Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hotels = pd.read_csv(\"data/hotels.csv\", sep=\",\")\n",
    "hotels[\"launch_date\"] = hotels[\"launch_date\"].astype(\"datetime64[ns]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of hotels with handy deployed: 2530\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of hotels with handy deployed: {}\".format(hotels.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launch date range: 2012-12-08 to 2018-10-26\n"
     ]
    }
   ],
   "source": [
    "print(\"Launch date range: {} to {}\".format(min(hotels[\"launch_date\"].dt.date), max(hotels[\"launch_date\"].dt.date)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's see how handy has been growing in terms of hotel deployment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>destination_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>launch_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-28</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-31</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-31</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-30</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-31</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-30</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-31</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-30</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-31</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-28</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-30</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-31</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-30</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-30</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-31</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-31</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-31</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-31</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-30</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-30</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-31</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-31</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31</th>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30</th>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-28</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             destination_id\n",
       "launch_date                \n",
       "2012-12-31                1\n",
       "2013-01-31                0\n",
       "2013-02-28                0\n",
       "2013-03-31                0\n",
       "2013-04-30                0\n",
       "2013-05-31                0\n",
       "2013-06-30                0\n",
       "2013-07-31                0\n",
       "2013-08-31                1\n",
       "2013-09-30                1\n",
       "2013-10-31                0\n",
       "2013-11-30                0\n",
       "2013-12-31                0\n",
       "2014-01-31                1\n",
       "2014-02-28                0\n",
       "2014-03-31                3\n",
       "2014-04-30                2\n",
       "2014-05-31                4\n",
       "2014-06-30                3\n",
       "2014-07-31                0\n",
       "2014-08-31                1\n",
       "2014-09-30                0\n",
       "2014-10-31                1\n",
       "2014-11-30                1\n",
       "2014-12-31                2\n",
       "2015-01-31                6\n",
       "2015-02-28               12\n",
       "2015-03-31               13\n",
       "2015-04-30                7\n",
       "2015-05-31                5\n",
       "...                     ...\n",
       "2016-02-29                7\n",
       "2016-03-31               39\n",
       "2016-04-30               55\n",
       "2016-05-31               35\n",
       "2016-06-30               34\n",
       "2016-07-31               42\n",
       "2016-08-31               35\n",
       "2016-09-30               40\n",
       "2016-10-31               44\n",
       "2016-11-30               40\n",
       "2016-12-31               16\n",
       "2017-01-31               17\n",
       "2017-02-28               12\n",
       "2017-03-31               22\n",
       "2017-04-30               24\n",
       "2017-05-31               14\n",
       "2017-06-30               41\n",
       "2017-07-31               69\n",
       "2017-08-31               99\n",
       "2017-09-30              234\n",
       "2017-10-31              203\n",
       "2017-11-30              222\n",
       "2017-12-31              209\n",
       "2018-01-31              171\n",
       "2018-02-28              200\n",
       "2018-03-31              201\n",
       "2018-04-30              152\n",
       "2018-05-31               79\n",
       "2018-06-30               46\n",
       "2018-07-31               34\n",
       "\n",
       "[68 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from datetime import date\n",
    "\n",
    "before_201808 = hotels[\"launch_date\"] < date(2018, 8, 1)\n",
    "hotels_sum = hotels[before_201808][[\"launch_date\", \"destination_id\"]].set_index(\"launch_date\").resample(\"M\").count()\n",
