Clustering and Analyses of Tokyo Hostels - Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project - The Battle of the Neighborhoods (Week 2)\n",
    "### Applied Data Science Capstone by IBM/Coursera"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "* [Introduction: Business Problem](#introduction)\n",
    "* [Data](#data)\n",
    "* [Methodology](#methodology)\n",
    "* [Analysis](#analysis)\n",
    "* [Results and Discussion](#results)\n",
    "* [Conclusion](#conclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction: Business Problem <a name=\"introduction\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the 2020 Tokyo Olympics coming, the number of tourists planning to go to Tokyo will rise significantly. Many of those tourits don’t know what to expect in Tokyo Neighborhoods or don’t how to choose a hostel that could fill their needs and that could lead to a poor choice that could potentially ruin the entire experience of travelling to another country.\n",
    "\n",
    "We will analyse and divide Tokyo Hostels in Clusters and this report will be targeted to stakeholders interested in using the data for guidance to choose the best hostel to supply \n",
    "its needs or to offer it to it's clients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data <a name=\"data\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on definition of our problem, factors that will influence our decision are:\n",
    "* number and variety of venues around the hostels\n",
    "* distance to the National Olympic Stadium and to the city center\n",
    "* Hostel prices\n",
    "\n",
    "The Following data sources will be needed to extract/generate the required information:\n",
    "* name and location of tokyo's major districts used for reference in our visualization posted by **suvoooo** on [GitHub](https://github.com/suvoooo/Coursera_Capstone/blob/master/Week5/Tokyo_df_Coord.csv) \n",
    "* number of venues and their type and location for every hostel coordinate in a 500m radius will be obtained using **Foursquare API**\n",
    "* a dataset containing Japan Hostels by **HostelWorld** posted by **thatdatastudent**  on [kaggle](https://www.kaggle.com/koki25ando/hostel-world-dataset) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries and data\n",
    "\n",
    "Let's import every library and dataset needed for the project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /opt/conda/envs/Python36\n",
      "\n",
      "  added / updated specs: \n",
      "    - folium=0.5.0\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    ca-certificates-2019.11.28 |       hecc5488_0         145 KB  conda-forge\n",
      "    certifi-2019.11.28         |           py36_0         149 KB  conda-forge\n",
      "    folium-0.5.0               |             py_0          45 KB  conda-forge\n",
      "    branca-0.4.0               |             py_0          26 KB  conda-forge\n",
      "    openssl-1.1.1d             |       h516909a_0         2.1 MB  conda-forge\n",
      "    altair-4.0.1               |             py_0         575 KB  conda-forge\n",
      "    vincent-0.4.4              |             py_1          28 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         3.0 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "    altair:          4.0.1-py_0        conda-forge\n",
      "    branca:          0.4.0-py_0        conda-forge\n",
      "    folium:          0.5.0-py_0        conda-forge\n",
      "    vincent:         0.4.4-py_1        conda-forge\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    ca-certificates: 2019.11.27-0                  --> 2019.11.28-hecc5488_0 conda-forge\n",
      "    certifi:         2019.11.28-py36_0             --> 2019.11.28-py36_0     conda-forge\n",
      "\n",
      "The following packages will be DOWNGRADED:\n",
      "\n",
      "    openssl:         1.1.1d-h7b6447c_3             --> 1.1.1d-h516909a_0     conda-forge\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "ca-certificates-2019 | 145 KB    | ##################################### | 100% \n",
      "certifi-2019.11.28   | 149 KB    | ##################################### | 100% \n",
      "folium-0.5.0         | 45 KB     | ##################################### | 100% \n",
      "branca-0.4.0         | 26 KB     | ##################################### | 100% \n",
      "openssl-1.1.1d       | 2.1 MB    | ##################################### | 100% \n",
      "altair-4.0.1         | 575 KB    | ##################################### | 100% \n",
      "vincent-0.4.4        | 28 KB     | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n"
     ]
    }
   ],
   "source": [
    "# Libraries\n",
    "import types\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from botocore.client import Config\n",
    "from sklearn.cluster import KMeans\n",
    "import requests\n",
    "import ibm_boto3\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from math import sqrt\n",
    "#!conda install -c conda-forge folium=0.5.0 --yes \n",
    "import folium\n",
    "import types\n",
    "import pandas as pd\n",
    "from botocore.client import Config\n",
    "import ibm_boto3\n",
    "import geopy.distance\n",
    "\n",
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share the notebook.\n",
    "client_7a207381b1af4d15b126270709dfd5b8 = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id='swDJ2uE3GTLgHqlF6qmLuM9RVZEM-dAnwtTg7upix0qM',\n",
    "    ibm_auth_endpoint=\"https://iam.ng.bluemix.net/oidc/token\",\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')\n",
    "\n",
    "body = client_7a207381b1af4d15b126270709dfd5b8.get_object(Bucket='courseracapstoneproject-donotdelete-pr-x1dwa7gkvsd5xa',Key='Hostel.csv')['Body']\n",
    "# add missing __iter__ method, so pandas accepts body as file-like object\n",
    "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
    "\n",
    "df_hostel = pd.read_csv(body)\n",
    "\n",
    "body = client_7a207381b1af4d15b126270709dfd5b8.get_object(Bucket='courseracapstoneproject-donotdelete-pr-x1dwa7gkvsd5xa',Key='Tokyo_df_Coord.csv')['Body']\n",
    "# add missing __iter__ method, so pandas accepts body as file-like object\n",
    "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
    "\n",
    "df_tokyo = pd.read_csv(body)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning df_hostel data\n",
    "Now Let's begin cleaning our first dataset, we are going to filter so we end up with only the hostels located in Tokyo, also we will remove every hostel that doesn't have coordinates,categorize the *rating.band* column, remove the first column that worked as an *id* and remove strings from the *Distance* column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filtering City to only show hostels from Tokyo City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hostel = df_hostel[df_hostel.City == 'Tokyo']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing rows where longitude is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hostel = df_hostel[df_hostel['lon'].notna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorizing rating.band column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hostel['rating.band'] = df_hostel['rating.band'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hostel['rating.band'] = df_hostel['rating.band'].replace({'Rating':0,'Good': 1, 'Very Good': 2,'Fabulous':3,'Superb':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hostel = df_hostel.reset_index(drop=True)\n",
    "df_hostel = df_hostel.drop(df_hostel.columns[0],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing string from distance column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\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>hostel.name</th>\n",
       "      <th>City</th>\n",
       "      <th>price.from</th>\n",
       "      <th>distance.from.city.centre</th>\n",
       "      <th>summary.score</th>\n",
       "      <th>rating.band</th>\n",
       "      <th>atmosphere</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>facilities</th>\n",
       "      <th>location.y</th>\n",
       "      <th>security</th>\n",
       "      <th>staff</th>\n",
       "      <th>valueformoney</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3600</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>139.777472</td>\n",
       "      <td>35.697447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&amp;And Hostel Ueno</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2600</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>139.783667</td>\n",
       "      <td>35.712716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&amp;And Hostel-Asakusa North-</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1500</td>\n",
       "      <td>10.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>139.798371</td>\n",
       "      <td>35.727898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1night1980hostel Tokyo</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2100</td>\n",
       "      <td>9.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>139.786950</td>\n",
       "      <td>35.724384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>328 Hostel &amp; Lounge</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3300</td>\n",
       "      <td>16.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>139.745467</td>\n",
       "      <td>35.548044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  hostel.name   City  price.from distance.from.city.centre  \\\n",
       "0       &And Hostel Akihabara  Tokyo        3600                       7.8   \n",
       "1            &And Hostel Ueno  Tokyo        2600                       8.7   \n",
       "2  &And Hostel-Asakusa North-  Tokyo        1500                      10.5   \n",
       "3      1night1980hostel Tokyo  Tokyo        2100                       9.4   \n",
       "4         328 Hostel & Lounge  Tokyo        3300                      16.5   \n",
       "\n",
       "   summary.score  rating.band  atmosphere  cleanliness  facilities  \\\n",
       "0            8.7            3         8.0          7.0         9.0   \n",
       "1            7.4            2         8.0          7.5         7.5   \n",
       "2            9.4            4         9.5          9.5         9.0   \n",
       "3            7.0            2         5.5          8.0         6.0   \n",
       "4            9.3            4         8.7          9.7         9.3   \n",
       "\n",
       "   location.y  security  staff  valueformoney         lon        lat  \n",
       "0         8.0      10.0   10.0            9.0  139.777472  35.697447  \n",
       "1         7.5       7.0    8.0            6.5  139.783667  35.712716  \n",
       "2         9.0       9.5   10.0            9.5  139.798371  35.727898  \n",
       "3         6.0       8.5    8.5            6.5  139.786950  35.724384  \n",
       "4         9.1       9.3    9.7            8.9  139.745467  35.548044  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hostel['Distance'] = df_hostel['Distance'].map(lambda x: x.rstrip('km from city centre'))\n",
    "df_hostel = df_hostel.rename(columns={'Distance': 'distance.from.city.centre'})\n",
    "df_hostel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating distance from hostel to the National Olympic Stadium\n",
    "Let's now calculate the distance from every hostel to the National Olympic Stadium in kilometers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = []\n",
    "for i in range (len(df_hostel)):\n",
    "    stadium_coord = (35.573497306, 139.708830498)\n",
    "    hostel_coord = (df_hostel.iloc[i][14],df_hostel.iloc[i][13])\n",
    "    d.append(geopy.distance.distance(stadium_coord, hostel_coord).km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_distance = pd.Series( (v for v in d) )\n",
    "df_distance = pd.DataFrame(df_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>hostel.name</th>\n",
       "      <th>City</th>\n",
       "      <th>price.from</th>\n",
       "      <th>distance.from.city.centre</th>\n",
       "      <th>summary.score</th>\n",
       "      <th>rating.band</th>\n",
       "      <th>atmosphere</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>facilities</th>\n",
       "      <th>location.y</th>\n",
       "      <th>security</th>\n",
       "      <th>staff</th>\n",
       "      <th>valueformoney</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>distance.from.stadium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3600</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.7</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>139.777472</td>\n",
       "      <td>35.697447</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&amp;And Hostel Ueno</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2600</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>139.783667</td>\n",
       "      <td>35.712716</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&amp;And Hostel-Asakusa North-</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1500</td>\n",
       "      <td>10.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>139.798371</td>\n",
       "      <td>35.727898</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1night1980hostel Tokyo</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2100</td>\n",
       "      <td>9.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>139.786950</td>\n",
       "      <td>35.724384</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>328 Hostel &amp; Lounge</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3300</td>\n",
       "      <td>16.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>139.745467</td>\n",
       "      <td>35.548044</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ace Inn Shinjuku</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2200</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>139.724304</td>\n",
       "      <td>35.692512</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Aizuya Inn</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2000</td>\n",
       "      <td>10.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>139.800993</td>\n",
       "      <td>35.727547</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Akihabara Hotel 3000</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2200</td>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>139.779382</td>\n",
       "      <td>35.697490</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Almond hostel &amp; cafe Shibuya</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2900</td>\n",
       "      <td>2.2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>139.687535</td>\n",
       "      <td>35.670009</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Anne Hostel Asakusabashi</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2000</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>139.789359</td>\n",
       "      <td>35.698945</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Anne Hostel Yokozuna</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1800</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>139.796798</td>\n",
       "      <td>35.695490</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ARTnSHELTER</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2400</td>\n",
       "      <td>10.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>139.742108</td>\n",
       "      <td>35.604242</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AS House (Asakusa Smile)</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1600</td>\n",
       "      <td>10</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>139.800616</td>\n",
       "      <td>35.707363</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Asakusa Hostel Toukaisou</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1300</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>9.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.2</td>\n",
       "      <td>139.790810</td>\n",
       "      <td>35.713177</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Asakusa Ryokan Toukaisou</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3600</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>139.790810</td>\n",
       "      <td>35.713177</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Asakusahotel Wasou</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2000</td>\n",
       "      <td>9.9</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>139.796697</td>\n",
       "      <td>35.711877</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Astro Station</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3500</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>139.703693</td>\n",
       "      <td>35.714396</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Backpackers Hostel K's House Tokyo</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2100</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>139.792253</td>\n",
       "      <td>35.705066</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Beagle Tokyo Hostel &amp; Apartments</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3800</td>\n",
       "      <td>16.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>139.741078</td>\n",
       "      <td>35.550492</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Book And Bed Tokyo Asakusa</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>4100</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.6</td>\n",
       "      <td>139.794981</td>\n",
       "      <td>35.710797</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Book And Bed Tokyo Ikebukuro</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>4100</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>139.708539</td>\n",
       "      <td>35.731081</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bunka Hostel Tokyo</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2200</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.2</td>\n",
       "      <td>139.793220</td>\n",
       "      <td>35.712108</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Capsule Inn Kinshichou</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2600</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>139.811980</td>\n",
       "      <td>35.698122</td>\n",
       "      <td>16.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Capsule Value Kanda</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3000</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>139.770764</td>\n",
       "      <td>35.689595</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Centurion Hotel Residential Akasaka</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2600</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>139.737697</td>\n",
       "      <td>35.673846</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Centurion Ladies Hostel Uenopark (Female only)</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2500</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>139.770129</td>\n",
       "      <td>35.709199</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CITAN Hostel</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2500</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.3</td>\n",
       "      <td>139.781352</td>\n",
       "      <td>35.691571</td>\n",
       "      <td>14.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edo Tokyo Hostel</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1800</td>\n",
       "      <td>15.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>139.863871</td>\n",
       "      <td>35.712953</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Emblem Hostel Nishiarai</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2000</td>\n",
       "      <td>13.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>139.791216</td>\n",
       "      <td>35.778200</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ENAKA Asakusa Central Hostel</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2600</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>139.795301</td>\n",
       "      <td>35.712184</td>\n",
       "      <td>17.3</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",
       "      <td>...</td>\n",
       "      <td>...</td>\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>86</th>\n",
       "      <td>RYOKAN＆HOSTEL WASABI Nippori</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2000</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>139.777723</td>\n",
       "      <td>35.732782</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Sakura Hostel Asakusa</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2400</td>\n",
       "      <td>9.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>139.794826</td>\n",
       "      <td>35.716022</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Sakura Hotel Ikebukuro</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2300</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>139.708803</td>\n",
       "      <td>35.733614</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Sakura Hotel Nippori</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1900</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>139.764175</td>\n",
       "      <td>35.728169</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Sheena and Ippei</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3700</td>\n",
       "      <td>4.2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>139.693004</td>\n",
       "      <td>35.727355</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Shibamata FU-TEN Bed and Local</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3000</td>\n",
       "      <td>18.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>139.877880</td>\n",
       "      <td>35.759783</td>\n",
       "      <td>25.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Space Hostel Tokyo</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2200</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>139.785652</td>\n",
       "      <td>35.718662</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Stayto (former: Oak Hotel)</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2500</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>139.784620</td>\n",
       "      <td>35.711382</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Tadaima Japan Shinjuku Ryokan</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>7600</td>\n",
       "      <td>2.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>139.722998</td>\n",
       "      <td>35.689721</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>The Millennials Shibuya</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>6300</td>\n",
       "      <td>3.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.7</td>\n",
       "      <td>139.699695</td>\n",
       "      <td>35.662197</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>The Prime Pod Ginza Tokyo</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3700</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>139.767087</td>\n",
       "      <td>35.669269</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Toco Tokyo Heritage Hostel</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3000</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.2</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>139.783928</td>\n",
       "      <td>35.722037</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Tokyo Central Youth Hostel</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1003200</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>139.743522</td>\n",
       "      <td>35.700821</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Tokyo Hostel ENISHI</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3300</td>\n",
       "      <td>4.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>139.679436</td>\n",
       "      <td>35.648204</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Tokyo Hostel Fuji</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.2</td>\n",
       "      <td>139.674336</td>\n",
       "      <td>35.692835</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Tokyo House Inn</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3800</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>139.703819</td>\n",
       "      <td>35.699006</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Tokyo Hutte</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3000</td>\n",
       "      <td>11.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>139.815004</td>\n",
       "      <td>35.708883</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Tokyo Sumidagawa Youth Hostel</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2500</td>\n",
       "      <td>8.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>139.789223</td>\n",
       "      <td>35.699131</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>TOKYO-W-INN Asakusa</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2500</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>139.792309</td>\n",
       "      <td>35.708316</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Train Hostel HOKUTOSEI</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2100</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>139.782843</td>\n",
       "      <td>35.694048</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Turn Table Hostel</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>5400</td>\n",
       "      <td>3.8</td>\n",
       "      <td>9.6</td>\n",
       "      <td>4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.3</td>\n",
       "      <td>139.692916</td>\n",
