Capstone Project - The Battle of Neighborhoods - Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project - The Battle of Neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introduction where you discuss the business problem and who would be interested in this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"Would you recommend a location in Hong Kong to open a new cinema?\"  \n",
    "My boss, the stakeholder wants to **open a new cinema as company's new business**.  \n",
    "  \n",
    "He explains that watching movie is a part of whole afternoon or night activities. Cinema should has **many restaurants and shopping places nearby**. Transportation is also an important factor. Customer can walk to cinema within **5 minutes** from **public transport facilities** is perfect.  \n",
    "  \n",
    "He wants me concentrated on selection of cinema location according to its nearby environment. Cinema facility and rental price is not my concern. He lists out his **top 10 favorite cinemas** in Hong Kong with rating.  \n",
    "\n",
    "I work with my teammates and select **5 possible locations** to build the cinema. Which location should be suggested to the stakeholder?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data where you describe the data that will be used to solve the problem and the source of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the question, following data are required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Geographic coordinate of Hong Kong cinemas\n",
    "\n",
    "I need to **compare 5 possible locations with current cinemas** in Hong Kong. Therefore, I need to find a list of Hong Kong cinema and cinemas' geographic coordinates. Luckily, I can find the list and coordinates from the website https://hkmovie6.com/cinema ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary library\n",
    "import json\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-10-03 11:44:29--  https://hkmovie6.com/api/cinemas/lists\n",
      "Resolving hkmovie6.com (hkmovie6.com)... 172.64.164.18, 172.64.165.18, 2606:4700:30::ac40:a412, ...\n",
      "Connecting to hkmovie6.com (hkmovie6.com)|172.64.164.18|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: unspecified [application/json]\n",
      "Saving to: ‘hk_cinema_list.json’\n",
      "\n",
      "hk_cinema_list.json     [ <=>                  ]  51.76K   286KB/s   in 0.2s   \n",
      "\n",
      "2018-10-03 11:44:31 (286 KB/s) - ‘hk_cinema_list.json’ saved [53003]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Download the cinema list\n",
    "!wget -O hk_cinema_list.json https://hkmovie6.com/api/cinemas/lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the JSON data into DataFrmae\n",
    "cinemas_json = None\n",
    "with open('hk_cinema_list.json', 'r', encoding='utf-8') as f:\n",
    "    cinemas_json = json.load(f)\n",
    "    \n",
    "cinemas = []\n",
    "for data in cinemas_json['data']:    \n",
    "    cinemas.append({\n",
    "        'Name': data['name'],\n",
    "        'ChiName': data['chiName'],\n",
    "        'Address': data['address'],\n",
    "        'Latitude': data['lat'],\n",
    "        'Longitude': data['lon']\n",
    "    })\n",
    "df_cinemas = pd.DataFrame(cinemas, columns=['Name','ChiName','Address','Latitude','Longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 68 cinemas in Hong Kong\n"
     ]
    }
   ],
   "source": [
    "print('There are {} cinemas in Hong Kong'.format(len(df_cinemas)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First five records of Hong Kong cinemas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ChiName</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>英皇戲院 - 娛樂行</td>\n",
       "      <td>3/F, Emperor Cinemas Entertainment Building, 3...</td>\n",
       "      <td>22.281453</td>\n",
       "      <td>114.154230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Coronet @ Emperor Cinemas - Entertainment ...</td>\n",
       "      <td>The Coronet @ 英皇戲院 - 娛樂行</td>\n",
       "      <td>3/F, Emperor Cinemas Entertainment Building, 3...</td>\n",
       "      <td>22.281453</td>\n",
       "      <td>114.154230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emperor Cinemas - Tuen Mun</td>\n",
       "      <td>英皇戲院 - 屯門新都商場</td>\n",
       "      <td>3/F, New Town Commercial Arcade, 2 Tuen Lee St...</td>\n",
       "      <td>22.390776</td>\n",
       "      <td>113.975983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Broadway Circuit - CYBERPORT</td>\n",
       "      <td>百老匯戲院 - 數碼港</td>\n",
       "      <td>Shop L1 - 3, Level 1, The Arcade, 100 Cyberpor...</td>\n",
       "      <td>22.261067</td>\n",
       "      <td>114.129825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Broadway Circuit - PALACE IFC</td>\n",
       "      <td>百老匯戲院 - PALACE IFC</td>\n",
       "      <td>Podium L1, IFC Mall, 8 Finance Street, Central</td>\n",
       "      <td>22.285545</td>\n",
       "      <td>114.157979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name  \\\n",
       "0           Emperor Cinemas - Entertainment Building   \n",
       "1  The Coronet @ Emperor Cinemas - Entertainment ...   \n",
       "2                         Emperor Cinemas - Tuen Mun   \n",
       "3                       Broadway Circuit - CYBERPORT   \n",
       "4                      Broadway Circuit - PALACE IFC   \n",
       "\n",
       "                    ChiName  \\\n",
       "0                英皇戲院 - 娛樂行   \n",
       "1  The Coronet @ 英皇戲院 - 娛樂行   \n",
       "2             英皇戲院 - 屯門新都商場   \n",
       "3               百老匯戲院 - 數碼港   \n",
       "4        百老匯戲院 - PALACE IFC   \n",
       "\n",
       "                                             Address   Latitude   Longitude  \n",
       "0  3/F, Emperor Cinemas Entertainment Building, 3...  22.281453  114.154230  \n",
       "1  3/F, Emperor Cinemas Entertainment Building, 3...  22.281453  114.154230  \n",
       "2  3/F, New Town Commercial Arcade, 2 Tuen Lee St...  22.390776  113.975983  \n",
       "3  Shop L1 - 3, Level 1, The Arcade, 100 Cyberpor...  22.261067  114.129825  \n",
       "4     Podium L1, IFC Mall, 8 Finance Street, Central  22.285545  114.157979  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cinemas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Geographic coordinates of 5 possible cinema addresses\n",
    "Geographic coordinates of 5 possible cinemas are required and I can use Google Map API to find this information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "possible_locations = [\n",
    "    { 'Location': 'L1', 'Address': 'Sau Mau Ping Shopping Centre, Sau Mau Ping'},\n",
    "    { 'Location': 'L2', 'Address': 'Tuen Mun Ferry, Tuen Mun'},\n",
    "    { 'Location': 'L3', 'Address': 'Un Chau Shopping Centre, Cheung Sha Wan'},\n",
    "    { 'Location': 'L4', 'Address': 'Prosperity Millennia Plaza, North Point'},\n",
    "    { 'Location': 'L5', 'Address': 'Tsuen Fung Centre Shopping Arcade, Tsuen Wan'},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting googlemaps\n",
      "  Downloading https://files.pythonhosted.org/packages/5a/3d/13b4230f3c1b8a586cdc8d8179f3c6af771c11247f8de9c166d1ab37f51d/googlemaps-3.0.2.tar.gz\n",
      "Requirement not upgraded as not directly required: requests<3.0,>=2.11.1 in /home/jupyterlab/conda/lib/python3.6/site-packages (from googlemaps) (2.18.4)\n",
      "Requirement not upgraded as not directly required: chardet<3.1.0,>=3.0.2 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests<3.0,>=2.11.1->googlemaps) (3.0.4)\n",
      "Requirement not upgraded as not directly required: idna<2.7,>=2.5 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests<3.0,>=2.11.1->googlemaps) (2.6)\n",
      "Requirement not upgraded as not directly required: urllib3<1.23,>=1.21.1 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests<3.0,>=2.11.1->googlemaps) (1.22)\n",
      "Requirement not upgraded as not directly required: certifi>=2017.4.17 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests<3.0,>=2.11.1->googlemaps) (2018.8.24)\n",
      "Building wheels for collected packages: googlemaps\n",
      "  Running setup.py bdist_wheel for googlemaps ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jupyterlab/.cache/pip/wheels/3c/3f/25/ce6d7722dba07e5d4a12d27ab38f3d7add65ef43171b02c819\n",
      "Successfully built googlemaps\n",
      "\u001b[31mdistributed 1.21.8 requires msgpack, which is not installed.\u001b[0m\n",
      "Installing collected packages: googlemaps\n",
      "Successfully installed googlemaps-3.0.2\n"
     ]
    }
   ],
   "source": [
    "# install the google map api client library\n",
    "!pip install -U googlemaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "google_act = None\n",
    "with open('google_map_act.json', 'r') as f:\n",
    "    google_act = json.load(f)\n",
    "    \n",
    "GOOGLE_MAP_API_KEY = google_act['api_key']    \n",
    "\n",
    "import googlemaps\n",
    "gmaps = googlemaps.Client(key=GOOGLE_MAP_API_KEY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve geolocation and create the dataframe of pending cinema addresses\n",
    "def getLatLng(address):\n",
    "    latlnt = gmaps.geocode('{}, Hong Kong'.format(address))\n",
    "    return (latlnt[0]['geometry']['location']['lat'], latlnt[0]['geometry']['location']['lng'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataframe of 5 target locations with geographic coordinates information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Location</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L1</td>\n",
       "      <td>Sau Mau Ping Shopping Centre, Sau Mau Ping</td>\n",
       "      <td>22.319503</td>\n",
       "      <td>114.232187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L2</td>\n",
       "      <td>Tuen Mun Ferry, Tuen Mun</td>\n",
       "      <td>22.371780</td>\n",
