-0.124721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prince Charles Cinema</td>\n",
       "      <td>Indie Movie Theater</td>\n",
       "      <td>51.511529</td>\n",
       "      <td>-0.130269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Imperial Treasure Fine Chinese Cuisine</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>51.507935</td>\n",
       "      <td>-0.132977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>The Original London Sightseeing Tour</td>\n",
       "      <td>Tour Provider</td>\n",
       "      <td>51.508011</td>\n",
       "      <td>-0.130052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Byron</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>51.508871</td>\n",
       "      <td>-0.131699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>CoCo Ichibanya</td>\n",
       "      <td>Japanese Curry Restaurant</td>\n",
       "      <td>51.511875</td>\n",
       "      <td>-0.127809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Waterloo Place</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>51.507235</td>\n",
       "      <td>-0.132530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Santa Nata</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>51.511016</td>\n",
       "      <td>-0.126260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Smith &amp; Wollensky</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>51.509557</td>\n",
       "      <td>-0.122241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Sticks'n'Sushi</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>51.511038</td>\n",
       "      <td>-0.123655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Wok to Walk</td>\n",
       "      <td>Noodle House</td>\n",
       "      <td>51.511610</td>\n",
       "      <td>-0.127759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Salt 'n Pepper</td>\n",
       "      <td>Pakistani Restaurant</td>\n",
       "      <td>51.509480</td>\n",
       "      <td>-0.129968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Burger &amp; Lobster</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>51.511029</td>\n",
       "      <td>-0.131605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>The Rose Lounge</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>51.507491</td>\n",
       "      <td>-0.132574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Comptoir Libanais</td>\n",
       "      <td>Lebanese Restaurant</td>\n",
       "      <td>51.508991</td>\n",
       "      <td>-0.132293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Porterhouse</td>\n",
       "      <td>Irish Pub</td>\n",
       "      <td>51.510410</td>\n",
       "      <td>-0.123653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Frenchie</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>51.510908</td>\n",
       "      <td>-0.124035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Paul</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>51.511226</td>\n",
       "      <td>-0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>DW Fitness First</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>51.510077</td>\n",
       "      <td>-0.123848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Aquavit</td>\n",
       "      <td>Modern European Restaurant</td>\n",
       "      <td>51.508812</td>\n",
       "      <td>-0.133335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Sartori</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>51.511784</td>\n",
       "      <td>-0.128021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Haidilao Hot Pot</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>51.510303</td>\n",
       "      <td>-0.132679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Pizza Pilgrims</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>51.511562</td>\n",
       "      <td>-0.126550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Waxy O'Connor's</td>\n",
       "      <td>Irish Pub</td>\n",
       "      <td>51.510715</td>\n",
       "      <td>-0.132489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Five Guys</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>51.511840</td>\n",
       "      <td>-0.126727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Theatre Royal Haymarket</td>\n",
       "      <td>Theater</td>\n",
       "      <td>51.508579</td>\n",
       "      <td>-0.131204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      name                  categories  \\\n",
       "0                         National Gallery                  Art Museum   \n",
       "1                         Trafalgar Square                       Plaza   \n",
       "2           East Trafalgar Square Fountain                    Fountain   \n",
       "3          Sainsbury Wing National Gallery                  Art Museum   \n",
       "4                   Trafalgar Square Lions           Outdoor Sculpture   \n",
       "5                          Nelson's Column         Monument / Landmark   \n",
       "6                  St Martin-in-the-Fields                      Church   \n",
       "7                National Portrait Gallery                 Art Gallery   \n",
       "8                          Corinthia Hotel                       Hotel   \n",
       "9                              Waterstones                   Bookstore   \n",
       "10                               Barrafina          Spanish Restaurant   \n",