       "      <td>35.655386</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Uno Ueno</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1500</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.6</td>\n",
       "      <td>139.779171</td>\n",
       "      <td>35.708746</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Unplan Kagurazaka</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3200</td>\n",
       "      <td>4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.9</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.6</td>\n",
       "      <td>139.731323</td>\n",
       "      <td>35.705065</td>\n",
       "      <td>14.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>WIRED HOTEL Asakusa</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>5200</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>139.793905</td>\n",
       "      <td>35.715853</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Wise Owl Hostels Shibuya</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2500</td>\n",
       "      <td>4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>139.689774</td>\n",
       "      <td>35.653700</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Wise Owl Hostels Tokyo</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2000</td>\n",
       "      <td>7.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>139.777275</td>\n",
       "      <td>35.674781</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>YADOYA Guesthouse Green</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2300</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.3</td>\n",
       "      <td>139.668125</td>\n",
       "      <td>35.702908</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>YADOYA Guesthouse Orange</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2000</td>\n",
       "      <td>2.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>3</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.4</td>\n",
       "      <td>139.667695</td>\n",
       "      <td>35.706513</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>YAWP! backpackers</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2500</td>\n",
       "      <td>17.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>139.869197</td>\n",
       "      <td>35.752885</td>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Zabutton Hostel</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2900</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>139.742116</td>\n",
       "      <td>35.655470</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        hostel.name   City  price.from  \\\n",
       "0                             &And Hostel Akihabara  Tokyo        3600   \n",
       "1                                  &And Hostel Ueno  Tokyo        2600   \n",
       "2                        &And Hostel-Asakusa North-  Tokyo        1500   \n",
       "3                            1night1980hostel Tokyo  Tokyo        2100   \n",
       "4                               328 Hostel & Lounge  Tokyo        3300   \n",
       "5                                  Ace Inn Shinjuku  Tokyo        2200   \n",
       "6                                        Aizuya Inn  Tokyo        2000   \n",
       "7                              Akihabara Hotel 3000  Tokyo        2200   \n",
       "8                      Almond hostel & cafe Shibuya  Tokyo        2900   \n",
       "9                          Anne Hostel Asakusabashi  Tokyo        2000   \n",
       "10                             Anne Hostel Yokozuna  Tokyo        1800   \n",
       "11                                      ARTnSHELTER  Tokyo        2400   \n",
       "12                         AS House (Asakusa Smile)  Tokyo        1600   \n",
       "13                         Asakusa Hostel Toukaisou  Tokyo        1300   \n",
       "14                         Asakusa Ryokan Toukaisou  Tokyo        3600   \n",
       "15                               Asakusahotel Wasou  Tokyo        2000   \n",
       "16                                    Astro Station  Tokyo        3500   \n",
       "17               Backpackers Hostel K's House Tokyo  Tokyo        2100   \n",
       "18                 Beagle Tokyo Hostel & Apartments  Tokyo        3800   \n",
       "19                       Book And Bed Tokyo Asakusa  Tokyo        4100   \n",
       "20                     Book And Bed Tokyo Ikebukuro  Tokyo        4100   \n",
       "21                               Bunka Hostel Tokyo  Tokyo        2200   \n",
       "22                           Capsule Inn Kinshichou  Tokyo        2600   \n",
       "23                              Capsule Value Kanda  Tokyo        3000   \n",
       "24              Centurion Hotel Residential Akasaka  Tokyo        2600   \n",
       "25   Centurion Ladies Hostel Uenopark (Female only)  Tokyo        2500   \n",
       "26                                     CITAN Hostel  Tokyo        2500   \n",
       "27                                 Edo Tokyo Hostel  Tokyo        1800   \n",
       "28                          Emblem Hostel Nishiarai  Tokyo        2000   \n",
       "29                     ENAKA Asakusa Central Hostel  Tokyo        2600   \n",
       "..                                              ...    ...         ...   \n",
       "86                     RYOKAN＆HOSTEL WASABI Nippori  Tokyo        2000   \n",
       "87                            Sakura Hostel Asakusa  Tokyo        2400   \n",
       "88                           Sakura Hotel Ikebukuro  Tokyo        2300   \n",
       "89                             Sakura Hotel Nippori  Tokyo        1900   \n",
       "90                                 Sheena and Ippei  Tokyo        3700   \n",
       "91                   Shibamata FU-TEN Bed and Local  Tokyo        3000   \n",
       "92                               Space Hostel Tokyo  Tokyo        2200   \n",
       "93                       Stayto (former: Oak Hotel)  Tokyo        2500   \n",
       "94                    Tadaima Japan Shinjuku Ryokan  Tokyo        7600   \n",
       "95                          The Millennials Shibuya  Tokyo        6300   \n",
       "96                        The Prime Pod Ginza Tokyo  Tokyo        3700   \n",
       "97                       Toco Tokyo Heritage Hostel  Tokyo        3000   \n",
       "98                       Tokyo Central Youth Hostel  Tokyo     1003200   \n",
       "99                              Tokyo Hostel ENISHI  Tokyo        3300   \n",
       "100                               Tokyo Hostel Fuji  Tokyo        2000   \n",
       "101                                 Tokyo House Inn  Tokyo        3800   \n",
       "102                                     Tokyo Hutte  Tokyo        3000   \n",
       "103                   Tokyo Sumidagawa Youth Hostel  Tokyo        2500   \n",
       "104                             TOKYO-W-INN Asakusa  Tokyo        2500   \n",
       "105                          Train Hostel HOKUTOSEI  Tokyo        2100   \n",
       "106                               Turn Table Hostel  Tokyo        5400   \n",
       "107                                        Uno Ueno  Tokyo        1500   \n",
       "108                               Unplan Kagurazaka  Tokyo        3200   \n",
       "109                             WIRED HOTEL Asakusa  Tokyo        5200   \n",
       "110                        Wise Owl Hostels Shibuya  Tokyo        2500   \n",
       "111                          Wise Owl Hostels Tokyo  Tokyo        2000   \n",
       "112                         YADOYA Guesthouse Green  Tokyo        2300   \n",
       "113                        YADOYA Guesthouse Orange  Tokyo        2000   \n",
       "114                               YAWP! backpackers  Tokyo        2500   \n",
       "115                                 Zabutton Hostel  Tokyo        2900   \n",
       "\n",
       "    distance.from.city.centre  summary.score  rating.band  atmosphere  \\\n",
       "0                         7.8            8.7            3         8.0   \n",
       "1                         8.7            7.4            2         8.0   \n",
       "2                        10.5            9.4            4         9.5   \n",
       "3                         9.4            7.0            2         5.5   \n",
       "4                        16.5            9.3            4         8.7   \n",
       "5                           3            7.7            2         6.7   \n",
       "6                        10.6            8.5            3         8.1   \n",
       "7                           8           10.0            4        10.0   \n",
       "8                         2.2            9.3            4         9.1   \n",
       "9                         8.9            9.1            4         8.8   \n",
       "10                        9.5            9.1            4         8.8   \n",
       "11                       10.5            8.3            3         8.2   \n",
       "12                         10            7.8            2         7.9   \n",
       "13                        9.3            8.9            3         7.9   \n",
       "14                        9.3            8.7            3         8.0   \n",
       "15                        9.9            9.4            4         8.7   \n",
       "16                        3.8            8.4            3         7.1   \n",
       "17                        9.2            9.2            4         9.1   \n",
       "18                       16.1            9.0            4         8.0   \n",
       "19                        9.6            9.1            4         8.7   \n",
       "20                        4.9            8.2            3         8.9   \n",
       "21                        9.5            9.3            4         8.8   \n",
       "22                       10.9            8.6            3         6.0   \n",
       "23                        7.1            8.6            3         8.0   \n",
       "24                        4.5            9.0            4         7.5   \n",
       "25                        7.4            8.9            3         7.7   \n",
       "26                        8.1            9.4            4         9.3   \n",
       "27                       15.8            8.1            3         8.0   \n",
       "28                       13.3            9.3            4         9.5   \n",
       "29                        9.7            9.3            4         8.9   \n",
       "..                        ...            ...          ...         ...   \n",
       "86                        9.1            8.7            3         8.2   \n",
       "87                        9.8            8.6            3         7.7   \n",
       "88                        5.1            8.3            3         7.3   \n",
       "89                        7.8            9.1            4         8.6   \n",
       "90                        4.2            9.1            4        10.0   \n",
       "91                       18.5            4.9            0         2.0   \n",
       "92                        9.1            9.1            4         8.6   \n",
       "93                        8.7            9.0            4         8.3   \n",
       "94                        2.8            8.4            3         8.0   \n",
       "95                        3.1            9.3            4         8.9   \n",
       "96                        7.2            9.0            4         7.9   \n",
       "97                        9.1            9.2            4        10.0   \n",
       "98                        4.8            8.0            3         6.0   \n",
       "99                        4.7            8.6            3         7.5   \n",
       "100                       1.6            9.2            4         8.8   \n",
       "101                       1.5            6.8            1         6.0   \n",
       "102                      11.3            7.1            2         8.0   \n",
       "103                       8.9            7.7            2         6.5   \n",
       "104                       9.3            8.9            3         8.2   \n",
       "105                       8.3            8.7            3         7.4   \n",
       "106                       3.8            9.6            4         9.4   \n",
       "107                       8.2            8.8            3         7.8   \n",
       "108                         4            9.5            4         9.2   \n",
       "109                       9.7            9.5            4         8.9   \n",
       "110                         4            9.1            4         8.9   \n",
       "111                       7.9            9.1            4         8.4   \n",
       "112                       2.6            8.2            3         7.9   \n",
       "113                       2.9            8.9            3         8.6   \n",
       "114                      17.5            9.3            4         9.5   \n",
       "115                       5.9            8.6            3         8.1   \n",
       "\n",
       "     cleanliness  facilities  location.y  security  staff  valueformoney  \\\n",
       "0            7.0         9.0         8.0      10.0   10.0            9.0   \n",
       "1            7.5         7.5         7.5       7.0    8.0            6.5   \n",
       "2            9.5         9.0         9.0       9.5   10.0            9.5   \n",
       "3            8.0         6.0         6.0       8.5    8.5            6.5   \n",
       "4            9.7         9.3         9.1       9.3    9.7            8.9   \n",
       "5            7.2         6.8         8.5       7.8    8.5            8.1   \n",
       "6            8.3         8.4         7.8       8.9    9.1            8.9   \n",
       "7           10.0        10.0        10.0      10.0   10.0           10.0   \n",
       "8            9.5         8.8         9.5       9.4    9.7            9.0   \n",
       "9            9.2         8.7         9.0       9.1    9.5            9.2   \n",
       "10           9.1         9.0         9.2       9.3    9.3            9.2   \n",
       "11           8.1         7.7         7.9       9.1    9.0            8.3   \n",
       "12           6.2         7.0         9.0       8.2    8.0            8.3   \n",
       "13           9.4         8.3         9.5       9.2    8.9            9.2   \n",
       "14           8.4         8.4         8.8       8.8    9.4            8.8   \n",
       "15           9.3         9.0         9.7      10.0   10.0            9.3   \n",
       "16           9.1         8.0         9.7       7.1    8.6            8.9   \n",
       "17           9.3         9.0         8.6       9.5    9.6            9.0   \n",
       "18          10.0         9.0         8.0       9.0    9.0           10.0   \n",
       "19           9.7         8.6         9.4       9.5    9.3            8.6   \n",
       "20           8.0         8.0         9.1       7.7    8.6            7.4   \n",
       "21           9.3         9.3         9.5       9.4    9.8            9.2   \n",
       "22          10.0         8.0         8.0      10.0    8.0           10.0   \n",
       "23           8.7         7.3         9.3       8.7    9.3            8.7   \n",
       "24           9.3         9.3         9.3       9.5    8.9            9.0   \n",
       "25           8.9         9.1         9.1       9.3    8.9            9.3   \n",
       "26           9.8         9.5         9.1       9.5    9.6            9.3   \n",
       "27           8.5         8.0         7.5       7.0    8.5            9.0   \n",
       "28           9.8         9.7         7.4       9.8    9.8            9.5   \n",
       "29           9.8         9.3         9.6       9.6    9.1            8.9   \n",
       "..           ...         ...         ...       ...    ...            ...   \n",
       "86           8.6         8.9         8.5       8.8    8.9            8.9   \n",
       "87           8.6         8.4         8.9       9.0    9.0            8.7   \n",
       "88           8.2         7.9         9.0       7.8    9.1            8.8   \n",
       "89           9.4         8.9         9.1       9.7    9.4            8.6   \n",
       "90          10.0         8.0         8.0       8.0   10.0           10.0   \n",
       "91           4.0         2.0         2.0      10.0   10.0            4.0   \n",
       "92           9.6         9.0         8.7       9.2    9.4            9.2   \n",
       "93           9.1         8.5         9.2       9.4    9.5            8.8   \n",
       "94           9.0         9.0         8.0       8.0    9.0            8.0   \n",
       "95          10.0         9.3         9.8       9.5    8.9            8.7   \n",
       "96           9.2         9.2         9.5       9.6    9.0            8.4   \n",
       "97          10.0         7.3         8.7       8.7   10.0           10.0   \n",
       "98          10.0        10.0         8.0       8.0    8.0            6.0   \n",
       "99           9.0         9.0         8.5       9.0    9.0            8.5   \n",
       "100          9.3         9.0         9.0       9.4    9.8            9.2   \n",
       "101          6.7         6.7         8.0       6.0    7.3            6.7   \n",
       "102          6.0         4.0         8.0      10.0    8.0            6.0   \n",
       "103          6.0         8.0         8.0       9.0    9.0            7.5   \n",
       "104          9.2         8.9         8.9       8.8    9.4            9.2   \n",
       "105          9.3         9.0         8.7       8.8    9.2            8.8   \n",
       "106          9.9         9.7         9.7       9.6    9.6            9.3   \n",
       "107          9.3         8.5         8.9       9.2    9.3            8.6   \n",
       "108          9.9         9.6         8.8       9.6    9.8            9.6   \n",
       "109         10.0         9.9         9.1       9.7    9.6            9.5   \n",
       "110          9.6         9.1         8.4       9.6    9.5            8.8   \n",
       "111          9.5         9.1         9.1       9.4    9.3            9.1   \n",
       "112          7.7         6.9         8.9       8.9    8.8            8.3   \n",
       "113          9.0         7.8         9.4       9.0    9.2            9.4   \n",
       "114          9.3         9.4         8.5       9.5    9.2            9.6   \n",
       "115          8.5         7.8         9.0       8.9    9.5            8.5   \n",
       "\n",
       "            lon        lat  distance.from.stadium  \n",
       "0    139.777472  35.697447                   15.1  \n",
       "1    139.783667  35.712716                   16.9  \n",
       "2    139.798371  35.727898                   19.0  \n",
       "3    139.786950  35.724384                   18.2  \n",
       "4    139.745467  35.548044                    4.4  \n",
       "5    139.724304  35.692512                   13.3  \n",
       "6    139.800993  35.727547                   19.0  \n",
       "7    139.779382  35.697490                   15.2  \n",
       "8    139.687535  35.670009                   10.9  \n",
       "9    139.789359  35.698945                   15.7  \n",
       "10   139.796798  35.695490                   15.7  \n",
       "11   139.742108  35.604242                    4.6  \n",
       "12   139.800616  35.707363                   17.0  \n",
       "13   139.790810  35.713177                   17.2  \n",
       "14   139.790810  35.713177                   17.2  \n",
       "15   139.796697  35.711877                   17.3  \n",
       "16   139.703693  35.714396                   15.6  \n",
       "17   139.792253  35.705066                   16.4  \n",
       "18   139.741078  35.550492                    3.9  \n",
       "19   139.794981  35.710797                   17.1  \n",
       "20   139.708539  35.731081                   17.5  \n",
       "21   139.793220  35.712108                   17.2  \n",
       "22   139.811980  35.698122                   16.7  \n",
       "23   139.770764  35.689595                   14.0  \n",
       "24   139.737697  35.673846                   11.4  \n",
       "25   139.770129  35.709199                   16.0  \n",
       "26   139.781352  35.691571                   14.7  \n",
       "27   139.863871  35.712953                   20.9  \n",
       "28   139.791216  35.778200                   23.9  \n",
       "29   139.795301  35.712184                   17.3  \n",
       "..          ...        ...                    ...  \n",
       "86   139.777723  35.732782                   18.7  \n",
       "87   139.794826  35.716022                   17.6  \n",
       "88   139.708803  35.733614                   17.8  \n",
       "89   139.764175  35.728169                   17.9  \n",
       "90   139.693004  35.727355                   17.1  \n",
       "91   139.877880  35.759783                   25.7  \n",
       "92   139.785652  35.718662                   17.5  \n",
       "93   139.784620  35.711382                   16.8  \n",
       "94   139.722998  35.689721                   13.0  \n",
       "95   139.699695  35.662197                    9.9  \n",
       "96   139.767087  35.669269                   11.9  \n",
       "97   139.783928  35.722037                   17.8  \n",
       "98   139.743522  35.700821                   14.5  \n",
       "99   139.679436  35.648204                    8.7  \n",
       "100  139.674336  35.692835                   13.6  \n",
       "101  139.703819  35.699006                   13.9  \n",
       "102  139.815004  35.708883                   17.8  \n",
       "103  139.789223  35.699131                   15.7  \n",
       "104  139.792309  35.708316                   16.8  \n",
       "105  139.782843  35.694048                   15.0  \n",
       "106  139.692916  35.655386                    9.2  \n",
       "107  139.779171  35.708746                   16.3  \n",
       "108  139.731323  35.705065                   14.7  \n",
       "109  139.793905  35.715853                   17.6  \n",
       "110  139.689774  35.653700                    9.1  \n",
       "111  139.777275  35.674781                   12.8  \n",
       "112  139.668125  35.702908                   14.8  \n",
       "113  139.667695  35.706513                   15.2  \n",
       "114  139.869197  35.752885                   24.6  \n",
       "115  139.742116  35.655470                    9.6  \n",
       "\n",
       "[116 rows x 16 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hostel = df_hostel.merge(df_distance, left_on = df_hostel.index, right_on = df_distance.index)\n",
    "df_hostel = df_hostel.drop(columns = 'key_0')\n",
    "df_hostel = df_hostel.rename(columns={0: 'distance.from.stadium'})\n",
    "df_hostel['distance.from.stadium'] = round(df_hostel['distance.from.stadium'],1)\n",
    "df_hostel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning df_tokyo data\n",
    "Now let's clean our second dataset, removing unwanted columns and renaming the columns referencing Latitude and Longitude, we are also going to rearrange the order of the columns to better fit our needs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing unnamed, area and population, renaming latitude and longitude and changing column order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>Major_District</th>\n",
       "      <th>Ward</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nagatacho</td>\n",
       "      <td>Chiyoda</td>\n",
       "      <td>139.743469</td>\n",
       "      <td>35.675618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nihonbashi</td>\n",
       "      <td>Chuo</td>\n",
       "      <td>139.774501</td>\n",
       "      <td>35.684058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Odaiba</td>\n",
       "      <td>Minato</td>\n",
       "      <td>139.779403</td>\n",
       "      <td>35.619128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shinjuku</td>\n",
       "      <td>Shinjuku</td>\n",
       "      <td>139.703632</td>\n",
       "      <td>35.693763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hongo</td>\n",
       "      <td>Bunkyō</td>\n",
       "      <td>139.760100</td>\n",
       "      <td>35.708800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Major_District      Ward         lon        lat\n",
       "0      Nagatacho   Chiyoda  139.743469  35.675618\n",
       "1     Nihonbashi      Chuo  139.774501  35.684058\n",
       "2         Odaiba    Minato  139.779403  35.619128\n",
       "3       Shinjuku  Shinjuku  139.703632  35.693763\n",
       "4          Hongo    Bunkyō  139.760100  35.708800"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tokyo = df_tokyo.drop(columns = ['Unnamed: 0','Area_SqKm','Population'])\n",
    "df_tokyo = df_tokyo.rename(columns={'Dist_Latitude': 'lat','Dist_Longitude':'lon'})\n",
    "df_tokyo = df_tokyo.drop([20,21])\n",
    "cols = ['Major_District','Ward','lon','lat']\n",
    "df_tokyo = df_tokyo[cols]\n",
    "df_tokyo = df_tokyo.reset_index(drop=True)\n",
    "df_tokyo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the map using folium\n",
    "Let's create our map using the folium library, first we will create markers for every major district in Tokyo and also for the National Olympic Stadium and then we are going to identify the hostels in the dataset with circle markers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting map location and markers with the red marker being the National Olympic Stadium and the blue one being the Major Disctricts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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            marker_a8d282c105674b7b9c6b4d0adb6f1dcb.bindPopup(popup_fe661ea333464c449a3a91ec93fd174a);