       "      <td>113.966039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L3</td>\n",
       "      <td>Un Chau Shopping Centre, Cheung Sha Wan</td>\n",
       "      <td>22.337280</td>\n",
       "      <td>114.156457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L4</td>\n",
       "      <td>Prosperity Millennia Plaza, North Point</td>\n",
       "      <td>22.291698</td>\n",
       "      <td>114.208168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L5</td>\n",
       "      <td>Tsuen Fung Centre Shopping Arcade, Tsuen Wan</td>\n",
       "      <td>22.372112</td>\n",
       "      <td>114.119317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Location                                       Address   Latitude  \\\n",
       "0       L1    Sau Mau Ping Shopping Centre, Sau Mau Ping  22.319503   \n",
       "1       L2                      Tuen Mun Ferry, Tuen Mun  22.371780   \n",
       "2       L3       Un Chau Shopping Centre, Cheung Sha Wan  22.337280   \n",
       "3       L4       Prosperity Millennia Plaza, North Point  22.291698   \n",
       "4       L5  Tsuen Fung Centre Shopping Arcade, Tsuen Wan  22.372112   \n",
       "\n",
       "    Longitude  \n",
       "0  114.232187  \n",
       "1  113.966039  \n",
       "2  114.156457  \n",
       "3  114.208168  \n",
       "4  114.119317  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for loc in possible_locations:        \n",
    "    (lat, lng) = getLatLng(loc['Address'])\n",
    "    loc['Latitude'] = lat\n",
    "    loc['Longitude'] = lng\n",
    "    \n",
    "df_possible_locations = pd.DataFrame(possible_locations, columns=['Location', 'Address', 'Latitude', 'Longitude'])\n",
    "df_possible_locations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Favorite cinema list of stakeholder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The favorite cinema list of stakeholder is an important information that I can **use it as profile to select the best location**.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Broadway Circuit - MONGKOK</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Broadway Circuit - The ONE</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grand Ocean</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Grand Cinema</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UA IMAX @ Airport</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name  Rating\n",
       "0  Broadway Circuit - MONGKOK     4.5\n",
       "1  Broadway Circuit - The ONE     4.5\n",
       "2                 Grand Ocean     4.3\n",
       "3            The Grand Cinema     3.4\n",
       "4           AMC Pacific Place     2.3\n",
       "5           UA IMAX @ Airport     1.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boss_favorite = [\n",
    "    {'Name': 'Broadway Circuit - MONGKOK', 'Rating': 4.5},\n",
    "    {'Name': 'Broadway Circuit - The ONE', 'Rating': 4.5},\n",
    "    {'Name': 'Grand Ocean', 'Rating': 4.3},\n",
    "    {'Name': 'The Grand Cinema', 'Rating': 3.4},\n",
    "    {'Name': 'AMC Pacific Place', 'Rating': 2.3},\n",
    "    {'Name': 'UA IMAX @ Airport', 'Rating': 1.5},\n",
    "]\n",
    "\n",
    "df_boss_favorite = pd.DataFrame(boss_favorite, columns=['Name','Rating'])\n",
    "df_boss_favorite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Eating, Shopping and Public transportation facility around cinema\n",
    "The recommended cinema location needs to have many eating and shopping venues nearby. Convenient public transport is also required.  \n",
    "These data can be found by using FourSquare API to find these venues around the location. The radius of exploration distance is set to 500 meters, which is about 5 minutes walking distance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following type of venue category will be used to search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs_categories = {\n",
    "    'Food': '4d4b7105d754a06374d81259',\n",
    "    'Shop & Service': '4d4b7105d754a06378d81259',\n",
    "    'Bus Stop': '52f2ab2ebcbc57f1066b8b4f',\n",
    "    'Metro Station': '4bf58dd8d48988d1fd931735',\n",
    "    'Nightlife Spot': '4d4b7105d754a06376d81259',\n",
    "    'Arts & Entertainment': '4d4b7104d754a06370d81259'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Food, Shop & Service, Bus Stop, Metro Station, Nightlife Spot, Arts & Entertainment'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "', '.join([ cat for cat in fs_categories])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cinema = df_cinemas.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use the first cinema \"Emperor Cinemas - Entertainment Building\" in the list as example to explore venues nearyby\n"
     ]
    }
   ],
   "source": [
    "print('Use the first cinema \"{}\" in the list as example to explore venues nearyby'.format(cinema['Name']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting foursquare\n",
      "  Downloading https://files.pythonhosted.org/packages/7e/9f/21ef283c50eb576eaebb0525d8a988baffe4d59ac2bbb1f9d84434bdf616/foursquare-1%212016.9.12.tar.gz\n",
      "Requirement already satisfied: requests>=2.1 in /home/jupyterlab/conda/lib/python3.6/site-packages (from foursquare) (2.18.4)\n",
      "Requirement already satisfied: six in /home/jupyterlab/conda/lib/python3.6/site-packages (from foursquare) (1.11.0)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests>=2.1->foursquare) (3.0.4)\n",
      "Requirement already satisfied: idna<2.7,>=2.5 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests>=2.1->foursquare) (2.6)\n",
      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests>=2.1->foursquare) (1.22)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests>=2.1->foursquare) (2018.8.24)\n",
      "Building wheels for collected packages: foursquare\n",
      "  Running setup.py bdist_wheel for foursquare ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jupyterlab/.cache/pip/wheels/c1/a4/ff/e07a4f4f02ef7189c5b1e0738a09131f6c5f2de811ce3a39a0\n",
      "Successfully built foursquare\n",
      "\u001b[31mdistributed 1.21.8 requires msgpack, which is not installed.\u001b[0m\n",
      "Installing collected packages: foursquare\n",
      "Successfully installed foursquare-1!2016.9.12\n"
     ]
    }
   ],
   "source": [
    "# Install FourSquare client library\n",
    "!pip install foursquare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs_act = None\n",
    "with open('fs_act.json') as json_data:\n",
    "    fs_act = json.load(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import foursquare\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "fs = foursquare.Foursquare(client_id=fs_act['client_id'], client_secret=fs_act['client_secret'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "RADIUS = 500 # 500m, around 5 minutes walking time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to search nearby information and convert the result as dataframe\n",
    "def venues_nearby(latitude, longitude, category, verbose=True):    \n",
    "    results = fs.venues.search(\n",
    "        params = {\n",
    "            'query': category, \n",
    "            'll': '{},{}'.format(latitude, longitude),\n",
    "            'radius': RADIUS,\n",
    "            'categoryId': fs_categories[category]\n",
    "        }\n",
    "    )    \n",
    "    df = json_normalize(results['venues'])\n",
    "    cols = ['Name','Latitude','Longitude','Tips','Users','Visits']    \n",
    "    if( len(df) == 0 ):        \n",
    "        df = pd.DataFrame(columns=cols)\n",
    "    else:        \n",
    "        df = df[['name','location.lat','location.lng','stats.tipCount','stats.usersCount','stats.visitsCount']]\n",
    "        df.columns = cols\n",
    "    if( verbose ):\n",
    "        print('{} \"{}\" venues are found within {}m of location'.format(len(df), category, RADIUS))\n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find Metro Station around the cinema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 \"Metro Station\" venues are found within 500m of location\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MTR Central Station (港鐵中環站)</td>\n",
       "      <td>22.281911</td>\n",
       "      <td>114.158406</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MTR Hong Kong Station (港鐵香港站)</td>\n",
       "      <td>22.284926</td>\n",
       "      <td>114.158314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Name   Latitude   Longitude  Tips  Users  Visits\n",
       "0    MTR Central Station (港鐵中環站)  22.281911  114.158406     0      0       0\n",
       "1  MTR Hong Kong Station (港鐵香港站)  22.284926  114.158314     0      0       0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_nearby(cinema['Latitude'], cinema['Longitude'], 'Metro Station').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find Bus Stop around the cinema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 \"Bus Stop\" venues are found within 500m of location\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seymour Road / Robinson Road Bus Stop 西摩道／羅便臣道巴士站</td>\n",
       "      <td>22.280465</td>\n",
       "      <td>114.150347</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Douglas Street Bus Stop 德忌利士街巴士站</td>\n",
       "      <td>22.283273</td>\n",
       "      <td>114.156910</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hang Seng Bank Headquarters / Connaught Road C...</td>\n",
       "      <td>22.284741</td>\n",
       "      <td>114.156404</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HSBC Headquarters Bus Stop 匯豐總行巴士站</td>\n",
       "      <td>22.280577</td>\n",
       "      <td>114.159446</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dr. Sun Yat-Sen Museum Bus Stop 孫中山紀念館巴士站</td>\n",
       "      <td>22.279132</td>\n",
       "      <td>114.152743</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name   Latitude   Longitude  \\\n",