       "11    Institute of Contemporary Arts (ICA)                 Art Gallery   \n",
       "12                         Haymarket Hotel                       Hotel   \n",
       "13                             Thai Square             Thai Restaurant   \n",
       "14                      Tandoor Chop House     North Indian Restaurant   \n",
       "15                  English National Opera                 Opera House   \n",
       "16                     The London Coliseum                     Theater   \n",
       "17                   Her Majesty's Theatre                     Theater   \n",
       "18                                The Harp                         Pub   \n",
       "19                           The Admiralty                         Pub   \n",
       "20                     Horse Guards Parade                       Plaza   \n",
       "21                          Mall Galleries                 Art Gallery   \n",
       "22                           Pret A Manger              Sandwich Place   \n",
       "23                       Gordon's Wine Bar                    Wine Bar   \n",
       "24                              ICA Cinema         Indie Movie Theater   \n",
       "25                       Café In The Crypt                        Café   \n",
       "26                                Terroirs                    Wine Bar   \n",
       "27                 Sofitel London St James                       Hotel   \n",
       "28                     Dover Street Market                    Boutique   \n",
       "29                   The Royal Horseguards                       Hotel   \n",
       "30                                 Amorino              Ice Cream Shop   \n",
       "31                               Five Guys                Burger Joint   \n",
       "32                       Whitehall Gardens                      Garden   \n",
       "33                            Japan Centre         Japanese Restaurant   \n",
       "34                                  Brumus           French Restaurant   \n",
       "35                 Leicester Square Garden                      Garden   \n",
       "36                 St. Martin's Lane Hotel                       Hotel   \n",
       "37                   The London Beer House                         Bar   \n",
       "38                             Ole & Steen                      Bakery   \n",
       "39                   Harold Pinter Theatre                     Theater   \n",
       "40                                  So Spa                         Spa   \n",
       "41                             Steak & Co.                  Steakhouse   \n",
       "42                                   Milos            Greek Restaurant   \n",
       "43                                  Wahaca          Mexican Restaurant   \n",
       "44                 Prince of Wales Theatre                     Theater   \n",
       "45                        Leicester Square                       Plaza   \n",
       "46                          The LEGO Store            Toy / Game Store   \n",
       "47             Victoria Embankment Gardens                        Park   \n",
       "48                               Retro Bar                     Gay Bar   \n",
       "49                  The Z Hotel Piccadilly                       Hotel   \n",
       "50                 Pall Mall Fine Wine Bar                    Wine Bar   \n",
       "51                     Noel Coward Theatre                     Theater   \n",
       "52                               Kanada-Ya            Ramen Restaurant   \n",
       "53                            La Gelatiera              Ice Cream Shop   \n",
       "54                         Sandwich Centre              Sandwich Place   \n",
       "55                       Playhouse Theatre                     Theater   \n",
       "56                                  Scully                  Restaurant   \n",
       "57                               Superdrug                    Pharmacy   \n",
       "58                                  Gymbox        Gym / Fitness Center   \n",
       "59                         Guards Memorial         Monument / Landmark   \n",
       "60                           The Alchemist                Cocktail Bar   \n",
       "61                            St James Bar                Cocktail Bar   \n",
       "62                                Lao Café            Asian Restaurant   \n",
       "63                               Flat Iron                  Steakhouse   \n",
       "64                                 Machiya         Japanese Restaurant   \n",
       "65          The Royal Trafalgar by Thistle                       Hotel   \n",
       "66                         Adelphi Theatre                     Theater   \n",
       "67                       Wyndham's Theatre                     Theater   \n",
       "68                 Rochelle Canteen at ICA          English Restaurant   \n",
       "69                                 Amorino              Ice Cream Shop   \n",
       "70                             M&M's World                 Candy Store   \n",
       "71                     The Travellers Club                      Lounge   \n",
       "72                       The Espresso Room                 Coffee Shop   \n",
       "73                          Koshari Street          African Restaurant   \n",
       "74                                wagamama            Asian Restaurant   \n",