            
        
    

            var marker_1e944f9ccf314839b209733dfffe1183 = L.marker(
                [35.774143,139.681209],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_b01fbdcca6f641f0bf10abc5fa8be64a);
            
    
            var popup_4ab23d97603e457ab38df31da7414e89 = L.popup({maxWidth: '300'});

            
                var html_b4268ab4fa784214b96c6808dc95838f = $('<div id="html_b4268ab4fa784214b96c6808dc95838f" style="width: 100.0%; height: 100.0%;">Itabashi</div>')[0];
                popup_4ab23d97603e457ab38df31da7414e89.setContent(html_b4268ab4fa784214b96c6808dc95838f);
            

            marker_1e944f9ccf314839b209733dfffe1183.bindPopup(popup_4ab23d97603e457ab38df31da7414e89);

            
        
    

            var marker_8d3363935d8e499399c97bb442eb0dd7 = L.marker(
                [35.74836,139.638735],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_b01fbdcca6f641f0bf10abc5fa8be64a);
            
    
            var popup_d39730f13e2d4566a123cdbf6a2c3efe = L.popup({maxWidth: '300'});

            
                var html_62c3066901b344b2a13d2ed92f8ba687 = $('<div id="html_62c3066901b344b2a13d2ed92f8ba687" style="width: 100.0%; height: 100.0%;">Nerima</div>')[0];
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            var marker_b51f866ac8594304b8c97dd6c84ea57d = L.marker(
                [35.6634,139.8731],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_b01fbdcca6f641f0bf10abc5fa8be64a);
            
    
            var popup_cf28c9393d7741fc9210a81285dc6fe1 = L.popup({maxWidth: '300'});

            
                var html_0ccf231929ca4a2f9f0a8e3c1903b6c9 = $('<div id="html_0ccf231929ca4a2f9f0a8e3c1903b6c9" style="width: 100.0%; height: 100.0%;">Kasai</div>')[0];
                popup_cf28c9393d7741fc9210a81285dc6fe1.setContent(html_0ccf231929ca4a2f9f0a8e3c1903b6c9);
            

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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 0x7f21c6ed10f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_tokyo_stadium = folium.Map(location=[35.573497306, 139.708830498],tiles='https://{s}.tile.openstreetmap.fr/osmfr/{z}/{x}/{y}.png',attr=\"https://www.openstreetmap.org/copyright\", zoom_start=11)\n",
    "folium.Marker([35.673497306, 139.708830498], popup='National Olympic Stadium',icon = folium.map.Icon(color='red')).add_to(map_tokyo_stadium)\n",
    "for i in range(0,len(df_tokyo)):\n",
    "    folium.Marker([df_tokyo.iloc[i]['lat'], df_tokyo.iloc[i]['lon']], popup=df_tokyo.iloc[i]['Major_District']).add_to(map_tokyo_stadium)\n",
    "    \n",
    "map_tokyo_stadium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding hostels locations to the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS = false; L_NO_TOUCH = false; L_DISABLE_3D = false;</script>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
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    <link rel="stylesheet" href="https://rawgit.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
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  "noWrap": false,
  "subdomains": "abc"
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                    extraClasses: 'fa-rotate-0'
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                popup_78887eb0aa694f50a724687cd6f67376.setContent(html_01a970c4e36649868f58a4c5ae27b2fa);
            

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                .addTo(map_b01fbdcca6f641f0bf10abc5fa8be64a);
            
    
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                var html_12a729d024a84cc5aadf2b5360b7d074 = $('<div id="html_12a729d024a84cc5aadf2b5360b7d074" style="width: 100.0%; height: 100.0%;">Kinshicho</div>')[0];
                popup_57be348655094231b333ed471421e58c.setContent(html_12a729d024a84cc5aadf2b5360b7d074);
            

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                var html_77cb8058ff4e430697f1eac650ad5a66 = $('<div id="html_77cb8058ff4e430697f1eac650ad5a66" style="width: 100.0%; height: 100.0%;">Kiba</div>')[0];
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                .addTo(map_b01fbdcca6f641f0bf10abc5fa8be64a);
            
    
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                var html_676f197287f04043aecb131d499adccb = $('<div id="html_676f197287f04043aecb131d499adccb" style="width: 100.0%; height: 100.0%;">Shinagawa</div>')[0];
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            var popup_9e3e12b47c064226a8994721dd97d05e = L.popup({maxWidth: '300'});

            
                var html_7e065cb1d2bd46ea9491bdb8e9c8291f = $('<div id="html_7e065cb1d2bd46ea9491bdb8e9c8291f" style="width: 100.0%; height: 100.0%;">Shibuya</div>')[0];
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            var popup_f2ce5bdea2e44bff9219d0c5652a2be9 = L.popup({maxWidth: '300'});

            
                var html_149a74bd69cb44549520cf3e55373694 = $('<div id="html_149a74bd69cb44549520cf3e55373694" style="width: 100.0%; height: 100.0%;">Nakano</div>')[0];
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            marker_55a10824f1b8485a94da1db3b881851e.bindPopup(popup_f2ce5bdea2e44bff9219d0c5652a2be9);

            
        
    

            var marker_423345c424e64295894985c4fada241c = L.marker(
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            var popup_1b5ab2e2774a45c6b39c1b1a4e99ed4a = L.popup({maxWidth: '300'});