       "0  Seymour Road / Robinson Road Bus Stop 西摩道／羅便臣道巴士站  22.280465  114.150347   \n",
       "1                   Douglas Street Bus Stop 德忌利士街巴士站  22.283273  114.156910   \n",
       "2  Hang Seng Bank Headquarters / Connaught Road C...  22.284741  114.156404   \n",
       "3                 HSBC Headquarters Bus Stop 匯豐總行巴士站  22.280577  114.159446   \n",
       "4          Dr. Sun Yat-Sen Museum Bus Stop 孫中山紀念館巴士站  22.279132  114.152743   \n",
       "\n",
       "   Tips  Users  Visits  \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  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_nearby(cinema['Latitude'], cinema['Longitude'], 'Bus Stop').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find eating places around the cinema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 \"Food\" venues are found within 500m of location\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mana! Fast Slow Food</td>\n",
       "      <td>22.282921</td>\n",
       "      <td>114.154651</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Good Luck Thai Food (鴻運泰國美食)</td>\n",
       "      <td>22.281165</td>\n",
       "      <td>114.155296</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Soul Food</td>\n",
       "      <td>22.281668</td>\n",
       "      <td>114.152495</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chiu Lung Fast Food (昭隆美食)</td>\n",
       "      <td>22.282659</td>\n",
       "      <td>114.156753</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sun Hing Fast Food (新興美食)</td>\n",
       "      <td>22.282521</td>\n",
       "      <td>114.156717</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name   Latitude   Longitude  Tips  Users  Visits\n",
       "0          Mana! Fast Slow Food  22.282921  114.154651     0      0       0\n",
       "1  Good Luck Thai Food (鴻運泰國美食)  22.281165  114.155296     0      0       0\n",
       "2                     Soul Food  22.281668  114.152495     0      0       0\n",
       "3    Chiu Lung Fast Food (昭隆美食)  22.282659  114.156753     0      0       0\n",
       "4     Sun Hing Fast Food (新興美食)  22.282521  114.156717     0      0       0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_nearby(cinema['Latitude'], cinema['Longitude'], 'Food').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 \"Arts & Entertainment\" venues are found within 500m of location\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tai Kwun Centre for Heritage and Arts (大館古蹟及藝術館)</td>\n",
       "      <td>22.281668</td>\n",
       "      <td>114.154216</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wah Tung China Arts Limited (華通陶瓷藝術有限公司)</td>\n",
       "      <td>22.283046</td>\n",
       "      <td>114.152723</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ravenel Fine Arts Limited 睿芙奧</td>\n",
       "      <td>22.281819</td>\n",
       "      <td>114.156906</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ben Brown Fine Arts</td>\n",
       "      <td>22.281853</td>\n",
       "      <td>114.157285</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KONG Arts Space</td>\n",
       "      <td>22.281751</td>\n",
       "      <td>114.153300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Name   Latitude   Longitude  \\\n",
       "0  Tai Kwun Centre for Heritage and Arts (大館古蹟及藝術館)  22.281668  114.154216   \n",
       "1          Wah Tung China Arts Limited (華通陶瓷藝術有限公司)  22.283046  114.152723   \n",
       "2                     Ravenel Fine Arts Limited 睿芙奧  22.281819  114.156906   \n",
       "3                               Ben Brown Fine Arts  22.281853  114.157285   \n",
       "4                                   KONG Arts Space  22.281751  114.153300   \n",
       "\n",
       "   Tips  Users  Visits  \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  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_nearby(cinema['Latitude'], cinema['Longitude'], 'Arts & Entertainment').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Methodology section which represents the main component of the report where you discuss and describe any exploratory data analysis that you did, any inferential statistical testing that you performed, and what machine learnings were used and why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With above data, I can use content-based recommendation technique to resolve the problem.\n",
    "\n",
    "Combine with FourSquare API which provides how many venues in different category of Hong Kong cinemas, a matrix which captured characteristic of venues nearby cinema are built. Stakeholder's favorite list is the profile to combine with the matrix to become a weighted matrix of favorite cinema.\n",
    "\n",
    "The weighted matrix can be applied on 5 target locations with venues information to generate a ranking result. The the top one on the ranking list can be recommended to the stakeholder.\n",
    "\n",
    "Before building the matrix, I have to prepare the required data and apply some data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Cleansing and Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the cinemas dataset contains any duplicated address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ChiName</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cinema City VICTORIA (Causeway Bay)</td>\n",
       "      <td>Cinema City VICTORIA (銅鑼灣)</td>\n",
       "      <td>2-8 Sugar Street, Causeway Bay, Hong Kong</td>\n",
       "      <td>22.279805</td>\n",
       "      <td>114.187126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diamond Suite VIP House @ Cinema City VICTORIA...</td>\n",
       "      <td>Diamond Suite VIP House @ Cinema City VICTORIA...</td>\n",
       "      <td>2-8 Sugar Street, Causeway Bay, Hong Kong</td>\n",
       "      <td>22.279805</td>\n",
       "      <td>114.187126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>The Grand Cinema</td>\n",
       "      <td>The Grand Cinema</td>\n",
       "      <td>2/F, Elements, 1 Austin Road West, Kowloon</td>\n",
       "      <td>22.304118</td>\n",
       "      <td>114.161466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>The Grand SC Starsuite</td>\n",
       "      <td>The Grand SC Starsuite</td>\n",
       "      <td>2/F, Elements, 1 Austin Road West, Kowloon</td>\n",
       "      <td>22.304118</td>\n",
       "      <td>114.161466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>英皇戲院 - 娛樂行</td>\n",
       "      <td>3/F, Emperor Cinemas Entertainment Building, 3...</td>\n",
       "      <td>22.281453</td>\n",
       "      <td>114.154230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Coronet @ Emperor Cinemas - Entertainment ...</td>\n",
       "      <td>The Coronet @ 英皇戲院 - 娛樂行</td>\n",
       "      <td>3/F, Emperor Cinemas Entertainment Building, 3...</td>\n",
       "      <td>22.281453</td>\n",
       "      <td>114.154230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>BEA IMAX @ UA iSQUARE</td>\n",
       "      <td>BEA IMAX @ UA iSQUARE</td>\n",
       "      <td>7/F, iSQUARE, 63 Nathan Road, Tsimshatsui</td>\n",
       "      <td>22.296648</td>\n",
       "      <td>114.171974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Phoenix Club @ UA iSQUARE</td>\n",
       "      <td>鳯凰影院 @ UA iSQUARE</td>\n",
       "      <td>7/F, iSQUARE, 63 Nathan Road, Tsimshatsui</td>\n",
       "      <td>22.296648</td>\n",
       "      <td>114.171974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>UA iSQUARE</td>\n",
       "      <td>UA iSQUARE</td>\n",
       "      <td>7/F, iSQUARE, 63 Nathan Road, Tsimshatsui</td>\n",
       "      <td>22.296648</td>\n",
       "      <td>114.171974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>BEA IMAX @ UA Cine Moko</td>\n",
       "      <td>BEA IMAX @ UA Cine Moko</td>\n",
       "      <td>L4, MOKO, 193 Prince Edward Road West, Mongkok...</td>\n",
       "      <td>22.323800</td>\n",
       "      <td>114.172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>UA Cine Moko</td>\n",
       "      <td>UA Cine Moko</td>\n",
       "      <td>L4, MOKO, 193 Prince Edward Road West, Mongkok...</td>\n",
       "      <td>22.323800</td>\n",
       "      <td>114.172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>BEA IMAX @ UA MegaBox</td>\n",
       "      <td>BEA IMAX @ UA MegaBox</td>\n",
       "      <td>Level 11, MegaBox, Enterprise Square 5, 38 Wan...</td>\n",
       "      <td>22.319533</td>\n",
       "      <td>114.208555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>BEA Oscars Club @ UA MegaBox</td>\n",
       "      <td>BEA Oscars Club @ UA MegaBox</td>\n",
       "      <td>Level 11, MegaBox, Enterprise Square 5, 38 Wan...</td>\n",
       "      <td>22.319533</td>\n",
       "      <td>114.208555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>UA MegaBox</td>\n",
       "      <td>UA MegaBox</td>\n",
       "      <td>Level 11, MegaBox, Enterprise Square 5, 38 Wan...</td>\n",
       "      <td>22.319533</td>\n",
       "      <td>114.208555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Name  \\\n",
       "5                 Cinema City VICTORIA (Causeway Bay)   \n",
       "6   Diamond Suite VIP House @ Cinema City VICTORIA...   \n",
       "28                                   The Grand Cinema   \n",
       "29                             The Grand SC Starsuite   \n",
       "0            Emperor Cinemas - Entertainment Building   \n",
       "1   The Coronet @ Emperor Cinemas - Entertainment ...   \n",
       "43                              BEA IMAX @ UA iSQUARE   \n",
       "46                          Phoenix Club @ UA iSQUARE   \n",
       "49                                         UA iSQUARE   \n",
       "42                            BEA IMAX @ UA Cine Moko   \n",
       "48                                       UA Cine Moko   \n",
       "44                              BEA IMAX @ UA MegaBox   \n",