       "75                   Prince Charles Cinema         Indie Movie Theater   \n",
       "76  Imperial Treasure Fine Chinese Cuisine          Chinese Restaurant   \n",
       "77    The Original London Sightseeing Tour               Tour Provider   \n",
       "78                                   Byron                Burger Joint   \n",
       "79                          CoCo Ichibanya   Japanese Curry Restaurant   \n",
       "80                          Waterloo Place                       Plaza   \n",
       "81                              Santa Nata                      Bakery   \n",
       "82                       Smith & Wollensky                  Steakhouse   \n",
       "83                          Sticks'n'Sushi            Sushi Restaurant   \n",
       "84                             Wok to Walk                Noodle House   \n",
       "85                          Salt 'n Pepper        Pakistani Restaurant   \n",
       "86                        Burger & Lobster          Seafood Restaurant   \n",
       "87                         The Rose Lounge                    Tea Room   \n",
       "88                       Comptoir Libanais         Lebanese Restaurant   \n",
       "89                             Porterhouse                   Irish Pub   \n",
       "90                                Frenchie           French Restaurant   \n",
       "91                                    Paul                      Bakery   \n",
       "92                        DW Fitness First        Gym / Fitness Center   \n",
       "93                                 Aquavit  Modern European Restaurant   \n",
       "94                                 Sartori          Italian Restaurant   \n",
       "95                        Haidilao Hot Pot          Chinese Restaurant   \n",
       "96                          Pizza Pilgrims                 Pizza Place   \n",
       "97                         Waxy O'Connor's                   Irish Pub   \n",
       "98                               Five Guys                Burger Joint   \n",
       "99                 Theatre Royal Haymarket                     Theater   \n",
       "\n",
       "          lat       lng  \n",
       "0   51.508876 -0.128478  \n",
       "1   51.507987 -0.128048  \n",
       "2   51.508088 -0.127700  \n",
       "3   51.508384 -0.129001  \n",
       "4   51.507641 -0.127888  \n",
       "5   51.507744 -0.127931  \n",
       "6   51.508746 -0.126507  \n",
       "7   51.509438 -0.128032  \n",
       "8   51.506607 -0.124460  \n",
       "9   51.507650 -0.127130  \n",
       "10  51.509427 -0.125894  \n",
       "11  51.506265 -0.130817  \n",
       "12  51.508267 -0.131315  \n",
       "13  51.507656 -0.129830  \n",
       "14  51.509192 -0.125638  \n",
       "15  51.509804 -0.126945  \n",
       "16  51.509846 -0.127005  \n",
       "17  51.508289 -0.131641  \n",
       "18  51.509719 -0.125943  \n",
       "19  51.507303 -0.128847  \n",
       "20  51.504847 -0.126590  \n",
       "21  51.506370 -0.129776  \n",
       "22  51.508114 -0.129910  \n",
       "23  51.507911 -0.123293  \n",
       "24  51.506018 -0.131068  \n",
       "25  51.508962 -0.126729  \n",
       "26  51.509547 -0.125250  \n",
       "27  51.507636 -0.132367  \n",
       "28  51.508981 -0.131867  \n",
       "29  51.506127 -0.124224  \n",
       "30  51.510427 -0.128401  \n",
       "31  51.508520 -0.124363  \n",
       "32  51.506354 -0.122900  \n",
       "33  51.509761 -0.131273  \n",
       "34  51.508198 -0.131485  \n",
       "35  51.510361 -0.130127  \n",
       "36  51.510526 -0.126705  \n",
       "37  51.508214 -0.132578  \n",
       "38  51.509219 -0.132597  \n",
       "39  51.509443 -0.131644  \n",
       "40  51.507601 -0.132690  \n",
       "41  51.509923 -0.128494  \n",
       "42  51.508117 -0.133341  \n",
       "43  51.510221 -0.124524  \n",
       "44  51.510132 -0.131594  \n",
       "45  51.510519 -0.130098  \n",
       "46  51.510497 -0.131001  \n",
       "47  51.508135 -0.122079  \n",
       "48  51.509153 -0.123533  \n",
       "49  51.509009 -0.131564  \n",
       "50  51.507660 -0.132015  \n",
       "51  51.511042 -0.127619  \n",
       "52  51.509359 -0.132215  \n",
       "53  51.511046 -0.126828  \n",
       "54  51.508174 -0.132451  \n",
       "55  51.506798 -0.123479  \n",
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       "58  51.510468 -0.126803  \n",
       "59  51.504595 -0.129438  \n",
       "60  51.511458 -0.126955  \n",
       "61  51.507442 -0.132402  \n",
       "62  51.509987 -0.124891  \n",
       "63  51.510841 -0.124093  \n",
       "64  51.509413 -0.131994  \n",
       "65  51.509087 -0.130328  \n",
       "66  51.510383 -0.123068  \n",
       "67  51.511072 -0.127990  \n",
       "68  51.506258 -0.130640  \n",
       "69  51.511510 -0.125794  \n",
       "70  51.510599 -0.131149  \n",
       "71  51.506654 -0.132939  \n",
       "72  51.511074 -0.126568  \n",
       "73  51.510889 -0.127005  \n",
       "74  51.510792 -0.124721  \n",
       "75  51.511529 -0.130269  \n",
       "76  51.507935 -0.132977  \n",
       "77  51.508011 -0.130052  \n",
       "78  51.508871 -0.131699  \n",
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       "80  51.507235 -0.132530  \n",
       "81  51.511016 -0.126260  \n",
       "82  51.509557 -0.122241  \n",
       "83  51.511038 -0.123655  \n",
       "84  51.511610 -0.127759  \n",
       "85  51.509480 -0.129968  \n",
       "86  51.511029 -0.131605  \n",