            
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            var circle_marker_dfd4241fbe6840dc8fc8530e696af8af = L.circleMarker(
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                var html_df1ca7c14cd6488594a671bc563cbd93 = $('<div id="html_df1ca7c14cd6488594a671bc563cbd93" style="width: 100.0%; height: 100.0%;">YADOYA Guesthouse Orange</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 0x7f21c6ed10f0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add markers to map\n",
    "for lat, lng, label in zip(df_hostel['lat'], df_hostel['lon'], df_hostel['hostel.name']):\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='orange',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_tokyo_stadium)  \n",
    "    \n",
    "map_tokyo_stadium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Foursquare\n",
    "With our data cleaned, let's use Foursquare API to get info on venues near the hostels.\n",
    "\n",
    "We are going to get information of every venue in a 500m radius from the hostels, except those in *Hotel*, *Hostel* and *Motel* categories. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Foursquare credentials are defined in hidden cell bellow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_ID = '###' \n",
    "CLIENT_SECRET = '###' \n",
    "VERSION = '###'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function to get nearby venues with a 500m radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Hostel Name', \n",
    "                  'Hostel Latitude', \n",
    "                  'Hostel Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting venues near the hostels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "&And Hostel Akihabara\n",
      "&And Hostel Ueno\n",
      "&And Hostel-Asakusa North-\n",
      "1night1980hostel Tokyo\n",
      "328 Hostel & Lounge\n",
      "Ace Inn Shinjuku\n",
      "Aizuya Inn\n",
      "Akihabara Hotel 3000\n",
      "Almond hostel & cafe Shibuya\n",
      "Anne Hostel Asakusabashi\n",
      "Anne Hostel Yokozuna\n",
      "ARTnSHELTER\n",
      "AS House (Asakusa Smile)\n",
      "Asakusa Hostel Toukaisou\n",
      "Asakusa Ryokan Toukaisou\n",
      "Asakusahotel Wasou\n",
      "Astro Station\n",
      "Backpackers Hostel K's House Tokyo\n",
      "Beagle Tokyo Hostel & Apartments\n",
      "Book And Bed Tokyo Asakusa\n",
      "Book And Bed Tokyo Ikebukuro\n",
      "Bunka Hostel Tokyo\n",
      "Capsule Inn Kinshichou\n",
      "Capsule Value Kanda\n",
      "Centurion Hotel Residential Akasaka\n",
      "Centurion Ladies Hostel Uenopark (Female only)\n",
      "CITAN Hostel\n",
      "Edo Tokyo Hostel\n",
      "Emblem Hostel Nishiarai\n",
      "ENAKA Asakusa Central Hostel\n",
      "Family & bAKpAK Tokyo Hostel\n",
      "Good Diner Inn Copain\n",
      "GRIDS TOKYO AKIHABARA HOTEL&HOSTEL\n",
      "Grids Tokyo Asakusa-Bashi Hotel&Hostel\n",
      "GRIDS TOKYO NIHOMBASHI EAST HOTEL&HOSTEL\n",
      "Guest House Denchi\n",
      "Guest House Shinagawa-shuku Tokyo\n",
      "Guesthouse Fete\n",
      "Guesthouse Kagaribi\n",
      "Guesthouse Sensu\n",
      "Hikari House\n",
      "Hiromas Hostel in Akiba\n",
      "Hiromas Hostel in Kanda\n",
      "Hiromas Inn  Chitose-Karasuyama\n",
      "Homeikan\n",
      "Hop Step Inn\n",
      "Hostel bedgasm\n",
      "Hostel Chapter Two Tokyo\n",
      "hostel DEN\n",
      "Hostel Pumpkey Tokyo\n",
      "Hostel WAQWAQ Tokyo Ueno Asakusa\n",
      "Hostel＆Café East57 Asakusabashi\n",
      "Hotel Graphy Nezu\n",
      "Hotel&Hostel On the Marks Tokyo Kawasaki\n",
      "Ikidane House\n",
      "Ikidane House Asakusa Hatago\n",
      "Imano Tokyo Ginza Hostel\n",
      "Imano Tokyo Hostel\n",
      "IRORI Nihonbashi Hostel and Kitchen\n",
      "Iza Tokyo Asakusa Guesthouse\n",
      "J's Backpackers\n",
      "Japanize Guest House\n",
      "JGH Tokyo\n",
      "Jimbocho Sakura Hotel\n",
      "Juyoh Hotel\n",
      "Kaisu Hostel\n",
      "Khaosan Tokyo Kabuki\n",
      "Khaosan Tokyo Laboratory\n",
      "Khaosan Tokyo Origami\n",
      "Khaosan Tokyo Samurai\n",
      "Khaosan World Asakusa RYOKAN & HOSTEL\n",
      "Khaosan World Ryogoku Hostel\n",
      "Lyuro Tokyo Kiyosumi -The Share Hotels-\n",
      "MyCUBE by MYSTAYS Asakusa-Kuramae\n",
      "Nadeshiko Hotel Shibuya (Female Only)\n",
      "Nihonbashi Muromachi BAY HOTEL\n",
      "Nui. Hostel & Bar Lounge\n",
      "Oak Hostel Zen\n",
      "Oak Hotel Edo\n",
      "Oakhostel Cabin\n",
      "Oakhostel Fuji\n",
      "Oakhostel Sakura\n",
      "Obi Hostel\n",
      "Planetyze Hostel\n",
      "Quality Hostel K's House Tokyo Oasis\n",
      "Retrometro Backpackers\n",
      "RYOKAN＆HOSTEL WASABI Nippori\n",
      "Sakura Hostel Asakusa\n",
      "Sakura Hotel Ikebukuro\n",
      "Sakura Hotel Nippori\n",
      "Sheena and Ippei\n",
      "Shibamata FU-TEN Bed and Local\n",
      "Space Hostel Tokyo\n",
      "Stayto (former: Oak Hotel)\n",
      "Tadaima Japan Shinjuku Ryokan\n",
      "The Millennials Shibuya\n",
      "The Prime Pod Ginza Tokyo\n",
      "Toco Tokyo Heritage Hostel\n",
      "Tokyo Central Youth Hostel\n",
      "Tokyo Hostel ENISHI\n",
      "Tokyo Hostel Fuji\n",
      "Tokyo House Inn\n",
      "Tokyo Hutte\n",
      "Tokyo Sumidagawa Youth Hostel\n",
      "TOKYO-W-INN Asakusa\n",
      "Train Hostel HOKUTOSEI\n",
      "Turn Table Hostel\n",
      "Uno Ueno\n",
      "Unplan Kagurazaka\n",
      "WIRED HOTEL Asakusa\n",
      "Wise Owl Hostels Shibuya\n",
      "Wise Owl Hostels Tokyo\n",
      "YADOYA Guesthouse Green\n",
      "YADOYA Guesthouse Orange\n",
      "YAWP! backpackers\n",
      "Zabutton Hostel\n"
     ]
    }
   ],
   "source": [
    "LIMIT = 116;\n",
    "hostel_venues = getNearbyVenues(names=df_hostel['hostel.name'],\n",
    "                                   latitudes=df_hostel['lat'],\n",
    "                                   longitudes=df_hostel['lon']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have gathered the necessary data. Let's begin our analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology <a name=\"methodology\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we want to offer our StakeHolder some choices based on hostels data and nearby venues so they will be able to pick the best place to fill their needs.  \n",
    "\n",
    "To do so we have collected the required **data** from our **hostels dataset** and the **location and type of every venue within 500m radius from the hostels using the FourSquare API**.\n",
    "\n",
    "Next, in our analysis, we will find the optmized cluster numbers for our data and create them based on hostels and venues nearby.\n",
    "\n",
    "And finally we are going to compare the clusters and show the most common venues in each of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis <a name=\"analysis\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at how many venues we found for each Hostel"
   ]
  },
  {
   "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>Hostel Latitude</th>\n",
       "      <th>Hostel Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hostel Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&amp;And Hostel Akihabara</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&amp;And Hostel Ueno</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&amp;And Hostel-Asakusa North-</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1night1980hostel Tokyo</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328 Hostel &amp; Lounge</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARTnSHELTER</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS House (Asakusa Smile)</th>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ace Inn Shinjuku</th>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aizuya Inn</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akihabara Hotel 3000</th>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Almond hostel &amp; cafe Shibuya</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anne Hostel Asakusabashi</th>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anne Hostel Yokozuna</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asakusa Hostel Toukaisou</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asakusa Ryokan Toukaisou</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asakusahotel Wasou</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Astro Station</th>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Backpackers Hostel K's House Tokyo</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beagle Tokyo Hostel &amp; Apartments</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Book And Bed Tokyo Asakusa</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Book And Bed Tokyo Ikebukuro</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bunka Hostel Tokyo</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITAN Hostel</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capsule Inn Kinshichou</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capsule Value Kanda</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centurion Hotel Residential Akasaka</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centurion Ladies Hostel Uenopark (Female only)</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENAKA Asakusa Central Hostel</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edo Tokyo Hostel</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emblem Hostel Nishiarai</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sakura Hostel Asakusa</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sakura Hotel Ikebukuro</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sakura Hotel Nippori</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sheena and Ippei</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shibamata FU-TEN Bed and Local</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Space Hostel Tokyo</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stayto (former: Oak Hotel)</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOKYO-W-INN Asakusa</th>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tadaima Japan Shinjuku Ryokan</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Millennials Shibuya</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Prime Pod Ginza Tokyo</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toco Tokyo Heritage Hostel</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo Central Youth Hostel</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo Hostel ENISHI</th>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo Hostel Fuji</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo House Inn</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo Hutte</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo Sumidagawa Youth Hostel</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train Hostel HOKUTOSEI</th>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turn Table Hostel</th>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uno Ueno</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unplan Kagurazaka</th>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIRED HOTEL Asakusa</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wise Owl Hostels Shibuya</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wise Owl Hostels Tokyo</th>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YADOYA Guesthouse Green</th>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YADOYA Guesthouse Orange</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAWP! backpackers</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zabutton Hostel</th>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hostel DEN</th>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Hostel Latitude  \\\n",
       "Hostel Name                                                       \n",
       "&And Hostel Akihabara                                       100   \n",
       "&And Hostel Ueno                                             80   \n",
       "&And Hostel-Asakusa North-                                   39   \n",
       "1night1980hostel Tokyo                                       29   \n",
       "328 Hostel & Lounge                                          60   \n",
       "ARTnSHELTER                                                  33   \n",
       "AS House (Asakusa Smile)                                     64   \n",
       "Ace Inn Shinjuku                                             73   \n",
       "Aizuya Inn                                                   40   \n",
       "Akihabara Hotel 3000                                         99   \n",
       "Almond hostel & cafe Shibuya                                 76   \n",
       "Anne Hostel Asakusabashi                                     54   \n",
       "Anne Hostel Yokozuna                                         83   \n",
       "Asakusa Hostel Toukaisou                                    100   \n",
       "Asakusa Ryokan Toukaisou                                    100   \n",
       "Asakusahotel Wasou                                          100   \n",
       "Astro Station                                                96   \n",
       "Backpackers Hostel K's House Tokyo                           83   \n",
       "Beagle Tokyo Hostel & Apartments                             39   \n",
       "Book And Bed Tokyo Asakusa                                  100   \n",
       "Book And Bed Tokyo Ikebukuro                                100   \n",
       "Bunka Hostel Tokyo                                          100   \n",
       "CITAN Hostel                                                100   \n",
       "Capsule Inn Kinshichou                                      100   \n",
       "Capsule Value Kanda                                         100   \n",
       "Centurion Hotel Residential Akasaka                         100   \n",
       "Centurion Ladies Hostel Uenopark (Female only)              100   \n",
       "ENAKA Asakusa Central Hostel                                100   \n",
       "Edo Tokyo Hostel                                             47   \n",
       "Emblem Hostel Nishiarai                                      45   \n",
       "...                                                         ...   \n",
       "Sakura Hostel Asakusa                                       100   \n",
       "Sakura Hotel Ikebukuro                                      100   \n",
       "Sakura Hotel Nippori                                         51   \n",
       "Sheena and Ippei                                             30   \n",
       "Shibamata FU-TEN Bed and Local                               44   \n",
       "Space Hostel Tokyo                                           47   \n",
       "Stayto (former: Oak Hotel)                                   41   \n",
       "TOKYO-W-INN Asakusa                                          92   \n",
       "Tadaima Japan Shinjuku Ryokan                               100   \n",
       "The Millennials Shibuya                                     100   \n",
       "The Prime Pod Ginza Tokyo                                   100   \n",
       "Toco Tokyo Heritage Hostel                                   83   \n",
       "Tokyo Central Youth Hostel                                  100   \n",
       "Tokyo Hostel ENISHI                                          52   \n",
       "Tokyo Hostel Fuji                                            25   \n",
       "Tokyo House Inn                                             100   \n",
       "Tokyo Hutte                                                  83   \n",
       "Tokyo Sumidagawa Youth Hostel                                51   \n",
       "Train Hostel HOKUTOSEI                                       97   \n",
       "Turn Table Hostel                                            92   \n",
       "Uno Ueno                                                    100   \n",
       "Unplan Kagurazaka                                            75   \n",
       "WIRED HOTEL Asakusa                                         100   \n",
       "Wise Owl Hostels Shibuya                                     71   \n",
       "Wise Owl Hostels Tokyo                                       55   \n",
       "YADOYA Guesthouse Green                                      52   \n",
       "YADOYA Guesthouse Orange                                    100   \n",
       "YAWP! backpackers                                            22   \n",
       "Zabutton Hostel                                              73   \n",
       "hostel DEN                                                   78   \n",
       "\n",
       "                                                Hostel Longitude  Venue  \\\n",
       "Hostel Name                                                               \n",
       "&And Hostel Akihabara                                        100    100   \n",
       "&And Hostel Ueno                                              80     80   \n",
       "&And Hostel-Asakusa North-                                    39     39   \n",
       "1night1980hostel Tokyo                                        29     29   \n",
       "328 Hostel & Lounge                                           60     60   \n",
       "ARTnSHELTER                                                   33     33   \n",
       "AS House (Asakusa Smile)                                      64     64   \n",
       "Ace Inn Shinjuku                                              73     73   \n",
       "Aizuya Inn                                                    40     40   \n",
       "Akihabara Hotel 3000                                          99     99   \n",
       "Almond hostel & cafe Shibuya                                  76     76   \n",
       "Anne Hostel Asakusabashi                                      54     54   \n",
       "Anne Hostel Yokozuna                                          83     83   \n",
       "Asakusa Hostel Toukaisou                                     100    100   \n",
       "Asakusa Ryokan Toukaisou                                     100    100   \n",