       "45                       BEA Oscars Club @ UA MegaBox   \n",
       "51                                         UA MegaBox   \n",
       "\n",
       "                                              ChiName  \\\n",
       "5                          Cinema City VICTORIA (銅鑼灣)   \n",
       "6   Diamond Suite VIP House @ Cinema City VICTORIA...   \n",
       "28                                   The Grand Cinema   \n",
       "29                             The Grand SC Starsuite   \n",
       "0                                          英皇戲院 - 娛樂行   \n",
       "1                            The Coronet @ 英皇戲院 - 娛樂行   \n",
       "43                              BEA IMAX @ UA iSQUARE   \n",
       "46                                  鳯凰影院 @ UA iSQUARE   \n",
       "49                                         UA iSQUARE   \n",
       "42                            BEA IMAX @ UA Cine Moko   \n",
       "48                                       UA Cine Moko   \n",
       "44                              BEA IMAX @ UA MegaBox   \n",
       "45                       BEA Oscars Club @ UA MegaBox   \n",
       "51                                         UA MegaBox   \n",
       "\n",
       "                                              Address   Latitude   Longitude  \n",
       "5           2-8 Sugar Street, Causeway Bay, Hong Kong  22.279805  114.187126  \n",
       "6           2-8 Sugar Street, Causeway Bay, Hong Kong  22.279805  114.187126  \n",
       "28         2/F, Elements, 1 Austin Road West, Kowloon  22.304118  114.161466  \n",
       "29         2/F, Elements, 1 Austin Road West, Kowloon  22.304118  114.161466  \n",
       "0   3/F, Emperor Cinemas Entertainment Building, 3...  22.281453  114.154230  \n",
       "1   3/F, Emperor Cinemas Entertainment Building, 3...  22.281453  114.154230  \n",
       "43          7/F, iSQUARE, 63 Nathan Road, Tsimshatsui  22.296648  114.171974  \n",
       "46          7/F, iSQUARE, 63 Nathan Road, Tsimshatsui  22.296648  114.171974  \n",
       "49          7/F, iSQUARE, 63 Nathan Road, Tsimshatsui  22.296648  114.171974  \n",
       "42  L4, MOKO, 193 Prince Edward Road West, Mongkok...  22.323800  114.172000  \n",
       "48  L4, MOKO, 193 Prince Edward Road West, Mongkok...  22.323800  114.172000  \n",
       "44  Level 11, MegaBox, Enterprise Square 5, 38 Wan...  22.319533  114.208555  \n",
       "45  Level 11, MegaBox, Enterprise Square 5, 38 Wan...  22.319533  114.208555  \n",
       "51  Level 11, MegaBox, Enterprise Square 5, 38 Wan...  22.319533  114.208555  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicated = df_cinemas.duplicated('Address', keep=False)\n",
    "df_cinemas[duplicated].sort_values('Address')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some \"special house\" in cinema are separated as a new cinema in www.hkmovie6.com  \n",
    "These records are duplicated in my case and should be corrected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Grand SC Starsuite -> The Grand Cinema\n",
    "df_cinemas.loc[29, 'Name'] = 'The Grand Cinema'\n",
    "\n",
    "# XXX @ UA MegaBox -> UA MegaBox\n",
    "df_cinemas.loc[44, 'Name'] = 'UA MegaBox'\n",
    "df_cinemas.loc[45, 'Name'] = 'UA MegaBox'\n",
    "\n",
    "# BEA IMAX @ UA Cine Moko -> UA Cine Moko\n",
    "df_cinemas.loc[42, 'Name'] = 'UA Cine Moko'\n",
    "\n",
    "# XXX @ UA iSQUARE -> iSQUARE\n",
    "df_cinemas.loc[43, 'Name'] = 'UA iSQUARE'\n",
    "df_cinemas.loc[46, 'Name'] = 'UA iSQUARE'\n",
    "\n",
    "# Emperor Cinemas - Entertainment Building\n",
    "df_cinemas.loc[1, 'Name'] = 'Emperor Cinemas - Entertainment Building'\n",
    "\n",
    "# Cinema City VICTORIA (Causeway Bay)\n",
    "df_cinemas.loc[6, 'Name'] = 'Cinema City VICTORIA (Causeway Bay)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ChiName</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>英皇戲院 - 娛樂行</td>\n",
       "      <td>3/F, Emperor Cinemas Entertainment Building, 3...</td>\n",
       "      <td>22.281453</td>\n",
       "      <td>114.154230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>The Coronet @ 英皇戲院 - 娛樂行</td>\n",
       "      <td>3/F, Emperor Cinemas Entertainment Building, 3...</td>\n",
       "      <td>22.281453</td>\n",
       "      <td>114.154230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cinema City VICTORIA (Causeway Bay)</td>\n",
       "      <td>Cinema City VICTORIA (銅鑼灣)</td>\n",
       "      <td>2-8 Sugar Street, Causeway Bay, Hong Kong</td>\n",
       "      <td>22.279805</td>\n",
       "      <td>114.187126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cinema City VICTORIA (Causeway Bay)</td>\n",
       "      <td>Diamond Suite VIP House @ Cinema City VICTORIA...</td>\n",
       "      <td>2-8 Sugar Street, Causeway Bay, Hong Kong</td>\n",
       "      <td>22.279805</td>\n",
       "      <td>114.187126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>The Grand Cinema</td>\n",
       "      <td>The Grand Cinema</td>\n",
       "      <td>2/F, Elements, 1 Austin Road West, Kowloon</td>\n",
       "      <td>22.304118</td>\n",
       "      <td>114.161466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>The Grand Cinema</td>\n",
       "      <td>The Grand SC Starsuite</td>\n",
       "      <td>2/F, Elements, 1 Austin Road West, Kowloon</td>\n",
       "      <td>22.304118</td>\n",
       "      <td>114.161466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>UA Cine Moko</td>\n",
       "      <td>BEA IMAX @ UA Cine Moko</td>\n",
       "      <td>L4, MOKO, 193 Prince Edward Road West, Mongkok...</td>\n",
       "      <td>22.323800</td>\n",
       "      <td>114.172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>UA iSQUARE</td>\n",
       "      <td>BEA IMAX @ UA iSQUARE</td>\n",
       "      <td>7/F, iSQUARE, 63 Nathan Road, Tsimshatsui</td>\n",
       "      <td>22.296648</td>\n",
       "      <td>114.171974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>UA MegaBox</td>\n",
       "      <td>BEA IMAX @ UA MegaBox</td>\n",
       "      <td>Level 11, MegaBox, Enterprise Square 5, 38 Wan...</td>\n",
       "      <td>22.319533</td>\n",
       "      <td>114.208555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>UA MegaBox</td>\n",
       "      <td>BEA Oscars Club @ UA MegaBox</td>\n",
       "      <td>Level 11, MegaBox, Enterprise Square 5, 38 Wan...</td>\n",
       "      <td>22.319533</td>\n",
       "      <td>114.208555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>UA iSQUARE</td>\n",
       "      <td>鳯凰影院 @ UA iSQUARE</td>\n",
       "      <td>7/F, iSQUARE, 63 Nathan Road, Tsimshatsui</td>\n",
       "      <td>22.296648</td>\n",
       "      <td>114.171974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>UA Cine Moko</td>\n",
       "      <td>UA Cine Moko</td>\n",
       "      <td>L4, MOKO, 193 Prince Edward Road West, Mongkok...</td>\n",
       "      <td>22.323800</td>\n",
       "      <td>114.172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>UA iSQUARE</td>\n",
       "      <td>UA iSQUARE</td>\n",
       "      <td>7/F, iSQUARE, 63 Nathan Road, Tsimshatsui</td>\n",
       "      <td>22.296648</td>\n",
       "      <td>114.171974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>UA MegaBox</td>\n",
       "      <td>UA MegaBox</td>\n",
       "      <td>Level 11, MegaBox, Enterprise Square 5, 38 Wan...</td>\n",
       "      <td>22.319533</td>\n",
       "      <td>114.208555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name  \\\n",
       "0   Emperor Cinemas - Entertainment Building   \n",
       "1   Emperor Cinemas - Entertainment Building   \n",
       "5        Cinema City VICTORIA (Causeway Bay)   \n",
       "6        Cinema City VICTORIA (Causeway Bay)   \n",
       "28                          The Grand Cinema   \n",
       "29                          The Grand Cinema   \n",
       "42                              UA Cine Moko   \n",
       "43                                UA iSQUARE   \n",
       "44                                UA MegaBox   \n",
       "45                                UA MegaBox   \n",
       "46                                UA iSQUARE   \n",
       "48                              UA Cine Moko   \n",
       "49                                UA iSQUARE   \n",
       "51                                UA MegaBox   \n",
       "\n",
       "                                              ChiName  \\\n",
       "0                                          英皇戲院 - 娛樂行   \n",
       "1                            The Coronet @ 英皇戲院 - 娛樂行   \n",
       "5                          Cinema City VICTORIA (銅鑼灣)   \n",
       "6   Diamond Suite VIP House @ Cinema City VICTORIA...   \n",
       "28                                   The Grand Cinema   \n",
       "29                             The Grand SC Starsuite   \n",
       "42                            BEA IMAX @ UA Cine Moko   \n",
       "43                              BEA IMAX @ UA iSQUARE   \n",
       "44                              BEA IMAX @ UA MegaBox   \n",
       "45                       BEA Oscars Club @ UA MegaBox   \n",
       "46                                  鳯凰影院 @ UA iSQUARE   \n",
       "48                                       UA Cine Moko   \n",
       "49                                         UA iSQUARE   \n",
       "51                                         UA MegaBox   \n",
       "\n",
       "                                              Address   Latitude   Longitude  \n",
       "0   3/F, Emperor Cinemas Entertainment Building, 3...  22.281453  114.154230  \n",
       "1   3/F, Emperor Cinemas Entertainment Building, 3...  22.281453  114.154230  \n",
       "5           2-8 Sugar Street, Causeway Bay, Hong Kong  22.279805  114.187126  \n",
       "6           2-8 Sugar Street, Causeway Bay, Hong Kong  22.279805  114.187126  \n",