       "87  51.507491 -0.132574  \n",
       "88  51.508991 -0.132293  \n",
       "89  51.510410 -0.123653  \n",
       "90  51.510908 -0.124035  \n",
       "91  51.511226 -0.125000  \n",
       "92  51.510077 -0.123848  \n",
       "93  51.508812 -0.133335  \n",
       "94  51.511784 -0.128021  \n",
       "95  51.510303 -0.132679  \n",
       "96  51.511562 -0.126550  \n",
       "97  51.510715 -0.132489  \n",
       "98  51.511840 -0.126727  \n",
       "99  51.508579 -0.131204  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the needed data and put it in a pandas dataframe\n",
    "restaurants = results2['response']['groups'][0]['items']\n",
    "    \n",
    "restaurants_df = json_normalize(restaurants) # flatten JSON\n",
    "\n",
    "# filter columns\n",
    "filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
    "restaurants_df = restaurants_df.loc[:, filtered_columns]\n",
    "\n",
    "# filter the category for each row\n",
    "restaurants_df['venue.categories'] = restaurants_df.apply(get_category_type, axis=1)\n",
    "\n",
    "# clean columns\n",
    "restaurants_df.columns = [col.split(\".\")[-1] for col in restaurants_df.columns]\n",
    "\n",
    "restaurants_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 4)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of venues recieved\n",
    "restaurants_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 4)\n"
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barrafina</td>\n",
       "      <td>Spanish Restaurant</td>\n",
       "      <td>51.509427</td>\n",
       "      <td>-0.125894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thai Square</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>51.507656</td>\n",
       "      <td>-0.129830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tandoor Chop House</td>\n",
       "      <td>North Indian Restaurant</td>\n",
       "      <td>51.509192</td>\n",
       "      <td>-0.125638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Japan Centre</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>51.509761</td>\n",
       "      <td>-0.131273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brumus</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>51.508198</td>\n",
       "      <td>-0.131485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name               categories        lat       lng\n",
       "0           Barrafina       Spanish Restaurant  51.509427 -0.125894\n",
       "1         Thai Square          Thai Restaurant  51.507656 -0.129830\n",
       "2  Tandoor Chop House  North Indian Restaurant  51.509192 -0.125638\n",
       "3        Japan Centre      Japanese Restaurant  51.509761 -0.131273\n",
       "4              Brumus        French Restaurant  51.508198 -0.131485"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#keeping only the venues with categories containing \"Restaurant\"\n",
    "restaurants_df_cleaned = restaurants_df[restaurants_df['categories'].str.contains('Restaurant')].reset_index(drop=True)\n",
    "print(restaurants_df_cleaned.shape) #number of restaurants\n",
    "restaurants_df_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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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 0x15b7f97db38>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a map of restaurants\n",
    "restaurants_map = folium.Map(location=[london_lat, london_lon], zoom_start=15) # generate map centred around London\n",
    "\n",
    "# add the hotels as yellow circle markers\n",
    "for lat, lng, label in zip(restaurants_df_cleaned.lat, restaurants_df_cleaned.lng, restaurants_df_cleaned.categories):\n",
    "    folium.features.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        color='yellow',\n",
    "        popup=label,\n",
    "        fill = True,\n",
    "        fill_color='yellow',\n",
    "        fill_opacity=0.6\n",
    "    ).add_to(restaurants_map)\n",
    "\n",
    "# display map\n",
    "restaurants_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 2)\n"
     ]
    },
    {
     "data": {
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       "\n",
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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>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.509427</td>\n",
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       "      <th>8</th>\n",
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       "      <th>9</th>\n",
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       "      <td>-0.124891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat       lng\n",
       "0  51.509427 -0.125894\n",
       "1  51.507656 -0.129830\n",
       "2  51.509192 -0.125638\n",
       "3  51.509761 -0.131273\n",
       "4  51.508198 -0.131485\n",
       "5  51.508117 -0.133341\n",
       "6  51.510221 -0.124524\n",
       "7  51.509359 -0.132215\n",
       "8  51.508744 -0.133083\n",
       "9  51.509987 -0.124891"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preparing the data that will be used in KNN classification\n",
    "Xhat=restaurants_df_cleaned.loc[:,['lat','lng']] #classification based on the location coordinates\n",
    "print(Xhat.shape) #number of restaurants\n",
    "Xhat.head(10) #show dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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>c_lat</th>\n",
       "      <th>c_lon</th>\n",