       "Asakusahotel Wasou                                           100    100   \n",
       "Astro Station                                                 96     96   \n",
       "Backpackers Hostel K's House Tokyo                            83     83   \n",
       "Beagle Tokyo Hostel & Apartments                              39     39   \n",
       "Book And Bed Tokyo Asakusa                                   100    100   \n",
       "Book And Bed Tokyo Ikebukuro                                 100    100   \n",
       "Bunka Hostel Tokyo                                           100    100   \n",
       "CITAN Hostel                                                 100    100   \n",
       "Capsule Inn Kinshichou                                       100    100   \n",
       "Capsule Value Kanda                                          100    100   \n",
       "Centurion Hotel Residential Akasaka                          100    100   \n",
       "Centurion Ladies Hostel Uenopark (Female only)               100    100   \n",
       "ENAKA Asakusa Central Hostel                                 100    100   \n",
       "Edo Tokyo Hostel                                              47     47   \n",
       "Emblem Hostel Nishiarai                                       45     45   \n",
       "...                                                          ...    ...   \n",
       "Sakura Hostel Asakusa                                        100    100   \n",
       "Sakura Hotel Ikebukuro                                       100    100   \n",
       "Sakura Hotel Nippori                                          51     51   \n",
       "Sheena and Ippei                                              30     30   \n",
       "Shibamata FU-TEN Bed and Local                                44     44   \n",
       "Space Hostel Tokyo                                            47     47   \n",
       "Stayto (former: Oak Hotel)                                    41     41   \n",
       "TOKYO-W-INN Asakusa                                           92     92   \n",
       "Tadaima Japan Shinjuku Ryokan                                100    100   \n",
       "The Millennials Shibuya                                      100    100   \n",
       "The Prime Pod Ginza Tokyo                                    100    100   \n",
       "Toco Tokyo Heritage Hostel                                    83     83   \n",
       "Tokyo Central Youth Hostel                                   100    100   \n",
       "Tokyo Hostel ENISHI                                           52     52   \n",
       "Tokyo Hostel Fuji                                             25     25   \n",
       "Tokyo House Inn                                              100    100   \n",
       "Tokyo Hutte                                                   83     83   \n",
       "Tokyo Sumidagawa Youth Hostel                                 51     51   \n",
       "Train Hostel HOKUTOSEI                                        97     97   \n",
       "Turn Table Hostel                                             92     92   \n",
       "Uno Ueno                                                     100    100   \n",
       "Unplan Kagurazaka                                             75     75   \n",
       "WIRED HOTEL Asakusa                                          100    100   \n",
       "Wise Owl Hostels Shibuya                                      71     71   \n",
       "Wise Owl Hostels Tokyo                                        55     55   \n",
       "YADOYA Guesthouse Green                                       52     52   \n",
       "YADOYA Guesthouse Orange                                     100    100   \n",
       "YAWP! backpackers                                             22     22   \n",
       "Zabutton Hostel                                               73     73   \n",
       "hostel DEN                                                    78     78   \n",
       "\n",
       "                                                Venue Latitude  \\\n",
       "Hostel Name                                                      \n",
       "&And Hostel Akihabara                                      100   \n",
       "&And Hostel Ueno                                            80   \n",
       "&And Hostel-Asakusa North-                                  39   \n",
       "1night1980hostel Tokyo                                      29   \n",
       "328 Hostel & Lounge                                         60   \n",
       "ARTnSHELTER                                                 33   \n",
       "AS House (Asakusa Smile)                                    64   \n",
       "Ace Inn Shinjuku                                            73   \n",
       "Aizuya Inn                                                  40   \n",
       "Akihabara Hotel 3000                                        99   \n",
       "Almond hostel & cafe Shibuya                                76   \n",
       "Anne Hostel Asakusabashi                                    54   \n",
       "Anne Hostel Yokozuna                                        83   \n",
       "Asakusa Hostel Toukaisou                                   100   \n",
       "Asakusa Ryokan Toukaisou                                   100   \n",
       "Asakusahotel Wasou                                         100   \n",
       "Astro Station                                               96   \n",
       "Backpackers Hostel K's House Tokyo                          83   \n",
       "Beagle Tokyo Hostel & Apartments                            39   \n",
       "Book And Bed Tokyo Asakusa                                 100   \n",
       "Book And Bed Tokyo Ikebukuro                               100   \n",
       "Bunka Hostel Tokyo                                         100   \n",
       "CITAN Hostel                                               100   \n",
       "Capsule Inn Kinshichou                                     100   \n",
       "Capsule Value Kanda                                        100   \n",
       "Centurion Hotel Residential Akasaka                        100   \n",
       "Centurion Ladies Hostel Uenopark (Female only)             100   \n",
       "ENAKA Asakusa Central Hostel                               100   \n",
       "Edo Tokyo Hostel                                            47   \n",
       "Emblem Hostel Nishiarai                                     45   \n",
       "...                                                        ...   \n",
       "Sakura Hostel Asakusa                                      100   \n",
       "Sakura Hotel Ikebukuro                                     100   \n",
       "Sakura Hotel Nippori                                        51   \n",
       "Sheena and Ippei                                            30   \n",
       "Shibamata FU-TEN Bed and Local                              44   \n",
       "Space Hostel Tokyo                                          47   \n",
       "Stayto (former: Oak Hotel)                                  41   \n",
       "TOKYO-W-INN Asakusa                                         92   \n",
       "Tadaima Japan Shinjuku Ryokan                              100   \n",
       "The Millennials Shibuya                                    100   \n",
       "The Prime Pod Ginza Tokyo                                  100   \n",
       "Toco Tokyo Heritage Hostel                                  83   \n",
       "Tokyo Central Youth Hostel                                 100   \n",
       "Tokyo Hostel ENISHI                                         52   \n",
       "Tokyo Hostel Fuji                                           25   \n",
       "Tokyo House Inn                                            100   \n",
       "Tokyo Hutte                                                 83   \n",
       "Tokyo Sumidagawa Youth Hostel                               51   \n",
       "Train Hostel HOKUTOSEI                                      97   \n",
       "Turn Table Hostel                                           92   \n",
       "Uno Ueno                                                   100   \n",
       "Unplan Kagurazaka                                           75   \n",
       "WIRED HOTEL Asakusa                                        100   \n",
       "Wise Owl Hostels Shibuya                                    71   \n",
       "Wise Owl Hostels Tokyo                                      55   \n",
       "YADOYA Guesthouse Green                                     52   \n",
       "YADOYA Guesthouse Orange                                   100   \n",
       "YAWP! backpackers                                           22   \n",
       "Zabutton Hostel                                             73   \n",
       "hostel DEN                                                  78   \n",
       "\n",
       "                                                Venue Longitude  \\\n",
       "Hostel Name                                                       \n",
       "&And Hostel Akihabara                                       100   \n",
       "&And Hostel Ueno                                             80   \n",
       "&And Hostel-Asakusa North-                                   39   \n",
       "1night1980hostel Tokyo                                       29   \n",
       "328 Hostel & Lounge                                          60   \n",
       "ARTnSHELTER                                                  33   \n",
       "AS House (Asakusa Smile)                                     64   \n",
       "Ace Inn Shinjuku                                             73   \n",
       "Aizuya Inn                                                   40   \n",
       "Akihabara Hotel 3000                                         99   \n",
       "Almond hostel & cafe Shibuya                                 76   \n",
       "Anne Hostel Asakusabashi                                     54   \n",
       "Anne Hostel Yokozuna                                         83   \n",
       "Asakusa Hostel Toukaisou                                    100   \n",
       "Asakusa Ryokan Toukaisou                                    100   \n",
       "Asakusahotel Wasou                                          100   \n",
       "Astro Station                                                96   \n",
       "Backpackers Hostel K's House Tokyo                           83   \n",
       "Beagle Tokyo Hostel & Apartments                             39   \n",
       "Book And Bed Tokyo Asakusa                                  100   \n",
       "Book And Bed Tokyo Ikebukuro                                100   \n",
       "Bunka Hostel Tokyo                                          100   \n",
       "CITAN Hostel                                                100   \n",
       "Capsule Inn Kinshichou                                      100   \n",
       "Capsule Value Kanda                                         100   \n",
       "Centurion Hotel Residential Akasaka                         100   \n",
       "Centurion Ladies Hostel Uenopark (Female only)              100   \n",
       "ENAKA Asakusa Central Hostel                                100   \n",
       "Edo Tokyo Hostel                                             47   \n",
       "Emblem Hostel Nishiarai                                      45   \n",
       "...                                                         ...   \n",
       "Sakura Hostel Asakusa                                       100   \n",
       "Sakura Hotel Ikebukuro                                      100   \n",
       "Sakura Hotel Nippori                                         51   \n",
       "Sheena and Ippei                                             30   \n",
       "Shibamata FU-TEN Bed and Local                               44   \n",
       "Space Hostel Tokyo                                           47   \n",
       "Stayto (former: Oak Hotel)                                   41   \n",
       "TOKYO-W-INN Asakusa                                          92   \n",
       "Tadaima Japan Shinjuku Ryokan                               100   \n",
       "The Millennials Shibuya                                     100   \n",
       "The Prime Pod Ginza Tokyo                                   100   \n",
       "Toco Tokyo Heritage Hostel                                   83   \n",
       "Tokyo Central Youth Hostel                                  100   \n",
       "Tokyo Hostel ENISHI                                          52   \n",
       "Tokyo Hostel Fuji                                            25   \n",
       "Tokyo House Inn                                             100   \n",
       "Tokyo Hutte                                                  83   \n",
       "Tokyo Sumidagawa Youth Hostel                                51   \n",
       "Train Hostel HOKUTOSEI                                       97   \n",
       "Turn Table Hostel                                            92   \n",
       "Uno Ueno                                                    100   \n",
       "Unplan Kagurazaka                                            75   \n",
       "WIRED HOTEL Asakusa                                         100   \n",
       "Wise Owl Hostels Shibuya                                     71   \n",
       "Wise Owl Hostels Tokyo                                       55   \n",
       "YADOYA Guesthouse Green                                      52   \n",
       "YADOYA Guesthouse Orange                                    100   \n",
       "YAWP! backpackers                                            22   \n",
       "Zabutton Hostel                                              73   \n",
       "hostel DEN                                                   78   \n",
       "\n",
       "                                                Venue Category  \n",
       "Hostel Name                                                     \n",
       "&And Hostel Akihabara                                      100  \n",
       "&And Hostel Ueno                                            80  \n",
       "&And Hostel-Asakusa North-                                  39  \n",
       "1night1980hostel Tokyo                                      29  \n",
       "328 Hostel & Lounge                                         60  \n",
       "ARTnSHELTER                                                 33  \n",
       "AS House (Asakusa Smile)                                    64  \n",
       "Ace Inn Shinjuku                                            73  \n",
       "Aizuya Inn                                                  40  \n",
       "Akihabara Hotel 3000                                        99  \n",
       "Almond hostel & cafe Shibuya                                76  \n",
       "Anne Hostel Asakusabashi                                    54  \n",
       "Anne Hostel Yokozuna                                        83  \n",
       "Asakusa Hostel Toukaisou                                   100  \n",
       "Asakusa Ryokan Toukaisou                                   100  \n",
       "Asakusahotel Wasou                                         100  \n",
       "Astro Station                                               96  \n",
       "Backpackers Hostel K's House Tokyo                          83  \n",
       "Beagle Tokyo Hostel & Apartments                            39  \n",
       "Book And Bed Tokyo Asakusa                                 100  \n",
       "Book And Bed Tokyo Ikebukuro                               100  \n",
       "Bunka Hostel Tokyo                                         100  \n",
       "CITAN Hostel                                               100  \n",
       "Capsule Inn Kinshichou                                     100  \n",
       "Capsule Value Kanda                                        100  \n",
       "Centurion Hotel Residential Akasaka                        100  \n",
       "Centurion Ladies Hostel Uenopark (Female only)             100  \n",
       "ENAKA Asakusa Central Hostel                               100  \n",
       "Edo Tokyo Hostel                                            47  \n",
       "Emblem Hostel Nishiarai                                     45  \n",
       "...                                                        ...  \n",
       "Sakura Hostel Asakusa                                      100  \n",
       "Sakura Hotel Ikebukuro                                     100  \n",
       "Sakura Hotel Nippori                                        51  \n",
       "Sheena and Ippei                                            30  \n",
       "Shibamata FU-TEN Bed and Local                              44  \n",
       "Space Hostel Tokyo                                          47  \n",
       "Stayto (former: Oak Hotel)                                  41  \n",
       "TOKYO-W-INN Asakusa                                         92  \n",
       "Tadaima Japan Shinjuku Ryokan                              100  \n",
       "The Millennials Shibuya                                    100  \n",
       "The Prime Pod Ginza Tokyo                                  100  \n",
       "Toco Tokyo Heritage Hostel                                  83  \n",
       "Tokyo Central Youth Hostel                                 100  \n",
       "Tokyo Hostel ENISHI                                         52  \n",
       "Tokyo Hostel Fuji                                           25  \n",
       "Tokyo House Inn                                            100  \n",
       "Tokyo Hutte                                                 83  \n",
       "Tokyo Sumidagawa Youth Hostel                               51  \n",
       "Train Hostel HOKUTOSEI                                      97  \n",
       "Turn Table Hostel                                           92  \n",
       "Uno Ueno                                                   100  \n",
       "Unplan Kagurazaka                                           75  \n",
       "WIRED HOTEL Asakusa                                        100  \n",
       "Wise Owl Hostels Shibuya                                    71  \n",