    "hotels_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd6c828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hotels_cum_sum = hotels_sum.cumsum()\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(16, 9)\n",
    "fig.suptitle(\"Cumulative Hotels by Date\", fontsize=18)\n",
    "plt.xlabel(\"Date\", fontsize=16)\n",
    "plt.ylabel(\"Number of Hotels\", fontsize=16)\n",
    "plt.plot(hotels_cum_sum)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### List of cities with more than 10 hotels deployed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "g186338     208\n",
       "g294265     144\n",
       "g294217     134\n",
       "g187791      81\n",
       "g298566      66\n",
       "g187147      60\n",
       "g298564      45\n",
       "g298560      31\n",
       "g298207      31\n",
       "g1066451     31\n",
       "g274887      30\n",
       "g1066444     29\n",
       "g664891      27\n",
       "g187849      27\n",
       "g293916      25\n",
       "g298224      25\n",
       "g187895      23\n",
       "g274707      22\n",
       "g189400      21\n",
       "g187497      21\n",
       "g293913      20\n",
       "g293974      20\n",
       "g298106      18\n",
       "g190454      18\n",
       "g294458      18\n",
       "g298562      17\n",
       "g298249      16\n",
       "g294197      15\n",
       "g1066443     15\n",
       "g188590      15\n",
       "g298171      15\n",
       "g293797      15\n",
       "g187514      14\n",
       "g1066457     14\n",
       "g188057      12\n",
       "g635613      11\n",
       "g298173      11\n",
       "g1066461     11\n",
       "g298151      11\n",
       "Name: geo_id, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotels[\"geo_id\"].value_counts()[hotels[\"geo_id\"].value_counts() > 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### So where are the handy cities? Without access to TripAdvisor API for the time being, let's try to obtain the list of locations through web crawling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By passing just the `geo_id` after `https://en.tripadvisor.com.hk/Hotels-`, it'll be redirected to the page with list of hotels for that location, e.g. https://en.tripadvisor.com.hk/Hotels-g186338"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![g186338 is London](img/geo_id_redirect.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`g186338`, the city with most number of hotels with handy deployed is London. Now let's hit them all!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import re\n",
    "import csv\n",
    "from tqdm import tqdm\n",
    "\n",
    "with open(\"data/locations.csv\", \"w+\") as f:\n",
    "    writer = csv.writer(f, delimiter=\",\")\n",
    "    writer.writerow([\"geo_id\", \"location\"])\n",
    "\n",
    "    for geo_id in tqdm(hotels[\"geo_id\"].unique()):\n",
    "        r = requests.get(\"https://en.tripadvisor.com.hk/Hotels-\" + geo_id)\n",
    "        # r.url sample : https://en.tripadvisor.com.hk/Hotels-g1010802-Garching_bei_Munchen_Upper_Bavaria_Bavaria-Hotels.html\n",
    "        # extract the location data and replace _ with space, which will be used later in Google Maps search\n",
    "        location = re.match(\"https:\\/\\/en.tripadvisor.com.hk\\/Hotels-g\\d+-(.+)-Hotels.html\", r.url).group(1).replace(\"_\", \" \")\n",
    "        writer.writerow([geo_id, location])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the list for later, which may come handy.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = pd.read_csv(\"data/locations.csv\", sep=\",\")\n",
    "hotels = hotels.merge(locations, on=\"geo_id\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "London England                                              208\n",
       "Singapore                                                   144\n",
       "Hong Kong                                                   134\n",
       "Rome Lazio                                                   81\n",
       "Osaka Osaka Prefecture Kinki                                 66\n",
       "Paris Ile de France                                          60\n",
       "Kyoto Kyoto Prefecture Kinki                                 45\n",
       "Minato Tokyo Tokyo Prefecture Kanto                          31\n",
       "Sapporo Hokkaido                                             31\n",
       "Fukuoka Fukuoka Prefecture Kyushu                            31\n",
       "Budapest Central Hungary                                     30\n",
       "Chuo Tokyo Tokyo Prefecture Kanto                            29\n",
       "Macau                                                        27\n",
       "Milan Lombardy                                               27\n",
       "Bangkok                                                      25\n",
       "Naha Okinawa Prefecture                                      25\n",
       "Florence Tuscany                                             23\n",
       "Prague Bohemia                                               22\n",
       "Barcelona Catalonia                                          21\n",
       "Athens Attica                                                21\n",
       "Istanbul                                                     20\n",
       "Taipei                                                       20\n",