       "28         2/F, Elements, 1 Austin Road West, Kowloon  22.304118  114.161466  \n",
       "29         2/F, Elements, 1 Austin Road West, Kowloon  22.304118  114.161466  \n",
       "42  L4, MOKO, 193 Prince Edward Road West, Mongkok...  22.323800  114.172000  \n",
       "43          7/F, iSQUARE, 63 Nathan Road, Tsimshatsui  22.296648  114.171974  \n",
       "44  Level 11, MegaBox, Enterprise Square 5, 38 Wan...  22.319533  114.208555  \n",
       "45  Level 11, MegaBox, Enterprise Square 5, 38 Wan...  22.319533  114.208555  \n",
       "46          7/F, iSQUARE, 63 Nathan Road, Tsimshatsui  22.296648  114.171974  \n",
       "48  L4, MOKO, 193 Prince Edward Road West, Mongkok...  22.323800  114.172000  \n",
       "49          7/F, iSQUARE, 63 Nathan Road, Tsimshatsui  22.296648  114.171974  \n",
       "51  Level 11, MegaBox, Enterprise Square 5, 38 Wan...  22.319533  114.208555  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cinemas[duplicated]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cinemas.drop_duplicates('Address', inplace=True, keep='first')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the duplicated cinema records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ChiName</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Name, ChiName, Address, Latitude, Longitude]\n",
       "Index: []"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cinemas[df_cinemas.duplicated('Name')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ChiName</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>英皇戲院 - 娛樂行</td>\n",
       "      <td>3/F, Emperor Cinemas Entertainment Building, 3...</td>\n",
       "      <td>22.281453</td>\n",
       "      <td>114.154230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emperor Cinemas - Tuen Mun</td>\n",
       "      <td>英皇戲院 - 屯門新都商場</td>\n",
       "      <td>3/F, New Town Commercial Arcade, 2 Tuen Lee St...</td>\n",
       "      <td>22.390776</td>\n",
       "      <td>113.975983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Broadway Circuit - CYBERPORT</td>\n",
       "      <td>百老匯戲院 - 數碼港</td>\n",
       "      <td>Shop L1 - 3, Level 1, The Arcade, 100 Cyberpor...</td>\n",
       "      <td>22.261067</td>\n",
       "      <td>114.129825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Broadway Circuit - PALACE IFC</td>\n",
       "      <td>百老匯戲院 - PALACE IFC</td>\n",
       "      <td>Podium L1, IFC Mall, 8 Finance Street, Central</td>\n",
       "      <td>22.285545</td>\n",
       "      <td>114.157979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cinema City VICTORIA (Causeway Bay)</td>\n",
       "      <td>Cinema City VICTORIA (銅鑼灣)</td>\n",
       "      <td>2-8 Sugar Street, Causeway Bay, Hong Kong</td>\n",
       "      <td>22.279805</td>\n",
       "      <td>114.187126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Name                     ChiName  \\\n",
       "0  Emperor Cinemas - Entertainment Building                  英皇戲院 - 娛樂行   \n",
       "2                Emperor Cinemas - Tuen Mun               英皇戲院 - 屯門新都商場   \n",
       "3              Broadway Circuit - CYBERPORT                 百老匯戲院 - 數碼港   \n",
       "4             Broadway Circuit - PALACE IFC          百老匯戲院 - PALACE IFC   \n",
       "5       Cinema City VICTORIA (Causeway Bay)  Cinema City VICTORIA (銅鑼灣)   \n",
       "\n",
       "                                             Address   Latitude   Longitude  \n",
       "0  3/F, Emperor Cinemas Entertainment Building, 3...  22.281453  114.154230  \n",
       "2  3/F, New Town Commercial Arcade, 2 Tuen Lee St...  22.390776  113.975983  \n",
       "3  Shop L1 - 3, Level 1, The Arcade, 100 Cyberpor...  22.261067  114.129825  \n",
       "4     Podium L1, IFC Mall, 8 Finance Street, Central  22.285545  114.157979  \n",
       "5          2-8 Sugar Street, Causeway Bay, Hong Kong  22.279805  114.187126  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cinemas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>ChiName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>英皇戲院 - 娛樂行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>英皇戲院 - 屯門新都商場</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>百老匯戲院 - 數碼港</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>百老匯戲院 - PALACE IFC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cinema City VICTORIA (銅鑼灣)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>百老匯戲院 - 電影中心</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>百老匯戲院 - 荷里活</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>百老匯戲院 - 旺角</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>百老匯戲院 - MOViE MOViE Cityplaza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>百老匯戲院 - PALACE apm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>百老匯戲院 - The ONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>百老匯戲院 - 嘉湖銀座</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>百老匯戲院 - 葵芳</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>百老匯戲院 - MY CINEMA YOHO MALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>百老匯戲院 - 荃灣</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>康怡戲院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>皇室戲院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MCL 粉嶺戲院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MCL 海怡戲院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MOVIE TOWN - 新城市廣場</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MCL 德福</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Festival Grand Cinema</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CGV Cinemas D2 Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MCL 新都城戲院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>STAR Cinema</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>The Grand Cinema</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>嘉禾粉嶺</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>嘉禾黃埔</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>海運戲院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>StagE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>the sky (奧海城)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>嘉禾荃新天地</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>星影匯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CANDY PARK BY CINEMA CITY (愉景新城)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Cinema City 柴灣</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Cinema City JP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Cinema City 朗豪坊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>UA Cine Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>BEA IMAX @ UA Cine Moko</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>BEA IMAX @ UA iSQUARE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>BEA IMAX @ UA MegaBox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>UA 淘大</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>UA 青衣城</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>機場UA IMAX 影院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>總統</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>豪華</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>新寶</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>香港科學館演講廳</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>凱都</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>巴黎倫敦紐約米蘭戲院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>元朗戲院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>馬鞍山戲院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>L Cinema Shau Kei Wan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>饒館新一天院線</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>香港藝術中心古天樂電影院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>寶石戲院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>新光戲院大劇場</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Super 3 影院</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>大館</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             ChiName\n",
       "0                         英皇戲院 - 娛樂行\n",
       "2                      英皇戲院 - 屯門新都商場\n",
       "3                        百老匯戲院 - 數碼港\n",
       "4                 百老匯戲院 - PALACE IFC\n",
       "5         Cinema City VICTORIA (銅鑼灣)\n",
       "7                       百老匯戲院 - 電影中心\n",
       "8                        百老匯戲院 - 荷里活\n",
       "9                         百老匯戲院 - 旺角\n",
       "10     百老匯戲院 - MOViE MOViE Cityplaza\n",
       "11                百老匯戲院 - PALACE apm\n",
       "12                   百老匯戲院 - The ONE\n",
       "13                      百老匯戲院 - 嘉湖銀座\n",
       "14                        百老匯戲院 - 葵芳\n",
       "15       百老匯戲院 - MY CINEMA YOHO MALL\n",
       "16                        百老匯戲院 - 荃灣\n",
       "17                 AMC Pacific Place\n",
       "18                              康怡戲院\n",
       "19                              皇室戲院\n",
       "20                          MCL 粉嶺戲院\n",
       "21                          MCL 海怡戲院\n",
       "22                MOVIE TOWN - 新城市廣場\n",
       "23                            MCL 德福\n",
       "24             Festival Grand Cinema\n",
       "25              CGV Cinemas D2 Place\n",
       "26                         MCL 新都城戲院\n",
       "27                       STAR Cinema\n",
       "28                  The Grand Cinema\n",
       "30                              嘉禾粉嶺\n",
       "31                              嘉禾黃埔\n",
       "32                              海運戲院\n",
       "33                             StagE\n",
       "34                     the sky (奧海城)\n",
       "35                            嘉禾荃新天地\n",
       "36                               星影匯\n",
       "37  CANDY PARK BY CINEMA CITY (愉景新城)\n",
       "38                    Cinema City 柴灣\n",
       "39                    Cinema City JP\n",
       "40                   Cinema City 朗豪坊\n",
       "41                     UA Cine Times\n",
       "42           BEA IMAX @ UA Cine Moko\n",