       "Wise Owl Hostels Tokyo                                      55  \n",
       "YADOYA Guesthouse Green                                     52  \n",
       "YADOYA Guesthouse Orange                                   100  \n",
       "YAWP! backpackers                                           22  \n",
       "Zabutton Hostel                                             73  \n",
       "hostel DEN                                                  78  \n",
       "\n",
       "[116 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hostel_venues.groupby('Hostel Name').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now Let's manipulate our data for a better analytical approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing Hotel and Hostel from nearby venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "hostel_venues = hostel_venues[hostel_venues['Venue Category'] != 'Hostel'] \n",
    "hostel_venues = hostel_venues[hostel_venues['Venue Category'] != 'Hotel'] \n",
    "hostel_venues = hostel_venues[hostel_venues['Venue Category'] != 'Motel'] \n",
    "hostel_venues = hostel_venues.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 276 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} uniques categories.'.format(len(hostel_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding venues as columns to hostel_venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Hostel Name</th>\n",
       "      <th>ATM</th>\n",
       "      <th>Accessories Store</th>\n",
       "      <th>Adult Boutique</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Antique Shop</th>\n",
       "      <th>Arcade</th>\n",
       "      <th>Argentinian Restaurant</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Art Museum</th>\n",
       "      <th>...</th>\n",
       "      <th>Wagashi Place</th>\n",
       "      <th>Waterfront</th>\n",
       "      <th>Whisky Bar</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Wings Joint</th>\n",
       "      <th>Women's Store</th>\n",
       "      <th>Yakitori Restaurant</th>\n",
       "      <th>Yoshoku Restaurant</th>\n",
       "      <th>Zoo</th>\n",
       "      <th>Zoo Exhibit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 277 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Hostel Name  ATM  Accessories Store  Adult Boutique  \\\n",
       "0  &And Hostel Akihabara    0                  0               0   \n",
       "1  &And Hostel Akihabara    0                  0               0   \n",
       "2  &And Hostel Akihabara    0                  0               0   \n",
       "3  &And Hostel Akihabara    0                  0               0   \n",
       "4  &And Hostel Akihabara    0                  0               0   \n",
       "\n",
       "   American Restaurant  Antique Shop  Arcade  Argentinian Restaurant  \\\n",
       "0                    0             0       0                       0   \n",
       "1                    0             0       0                       0   \n",
       "2                    0             0       0                       0   \n",
       "3                    0             0       0                       0   \n",
       "4                    0             0       0                       0   \n",
       "\n",
       "   Art Gallery  Art Museum  ...  Wagashi Place  Waterfront  Whisky Bar  \\\n",
       "0            0           0  ...              0           0           0   \n",
       "1            0           0  ...              0           0           0   \n",
       "2            0           0  ...              0           0           0   \n",
       "3            0           0  ...              0           0           0   \n",
       "4            0           0  ...              0           0           0   \n",
       "\n",
       "   Wine Bar  Wings Joint  Women's Store  Yakitori Restaurant  \\\n",
       "0         0            0              0                    0   \n",
       "1         0            0              0                    0   \n",
       "2         0            0              0                    0   \n",
       "3         0            0              0                    0   \n",
       "4         0            0              0                    0   \n",
       "\n",
       "   Yoshoku Restaurant  Zoo  Zoo Exhibit  \n",
       "0                   0    0            0  \n",
       "1                   0    0            0  \n",
       "2                   0    0            0  \n",
       "3                   0    0            0  \n",
       "4                   0    0            0  \n",
       "\n",
       "[5 rows x 277 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "hostel_onehot = pd.get_dummies(hostel_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add Hostel name column back to dataframe\n",
    "hostel_onehot['Hostel Name'] = hostel_venues['Hostel Name'] \n",
    "\n",
    "# move hostel name column to the first column\n",
    "fixed_columns = [hostel_onehot.columns[-1]] + list(hostel_onehot.columns[:-1])\n",
    "hostel_onehot = hostel_onehot[fixed_columns]\n",
    "\n",
    "hostel_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8118, 277)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hostel_onehot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouping by Hostel Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Hostel Name</th>\n",
       "      <th>ATM</th>\n",
       "      <th>Accessories Store</th>\n",
       "      <th>Adult Boutique</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Antique Shop</th>\n",
       "      <th>Arcade</th>\n",
       "      <th>Argentinian Restaurant</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Art Museum</th>\n",
       "      <th>...</th>\n",
       "      <th>summary.score</th>\n",
       "      <th>rating.band</th>\n",
       "      <th>atmosphere</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>facilities</th>\n",
       "      <th>location.y</th>\n",
       "      <th>security</th>\n",
       "      <th>staff</th>\n",
       "      <th>valueformoney</th>\n",
       "      <th>distance.from.stadium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
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       "      <td>...</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&amp;And Hostel Ueno</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&amp;And Hostel-Asakusa North-</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1night1980hostel Tokyo</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>328 Hostel &amp; Lounge</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 289 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Hostel Name       ATM  Accessories Store  Adult Boutique  \\\n",
       "0       &And Hostel Akihabara  0.000000                0.0        0.010204   \n",
       "1            &And Hostel Ueno  0.000000                0.0        0.000000   \n",
       "2  &And Hostel-Asakusa North-  0.000000                0.0        0.000000   \n",
       "3      1night1980hostel Tokyo  0.000000                0.0        0.000000   \n",
       "4         328 Hostel & Lounge  0.017857                0.0        0.000000   \n",
       "\n",
       "   American Restaurant  Antique Shop  Arcade  Argentinian Restaurant  \\\n",
       "0             0.000000           0.0     0.0                     0.0   \n",
       "1             0.013514           0.0     0.0                     0.0   \n",
       "2             0.000000           0.0     0.0                     0.0   \n",
       "3             0.000000           0.0     0.0                     0.0   \n",
       "4             0.000000           0.0     0.0                     0.0   \n",
       "\n",
       "   Art Gallery  Art Museum  ...  summary.score  rating.band  atmosphere  \\\n",
       "0          0.0         0.0  ...            8.7            3         8.0   \n",
       "1          0.0         0.0  ...            7.4            2         8.0   \n",
       "2          0.0         0.0  ...            9.4            4         9.5   \n",
       "3          0.0         0.0  ...            7.0            2         5.5   \n",
       "4          0.0         0.0  ...            9.3            4         8.7   \n",
       "\n",
       "   cleanliness  facilities  location.y  security  staff  valueformoney  \\\n",
       "0          7.0         9.0         8.0      10.0   10.0            9.0   \n",
       "1          7.5         7.5         7.5       7.0    8.0            6.5   \n",
       "2          9.5         9.0         9.0       9.5   10.0            9.5   \n",
       "3          8.0         6.0         6.0       8.5    8.5            6.5   \n",
       "4          9.7         9.3         9.1       9.3    9.7            8.9   \n",
       "\n",
       "   distance.from.stadium  \n",
       "0                   15.1  \n",
       "1                   16.9  \n",
       "2                   19.0  \n",
       "3                   18.2  \n",
       "4                    4.4  \n",
       "\n",
       "[5 rows x 289 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hostel_grouped = hostel_onehot.groupby('Hostel Name').mean().reset_index()\n",
    "hostel_grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>Cluster Labels</th>\n",
       "      <th>Hostel Name</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Soba Restaurant</td>\n",
       "      <td>Japanese Curry Restaurant</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>&amp;And Hostel Ueno</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Soba Restaurant</td>\n",
       "      <td>Donburi Restaurant</td>\n",
       "      <td>Bath House</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>&amp;And Hostel-Asakusa North-</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Nabe Restaurant</td>\n",
       "      <td>Outdoor Sculpture</td>\n",
       "      <td>Tempura Restaurant</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1night1980hostel Tokyo</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Shabu-Shabu Restaurant</td>\n",
       "      <td>Drugstore</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Soba Restaurant</td>\n",
       "      <td>Donburi Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>328 Hostel &amp; Lounge</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Canal Lock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cluster Labels                 Hostel Name 1st Most Common Venue  \\\n",
       "0               2       &And Hostel Akihabara              Sake Bar   \n",
       "1               0            &And Hostel Ueno     Convenience Store   \n",
       "2               0  &And Hostel-Asakusa North-     Convenience Store   \n",
       "3               0      1night1980hostel Tokyo     Convenience Store   \n",
       "4               2         328 Hostel & Lounge     Convenience Store   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue      4th Most Common Venue  \\\n",
       "0      Ramen Restaurant       Soba Restaurant  Japanese Curry Restaurant   \n",
       "1      Ramen Restaurant    Chinese Restaurant                Coffee Shop   \n",
       "2              Sake Bar         Grocery Store                   Bus Stop   \n",
       "3         Grocery Store          Intersection                   Sake Bar   \n",
       "4   Japanese Restaurant      Ramen Restaurant                       Café   \n",
       "\n",
       "    5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "0            Dessert Shop             BBQ Joint   Japanese Restaurant   \n",
       "1     Japanese Restaurant       Soba Restaurant    Donburi Restaurant   \n",
       "2         Bed & Breakfast         Shopping Mall       Nabe Restaurant   \n",
       "3  Shabu-Shabu Restaurant             Drugstore       Bed & Breakfast   \n",
       "4            Intersection              Sake Bar              Platform   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "0                  Café    Chinese Restaurant     Italian Restaurant  \n",
       "1            Bath House       Bed & Breakfast                   Café  \n",
       "2     Outdoor Sculpture    Tempura Restaurant                   Park  \n",
       "3       Soba Restaurant    Donburi Restaurant                 Bakery  \n",
       "4       Bed & Breakfast              Bus Stop             Canal Lock  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Hostel Name']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "hostel_venues_sorted = pd.DataFrame(columns=columns)\n",
    "hostel_venues_sorted['Hostel Name'] = hostel_grouped['Hostel Name']\n",
    "\n",
    "for ind in np.arange(hostel_grouped.shape[0]):\n",
    "    hostel_venues_sorted.iloc[ind, 1:] = return_most_common_venues(hostel_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "hostel_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>ATM</th>\n",
       "      <th>Accessories Store</th>\n",
       "      <th>Adult Boutique</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Antique Shop</th>\n",
       "      <th>Arcade</th>\n",
       "      <th>Argentinian Restaurant</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Art Museum</th>\n",
       "      <th>Arts &amp; Crafts Store</th>\n",
       "      <th>...</th>\n",
       "      <th>summary.score</th>\n",
       "      <th>rating.band</th>\n",
       "      <th>atmosphere</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>facilities</th>\n",
       "      <th>location.y</th>\n",
       "      <th>security</th>\n",
       "      <th>staff</th>\n",
       "      <th>valueformoney</th>\n",
       "      <th>distance.from.stadium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>...</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.017857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 288 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ATM  Accessories Store  Adult Boutique  American Restaurant  \\\n",
       "0  0.000000                0.0        0.010204             0.000000   \n",
       "1  0.000000                0.0        0.000000             0.013514   \n",
       "2  0.000000                0.0        0.000000             0.000000   \n",
       "3  0.000000                0.0        0.000000             0.000000   \n",
       "4  0.017857                0.0        0.000000             0.000000   \n",
       "\n",
       "   Antique Shop  Arcade  Argentinian Restaurant  Art Gallery  Art Museum  \\\n",
       "0           0.0     0.0                     0.0          0.0         0.0   \n",
       "1           0.0     0.0                     0.0          0.0         0.0   \n",
       "2           0.0     0.0                     0.0          0.0         0.0   \n",
       "3           0.0     0.0                     0.0          0.0         0.0   \n",
       "4           0.0     0.0                     0.0          0.0         0.0   \n",
       "\n",
       "   Arts & Crafts Store  ...  summary.score  rating.band  atmosphere  \\\n",
       "0             0.000000  ...            8.7            3         8.0   \n",
       "1             0.013514  ...            7.4            2         8.0   \n",
       "2             0.000000  ...            9.4            4         9.5   \n",
       "3             0.000000  ...            7.0            2         5.5   \n",
       "4             0.000000  ...            9.3            4         8.7   \n",
       "\n",
       "   cleanliness  facilities  location.y  security  staff  valueformoney  \\\n",
       "0          7.0         9.0         8.0      10.0   10.0            9.0   \n",
       "1          7.5         7.5         7.5       7.0    8.0            6.5   \n",
       "2          9.5         9.0         9.0       9.5   10.0            9.5   \n",
       "3          8.0         6.0         6.0       8.5    8.5            6.5   \n",
       "4          9.7         9.3         9.1       9.3    9.7            8.9   \n",
       "\n",
       "   distance.from.stadium  \n",
       "0                   15.1  \n",
       "1                   16.9  \n",
       "2                   19.0  \n",
       "3                   18.2  \n",
       "4                    4.4  \n",
       "\n",
       "[5 rows x 288 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hostel_grouped = hostel_grouped.join(df_hostel.set_index('hostel.name'), on='Hostel Name')\n",
    "hostel_grouped = hostel_grouped.drop(hostel_grouped.columns[[-2,-3,-15]],axis=1)\n",
    "hostel_grouped_clustering = hostel_grouped.drop('Hostel Name', 1)\n",
    "hostel_grouped_clustering.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using k-means elbow method for optimal k value\n",
    "Let's first calculate the optimal number of clusters for our data and validate it with the elbow method in our visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_wcss(data):\n",
    "    wcss = []\n",
    "    for n in range(1, 11):\n",
    "        kmeans = KMeans(n_clusters=n)\n",
    "        kmeans.fit(X=data)\n",
    "        wcss.append(kmeans.inertia_)\n",
    "\n",
    "    return wcss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimal_number_of_clusters(wcss):\n",
    "    x1, y1 = 1, wcss[0]\n",
    "    x2, y2 = 10, wcss[len(wcss)-1]\n",
    "\n",
    "    distances = []\n",
    "    for i in range(len(wcss)):\n",
    "        x0 = i+2\n",
    "        y0 = wcss[i]\n",
    "        numerator = abs((y2-y1)*x0 - (x2-x1)*y0 + x2*y1 - y2*x1)\n",
    "        denominator = sqrt((y2 - y1)**2 + (x2 - x1)**2)\n",
    "        distances.append(numerator/denominator)\n",
    "    \n",
    "    return distances.index(max(distances)) + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_of_squares = calculate_wcss(hostel_grouped_clustering)\n",
    "\n",
    "n = optimal_number_of_clusters(sum_of_squares)\n",
    "\n",
    "kmeans = KMeans(n_clusters=n)\n",