       "Nagoya Aichi Prefecture Tokai Chubu                          18\n",
       "Bucharest                                                    18\n",
       "Vienna                                                       18\n",
       "Kobe Hyogo Prefecture Kinki                                  17\n",
       "Sendai Miyagi Prefecture Tohoku                              16\n",
       "Accra Greater Accra                                          15\n",
       "Chiyoda Tokyo Tokyo Prefecture Kanto                         15\n",
       "Hakone machi Ashigarashimo gun Kanagawa Prefecture Kanto     15\n",
       "Amsterdam North Holland Province                             15\n",
       "Seoul                                                        15\n",
       "Shinjuku Tokyo Tokyo Prefecture Kanto                        14\n",
       "Madrid                                                       14\n",
       "Geneva                                                       12\n",
       "Rhodes Town Rhodes Dodecanese South Aegean                   11\n",
       "Taito Tokyo Tokyo Prefecture Kanto                           11\n",
       "Hakodate Hokkaido                                            11\n",
       "Yokohama Kanagawa Prefecture Kanto                           11\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotels[\"location\"].value_counts()[hotels[\"location\"].value_counts() > 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's plot a heatmap to visualize the handy hotels.. We'll need to obtain the `latitude` and `longitude` for each location in order to plot it with `gmaps`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gmaps\n",
    "\n",
    "with open(\"gmaps.key\", \"r\") as f:\n",
    "    gmaps_key = f.readline()\n",
    "gmaps.configure(api_key=gmaps_key) # Your Google API key\n",
    "\n",
    "def get_lat_long(location):\n",
    "    # sample request for Google Maps Geocoding API:\n",
    "    # https://maps.googleapis.com/maps/api/geocode/json?address=1600+Amphitheatre+Parkway,+Mountain+View,+CA&key=YOUR_API_KEY\n",
    "    try:\n",
    "        r = requests.get(\"https://maps.googleapis.com/maps/api/geocode/json?address=\" + location.replace(\" \", \",\") + \"&key=\" + gmaps_key)\n",
    "        lat = r.json()[\"results\"][0][\"geometry\"][\"location\"][\"lat\"]\n",
    "        long = r.json()[\"results\"][0][\"geometry\"][\"location\"][\"lng\"]\n",
    "        return lat, long\n",
    "    except:\n",
    "        print(location, r.json())    \n",
    "\n",
    "locations[\"latlong\"] = locations[\"location\"].apply(get_lat_long)\n",
    "locations[\"lat\"] = locations[\"latlong\"].apply(pd.Series)[0]\n",
    "locations[\"long\"] = locations[\"latlong\"].apply(pd.Series)[1]\n",
    "locations.drop([\"latlong\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "hotel_counts = hotels[\"geo_id\"].value_counts().reset_index()\n",
    "hotel_counts.columns = \"geo_id\", \"hotel_count\"\n",
    "locations = locations.merge(hotel_counts, on=\"geo_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations.to_csv(\"data/locations.csv\", sep=\",\", encoding=\"utf-8\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Locations with handy deployed\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "95c09700d3de424ea68d99fbc3261316",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_locations = locations.dropna()\n",
    "print(\"Locations with handy deployed\")\n",
    "\n",
    "fig = gmaps.figure()\n",
    "fig.add_layer(gmaps.heatmap_layer(valid_locations[[\"lat\", \"long\"]]))\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>geo_id</th>\n",
       "      <th>destination_id</th>\n",
       "      <th>launch_date</th>\n",
       "      <th>location</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>hotel_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>g2361615</td>\n",
       "      <td>d10006000</td>\n",
       "      <td>2018-05-04</td>\n",
       "      <td>Province of Malaga Andalucia</td>\n",
       "      <td>36.721111</td>\n",
       "      <td>-4.421031</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>g187438</td>\n",
       "      <td>d10006000</td>\n",
       "      <td>2018-05-04</td>\n",
       "      <td>Malaga Costa del Sol Province of Malaga Andalucia</td>\n",
       "      <td>36.524807</td>\n",
       "      <td>-5.039054</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>g14135010</td>\n",
       "      <td>d10128482</td>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>Dotombori Chuo Osaka Osaka Prefecture Kinki</td>\n",
       "      <td>34.667643</td>\n",
       "      <td>135.500052</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2186</th>\n",
       "      <td>g298566</td>\n",
       "      <td>d10128482</td>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>Osaka Osaka Prefecture Kinki</td>\n",
       "      <td>34.693738</td>\n",
       "      <td>135.502165</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>g14129743</td>\n",
       "      <td>d10223698</td>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>Akasaka Minato Tokyo Tokyo Prefecture Kanto</td>\n",
       "      <td>35.665529</td>\n",
       "      <td>139.730703</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>g1066451</td>\n",