       "      <th>cluster_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>51.509792</td>\n",
       "      <td>-0.130766</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.510464</td>\n",
       "      <td>-0.125776</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.506920</td>\n",
       "      <td>-0.125233</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       c_lat     c_lon  cluster_label\n",
       "0  51.509792 -0.130766              0\n",
       "1  51.510464 -0.125776              1\n",
       "2  51.506920 -0.125233              2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the centers dataframe to be used for training KNN\n",
    "centers_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#selecting classification features and target variable\n",
    "X_train = centers_df[['c_lat', 'c_lon']].values\n",
    "Y_train = centers_df['cluster_label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing KNN library\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 1 #classification based on the nearest center\n",
    "#Train Model and Predict  \n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,Y_train)\n",
    "neigh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#classify the restaurants based on the model created\n",
    "yhat = neigh.predict(Xhat)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>class</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>14</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    class\n",
       "0       1\n",
       "1       0\n",
       "2       1\n",
       "3       0\n",
       "4       0\n",
       "5       0\n",
       "6       1\n",
       "7       0\n",
       "8       0\n",
       "9       1\n",
       "10      0\n",
       "11      0\n",
       "12      1\n",
       "13      1\n",
       "14      0\n",
       "15      1\n",
       "16      1\n",
       "17      0\n",
       "18      0\n",
       "19      0\n",
       "20      1\n",
       "21      0\n",
       "22      1\n",
       "23      0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#putting the results in a dataframe\n",
    "KNN_results = pd.DataFrame(yhat, columns=['class'])\n",
    "KNN_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#joining the results with the restaurants dataframe\n",
    "restaurants_classified = pd.concat([restaurants_df_cleaned, KNN_results], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barrafina</td>\n",
       "      <td>Spanish Restaurant</td>\n",
       "      <td>51.509427</td>\n",
       "      <td>-0.125894</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thai Square</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>51.507656</td>\n",
       "      <td>-0.129830</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tandoor Chop House</td>\n",
       "      <td>North Indian Restaurant</td>\n",
       "      <td>51.509192</td>\n",
       "      <td>-0.125638</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Japan Centre</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>51.509761</td>\n",
       "      <td>-0.131273</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brumus</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>51.508198</td>\n",
       "      <td>-0.131485</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Milos</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>51.508117</td>\n",
       "      <td>-0.133341</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wahaca</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>51.510221</td>\n",
       "      <td>-0.124524</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kanada-Ya</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>51.509359</td>\n",
       "      <td>-0.132215</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scully</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>51.508744</td>\n",
       "      <td>-0.133083</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lao Café</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>51.509987</td>\n",
       "      <td>-0.124891</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Machiya</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>51.509413</td>\n",
       "      <td>-0.131994</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rochelle Canteen at ICA</td>\n",
       "      <td>English Restaurant</td>\n",
       "      <td>51.506258</td>\n",
       "      <td>-0.130640</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Koshari Street</td>\n",
       "      <td>African Restaurant</td>\n",
       "      <td>51.510889</td>\n",
       "      <td>-0.127005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>wagamama</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>51.510792</td>\n",
       "      <td>-0.124721</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Imperial Treasure Fine Chinese Cuisine</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>51.507935</td>\n",
       "      <td>-0.132977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CoCo Ichibanya</td>\n",
       "      <td>Japanese Curry Restaurant</td>\n",
       "      <td>51.511875</td>\n",
       "      <td>-0.127809</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sticks'n'Sushi</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>51.511038</td>\n",
       "      <td>-0.123655</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Salt 'n Pepper</td>\n",
       "      <td>Pakistani Restaurant</td>\n",