    "clusters = kmeans.fit_predict(hostel_grouped_clustering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ho1GydWs/3RgAAIC1ocxOWNcl99+fXHfd0EkAAAAWhzI7YV3Xb503CwAAsHaU2Ql7xjOSJz9ZmQUAAFhLyuyEVfWjsxaBAgAAWDvK7BSMRsmnP5184QtDJwEAAFgMyuwUOG8WAABgbSmzU3DeeckhhyizAAAAa0WZnYLDDks2bVJmAQAA1ooyOyVdl2zfnjz00NBJAAAA5p8yOyWjUfLww8mVVw6dBAAAYP4ps1OyZUu/9RU9AAAAB06ZnZKnPCV5xjOcNwsAALAWlNkpGo36Mtva0EkAAADmmzI7RV2X3Hln8ulPD50EAABgvimzUzQa9VtTjQEAAA6MMjtFZ52VHH20RaAAAAAOlDI7RQcd1K9qbGQWAADgwCizUzYaJdddl9x339BJAAAA5pcyO2Vd169mfNllQycBAACYX8rslF14YT/d2FRjAACA/afMTtlRRyXnnGMRKAAAgAOhzA6g65LLL08efXToJAAAAPNJmR3AaJR85SvJNdcMnQQAAGA+KbMD6Lp+67xZAACA/aPMDuDUU5OTTlJmAQAA9pcyO4CqfnTWIlAAAAD7R5kdSNclt92W7NgxdBIAAID5o8wOZDTqt6YaAwAA7DtldiAvfGFy+OHKLAAAwP5QZgdy8MHJBRcoswAAAPtDmR1Q1yVXXZU8+ODQSQAAAOaLMjugrksefTTZtm3oJAAAAPNFmR3Qli391lf0AAAA7BtldkDHHZc85znOmwUAANhXyuzARqNk69Zk586hkwAAAMwPZXZgXZfcc09y441DJwEAAJgfyuzAuq7fmmoMAACwesrswJ797OTYYy0CBQAAsC+U2YFV9aOzRmYBAABWT5mdAaNRf87s3XcPnQQAAGA+KLMzYOm82a1bh80BAAAwL5TZGbBpU7Jxo6nGAAAAq6XMzoAjjkjOO88iUAAAAKulzM6Irku2bUsefnjoJAAAALNPmZ0Ro1Hy0EPJ1VcPnQQAAGD2KbMzYmkRKFONAQAA9k6ZnREnnpicdppFoAAAAFZDmZ0ho1E/Mtva0EkAAABmmzI7Q7ou+dznkltvHToJAADAbFNmZ8ho1G9NNQYAAFiZMjtDnve85KijLAIFAACwN8rsDNm4Mdm82cgsAADA3iizM6brkmuuSb785aGTAAAAzK6JltmqemlV3VhVN1XVm3Zz/6lVdUlVXVVV11TVt0wyzzzoumTnzuTyy4dOAgAAMLsmVmarakOStyV5WZKzkry6qs7aZbefTnJRa+3cJK9K8l8nlWdebN6cVJlqDAAAsJJJjsxekOSm1trNrbWHk7w7ySt22aclOXp8+YlJ7phgnrnwxCf2C0FZBAoAAGDPJllmT0py+7LrO8a3LfezSb63qnYkuTjJD+/ugarq9VW1vaq233XXXZPIOlNGo+Syy5LHHhs6CQAAwGyaZJmt3dzWdrn+6iTvbK2dnORbkvxuVT0uU2vt7a21Ta21TSeccMIEos6Wrkvuvz+57rqhkwAAAMymSZbZHUlOWXb95Dx+GvHrklyUJK21rUkOS3L8BDPNha7rt86bBQAA2L1JltltSc6sqjOq6pD0Czy9b5d9bkvyzUlSVc9NX2YXfx7xXjz96clTnqLMAgAA7MnEymxr7dEkb0jygSQ3pF+1+LqqektVvXy8248n+SdV9fEk70ryj1tru05FXneq+tFZi0ABAADs3sZJPnhr7eL0Czstv+1nll2+Pslokhnm1WiU/MmfJJ//fPLUpw6dBgAAYLZMcpoxB8B5swAAAHumzM6o885LDj1UmQUAANgdZXZGHXposmmTMgsAALA7yuwM67rkYx9LHnpo6CQAAACzRZmdYaNR8vDDfaEFAADga5TZGbZlS7/1FT0AAABfT5mdYU9+cvLMZzpvFgAAYFfK7Iwbjfoy29rQSQAAAGaHMjvjui65667kppuGTgIAADA7lNkZNxr1W1ONAQAAvkaZnXHPfW7yxCdaBAoAAGA5ZXbGHXRQv6qxkVkAAICvUWbnwGiUXHddct99QycBAACYDcrsHOi6frt167A5AAAAZoUyOwcuuCDZsMFUYwAAgCXK7Bw46qjknHMsAgUAALBEmZ0TXZdcfnny6KNDJwEAABieMjsnRqPkwQeTa64ZOgkAAMDwlNk5sbQIlKnGAAAAyuzcOPXU5OSTLQIFAACQKLNzpeuMzAIAACTK7FzpuuT22/sfAACA9UyZnSOjUb/dunXYHAAAAENTZufIOeckRxxhqjEAAIAyO0cOPji54AKLQAEAACizc6brkquuSr7ylaGTAAAADEeZnTNdlzz2WLJt29BJAAAAhqPMzpktW/qtqcYAAMB6pszOmWOPTZ77XItAAQAA65syO4dGo/7reXbuHDoJAADAMJTZOdR1yb33Jn/zN0MnAQAAGIYyO4e6rt86bxYAAFivlNk59KxnJccdp8wCAADrlzI7h6r60VmLQAEAAOuVMjunRqPkk59M7r576CQAAADTp8zOKefNAgAA65kyO6c2bUoOPliZBQAA1idldk4dfnhy3nnKLAAAsD4ps3Os65Jt25KHHx46CQAAwHQps3NsNEoeeii56qqhkwAAAEyXMjvHlhaB8hU9AADAeqPMzrGnPS054wznzQIAAOuPMjvnuq4fmW1t6CQAAADTo8zOua5LPv/55JZbhk4CAAAwPcrsnBuN+q2pxgAAwHqizM655z0vecITLAIFAACsL8rsnNuwIdm82cgsAACwviizC6Drkk98Irn//qGTAAAATIcyuwC6Ltm5M7n88qGTAAAATIcyuwA2b06qTDUGAADWD2V2ARx9dPL851sECgAAWD+U2QUxGiWXXZY89tjQSQAAACZPmV0QXZd8+cvJddcNnQQAAGDylNkF0XX91lRjAABgPVBmF8QZZyRPfapFoAAAgPVBmV0QVf3orJFZAABgPVBmF8holHzmM8nnPjd0EgAAgMlSZhfI0nmzW7cOmwMAAGDSlNkFcu65yaGHmmoMAAAsPmV2gRx6aHL++RaBAgAAFp8yu2C6LvnYx5KvfnXoJAAAAJOjzC6Y0Sh55JG+0AIAACwqZXbBbNnSb503CwAALDJldsGccEJy5pnOmwUAABabMruARqO+zLY2dBIAAIDJUGYXUNcld9+dfOpTQycBAACYDGV2AY1G/dZUYwAAYFEpswvoOc9JjjnGIlAAAMDiUmYX0EEH9asaG5kFAAAWlTK7oEaj5Prrk3vvHToJAADA2ptoma2ql1bVjVV1U1W9aQ/7vLKqrq+q66rqDyaZZz3pun67deuwOQAAACZhYmW2qjYkeVuSlyU5K8mrq+qsXfY5M8mbk4xaa2cn+dFJ5VlvLrgg2bDBVGMAAGAxTXJk9oIkN7XWbm6tPZzk3Ulescs+/yTJ21pr9yZJa+3OCeZZV448MnnhCy0CBQAALKZJltmTkty+7PqO8W3LPSvJs6rqI1V1WVW9dIJ51p2uS664InnkkaGTAAAArK1JltnazW1tl+sbk5yZ5JuSvDrJb1bVMY97oKrXV9X2qtp+1113rXnQRTUaJQ8+mFxzzdBJAAAA1tYky+yOJKcsu35ykjt2s897W2uPtNY+k+TG9OX267TW3t5a29Ra23TCCSdMLPCiWVoEylRjAABg0UyyzG5LcmZVnVFVhyR5VZL37bLP/0ry4iSpquPTTzu+eYKZ1pVTTul/LAIFAAAsmomV2dbao0nekOQDSW5IclFr7bqqektVvXy82weSfLGqrk9ySZJ/1Vr74qQyrUddZ2QWAABYPBsn+eCttYuTXLzLbT+z7HJL8mPjHyag65I//MPk9tv7UVoAAIBFMMlpxsyA0ajfmmoMAAAsEmV2wb3gBckRR5hqDAAALBZldsEdfHBy4YVGZgEAgMWizK4DXZdcfXXywANDJwEAAFgbyuw60HXJY48l27YNnQQAAGBtKLPrwJYt/dZUYwAAYFEos+vAk56UnHWWRaAAAIDFocyuE6NRsnVrsnPn0EkAAAAOnDK7TnRdct99yQ03DJ0EAADgwCmz60TX9VvnzQIAAItAmV0nzjwzOf54ZRYAAFgMyuw6UdWPzloECgAAWATK7DoyGiWf+lRy111DJwEAADgwyuw64rxZAABgUSiz68imTcnBByuzAADA/FNm15HDDkte9CJlFgAAmH/K7DrTdcm2bcnf/u3QSQAAAPafMrvOjEZ9kb3qqqGTAAAA7D9ldp1ZWgTKV/QAAADzTJldZ5761OTpT3feLAAAMN+U2XWo6/oy29rQSQAAAPaPMrsOdV3y+c8nn/nM0EkAAAD2jzK7Do1G/dZUYwAAYF4ps+vQ2WcnRx9tESgAAGB+KbPr0IYNyebNRmYBAID5pcyuU12XfOITyf33D50EAABg362qzFbVkVV10Pjys6rq5VV18GSjMUld169mfNllQycBAADYd6sdmf1QksOq6qQkf5XktUneOalQTN6FFyYHHWSqMQAAMJ9WW2artfZgku9I8l9aa/8gyVmTi8WkHX108vznWwQKAACYT6sus1W1Jcn3JPnf49s2TiYS0zIa9dOMH3ts6CQAAAD7ZrVl9keTvDnJn7TWrquqpye5ZHKxmIauSx54ILn22qGTAAAA7JtVja621i5NcmmSjBeCuru19sZJBmPyuq7ffuQjyTnnDJsFAABgX6x2NeM/qKqjq+rIJNcnubGq/tVkozFpp5+ePO1pFoECAADmz2qnGZ/VWrs/ybcnuTjJqUleM7FUTEVVPzprESgAAGDerLbMHjz+XtlvT/Le1tojSdrkYjEto1Fyyy3JHXcMnQQAAGD1VltmfyPJLUmOTPKhqjotyf2TCsX0LJ03u3XrsDkAAAD2xarKbGvtV1trJ7XWvqW11pLcluTFk43GNJx7bnLYYaYaAwAA82W1C0D9fFUds+ymY5L87EQSMVWHHJKcf75FoAAAgPmy2mnGL2ut3bd0pbV2b5JvmUwkpq3rkiuvTL761aGTAAAArM5qy+yGqjp06UpVHZ7k0BX2Z46MRskjjyTbtw+dBAAAYHVWW2Z/L8lfVdXrquoHkvxlkt+eXCymacuWfmuqMQAAMC82rnRnVf1oko8k+aUk1yR5SZJK8nOttQ9MPh7TcPzxybOeZREoAABgfuxtZPbkJL+S5M4kb0rycJIPJtk24VxM2WjUj8w23x4MAADMgRXLbGvtJ1prXZKnJvmpJPck+YEk11bV9VPIx5R0XfLFLyaf/OTQSQAAAPZutefMHp7k6CRPHP/ckeTySYVi+kajfuu8WQAAYB7s7ZzZtyc5O8mX05fXjyb5pfFX87BAnv3s5ElP6svsa187dBoAAICV7W1k9tT0X8Hz+SSfTbIjyX0r/gZz6aCD+lWNLQIFAADMg72dM/vSJOcn+cXxTT+eZFtV/UVV/dtJh2O6RqPkhhuSe+4ZOgkAAMDK9nrObOtdm+TiJH+W/qt6npHkRyacjSnrun67deuwOQAAAPZmxTJbVW+sqndX1e1JPpTkW5PcmOQ7khw7hXxM0QUXJBs2WAQKAACYfSsuAJXk9CTvSfIvW2ufm3wchnTEEcm55yqzAADA7FuxzLbWfmxaQZgNXZe84x3JI48kBx88dBoAAIDdW+33zLJOjEbJV7+afPzjQycBAADYM2WWr7O0CJSv6AEAAGaZMsvXOfnk5NRTnTcLAADMNmWWx+k6ZRYAAJhtyiyP03XJjh3JbbcNnQQAAGD3lFkeZzTqt0ZnAQCAWaXM8jgveEFy5JEWgQIAAGaXMsvjbNyYXHihkVkAAGB2KbPsVtf13zX7wANDJwEAAHg8ZZbd6rrksceSK64YOgkAAMDjKbPs1pYt/dZUYwAAYBYps+zWMcckZ59tESgAAGA2KbPs0WiUbN2a7Nw5dBIAAICvp8yyR12XfOlLyQ03DJ0EAADg6ymz7NFo1G9NNQYAAGaNMssePeMZyQknWAQKAACYPcose1TVTzU2MgsAAMwaZZYVjUbJTTcld945dBIAAICvUWZZUdf1261bh80BAACw3ETLbFW9tKpurKqbqupNK+z3nVXVqmrTJPOw7170ouSQQ0w1BgAAZsvEymxVbUjytiQvS3JWkldX1Vm72e8JSd6Y5PJJZWH/HXZYX2gtAgUAAMxXyp0HAAATuklEQVSSSY7MXpDkptbaza21h5O8O8krdrPfzyV5a5KHJpiFA9B1yfbtyd/+7dBJAAAAepMssycluX3Z9R3j2/5OVZ2b5JTW2vsnmIMDNBr1RfbKK4dOAgAA0Jtkma3d3Nb+7s6qg5L85yQ/vtcHqnp9VW2vqu133XXXGkZkNZYWgTLVGAAAmBWTLLM7kpyy7PrJSe5Ydv0JSZ6X5K+r6pYkm5O8b3eLQLXW3t5a29Ra23TCCSdMMDK785SnJM94hkWgAACA2THJMrstyZlVdUZVHZLkVUnet3Rna+1LrbXjW2unt9ZOT3JZkpe31rZPMBP7qev6kdnW9r4vAADApE2szLbWHk3yhiQfSHJDkotaa9dV1Vuq6uWTel4mo+uSL3whufnmoZMAAAAkGyf54K21i5NcvMttP7OHfb9pklk4MKNRv/3oR/spxwAAAEOa5DRjFshZZyVHH20RKAAAYDYos6zKhg3Jli0WgQIAAGaDMsuqdV1y7bXJl740dBIAAGC9U2ZZta7rVzO+7LKhkwAAAOudMsuqXXhhctBBzpsFAACGp8yyak94QvKCFyizAADA8JRZ9slo1E8zfvTRoZMAAADrmTLLPum65IEH+oWgAAAAhqLMsk+6rt/6ih4AAGBIyiz75LTTkhNPdN4sAAAwLGWWfVLVj84amQUAAIakzLLPRqPk1luTz3526CQAAMB6pcyyz5bOm926ddgcAADA+qXMss9e+MLksMNMNQYAAIajzLLPDjkkueACi0ABAADDUWbZL12XXHll8uCDQycBAADWI2WW/TIaJY8+mmzfPnQSAABgPVJm2S+bN/dbU40BAIAhKLPsl+OPT579bItAAQAAw1Bm2W+jUT8y29rQSQAAgPVGmWW/dV1yzz3JjTcOnQQAAFhvlFn222jUb503CwAATJsyy3571rOSY49VZgEAgOlTZtlvBx2UbNliESgAAGD6lFkOyGiU/M3fJF/84tBJAACA9USZ5YB0Xb/dunXYHAAAwPqizHJAzj8/2bjRebMAAMB0KbMckCOOSM49V5kFAACmS5nlgHVdcsUVySOPDJ0EAABYL5RZDtholHz1q8nVVw+dBAAAWC+UWQ7Y0iJQvqIHAACYFmWWA3bSSclppzlvFgAAmB5lljXRdf3IbGtDJwEAANYDZZY1MRold9yR3Hbb0EkAAID1QJllTSydN2uqMQAAMA3KLGvi+c9PjjzSIlAAAMB0KLOsiY0bk82bjcwCAADTocyyZrou+fjHkwceGDoJAACw6JRZ1sxolOzcmVx++dBJAACARafMsmYuvDCpMtUYAACYPGWWNXPMMcnZZ1sECgAAmDxlljU1GiVbt/bTjQEAACZFmWVNdV1y//3J9dcPnQQAAFhkyixrajTqt6YaAwAAk6TMsqae/vTkyU+2CBQAADBZyixrqqqfamxkFgAAmCRlljU3GiWf/nTyhS8MnQQAAFhUyixrruv67datw+YAAAAWlzLLmnvRi5JDDjHVGAAAmBxlljV36KHJpk0WgQIAACZHmWUiui7Zvj156KGhkwAAAItImWUiRqPk4YeTK68cOgkAALCIlFkmYsuWfmuqMQAAMAnKLBPxlKckz3ymRaAAAIDJUGaZmK7rR2ZbGzoJAACwaJRZJqbrkjvvTD796aGTAAAAi0aZZWJGo37rvFkAAGCtKbNMzFlnJU98ojILAACsPWWWiTnooH5VY4tAAQAAa02ZZaK6LrnuuuS++4ZOAgAALBJllonqun4148suGzoJAACwSJRZJurCC/vpxs6bBQAA1pIyy0QddVRyzjnKLAAAsLaUWSZuNOqnGT/66NBJAACARaHMMnFdl3zlK8knPjF0EgAAYFEos0xc1/VbX9EDAACsFWWWiTv11OSkk5w3CwAArB1llomr6kdnlVkAAGCtKLNMxWiU3Hpr8tnPDp0EAABYBMosU7F03qzRWQAAYC0os0zFC1+YHH64RaAAAIC1MdEyW1Uvraobq+qmqnrTbu7/saq6vqquqaq/qqrTJpmH4Rx8cHLBBUZmAQCAtTGxMltVG5K8LcnLkpyV5NVVddYuu12VZFNr7QVJ3pPkrZPKw/C6LrnqquTBB4dOAgAAzLtJjsxekOSm1trNrbWHk7w7ySuW79Bau6S1tlRtLkty8gTzMLDRKHn00WTbtqGTAAAA826SZfakJLcvu75jfNuevC7Jn+3ujqp6fVVtr6rtd9111xpGZJo2b+63phoDAAAHapJltnZzW9vtjlXfm2RTkv+0u/tba29vrW1qrW064YQT1jAi03TccclznmMRKAAA4MBNsszuSHLKsusnJ7lj152q6iVJ/nWSl7fW/naCeZgBo1GydWuyc+fQSQAAgHk2yTK7LcmZVXVGVR2S5FVJ3rd8h6o6N8lvpC+yd04wCzOi65J77kk++cmhkwAAAPNsYmW2tfZokjck+UCSG5Jc1Fq7rqreUlUvH+/2n5IcleSPqurqqnrfHh6OBTEa9VtTjQEAgAOxcZIP3lq7OMnFu9z2M8suv2SSz8/sedazkmOP7ReBet3rhk4DAADMq0lOM4bHqeqnGhuZBQAADoQyy9SNRsmNNyZ33z10EgAAYF4ps0xd1/Xbyy4bNgcAADC/lFmm7vzzk40bTTUGAAD2nzLL1B1+eHLeef0iUAAAAPtDmWUQo1FyxRXJww8PnQQAAJhHyiyD6LrkoYeSq68eOgkAADCPlFkGsbQIlKnGAADA/lBmGcSJJyann24RKAAAYP8oswym6/qR2daGTgIAAMwbZZbBjEbJHXckt946dBIAAGDeKLMMxnmzAADA/lJmGczznpccdZQyCwAA7DtllsFs3Jhs3mwRKAAAYN8pswyq65Jrrkm+/OWhkwAAAPNEmWVQo1Gyc2dy+eVDJwEAAOaJMsugLrwwqXLeLAAAsG+UWQb1xCf2C0EpswAAwL5QZhncaJRs3Zo89tjQSQAAgHmhzDK4rkvuvz+5/vqhkwAAAPNCmWVwo1G/9RU9AADAaimzDO6MM5KnPMV5swAAwOopswyuqp9qrMwCAACrpcwyE0aj5NOfTr7whaGTAAAA80CZZSZ0Xb81OgsAAKyGMstMOO+85NBDLQIFAACsjjLLTDj00GTTJiOzAADA6iizzIyuSz72seShh4ZOAgAAzDpllpkxGiUPP9wXWgAAgJUos8yMLVv6ranGAADA3iizzIwnPzk580yLQAEAAHunzDJTuq4fmW1t6CQAAMAsU2aZKV2X3HVXctNNQycBAABmmTLLTBmN+q3zZgEAgJUos8yU5z43OeYYZRYAAFiZMstMOeigflVji0ABAAArUWaZOV2XXHddct99QycBAABmlTLLzOm6frt167A5AACA2aXMMnMuuCDZsMF5swAAwJ4ps8yco45KzjlHmQUAAPZMmWUmjUbJ5Zcnjz46dBIAAGAWKbPMpK5LvvKV5Jprhk4CAADMImWWmbS0CJSv6AEAAHZHmWUmnXpqcvLJzpsFAAB2T5llZnWdMgsAAOyeMsvMGo2S225LduwYOgkAADBrlFlm1tJ5s0ZnAQCAXSmzzKxzzkmOOMIiUAAAwOMps8ysgw9OLrjAyCwAAPB4yiwzreuSq67qv3MWAABgiTLLTBuNksceS7ZtGzoJAAAwS5RZZtrmzf3WVGMAAGA5ZZaZduyxyXOfaxEoAADg6ymzzLzRKNm6Ndm5c+gkAADArFBmmXldl9x7b3LjjUMnAQAAZoUyy8wbjfqtqcYAAMASZZaZd+aZyfHHWwQKAAD4GmWWmVfVTzU2MgsAACxRZpkLXZd88pPJ3XcPnQQAAJgFyixzoev67datw+YAAABmgzLLXNi0KTn4YFONAQCAnjLLXDj88OS88ywCBQAA9JRZ5sZolGzbljz88NBJAACAoSmzzI2uSx56KLnqqqGTAAAAQ1NmmRtLi0CZagwAACizzI2nPS054wyLQAEAAMosc6br+jLb2tBJAACAISmzzJXRKPn855Nbbhk6CQAAMCRllrnivFkAACBRZpkzz3te8oQnKLMAALDeTbTMVtVLq+rGqrqpqt60m/sPrao/HN9/eVWdPsk8zL8NG5LNmy0CBQAA693EymxVbUjytiQvS3JWkldX1Vm77Pa6JPe21p6Z5D8n+Y+TysNieOtb+1WNP/GJ5P77+9suuaS/nbXz1rf2x3U5x3ntOc7T4ThPj2M9HY7zdDjO0+E4T8eiHudJjsxekOSm1trNrbWHk7w7ySt22ecVSX57fPk9Sb65qmqCmZhz55+fvPe9yc6dyeWX9/8RvvKV/e2snfPP74/r0v/oOc6T4ThPh+M8PY71dDjO0+E4T4fjPB2LepyrTeg7TqrqO5O8tLX2g+Prr0lyYWvtDcv2uXa8z47x9U+P97l7T4+7adOmtn379olkZj68//3Jt31bcswxyQMPJM95Tn95tfbn45J9/Z1pPMekf+fee5Nrr01OOin57Gf785WPPXbfn+9ADPXR1jSf9557kmuuSU4+OdmxIznnnOkf5/XgnnuSj388OeWU5PbbHedJ2t2xPu64oVMtni9+0XGeBsd5Ohzn6Vh+nO+/P7noouTFLx461e5V1cdaa5v2tt/GSWbYzW27NufV7JOqen2S1yfJqaeeeuDJmGvf+q39qsYf/Why+unJk5+8ut/bn89tpvk7+/p7k8525JHJU5/afw3SyScnRxyRPPTQvj/n/hrqu4Sn/byHHNK/hz/zmf6Dg40bvzaFnrWzcWN/nG++2XGetF2P9YYNyX33DZ1q8WzY4DhPg+M8HY7zdCw/zm960+wW2X3SWpvIT5ItST6w7Pqbk7x5l30+kGTL+PLGJHdnPFq8p58XvehFjfXtgx9s7fjjW/s3/6bffvCDQydaTI7zdDjO0+E4T49jPR2O83Q4ztPhOE/HPB3nJNvbajrnanban59xOb05yRlJDkny8SRn77LPDyX5b+PLr0py0d4eV5ld35b+I1z6j2/X66wNx3k6HOfpcJynx7GeDsd5Ohzn6XCcp2PejvNqy+zEFoBqrT2a5A3j0dcbxkX1uqp6S1W9fLzbf09yXFXdlOTHkjzu63tguW3bvn5+/4tf3F/ftm3YXIvGcZ4Ox3k6HOfpcaynw3GeDsd5Ohzn6VjU4zyxBaAmxQJQAAAAi2u1C0BN8qt5AAAAYCKUWQAAAOaOMgsAAMDcUWYBAACYO8osAAAAc0eZBQAAYO4oswAAAMwdZRYAAIC5o8wCAAAwd5RZAAAA5o4yCwAAwNxRZgEAAJg7yiwAAABzR5kFAABg7iizAAAAzJ1qrQ2dYZ9U1V1Jbh06BzPh+CR3Dx0C1oj3M4vGe5pF4v3MIpmH9/NprbUT9rbT3JVZWFJV21trm4bOAWvB+5lF4z3NIvF+ZpEs0vvZNGMAAADmjjILAADA3FFmmWdvHzoArCHvZxaN9zSLxPuZRbIw72fnzAIAADB3jMwCAAAwd5RZ5kpVnVJVl1TVDVV1XVX9yNCZYC1U1Yaquqqq3j90FjgQVXVMVb2nqv5m/L/VW4bOBPurqv7l+N8b11bVu6rqsKEzwb6oqt+qqjur6tpltx1bVX9ZVZ8ab580ZMYDocwybx5N8uOttecm2Zzkh6rqrIEzwVr4kSQ3DB0C1sCvJPnz1tpzkpwT72vmVFWdlOSNSTa11p6XZEOSVw2bCvbZO5O8dJfb3pTkr1prZyb5q/H1uaTMMldaa59rrV05vvzl9P9IOmnYVHBgqurkJH8/yW8OnQUORFUdneQbk/z3JGmtPdxau2/YVHBANiY5vKo2JjkiyR0D54F90lr7UJJ7drn5FUl+e3z5t5N8+1RDrSFllrlVVacnOTfJ5cMmgQP2y0l+MsnOoYPAAXp6kruS/I/xtPnfrKojhw4F+6O19tkkv5jktiSfS/Kl1tpfDJsK1sRTWmufS/qBoiRPHjjPflNmmUtVdVSSP07yo621+4fOA/urqr41yZ2ttY8NnQXWwMYk5yX59dbauUm+kjmevsb6Nj6P8BVJzkhyYpIjq+p7h00FLKfMMneq6uD0Rfb3W2v/c+g8cIBGSV5eVbckeXeSv1dVvzdsJNhvO5LsaK0tzZh5T/pyC/PoJUk+01q7q7X2SJL/maQbOBOshS9U1dOSZLy9c+A8+02ZZa5UVaU/F+uG1tovDZ0HDlRr7c2ttZNba6enX1jkg601n/wzl1prn09ye1U9e3zTNye5fsBIcCBuS7K5qo4Y//vjm2NBMxbD+5J8//jy9yd574BZDsjGoQPAPholeU2ST1TV1ePbfqq1dvGAmQD4mh9O8vtVdUiSm5O8duA8sF9aa5dX1XuSXJn+2xSuSvL2YVPBvqmqdyX5piTHV9WOJP9vkv+Q5KKqel36D22+a7iEB6Zaa0NnAAAAgH1imjEAAABzR5kFAABg7iizAAAAzB1lFgAAgLmjzAIAADB3lFkAmEFVdXpVXTt0DgCYVcosAAAAc0eZBYAZV1VPr6qrqur8obMAwKxQZgFghlXVs5P8cZLXtta2DZ0HAGbFxqEDAAB7dEKS9yb5h62164YOAwCzxMgsAMyuLyW5Pclo6CAAMGuMzALA7Ho4ybcn+UBVPdBa+4OhAwHArFBmAWCGtda+UlXfmuQvq+orrbX3Dp0JAGZBtdaGzgAAAAD7xDmzAAAAzB1lFgAAgLmjzAIAADB3lFkAAADmjjILAADA3FFmAQAAmDvKLAAAAHNHmQUAAGDu/P9c+Hlo8zRyvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(range(1,11), sum_of_squares, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Wcss')\n",
    "plt.title('The Elbow Method showing the optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merging clustered with original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>hostel.name</th>\n",
       "      <th>City</th>\n",
       "      <th>price.from</th>\n",
       "      <th>distance.from.city.centre</th>\n",
       "      <th>summary.score</th>\n",
       "      <th>rating.band</th>\n",
       "      <th>atmosphere</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>facilities</th>\n",
       "      <th>location.y</th>\n",
       "      <th>...</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3600</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Soba Restaurant</td>\n",
       "      <td>Japanese Curry Restaurant</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&amp;And Hostel Ueno</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2600</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Soba Restaurant</td>\n",
       "      <td>Donburi Restaurant</td>\n",
       "      <td>Bath House</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&amp;And Hostel-Asakusa North-</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1500</td>\n",
       "      <td>10.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Nabe Restaurant</td>\n",
       "      <td>Outdoor Sculpture</td>\n",
       "      <td>Tempura Restaurant</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1night1980hostel Tokyo</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2100</td>\n",
       "      <td>9.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Shabu-Shabu Restaurant</td>\n",
       "      <td>Drugstore</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Soba Restaurant</td>\n",
       "      <td>Donburi Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>328 Hostel &amp; Lounge</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>3300</td>\n",
       "      <td>16.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Canal Lock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  hostel.name   City  price.from distance.from.city.centre  \\\n",
       "0       &And Hostel Akihabara  Tokyo        3600                       7.8   \n",
       "1            &And Hostel Ueno  Tokyo        2600                       8.7   \n",
       "2  &And Hostel-Asakusa North-  Tokyo        1500                      10.5   \n",
       "3      1night1980hostel Tokyo  Tokyo        2100                       9.4   \n",
       "4         328 Hostel & Lounge  Tokyo        3300                      16.5   \n",
       "\n",
       "   summary.score  rating.band  atmosphere  cleanliness  facilities  \\\n",
       "0            8.7            3         8.0          7.0         9.0   \n",
       "1            7.4            2         8.0          7.5         7.5   \n",
       "2            9.4            4         9.5          9.5         9.0   \n",
       "3            7.0            2         5.5          8.0         6.0   \n",
       "4            9.3            4         8.7          9.7         9.3   \n",
       "\n",
       "   location.y  ...  1st Most Common Venue  2nd Most Common Venue  \\\n",
       "0         8.0  ...               Sake Bar       Ramen Restaurant   \n",
       "1         7.5  ...      Convenience Store       Ramen Restaurant   \n",
       "2         9.0  ...      Convenience Store               Sake Bar   \n",
       "3         6.0  ...      Convenience Store          Grocery Store   \n",
       "4         9.1  ...      Convenience Store    Japanese Restaurant   \n",
       "\n",
       "   3rd Most Common Venue      4th Most Common Venue   5th Most Common Venue  \\\n",
       "0        Soba Restaurant  Japanese Curry Restaurant            Dessert Shop   \n",
       "1     Chinese Restaurant                Coffee Shop     Japanese Restaurant   \n",
       "2          Grocery Store                   Bus Stop         Bed & Breakfast   \n",
       "3           Intersection                   Sake Bar  Shabu-Shabu Restaurant   \n",
       "4       Ramen Restaurant                       Café            Intersection   \n",
       "\n",
       "   6th Most Common Venue  7th Most Common Venue 8th Most Common Venue  \\\n",
       "0              BBQ Joint    Japanese Restaurant                  Café   \n",
       "1        Soba Restaurant     Donburi Restaurant            Bath House   \n",
       "2          Shopping Mall        Nabe Restaurant     Outdoor Sculpture   \n",
       "3              Drugstore        Bed & Breakfast       Soba Restaurant   \n",
       "4               Sake Bar               Platform       Bed & Breakfast   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0    Chinese Restaurant     Italian Restaurant  \n",
       "1       Bed & Breakfast                   Café  \n",
       "2    Tempura Restaurant                   Park  \n",
       "3    Donburi Restaurant                 Bakery  \n",
       "4              Bus Stop             Canal Lock  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hostel_venues_sorted.insert(0,'Cluster Labels',kmeans.labels_)\n",
    "hostel_merged = df_hostel\n",
    "hostel_merged = hostel_merged.join(hostel_venues_sorted.set_index('Hostel Name'), on='hostel.name')\n",
    "hostel_merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating  map with clustered data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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                .addTo(map_41148db69e6e4e02afb41f5d0b1e1313);
            