       "      <td>d10223698</td>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>Minato Tokyo Tokyo Prefecture Kanto</td>\n",
       "      <td>35.658068</td>\n",
       "      <td>139.751599</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>g13806615</td>\n",
       "      <td>d10257221</td>\n",
       "      <td>2017-09-15</td>\n",
       "      <td>Gushan Kaohsiung</td>\n",
       "      <td>22.649587</td>\n",
       "      <td>120.268580</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768</th>\n",
       "      <td>g297908</td>\n",
       "      <td>d10257221</td>\n",
       "      <td>2017-09-15</td>\n",
       "      <td>Kaohsiung</td>\n",
       "      <td>22.627278</td>\n",
       "      <td>120.301435</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>g13808635</td>\n",
       "      <td>d1026343</td>\n",
       "      <td>2017-09-13</td>\n",
       "      <td>Yuchi Nantou</td>\n",
       "      <td>23.875385</td>\n",
       "      <td>120.918783</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>g304160</td>\n",
       "      <td>d1026343</td>\n",
       "      <td>2017-09-13</td>\n",
       "      <td>Nantou</td>\n",
       "      <td>23.960998</td>\n",
       "      <td>120.971864</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>g14129743</td>\n",
       "      <td>d1036724</td>\n",
       "      <td>2018-01-29</td>\n",
       "      <td>Akasaka Minato Tokyo Tokyo Prefecture Kanto</td>\n",
       "      <td>35.665529</td>\n",
       "      <td>139.730703</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>g1066451</td>\n",
       "      <td>d1036724</td>\n",
       "      <td>2018-01-29</td>\n",
       "      <td>Minato Tokyo Tokyo Prefecture Kanto</td>\n",
       "      <td>35.658068</td>\n",
       "      <td>139.751599</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>g14124519</td>\n",
       "      <td>d10441216</td>\n",
       "      <td>2018-03-09</td>\n",
       "      <td>Nakagyo Kyoto Kyoto Prefecture Kinki</td>\n",
       "      <td>35.010425</td>\n",
       "      <td>135.751411</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2137</th>\n",
       "      <td>g298564</td>\n",
       "      <td>d10441216</td>\n",
       "      <td>2018-03-09</td>\n",
       "      <td>Kyoto Kyoto Prefecture Kinki</td>\n",
       "      <td>35.011636</td>\n",
       "      <td>135.768029</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>g298207</td>\n",
       "      <td>d1053951</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>Fukuoka Fukuoka Prefecture Kyushu</td>\n",
       "      <td>33.590355</td>\n",
       "      <td>130.401715</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>g14127491</td>\n",
       "      <td>d1053951</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>Chuo Fukuoka Fukuoka Prefecture Kyushu</td>\n",
       "      <td>33.589216</td>\n",
       "      <td>130.392813</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2121</th>\n",
       "      <td>g298562</td>\n",
       "      <td>d1067557</td>\n",
       "      <td>2018-01-30</td>\n",
       "      <td>Kobe Hyogo Prefecture Kinki</td>\n",
       "      <td>34.690083</td>\n",
       "      <td>135.195511</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>g14127419</td>\n",
       "      <td>d1067557</td>\n",
       "      <td>2018-01-30</td>\n",
       "      <td>Chuo Kobe Hyogo Prefecture Kinki</td>\n",
       "      <td>34.678648</td>\n",
       "      <td>135.174021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>g14129258</td>\n",
       "      <td>d1079523</td>\n",
       "      <td>2018-03-17</td>\n",
       "      <td>Nakahara Kawasaki Kanagawa Prefecture Kanto</td>\n",
       "      <td>35.581360</td>\n",
       "      <td>139.640895</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>g298172</td>\n",
       "      <td>d1079523</td>\n",
       "      <td>2018-03-17</td>\n",
       "      <td>Kawasaki Kanagawa Prefecture Kanto</td>\n",
       "      <td>35.529820</td>\n",
       "      <td>139.702404</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2139</th>\n",
       "      <td>g298564</td>\n",
       "      <td>d1080314</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>Kyoto Kyoto Prefecture Kinki</td>\n",
       "      <td>35.011636</td>\n",
       "      <td>135.768029</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>g14124519</td>\n",
       "      <td>d1080314</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>Nakagyo Kyoto Kyoto Prefecture Kinki</td>\n",
       "      <td>35.010425</td>\n",
       "      <td>135.751411</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>g14127654</td>\n",
       "      <td>d1081427</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>Naniwa Osaka Osaka Prefecture Kinki</td>\n",
       "      <td>34.667643</td>\n",
       "      <td>135.500052</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>g298566</td>\n",
       "      <td>d1081427</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>Osaka Osaka Prefecture Kinki</td>\n",
       "      <td>34.693738</td>\n",
       "      <td>135.502165</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>g14126710</td>\n",
       "      <td>d1082462</td>\n",
       "      <td>2018-04-12</td>\n",