       "      <td>51.509480</td>\n",
       "      <td>-0.129968</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Burger &amp; Lobster</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>51.511029</td>\n",
       "      <td>-0.131605</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Comptoir Libanais</td>\n",
       "      <td>Lebanese Restaurant</td>\n",
       "      <td>51.508991</td>\n",
       "      <td>-0.132293</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Frenchie</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>51.510908</td>\n",
       "      <td>-0.124035</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aquavit</td>\n",
       "      <td>Modern European Restaurant</td>\n",
       "      <td>51.508812</td>\n",
       "      <td>-0.133335</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sartori</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>51.511784</td>\n",
       "      <td>-0.128021</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Haidilao Hot Pot</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>51.510303</td>\n",
       "      <td>-0.132679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      name                  categories  \\\n",
       "0                                Barrafina          Spanish Restaurant   \n",
       "1                              Thai Square             Thai Restaurant   \n",
       "2                       Tandoor Chop House     North Indian Restaurant   \n",
       "3                             Japan Centre         Japanese Restaurant   \n",
       "4                                   Brumus           French Restaurant   \n",
       "5                                    Milos            Greek Restaurant   \n",
       "6                                   Wahaca          Mexican Restaurant   \n",
       "7                                Kanada-Ya            Ramen Restaurant   \n",
       "8                                   Scully                  Restaurant   \n",
       "9                                 Lao Café            Asian Restaurant   \n",
       "10                                 Machiya         Japanese Restaurant   \n",
       "11                 Rochelle Canteen at ICA          English Restaurant   \n",
       "12                          Koshari Street          African Restaurant   \n",
       "13                                wagamama            Asian Restaurant   \n",
       "14  Imperial Treasure Fine Chinese Cuisine          Chinese Restaurant   \n",
       "15                          CoCo Ichibanya   Japanese Curry Restaurant   \n",
       "16                          Sticks'n'Sushi            Sushi Restaurant   \n",
       "17                          Salt 'n Pepper        Pakistani Restaurant   \n",
       "18                        Burger & Lobster          Seafood Restaurant   \n",
       "19                       Comptoir Libanais         Lebanese Restaurant   \n",
       "20                                Frenchie           French Restaurant   \n",
       "21                                 Aquavit  Modern European Restaurant   \n",
       "22                                 Sartori          Italian Restaurant   \n",
       "23                        Haidilao Hot Pot          Chinese Restaurant   \n",
       "\n",
       "          lat       lng  class  \n",
       "0   51.509427 -0.125894      1  \n",
       "1   51.507656 -0.129830      0  \n",
       "2   51.509192 -0.125638      1  \n",
       "3   51.509761 -0.131273      0  \n",
       "4   51.508198 -0.131485      0  \n",
       "5   51.508117 -0.133341      0  \n",
       "6   51.510221 -0.124524      1  \n",
       "7   51.509359 -0.132215      0  \n",
       "8   51.508744 -0.133083      0  \n",
       "9   51.509987 -0.124891      1  \n",
       "10  51.509413 -0.131994      0  \n",
       "11  51.506258 -0.130640      0  \n",
       "12  51.510889 -0.127005      1  \n",
       "13  51.510792 -0.124721      1  \n",
       "14  51.507935 -0.132977      0  \n",
       "15  51.511875 -0.127809      1  \n",
       "16  51.511038 -0.123655      1  \n",
       "17  51.509480 -0.129968      0  \n",
       "18  51.511029 -0.131605      0  \n",
       "19  51.508991 -0.132293      0  \n",
       "20  51.510908 -0.124035      1  \n",
       "21  51.508812 -0.133335      0  \n",
       "22  51.511784 -0.128021      1  \n",
       "23  51.510303 -0.132679      0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view resulting dataframe\n",
    "restaurants_classified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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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 0x15b7fa2f6d8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show restaurante in each area in different color\n",
    "map_rest_class = folium.Map(location=[london_lat, london_lon], zoom_start=15)\n",
    "\n",
    "# add markers to the map\n",
    "marker_colors = ['red','green','blue']\n",
    "for lat, lon, poi, cluster in zip(restaurants_classified['lat'], restaurants_classified['lng'], restaurants_classified['name'], restaurants_classified['class']):\n",
    "    label = folium.Popup(str(poi) + ' Class ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=marker_colors[cluster],\n",
    "        fill=True,\n",
    "        fill_color=marker_colors[cluster],\n",
    "        fill_opacity=0.7).add_to(map_rest_class)\n",
    "       \n",