    
            var popup_cf7d3c4aba7e404399cfa4ee2b54dedb = L.popup({maxWidth: '300'});

            
                var html_a018e9d4d4334f26a82d37b6dea89eb9 = $('<div id="html_a018e9d4d4334f26a82d37b6dea89eb9" style="width: 100.0%; height: 100.0%;">National Olympic Stadium</div>')[0];
                popup_cf7d3c4aba7e404399cfa4ee2b54dedb.setContent(html_a018e9d4d4334f26a82d37b6dea89eb9);
            

            marker_37fa570fc77047e5a1b55073d74a5ba0.bindPopup(popup_cf7d3c4aba7e404399cfa4ee2b54dedb);

            
        
</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 0x7f21c5238e10>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_clusters = folium.Map(location=[35.573497306, 139.708830498], zoom_start=11)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(n)\n",
    "ys = [i + x + (i*x)**2 for i in range(n)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(hostel_merged['lat'], hostel_merged['lon'], hostel_merged['hostel.name'], hostel_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "    \n",
    "folium.Marker([35.673497306, 139.708830498], popup='National Olympic Stadium').add_to(map_clusters)       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examinging Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>hostel.name</th>\n",
       "      <th>price.from</th>\n",
       "      <th>distance.from.city.centre</th>\n",
       "      <th>summary.score</th>\n",
       "      <th>rating.band</th>\n",
       "      <th>atmosphere</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>facilities</th>\n",
       "      <th>location.y</th>\n",
       "      <th>security</th>\n",
       "      <th>...</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&amp;And Hostel Ueno</td>\n",
       "      <td>2600</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Soba Restaurant</td>\n",
       "      <td>Donburi Restaurant</td>\n",
       "      <td>Bath House</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&amp;And Hostel-Asakusa North-</td>\n",
       "      <td>1500</td>\n",
       "      <td>10.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Nabe Restaurant</td>\n",
       "      <td>Outdoor Sculpture</td>\n",
       "      <td>Tempura Restaurant</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1night1980hostel Tokyo</td>\n",
       "      <td>2100</td>\n",
       "      <td>9.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Shabu-Shabu Restaurant</td>\n",
       "      <td>Drugstore</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Soba Restaurant</td>\n",
       "      <td>Donburi Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ace Inn Shinjuku</td>\n",
       "      <td>2200</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Café</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Dessert Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Aizuya Inn</td>\n",
       "      <td>2000</td>\n",
       "      <td>10.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Park</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Nabe Restaurant</td>\n",
       "      <td>Outdoor Sculpture</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  hostel.name  price.from distance.from.city.centre  \\\n",
       "1            &And Hostel Ueno        2600                       8.7   \n",
       "2  &And Hostel-Asakusa North-        1500                      10.5   \n",
       "3      1night1980hostel Tokyo        2100                       9.4   \n",
       "5            Ace Inn Shinjuku        2200                         3   \n",
       "6                  Aizuya Inn        2000                      10.6   \n",
       "\n",
       "   summary.score  rating.band  atmosphere  cleanliness  facilities  \\\n",
       "1            7.4            2         8.0          7.5         7.5   \n",
       "2            9.4            4         9.5          9.5         9.0   \n",
       "3            7.0            2         5.5          8.0         6.0   \n",
       "5            7.7            2         6.7          7.2         6.8   \n",
       "6            8.5            3         8.1          8.3         8.4   \n",
       "\n",
       "   location.y  security  ...  1st Most Common Venue  2nd Most Common Venue  \\\n",
       "1         7.5       7.0  ...      Convenience Store       Ramen Restaurant   \n",
       "2         9.0       9.5  ...      Convenience Store               Sake Bar   \n",
       "3         6.0       8.5  ...      Convenience Store          Grocery Store   \n",
       "5         8.5       7.8  ...      Convenience Store               Sake Bar   \n",
       "6         7.8       8.9  ...      Convenience Store          Grocery Store   \n",
       "\n",
       "   3rd Most Common Venue  4th Most Common Venue   5th Most Common Venue  \\\n",
       "1     Chinese Restaurant            Coffee Shop     Japanese Restaurant   \n",
       "2          Grocery Store               Bus Stop         Bed & Breakfast   \n",
       "3           Intersection               Sake Bar  Shabu-Shabu Restaurant   \n",
       "5       Ramen Restaurant              BBQ Joint     Japanese Restaurant   \n",
       "6               Sake Bar               Bus Stop            Intersection   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "1        Soba Restaurant    Donburi Restaurant            Bath House   \n",
       "2          Shopping Mall       Nabe Restaurant     Outdoor Sculpture   \n",
       "3              Drugstore       Bed & Breakfast       Soba Restaurant   \n",
       "5                    Bar            Steakhouse                  Café   \n",
       "6                   Park       Bed & Breakfast           Coffee Shop   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "1       Bed & Breakfast                   Café  \n",
       "2    Tempura Restaurant                   Park  \n",
       "3    Donburi Restaurant                 Bakery  \n",
       "5    Chinese Restaurant           Dessert Shop  \n",
       "6       Nabe Restaurant      Outdoor Sculpture  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = hostel_merged.loc[hostel_merged['Cluster Labels'] == 0, hostel_merged.columns[[0] + list(range(2, hostel_merged.shape[1]))]]\n",
    "c1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 venues from cluster 1 - Red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t1 = []\n",
    "for i in range(16,25):\n",
    "    t1.append(c1.iloc[:,i].value_counts())\n",
    "cv1 = pd.DataFrame(t1)\n",
    "cv1 = cv1.fillna(0)\n",
    "sum_cv1 = cv1.sum()\n",
    "sum_cv1 = sum_cv1.sort_values(ascending=False)\n",
    "plt.bar(sum_cv1.index[0:10],sum_cv1[0:10],color='Red')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>hostel.name</th>\n",
       "      <th>price.from</th>\n",
       "      <th>distance.from.city.centre</th>\n",
       "      <th>summary.score</th>\n",
       "      <th>rating.band</th>\n",
       "      <th>atmosphere</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>facilities</th>\n",
       "      <th>location.y</th>\n",
       "      <th>security</th>\n",
       "      <th>...</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Tokyo Central Youth Hostel</td>\n",
       "      <td>1003200</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Yakitori Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Soba Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   hostel.name  price.from distance.from.city.centre  \\\n",
       "98  Tokyo Central Youth Hostel     1003200                       4.8   \n",
       "\n",
       "    summary.score  rating.band  atmosphere  cleanliness  facilities  \\\n",
       "98            8.0            3         6.0         10.0        10.0   \n",
       "\n",
       "    location.y  security  ...  1st Most Common Venue  2nd Most Common Venue  \\\n",
       "98         8.0       8.0  ...     Italian Restaurant    Japanese Restaurant   \n",
       "\n",
       "    3rd Most Common Venue  4th Most Common Venue  5th Most Common Venue  \\\n",
       "98      French Restaurant              BBQ Joint               Sake Bar   \n",
       "\n",
       "    6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "98       Ramen Restaurant   Yakitori Restaurant    Chinese Restaurant   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "98       Soba Restaurant      Indian Restaurant  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2 = hostel_merged.loc[hostel_merged['Cluster Labels'] == 1, hostel_merged.columns[[0] + list(range(2, hostel_merged.shape[1]))]]\n",
    "c2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 venues from cluster 2 - Purple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8], <a list of 9 Text xticklabel objects>)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t2 = []\n",
    "for i in range(16,25):\n",
    "    t2.append(c2.iloc[:,i].value_counts())\n",
    "cv2 = pd.DataFrame(t2)\n",
    "cv2 = cv2.fillna(0)\n",
    "sum_cv2 = cv2.sum()\n",
    "sum_cv2 = sum_cv2.sort_values(ascending=False)\n",
    "plt.bar(sum_cv2.index[0:10],sum_cv2[0:10],color='Purple')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>hostel.name</th>\n",
       "      <th>price.from</th>\n",
       "      <th>distance.from.city.centre</th>\n",
       "      <th>summary.score</th>\n",
       "      <th>rating.band</th>\n",
       "      <th>atmosphere</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>facilities</th>\n",
       "      <th>location.y</th>\n",
       "      <th>security</th>\n",
       "      <th>...</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&amp;And Hostel Akihabara</td>\n",
       "      <td>3600</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Soba Restaurant</td>\n",
       "      <td>Japanese Curry Restaurant</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>328 Hostel &amp; Lounge</td>\n",
       "      <td>3300</td>\n",
       "      <td>16.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Canal Lock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Asakusa Ryokan Toukaisou</td>\n",
       "      <td>3600</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>...</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Café</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Wagashi Place</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Yoshoku Restaurant</td>\n",
       "      <td>Unagi Restaurant</td>\n",
       "      <td>Okonomiyaki Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Astro Station</td>\n",
       "      <td>3500</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>...</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Sake Bar</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Beer Bar</td>\n",
       "      <td>Japanese Curry Restaurant</td>\n",
       "      <td>Tonkatsu Restaurant</td>\n",
       "      <td>Music Venue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Beagle Tokyo Hostel &amp; Apartments</td>\n",
       "      <td>3800</td>\n",
       "      <td>16.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Donburi Restaurant</td>\n",
       "      <td>Bed &amp; Breakfast</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Drugstore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         hostel.name  price.from distance.from.city.centre  \\\n",
       "0              &And Hostel Akihabara        3600                       7.8   \n",
       "4                328 Hostel & Lounge        3300                      16.5   \n",
       "14          Asakusa Ryokan Toukaisou        3600                       9.3   \n",
       "16                     Astro Station        3500                       3.8   \n",
       "18  Beagle Tokyo Hostel & Apartments        3800                      16.1   \n",
       "\n",
       "    summary.score  rating.band  atmosphere  cleanliness  facilities  \\\n",
       "0             8.7            3         8.0          7.0         9.0   \n",
       "4             9.3            4         8.7          9.7         9.3   \n",
       "14            8.7            3         8.0          8.4         8.4   \n",
       "16            8.4            3         7.1          9.1         8.0   \n",
       "18            9.0            4         8.0         10.0         9.0   \n",
       "\n",
       "    location.y  security  ...  1st Most Common Venue  2nd Most Common Venue  \\\n",
       "0          8.0      10.0  ...               Sake Bar       Ramen Restaurant   \n",
       "4          9.1       9.3  ...      Convenience Store    Japanese Restaurant   \n",
       "14         8.8       8.8  ...            Coffee Shop              BBQ Joint   \n",
       "16         9.7       7.1  ...       Ramen Restaurant               Sake Bar   \n",
       "18         8.0       9.0  ...      Convenience Store       Ramen Restaurant   \n",
       "\n",
       "    3rd Most Common Venue      4th Most Common Venue  5th Most Common Venue  \\\n",
       "0         Soba Restaurant  Japanese Curry Restaurant           Dessert Shop   \n",
       "4        Ramen Restaurant                       Café           Intersection   \n",
       "14                   Café           Ramen Restaurant          Wagashi Place   \n",
       "16    Japanese Restaurant          Convenience Store     Chinese Restaurant   \n",
       "18    Japanese Restaurant                       Park             Restaurant   \n",
       "\n",
       "    6th Most Common Venue 7th Most Common Venue      8th Most Common Venue  \\\n",
       "0               BBQ Joint   Japanese Restaurant                       Café   \n",
       "4                Sake Bar              Platform            Bed & Breakfast   \n",
       "14    Japanese Restaurant    Yoshoku Restaurant           Unagi Restaurant   \n",
       "16     Italian Restaurant              Beer Bar  Japanese Curry Restaurant   \n",
       "18     Donburi Restaurant       Bed & Breakfast                        Pub   \n",
       "\n",
       "     9th Most Common Venue 10th Most Common Venue  \n",
       "0       Chinese Restaurant     Italian Restaurant  \n",
       "4                 Bus Stop             Canal Lock  \n",
       "14  Okonomiyaki Restaurant                    Bar  \n",
       "16     Tonkatsu Restaurant            Music Venue  \n",
       "18                    Pool              Drugstore  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c3 = hostel_merged.loc[hostel_merged['Cluster Labels'] == 2, hostel_merged.columns[[0] + list(range(2, hostel_merged.shape[1]))]]\n",
    "c3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 venues from cluster 3 - Green"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t3 = []\n",
    "for i in range(16,25):\n",
    "    t3.append(c3.iloc[:,i].value_counts())\n",
    "cv3 = pd.DataFrame(t3)\n",
    "cv3 = cv3.fillna(0)\n",
    "sum_cv3 = cv3.sum()\n",
    "sum_cv3 = sum_cv3.sort_values(ascending=False)\n",
    "plt.bar(sum_cv3.index[0:10],sum_cv3[0:10],color='Green')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average data between clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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",
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       "    }\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>price.from</th>\n",
       "      <th>distance.from.city.centre</th>\n",
       "      <th>summary.score</th>\n",
       "      <th>rating.band</th>\n",
       "      <th>atmosphere</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>facilities</th>\n",
       "      <th>location.y</th>\n",
       "      <th>security</th>\n",
       "      <th>staff</th>\n",
       "      <th>valueformoney</th>\n",
       "      <th>distance.from.stadium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2275.58</td>\n",
       "      <td>8.96</td>\n",
       "      <td>8.78</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.22</td>\n",
       "      <td>8.98</td>\n",
       "      <td>8.54</td>\n",
       "      <td>8.57</td>\n",
       "      <td>9.14</td>\n",
       "      <td>9.16</td>\n",
       "      <td>8.87</td>\n",
       "      <td>16.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003200.00</td>\n",
       "      <td>4.80</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3993.10</td>\n",
       "      <td>6.68</td>\n",
       "      <td>8.89</td>\n",
       "      <td>3.48</td>\n",
       "      <td>8.39</td>\n",
       "      <td>9.25</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.74</td>\n",
       "      <td>8.93</td>\n",
       "      <td>9.31</td>\n",
       "      <td>8.85</td>\n",
       "      <td>12.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                price.from  distance.from.city.centre  summary.score  \\\n",
       "Cluster Labels                                                         \n",
       "0                  2275.58                       8.96           8.78   \n",
       "1               1003200.00                       4.80           8.00   \n",
       "2                  3993.10                       6.68           8.89   \n",
       "\n",
       "                rating.band  atmosphere  cleanliness  facilities  location.y  \\\n",
       "Cluster Labels                                                                 \n",
       "0                      3.40        8.22         8.98        8.54        8.57   \n",
       "1                      3.00        6.00        10.00       10.00        8.00   \n",
       "2                      3.48        8.39         9.25        8.77        8.74   \n",
       "\n",
       "                security  staff  valueformoney  distance.from.stadium  \n",
       "Cluster Labels                                                         \n",
       "0                   9.14   9.16           8.87                  16.43  \n",
       "1                   8.00   8.00           6.00                  14.50  \n",
       "2                   8.93   9.31           8.85                  12.82  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_clusters = pd.DataFrame()\n",
    "for i in range(3):\n",
    "    locals()[\"c\"+str(i+1)].iloc[:,2] = locals()[\"c\"+str(i+1)].iloc[:,2].astype(float)\n",
    "    locals()[\"avg_c\"+str(i+1)] = locals()[\"c\"+str(i+1)]\n",
    "    locals()[\"avg_c\"+str(i+1)] = locals()[\"avg_c\"+str(i+1)].drop(locals()[\"avg_c\"+str(i+1)].columns[[0,12,13,16,17,18,19,20,21,22,23,24,25]],axis=1)\n",
    "    locals()[\"avg_c\"+str(i+1)] = locals()[\"avg_c\"+str(i+1)].groupby('Cluster Labels').mean()  \n",
    "    avg_clusters = avg_clusters.append(locals()[\"avg_c\"+str(i+1)])\n",
    "avg_clusters.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results and Discussion <a name=\"results\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our analysis shows that from the data that were available to us we could divide hostels in three clusters and that:\n",
    "* The main factor used to divide the clusters was the hostels price\n",
    "* The second (Purple) cluster can be considered an outliers because of the huge price difference comparing to the others, making it a cluster of 1 hostel only\n",
    "* The best cost-benefit hostels are in the Third (Green) cluster since they have intermediate price, higher hatings and score and tend to be closer to the center of the city as well as to the Olympic Stadium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion <a name=\"conclusion\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Purpose of this project was to aid our StakeHolders to find hostels that could best fill their needs based on a different number of features when visiting Tokyo for the Olympics. To do that, we gathered data from different datasets and the FourSquare API and then created clusters based on their attributes and compared them, narrowing the StakeHolder choices in the trip planning process."
   ]
  }
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