       "      <td>Chuo Sapporo Hokkaido</td>\n",
       "      <td>43.055460</td>\n",
       "      <td>141.340956</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086</th>\n",
       "      <td>g298560</td>\n",
       "      <td>d1082462</td>\n",
       "      <td>2018-04-12</td>\n",
       "      <td>Sapporo Hokkaido</td>\n",
       "      <td>43.062096</td>\n",
       "      <td>141.354376</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2124</th>\n",
       "      <td>g298562</td>\n",
       "      <td>d1083769</td>\n",
       "      <td>2017-10-23</td>\n",
       "      <td>Kobe Hyogo Prefecture Kinki</td>\n",
       "      <td>34.690083</td>\n",
       "      <td>135.195511</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>g14127420</td>\n",
       "      <td>d1083769</td>\n",
       "      <td>2017-10-23</td>\n",
       "      <td>Nagata Kobe Hyogo Prefecture Kinki</td>\n",
       "      <td>34.649544</td>\n",
       "      <td>135.138475</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>g14127491</td>\n",
       "      <td>d1088782</td>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>Chuo Fukuoka Fukuoka Prefecture Kyushu</td>\n",
       "      <td>33.589216</td>\n",
       "      <td>130.392813</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>g298207</td>\n",
       "      <td>d1088782</td>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>Fukuoka Fukuoka Prefecture Kyushu</td>\n",
       "      <td>33.590355</td>\n",
       "      <td>130.401715</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>g1432365</td>\n",
       "      <td>d5962184</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>New Taipei</td>\n",
       "      <td>25.016983</td>\n",
       "      <td>121.462787</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>g13792475</td>\n",
       "      <td>d5962184</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>Banqiao New Taipei</td>\n",
       "      <td>25.011409</td>\n",
       "      <td>121.461842</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>g14127623</td>\n",
       "      <td>d598268</td>\n",
       "      <td>2017-09-27</td>\n",
       "      <td>Chuo Osaka Osaka Prefecture Kinki</td>\n",
       "      <td>34.704068</td>\n",
       "      <td>135.502357</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>g298566</td>\n",
       "      <td>d598268</td>\n",
       "      <td>2017-09-27</td>\n",
       "      <td>Osaka Osaka Prefecture Kinki</td>\n",
       "      <td>34.693738</td>\n",
       "      <td>135.502165</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2524</th>\n",
       "      <td>g799531</td>\n",
       "      <td>d601703</td>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>Lido di Ostia Province of Rome Lazio</td>\n",
       "      <td>41.731118</td>\n",
       "      <td>12.286269</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>g187791</td>\n",
       "      <td>d601703</td>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>Rome Lazio</td>\n",
       "      <td>41.902784</td>\n",
       "      <td>12.496365</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>g1066444</td>\n",
       "      <td>d628577</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Chuo Tokyo Tokyo Prefecture Kanto</td>\n",
       "      <td>35.670651</td>\n",
       "      <td>139.771861</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>g14129573</td>\n",
       "      <td>d628577</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Ginza Chuo Tokyo Tokyo Prefecture Kanto</td>\n",
       "      <td>35.668321</td>\n",
       "      <td>139.764925</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>g1066451</td>\n",
       "      <td>d635033</td>\n",
       "      <td>2017-09-25</td>\n",
       "      <td>Minato Tokyo Tokyo Prefecture Kanto</td>\n",
       "      <td>35.658068</td>\n",
       "      <td>139.751599</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>g14129743</td>\n",
       "      <td>d635033</td>\n",
       "      <td>2017-09-25</td>\n",
       "      <td>Akasaka Minato Tokyo Tokyo Prefecture Kanto</td>\n",
       "      <td>35.665529</td>\n",
       "      <td>139.730703</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2175</th>\n",
       "      <td>g298564</td>\n",
       "      <td>d639822</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>Kyoto Kyoto Prefecture Kinki</td>\n",
       "      <td>35.011636</td>\n",
       "      <td>135.768029</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>g14124535</td>\n",
       "      <td>d639822</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>Fushimi Kyoto Kyoto Prefecture Kinki</td>\n",
       "      <td>34.936029</td>\n",
       "      <td>135.761410</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>g293913</td>\n",
       "      <td>d662919</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>25.032969</td>\n",
       "      <td>121.565418</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>g13808515</td>\n",
       "      <td>d662919</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>Xinyi District Taipei</td>\n",
       "      <td>25.028702</td>\n",
       "      <td>121.576957</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775</th>\n",
       "      <td>g297910</td>\n",