    "map_rest_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Finding the suitable spot</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will see what category of restaurants is most trending and desired in center of london. Since our restaurants data represent venues with highest foot-traffic then we will look at the frequency of restaurant category repetition as this are the venues recieving customers. \n",
    "\n",
    "Then we will look at the number of restaurants in each of the three areas to choose the cluster of hotels with the minimum level of competion, as well as the number of hotels in that cluster to correctly evaluate the opportunity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>class</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class  counts\n",
       "0      0      14\n",
       "1      1      10"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count number of restaurants in each area\n",
    "restaurants_count = restaurants_classified['class'].value_counts().rename_axis('class').reset_index(name='counts')\n",
    "restaurants_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>categories</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>North Indian Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Spanish Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>African Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Modern European Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>English Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Japanese Curry Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Lebanese Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Pakistani Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    categories  counts\n",
       "0             Asian Restaurant       2\n",
       "1            French Restaurant       2\n",
       "2          Japanese Restaurant       2\n",
       "3           Chinese Restaurant       2\n",
       "4           Seafood Restaurant       1\n",
       "5      North Indian Restaurant       1\n",
       "6           Italian Restaurant       1\n",
       "7             Greek Restaurant       1\n",
       "8           Spanish Restaurant       1\n",
       "9             Ramen Restaurant       1\n",
       "10          African Restaurant       1\n",
       "11  Modern European Restaurant       1\n",
       "12            Sushi Restaurant       1\n",
       "13          English Restaurant       1\n",
       "14          Mexican Restaurant       1\n",
       "15   Japanese Curry Restaurant       1\n",
       "16         Lebanese Restaurant       1\n",
       "17             Thai Restaurant       1\n",
       "18                  Restaurant       1\n",
       "19        Pakistani Restaurant       1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#counting number of trending restaurants of each unique category\n",
    "trending_restaurants = restaurants_classified['categories'].value_counts().rename_axis('categories').reset_index(name='counts')\n",
    "trending_restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keeping the five most frequent categories\n",
    "trending_restaurants = trending_restaurants.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>categories</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            categories  counts\n",
       "0     Asian Restaurant       2\n",
       "1    French Restaurant       2\n",
       "2  Japanese Restaurant       2\n",
       "3   Chinese Restaurant       2\n",
       "4   Seafood Restaurant       1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show top five categories\n",
    "trending_restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categories</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French Restaurant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japanese Restaurant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seafood Restaurant</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     counts\n",
       "categories                 \n",
       "Asian Restaurant          2\n",
       "French Restaurant         2\n",
       "Japanese Restaurant       2\n",
       "Chinese Restaurant        2\n",
       "Seafood Restaurant        1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make \"categories\" as index for visiualization\n",
    "trending_restaurants = trending_restaurants.set_index('categories')\n",
    "trending_restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing bar charts library\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the top five restaurant categories\n",
    "trending_restaurants.plot(kind='bar', figsize=(10, 6), legend=None)\n",
    "\n",
    "plt.xlabel('Category') # add to x-label to the plot\n",
    "plt.ylabel('Number of restaurants') # add y-label to the plot\n",
    "plt.title('Top five trending restaurants categories in London') # add title to the plot\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>cluster</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster  counts\n",
       "0        0      11\n",
       "1        2       8\n",
       "2        1       6"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#counting hotels in each area to evaluate potential\n",
    "hotels_count = clustered_hotels['cluster_label'].value_counts().rename_axis('cluster').reset_index(name='counts')\n",
    "hotels_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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 },
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}