       "43             BEA IMAX @ UA iSQUARE\n",
       "44             BEA IMAX @ UA MegaBox\n",
       "47                             UA 淘大\n",
       "50                            UA 青衣城\n",
       "52                      機場UA IMAX 影院\n",
       "53                                總統\n",
       "54                                豪華\n",
       "55                                新寶\n",
       "56                          香港科學館演講廳\n",
       "57                                凱都\n",
       "58                        巴黎倫敦紐約米蘭戲院\n",
       "59                              元朗戲院\n",
       "60                             馬鞍山戲院\n",
       "61             L Cinema Shau Kei Wan\n",
       "62                           饒館新一天院線\n",
       "63                      香港藝術中心古天樂電影院\n",
       "64                              寶石戲院\n",
       "65                           新光戲院大劇場\n",
       "66                        Super 3 影院\n",
       "67                                大館"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cinemas['ChiName'].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cinema '新光戲院大劇場' and '大館' should be considered as cinema in Hong Kong. These records must be rmeoved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cinemas.drop(index=[65,67], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cinemas.drop(axis=1, columns=['ChiName'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>3/F, Emperor Cinemas Entertainment Building, 3...</td>\n",
       "      <td>22.281453</td>\n",
       "      <td>114.154230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emperor Cinemas - Tuen Mun</td>\n",
       "      <td>3/F, New Town Commercial Arcade, 2 Tuen Lee St...</td>\n",
       "      <td>22.390776</td>\n",
       "      <td>113.975983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Broadway Circuit - CYBERPORT</td>\n",
       "      <td>Shop L1 - 3, Level 1, The Arcade, 100 Cyberpor...</td>\n",
       "      <td>22.261067</td>\n",
       "      <td>114.129825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Broadway Circuit - PALACE IFC</td>\n",
       "      <td>Podium L1, IFC Mall, 8 Finance Street, Central</td>\n",
       "      <td>22.285545</td>\n",
       "      <td>114.157979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cinema City VICTORIA (Causeway Bay)</td>\n",
       "      <td>2-8 Sugar Street, Causeway Bay, Hong Kong</td>\n",
       "      <td>22.279805</td>\n",
       "      <td>114.187126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Name  \\\n",
       "0  Emperor Cinemas - Entertainment Building   \n",
       "2                Emperor Cinemas - Tuen Mun   \n",
       "3              Broadway Circuit - CYBERPORT   \n",
       "4             Broadway Circuit - PALACE IFC   \n",
       "5       Cinema City VICTORIA (Causeway Bay)   \n",
       "\n",
       "                                             Address   Latitude   Longitude  \n",
       "0  3/F, Emperor Cinemas Entertainment Building, 3...  22.281453  114.154230  \n",
       "2  3/F, New Town Commercial Arcade, 2 Tuen Lee St...  22.390776  113.975983  \n",
       "3  Shop L1 - 3, Level 1, The Arcade, 100 Cyberpor...  22.261067  114.129825  \n",
       "4     Podium L1, IFC Mall, 8 Finance Street, Central  22.285545  114.157979  \n",
       "5          2-8 Sugar Street, Causeway Bay, Hong Kong  22.279805  114.187126  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cinemas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the shape of cinemas dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58, 4)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cinemas.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I can use the FourSquare API to explore nearby venues of Hong Kong cinemas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "venues_csv = Path('./cinemas_venues.csv')\n",
    "df_venues = None\n",
    "\n",
    "# check the venues data is explored and downloaded \n",
    "if( venues_csv.exists() ):\n",
    "    df_venues = pd.read_csv('./cinemas_venues.csv')\n",
    "else:    \n",
    "    # construct a dataframe to store data\n",
    "    df_venues = pd.DataFrame(columns=['Cinema Name', 'Category', 'Name', 'Latitude', 'Longitude', 'Tips', 'Users', 'Visits'])\n",
    "    for (name, address, latitude, longitude) in df_cinemas.itertuples(index=False):\n",
    "        for cat, cat_id in fs_categories.items():\n",
    "            df = venues_nearby(latitude, longitude, cat, verbose=False)\n",
    "            df['Cinema Name'] = name\n",
    "            df['Category'] = cat\n",
    "            df_venues = df_venues.append(df, sort=True)\n",
    "    df_venues.to_csv('cinemas_venues.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total 2223 of venues are found\n"
     ]
    }
   ],
   "source": [
    "print('Total {} of venues are found'.format(len(df_venues)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2223, 8)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of data\n",
    "df_venues.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Category</th>\n",
       "      <th>Cinema Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Name</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Food</td>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>22.282921</td>\n",
       "      <td>114.154651</td>\n",
       "      <td>Mana! Fast Slow Food</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Food</td>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>22.281165</td>\n",
       "      <td>114.155296</td>\n",
       "      <td>Good Luck Thai Food (鴻運泰國美食)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Food</td>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>22.281668</td>\n",
       "      <td>114.152495</td>\n",
       "      <td>Soul Food</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Food</td>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>22.282659</td>\n",
       "      <td>114.156753</td>\n",
       "      <td>Chiu Lung Fast Food (昭隆美食)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Food</td>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>22.282521</td>\n",
       "      <td>114.156717</td>\n",
       "      <td>Sun Hing Fast Food (新興美食)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category                               Cinema Name   Latitude   Longitude  \\\n",
       "0     Food  Emperor Cinemas - Entertainment Building  22.282921  114.154651   \n",
       "1     Food  Emperor Cinemas - Entertainment Building  22.281165  114.155296   \n",
       "2     Food  Emperor Cinemas - Entertainment Building  22.281668  114.152495   \n",
       "3     Food  Emperor Cinemas - Entertainment Building  22.282659  114.156753   \n",
       "4     Food  Emperor Cinemas - Entertainment Building  22.282521  114.156717   \n",
       "\n",
       "                           Name  Tips  Users  Visits  \n",
       "0          Mana! Fast Slow Food     0      0       0  \n",
       "1  Good Luck Thai Food (鴻運泰國美食)     0      0       0  \n",
       "2                     Soul Food     0      0       0  \n",
       "3    Chiu Lung Fast Food (昭隆美食)     0      0       0  \n",
       "4     Sun Hing Fast Food (新興美食)     0      0       0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check some data\n",
    "df_venues.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of venues in each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <td>874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bus Stop</th>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food</th>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metro Station</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nightlife Spot</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Count\n",
       "Shop & Service          874\n",
       "Bus Stop                704\n",
       "Food                    518\n",
       "Arts & Entertainment     65\n",
       "Metro Station            61\n",
       "Nightlife Spot            1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues['Category'].value_counts().to_frame(name='Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Cinema Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Name</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Category, Cinema Name, Latitude, Longitude, Name, Tips, Users, Visits]\n",
       "Index: []"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues[(df_venues.Tips > 0)|(df_venues.Users > 0)|(df_venues.Visits > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues.drop(columns=['Tips','Users','Visits'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Cinema Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Nightlife Spot</td>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>22.282246</td>\n",
       "      <td>114.152651</td>\n",
       "      <td>The Spot Bar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Category                               Cinema Name   Latitude  \\\n",
       "87  Nightlife Spot  Emperor Cinemas - Entertainment Building  22.282246   \n",
       "\n",
       "     Longitude          Name  \n",
       "87  114.152651  The Spot Bar  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues[df_venues.Category=='Nightlife Spot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues.drop(index=87, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comapred with other categories, only one 'Nightlife Spot' venue. This category is removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2222, 5)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore nearby venues of 5 possible/target locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_target_venues = pd.DataFrame(columns=['Location', 'Category', 'Name', 'Latitude', 'Longitude', 'Tips', 'Users', 'Visits'])\n",
    "for (location, address, latitude, longitude) in df_possible_locations.itertuples(index=False):\n",
    "    for cat, cat_id in fs_categories.items():\n",