       "      <td>d6847710</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>Taichung</td>\n",
       "      <td>24.147736</td>\n",
       "      <td>120.673648</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>g13808397</td>\n",
       "      <td>d6847710</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>West District Taichung</td>\n",
       "      <td>24.143060</td>\n",
       "      <td>120.663129</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>g13806900</td>\n",
       "      <td>d7102692</td>\n",
       "      <td>2017-09-27</td>\n",
       "      <td>Songshan Taipei</td>\n",
       "      <td>25.054159</td>\n",
       "      <td>121.563862</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>g293913</td>\n",
       "      <td>d7102692</td>\n",
       "      <td>2017-09-27</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>25.032969</td>\n",
       "      <td>121.565418</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>g1066444</td>\n",
       "      <td>d7684080</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>Chuo Tokyo Tokyo Prefecture Kanto</td>\n",
       "      <td>35.670651</td>\n",
       "      <td>139.771861</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>g14129573</td>\n",
       "      <td>d7684080</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>Ginza Chuo Tokyo Tokyo Prefecture Kanto</td>\n",
       "      <td>35.668321</td>\n",
       "      <td>139.764925</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>g293913</td>\n",
       "      <td>d7840139</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>25.032969</td>\n",
       "      <td>121.565418</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>g13808671</td>\n",
       "      <td>d7840139</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>Zhongshan District Taipei</td>\n",
       "      <td>25.079202</td>\n",
       "      <td>121.542709</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>g14124512</td>\n",
       "      <td>d8616610</td>\n",
       "      <td>2018-01-25</td>\n",
       "      <td>Shimogyo Kyoto Kyoto Prefecture Kinki</td>\n",
       "      <td>34.998821</td>\n",
       "      <td>135.760969</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2179</th>\n",
       "      <td>g298564</td>\n",
       "      <td>d8616610</td>\n",
       "      <td>2018-01-25</td>\n",
       "      <td>Kyoto Kyoto Prefecture Kinki</td>\n",
       "      <td>35.011636</td>\n",
       "      <td>135.768029</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>g13811269</td>\n",
       "      <td>d8706688</td>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>Da an Taipei</td>\n",
       "      <td>25.026158</td>\n",
       "      <td>121.542709</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>g293913</td>\n",
       "      <td>d8706688</td>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>25.032969</td>\n",
       "      <td>121.565418</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>g13808513</td>\n",
       "      <td>d8774286</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>Xinxing Kaohsiung</td>\n",
       "      <td>22.628389</td>\n",
       "      <td>120.306071</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771</th>\n",
       "      <td>g297908</td>\n",
       "      <td>d8774286</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>Kaohsiung</td>\n",
       "      <td>22.627278</td>\n",
       "      <td>120.301435</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>g13806753</td>\n",
       "      <td>d9723470</td>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>Nangang Taipei</td>\n",
       "      <td>25.031235</td>\n",
       "      <td>121.611195</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>g293913</td>\n",
       "      <td>d9723470</td>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>25.032969</td>\n",
       "      <td>121.565418</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         geo_id destination_id launch_date  \\\n",
       "1198   g2361615      d10006000  2018-05-04   \n",
       "805     g187438      d10006000  2018-05-04   \n",
       "420   g14135010      d10128482  2017-12-23   \n",
       "2186    g298566      d10128482  2017-12-23   \n",
       "410   g14129743      d10223698  2018-02-28   \n",
       "140    g1066451      d10223698  2018-02-28   \n",
       "332   g13806615      d10257221  2017-09-15   \n",
       "1768    g297908      d10257221  2017-09-15   \n",
       "350   g13808635       d1026343  2017-09-13   \n",
       "2289    g304160       d1026343  2017-09-13   \n",
       "411   g14129743       d1036724  2018-01-29   \n",
       "141    g1066451       d1036724  2018-01-29   \n",
       "364   g14124519      d10441216  2018-03-09   \n",
       "2137    g298564      d10441216  2018-03-09   \n",
       "1940    g298207       d1053951  2018-03-05   \n",
       "384   g14127491       d1053951  2018-03-05   \n",
       "2121    g298562       d1067557  2018-01-30   \n",
       "380   g14127419       d1067557  2018-01-30   \n",
       "402   g14129258       d1079523  2018-03-17   \n",
       "1897    g298172       d1079523  2018-03-17   \n",
       "2139    g298564       d1080314  2017-11-22   \n",
       "365   g14124519       d1080314  2017-11-22   \n",