    "        df = venues_nearby(latitude, longitude, cat, verbose=False)\n",
    "        df['Location'] = location\n",
    "        df['Category'] = cat\n",
    "        df_target_venues = df_target_venues.append(df, sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Category</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Location</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Name</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>22.319338</td>\n",
       "      <td>L1</td>\n",
       "      <td>114.231681</td>\n",
       "      <td>Sau Mau Ping Shopping Centre (秀茂坪商場)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>22.320360</td>\n",
       "      <td>L1</td>\n",
       "      <td>114.234489</td>\n",
       "      <td>On Tat Shopping Centre (安達商場)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>22.314968</td>\n",
       "      <td>L1</td>\n",
       "      <td>114.229987</td>\n",
       "      <td>Tsui Ping Shopping Circuit 翠屏商場</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>22.316921</td>\n",
       "      <td>L1</td>\n",
       "      <td>114.235785</td>\n",
       "      <td>Sau Fai House Bus Stop 秀暉樓巴士站</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Food</td>\n",
       "      <td>22.372900</td>\n",
       "      <td>L2</td>\n",
       "      <td>113.964900</td>\n",
       "      <td>Lee Kam Kee Vietnamese Food &amp; Drinks (李錦基越南飲食)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Category   Latitude Location   Longitude  \\\n",
       "0  Shop & Service  22.319338       L1  114.231681   \n",
       "1  Shop & Service  22.320360       L1  114.234489   \n",
       "2  Shop & Service  22.314968       L1  114.229987   \n",
       "0        Bus Stop  22.316921       L1  114.235785   \n",
       "0            Food  22.372900       L2  113.964900   \n",
       "\n",
       "                                             Name Tips Users Visits  \n",
       "0            Sau Mau Ping Shopping Centre (秀茂坪商場)    0     0      0  \n",
       "1                   On Tat Shopping Centre (安達商場)    0     0      0  \n",
       "2                 Tsui Ping Shopping Circuit 翠屏商場    0     0      0  \n",
       "0                   Sau Fai House Bus Stop 秀暉樓巴士站    0     0      0  \n",
       "0  Lee Kam Kee Vietnamese Food & Drinks (李錦基越南飲食)    0     0      0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Category</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Location</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Name</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Category, Latitude, Location, Longitude, Name, Tips, Users, Visits]\n",
       "Index: []"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target_venues[(df_target_venues.Tips > 0)|(df_target_venues.Users > 0)|(df_target_venues.Visits > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_target_venues.drop(columns=['Tips','Users','Visits'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bus Stop</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metro Station</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Count\n",
       "Bus Stop                 36\n",
       "Shop & Service           30\n",
       "Food                     12\n",
       "Metro Station             4\n",
       "Arts & Entertainment      1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target_venues['Category'].value_counts().to_frame(name='Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No venue is found for 'Nightlife Spot' category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83, 5)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target_venues.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I only interested in number of venues in each category of dataframe.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Cinema Name</th>\n",
       "      <th>Category</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "      <td>Arts &amp; Entertainment</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "      <td>Food</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "      <td>Metro Station</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cinema Name              Category  Count\n",
       "0  AMC Pacific Place  Arts & Entertainment      8\n",
       "1  AMC Pacific Place              Bus Stop     13\n",
       "2  AMC Pacific Place                  Food      8\n",
       "3  AMC Pacific Place         Metro Station      1\n",
       "4  AMC Pacific Place        Shop & Service     21"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues_count = df_venues.groupby(['Cinema Name','Category'], as_index=False).count()\n",
    "df_venues_count.drop(columns=['Latitude','Longitude'], inplace=True)\n",
    "df_venues_count.rename(columns={'Name':'Count'}, inplace=True)\n",
    "df_venues_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Category</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Food</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinema Name</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>AMC Pacific Place</th>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadway Circuit - CINEMATHEQUE</th>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadway Circuit - CYBERPORT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadway Circuit - HOLLYWOOD</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadway Circuit - KINGSWOOD GINZA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Category                            Arts & Entertainment  Bus Stop  Food  \\\n",
       "Cinema Name                                                                \n",
       "AMC Pacific Place                                    8.0      13.0   8.0   \n",
       "Broadway Circuit - CINEMATHEQUE                      3.0      25.0  13.0   \n",
       "Broadway Circuit - CYBERPORT                         0.0       2.0   1.0   \n",
       "Broadway Circuit - HOLLYWOOD                         1.0       7.0   0.0   \n",
       "Broadway Circuit - KINGSWOOD GINZA                   0.0       3.0   0.0   \n",
       "\n",
       "Category                            Metro Station  Shop & Service  \n",
       "Cinema Name                                                        \n",
       "AMC Pacific Place                             1.0            21.0  \n",
       "Broadway Circuit - CINEMATHEQUE               1.0            25.0  \n",
       "Broadway Circuit - CYBERPORT                  0.0             3.0  \n",
       "Broadway Circuit - HOLLYWOOD                  1.0             6.0  \n",
       "Broadway Circuit - KINGSWOOD GINZA            0.0             2.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues_count = df_venues_count.pivot(index='Cinema Name', columns='Category', values='Count').fillna(0)\n",
    "df_venues_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do the same process on target locations\n",
    "df_target_venues_count = df_target_venues.groupby(['Location','Category']).size().reset_index(name='Count')\n",
    "df_target_venues_count = df_target_venues_count.pivot(index='Location', columns='Category', values='Count').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Category</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Food</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</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>L1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Category  Arts & Entertainment  Bus Stop  Food  Metro Station  Shop & Service\n",
       "Location                                                                     \n",
       "L1                         0.0       1.0   0.0            0.0             3.0\n",
       "L2                         0.0       4.0   2.0            0.0             1.0\n",
       "L3                         0.0       9.0   4.0            1.0             9.0\n",
       "L4                         1.0       9.0   2.0            1.0             5.0\n",
       "L5                         0.0      13.0   4.0            2.0            12.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target_venues_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check boss's favorite cinema list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Name': 'Broadway Circuit - MONGKOK', 'Rating': 4.5},\n",
       " {'Name': 'Broadway Circuit - The ONE', 'Rating': 4.5},\n",
       " {'Name': 'Grand Ocean', 'Rating': 4.3},\n",
       " {'Name': 'The Grand Cinema', 'Rating': 3.4},\n",
       " {'Name': 'AMC Pacific Place', 'Rating': 2.3},\n",
       " {'Name': 'UA IMAX @ Airport', 'Rating': 1.5}]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boss_favorite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check boss's favorite cinemas are inside the hong kong cinemas dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the Hong Kong cinema list contains all stakeholder's favorite cinemas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Broadway Circuit - MONGKOK</td>\n",
       "      <td>6-12 Sai Yeung Choi Street, Mongkok, Kowloon</td>\n",
       "      <td>22.317077</td>\n",
       "      <td>114.170662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Broadway Circuit - The ONE</td>\n",
       "      <td>6-11/F, The ONE, No. 100 Nathan Road, Tsim Sha...</td>\n",
       "      <td>22.300058</td>\n",
       "      <td>114.172667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "      <td>Level 1, Pacific Place, 88 Queensway Road, Hon...</td>\n",
       "      <td>22.277673</td>\n",
       "      <td>114.165566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>The Grand Cinema</td>\n",
       "      <td>2/F, Elements, 1 Austin Road West, Kowloon</td>\n",
       "      <td>22.304118</td>\n",
       "      <td>114.161466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Grand Ocean</td>\n",
       "      <td>Ocean Centre, 3 Canton Road, Kowloon</td>\n",