       "396   g14127654       d1081427  2017-12-31   \n",
       "2191    g298566       d1081427  2017-12-31   \n",
       "369   g14126710       d1082462  2018-04-12   \n",
       "2086    g298560       d1082462  2018-04-12   \n",
       "2124    g298562       d1083769  2017-10-23   \n",
       "382   g14127420       d1083769  2017-10-23   \n",
       "385   g14127491       d1088782  2018-03-29   \n",
       "1949    g298207       d1088782  2018-03-29   \n",
       "...         ...            ...         ...   \n",
       "424    g1432365       d5962184  2017-11-29   \n",
       "323   g13792475       d5962184  2017-11-29   \n",
       "395   g14127623        d598268  2017-09-27   \n",
       "2250    g298566        d598268  2017-09-27   \n",
       "2524    g799531        d601703  2016-07-25   \n",
       "926     g187791        d601703  2016-07-25   \n",
       "125    g1066444        d628577  2018-03-31   \n",
       "408   g14129573        d628577  2018-03-31   \n",
       "167    g1066451        d635033  2017-09-25   \n",
       "413   g14129743        d635033  2017-09-25   \n",
       "2175    g298564        d639822  2018-02-01   \n",
       "367   g14124535        d639822  2018-02-01   \n",
       "1325    g293913        d662919  2017-11-25   \n",
       "346   g13808515        d662919  2017-11-25   \n",
       "1775    g297910       d6847710  2017-09-14   \n",
       "343   g13808397       d6847710  2017-09-14   \n",
       "340   g13806900       d7102692  2017-09-27   \n",
       "1326    g293913       d7102692  2017-09-27   \n",
       "130    g1066444       d7684080  2017-11-06   \n",
       "409   g14129573       d7684080  2017-11-06   \n",
       "1327    g293913       d7840139  2017-08-16   \n",
       "356   g13808671       d7840139  2017-08-16   \n",
       "363   g14124512       d8616610  2018-01-25   \n",
       "2179    g298564       d8616610  2018-01-25   \n",
       "361   g13811269       d8706688  2017-11-28   \n",
       "1328    g293913       d8706688  2017-11-28   \n",
       "345   g13808513       d8774286  2017-07-15   \n",
       "1771    g297908       d8774286  2017-07-15   \n",
       "336   g13806753       d9723470  2017-12-28   \n",
       "1329    g293913       d9723470  2017-12-28   \n",
       "\n",
       "                                               location        lat  \\\n",
       "1198                       Province of Malaga Andalucia  36.721111   \n",
       "805   Malaga Costa del Sol Province of Malaga Andalucia  36.524807   \n",
       "420         Dotombori Chuo Osaka Osaka Prefecture Kinki  34.667643   \n",
       "2186                       Osaka Osaka Prefecture Kinki  34.693738   \n",
       "410         Akasaka Minato Tokyo Tokyo Prefecture Kanto  35.665529   \n",
       "140                 Minato Tokyo Tokyo Prefecture Kanto  35.658068   \n",
       "332                                    Gushan Kaohsiung  22.649587   \n",
       "1768                                          Kaohsiung  22.627278   \n",
       "350                                        Yuchi Nantou  23.875385   \n",
       "2289                                             Nantou  23.960998   \n",
       "411         Akasaka Minato Tokyo Tokyo Prefecture Kanto  35.665529   \n",
       "141                 Minato Tokyo Tokyo Prefecture Kanto  35.658068   \n",
       "364                Nakagyo Kyoto Kyoto Prefecture Kinki  35.010425   \n",
       "2137                       Kyoto Kyoto Prefecture Kinki  35.011636   \n",
       "1940                  Fukuoka Fukuoka Prefecture Kyushu  33.590355   \n",
       "384              Chuo Fukuoka Fukuoka Prefecture Kyushu  33.589216   \n",
       "2121                        Kobe Hyogo Prefecture Kinki  34.690083   \n",
       "380                    Chuo Kobe Hyogo Prefecture Kinki  34.678648   \n",
       "402         Nakahara Kawasaki Kanagawa Prefecture Kanto  35.581360   \n",
       "1897                 Kawasaki Kanagawa Prefecture Kanto  35.529820   \n",
       "2139                       Kyoto Kyoto Prefecture Kinki  35.011636   \n",
       "365                Nakagyo Kyoto Kyoto Prefecture Kinki  35.010425   \n",
       "396                 Naniwa Osaka Osaka Prefecture Kinki  34.667643   \n",
       "2191                       Osaka Osaka Prefecture Kinki  34.693738   \n",
       "369                               Chuo Sapporo Hokkaido  43.055460   \n",
       "2086                                   Sapporo Hokkaido  43.062096   \n",
       "2124                        Kobe Hyogo Prefecture Kinki  34.690083   \n",
       "382                  Nagata Kobe Hyogo Prefecture Kinki  34.649544   \n",
       "385              Chuo Fukuoka Fukuoka Prefecture Kyushu  33.589216   \n",
       "1949                  Fukuoka Fukuoka Prefecture Kyushu  33.590355   \n",
       "...                                                 ...        ...   \n",
       "424                                          New Taipei  25.016983   \n",
       "323                                  Banqiao New Taipei  25.011409   \n",
       "395                   Chuo Osaka Osaka Prefecture Kinki  34.704068   \n",
       "2250                       Osaka Osaka Prefecture Kinki  34.693738   \n",
       "2524               Lido di Ostia Province of Rome Lazio  41.731118   \n",
       "926                                          Rome Lazio  41.902784   \n",
       "125                   Chuo Tokyo Tokyo Prefecture Kanto  35.670651   \n",
       "408             Ginza Chuo Tokyo Tokyo Prefecture Kanto  35.668321   \n",
       "167                 Minato Tokyo Tokyo Prefecture Kanto  35.658068   \n",
       "413         Akasaka Minato Tokyo Tokyo Prefecture Kanto  35.665529   \n",
       "2175                       Kyoto Kyoto Prefecture Kinki  35.011636   \n",
       "367                Fushimi Kyoto Kyoto Prefecture Kinki  34.936029   \n",
       "1325                                             Taipei  25.032969   \n",
       "346                               Xinyi District Taipei  25.028702   \n",
       "1775                                           Taichung  24.147736   \n",
       "343                              West District Taichung  24.143060   \n",
       "340                                     Songshan Taipei  25.054159   \n",
       "1326                                             Taipei  25.032969   \n",
       "130                   Chuo Tokyo Tokyo Prefecture Kanto  35.670651   \n",
       "409             Ginza Chuo Tokyo Tokyo Prefecture Kanto  35.668321   \n",
       "1327                                             Taipei  25.032969   \n",
       "356                           Zhongshan District Taipei  25.079202   \n",
       "363               Shimogyo Kyoto Kyoto Prefecture Kinki  34.998821   \n",
       "2179                       Kyoto Kyoto Prefecture Kinki  35.011636   \n",
       "361                                        Da an Taipei  25.026158   \n",
       "1328                                             Taipei  25.032969   \n",
       "345                                   Xinxing Kaohsiung  22.628389   \n",
       "1771                                          Kaohsiung  22.627278   \n",
       "336                                      Nangang Taipei  25.031235   \n",
       "1329                                             Taipei  25.032969   \n",
       "\n",
       "            long  hotel_count  \n",
       "1198   -4.421031            1  \n",
       "805    -5.039054            1  \n",
       "420   135.500052            2  \n",
       "2186  135.502165           66  \n",
       "410   139.730703            4  \n",
       "140   139.751599           31  \n",
       "332   120.268580            2  \n",
       "1768  120.301435            4  \n",
       "350   120.918783            2  \n",
       "2289  120.971864            2  \n",
       "411   139.730703            4  \n",
       "141   139.751599           31  \n",
       "364   135.751411            3  \n",
       "2137  135.768029           45  \n",
       "1940  130.401715           31  \n",
       "384   130.392813            2  \n",
       "2121  135.195511           17  \n",
       "380   135.174021            2  \n",
       "402   139.640895            2  \n",
       "1897  139.702404            6  \n",
       "2139  135.768029           45  \n",
       "365   135.751411            3  \n",
       "396   135.500052            4  \n",
       "2191  135.502165           66  \n",
       "369   141.340956            5  \n",
       "2086  141.354376           31  \n",
       "2124  135.195511           17  \n",
       "382   135.138475            1  \n",
       "385   130.392813            2  \n",
       "1949  130.401715           31  \n",
       "...          ...          ...  \n",
       "424   121.462787            3  \n",
       "323   121.461842            2  \n",
       "395   135.502357            4  \n",
       "2250  135.502165           66  \n",
       "2524   12.286269            1  \n",
       "926    12.496365           81  \n",
       "125   139.771861           29  \n",
       "408   139.764925            5  \n",
       "167   139.751599           31  \n",
       "413   139.730703            4  \n",
       "2175  135.768029           45  \n",
       "367   135.761410            1  \n",
       "1325  121.565418           20  \n",
       "346   121.576957            1  \n",
       "1775  120.673648            4  \n",
       "343   120.663129            2  \n",
       "340   121.563862            4  \n",
       "1326  121.565418           20  \n",
       "130   139.771861           29  \n",
       "409   139.764925            5  \n",
       "1327  121.565418           20  \n",
       "356   121.542709            5  \n",
       "363   135.760969            2  \n",
       "2179  135.768029           45  \n",
       "361   121.542709            3  \n",
       "1328  121.565418           20  \n",
       "345   120.306071            1  \n",
       "1771  120.301435            4  \n",
       "336   121.611195            2  \n",
       "1329  121.565418           20  \n",
       "\n",
       "[204 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotels[hotels.duplicated(\"destination_id\", keep=False)].sort_values(by=\"destination_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Turns out that `destination_id` is actually unique and is independent of `geo_id`. Which means that `https://en.tripadvisor.com.hk/Hotel_Review-destination_id` actually works."
   ]
  }
 ],
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