       "      <td>22.295165</td>\n",
       "      <td>114.169176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>UA IMAX @ Airport</td>\n",
       "      <td>6P059, Level 6, Terminal 2, 1 Sky Plaza Road, ...</td>\n",
       "      <td>22.316668</td>\n",
       "      <td>113.937787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Name  \\\n",
       "9   Broadway Circuit - MONGKOK   \n",
       "12  Broadway Circuit - The ONE   \n",
       "17           AMC Pacific Place   \n",
       "28            The Grand Cinema   \n",
       "32                 Grand Ocean   \n",
       "52           UA IMAX @ Airport   \n",
       "\n",
       "                                              Address   Latitude   Longitude  \n",
       "9        6-12 Sai Yeung Choi Street, Mongkok, Kowloon  22.317077  114.170662  \n",
       "12  6-11/F, The ONE, No. 100 Nathan Road, Tsim Sha...  22.300058  114.172667  \n",
       "17  Level 1, Pacific Place, 88 Queensway Road, Hon...  22.277673  114.165566  \n",
       "28         2/F, Elements, 1 Austin Road West, Kowloon  22.304118  114.161466  \n",
       "32               Ocean Centre, 3 Canton Road, Kowloon  22.295165  114.169176  \n",
       "52  6P059, Level 6, Terminal 2, 1 Sky Plaza Road, ...  22.316668  113.937787  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [ cinema['Name'] for cinema in boss_favorite ]\n",
    "df_cinemas[df_cinemas.Name.isin(names)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stakholder's favorite cinema list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Broadway Circuit - MONGKOK</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Broadway Circuit - The ONE</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grand Ocean</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Grand Cinema</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UA IMAX @ Airport</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name  Rating\n",
       "0  Broadway Circuit - MONGKOK     4.5\n",
       "1  Broadway Circuit - The ONE     4.5\n",
       "2                 Grand Ocean     4.3\n",
       "3            The Grand Cinema     3.4\n",
       "4           AMC Pacific Place     2.3\n",
       "5           UA IMAX @ Airport     1.5"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boss_favorite = pd.DataFrame(boss_favorite, columns=['Name','Rating'])\n",
    "df_boss_favorite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/jupyterlab/conda\n",
      "\n",
      "  added / updated specs: \n",
      "    - seaborn=0.9\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    seaborn-0.9.0              |           py36_0         379 KB\n",
      "    conda-4.5.11               |           py36_0         1.0 MB\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         1.4 MB\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    certifi:         2018.8.24-py36_1     conda-forge --> 2018.8.24-py36_1 \n",
      "    conda:           4.5.11-py36_0        conda-forge --> 4.5.11-py36_0    \n",
      "    openssl:         1.0.2p-h470a237_0    conda-forge --> 1.0.2p-h14c3975_0\n",
      "    seaborn:         0.8.1-py36hfad7ec4_0             --> 0.9.0-py36_0     \n",
      "\n",
      "The following packages will be DOWNGRADED:\n",
      "\n",
      "    ca-certificates: 2018.8.24-ha4d7672_0 conda-forge --> 2018.03.07-0     \n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "seaborn-0.9.0        | 379 KB    | ##################################### | 100% \n",
      "conda-4.5.11         | 1.0 MB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n"
     ]
    }
   ],
   "source": [
    "!conda install seaborn=0.9 --yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the data type of variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Data Type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bus Stop</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metro Station</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Data Type\n",
       "Category                      \n",
       "Arts & Entertainment   float64\n",
       "Bus Stop               float64\n",
       "Food                   float64\n",
       "Metro Station          float64\n",
       "Shop & Service         float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues_count.dtypes.to_frame(name='Data Type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All datatype is numeric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generates descriptive statistics that summarize the central tendency,\n",
    "dispersion and shape of a dataset's distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Category</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Food</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.140351</td>\n",
       "      <td>12.350877</td>\n",
       "      <td>9.087719</td>\n",
       "      <td>1.070175</td>\n",
       "      <td>15.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.430745</td>\n",
       "      <td>9.510316</td>\n",
       "      <td>8.018462</td>\n",
       "      <td>0.820706</td>\n",
       "      <td>10.736010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Category  Arts & Entertainment   Bus Stop       Food  Metro Station  \\\n",
       "count                57.000000  57.000000  57.000000      57.000000   \n",
       "mean                  1.140351  12.350877   9.087719       1.070175   \n",
       "std                   2.430745   9.510316   8.018462       0.820706   \n",
       "min                   0.000000   1.000000   0.000000       0.000000   \n",
       "25%                   0.000000   5.000000   2.000000       1.000000   \n",
       "50%                   0.000000   9.000000   6.000000       1.000000   \n",
       "75%                   1.000000  21.000000  16.000000       1.000000   \n",
       "max                  12.000000  30.000000  27.000000       4.000000   \n",
       "\n",
       "Category  Shop & Service  \n",
       "count          57.000000  \n",
       "mean           15.333333  \n",
       "std            10.736010  \n",
       "min             2.000000  \n",
       "25%             6.000000  \n",
       "50%            11.000000  \n",
       "75%            30.000000  \n",
       "max            30.000000  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues_count.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cinema really has many 'Bus Stop', 'Food', 'Shop & Service' venues around. However it is unusual that a cinema has 4 metro stations nearby (within 500 meters).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Cinema Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cinema Count\n",
       "0.0            13\n",
       "1.0            30\n",
       "2.0            12\n",
       "3.0             1\n",
       "4.0             1"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues_count['Metro Station'].value_counts().sort_index().to_frame('Cinema Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One cinema contains 4 Metro Station around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Category</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Food</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinema Name</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>Broadway Circuit - The ONE</th>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUX Theatre</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Category                    Arts & Entertainment  Bus Stop  Food  \\\n",
       "Cinema Name                                                        \n",
       "Broadway Circuit - The ONE                   3.0      21.0  27.0   \n",
       "LUX Theatre                                  0.0      12.0  12.0   \n",
       "\n",
       "Category                    Metro Station  Shop & Service  \n",
       "Cinema Name                                                \n",
       "Broadway Circuit - The ONE            3.0            30.0  \n",
       "LUX Theatre                           4.0            11.0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues_count[df_venues_count['Metro Station'] > 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Category</th>\n",
       "      <th>Cinema Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Metro Station</td>\n",
       "      <td>Broadway Circuit - The ONE</td>\n",
       "      <td>22.297150</td>\n",
       "      <td>114.172230</td>\n",
       "      <td>MTR Tsim Sha Tsui Station (港鐵尖沙咀站)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Metro Station</td>\n",
       "      <td>Broadway Circuit - The ONE</td>\n",
       "      <td>22.304787</td>\n",
       "      <td>114.171664</td>\n",
       "      <td>MTR Jordan Station (港鐵佐敦站)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>Metro Station</td>\n",
       "      <td>Broadway Circuit - The ONE</td>\n",
       "      <td>22.295573</td>\n",
       "      <td>114.173652</td>\n",
       "      <td>MTR East Tsim Sha Tsui Station (港鐵尖東站)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>Metro Station</td>\n",
       "      <td>LUX Theatre</td>\n",
       "      <td>22.305477</td>\n",
       "      <td>114.188624</td>\n",
       "      <td>MTR Whampoa Station (港鐵黃埔站)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>Metro Station</td>\n",
       "      <td>LUX Theatre</td>\n",
       "      <td>22.309115</td>\n",
       "      <td>114.182668</td>\n",
       "      <td>MTR Ho Man Tin Station (港鐵何文田站)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2182</th>\n",
       "      <td>Metro Station</td>\n",
       "      <td>LUX Theatre</td>\n",
       "      <td>22.303110</td>\n",
       "      <td>114.181630</td>\n",
       "      <td>Mtr Hung Hom Station Platform 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2183</th>\n",
       "      <td>Metro Station</td>\n",
       "      <td>LUX Theatre</td>\n",
       "      <td>22.303085</td>\n",
       "      <td>114.181160</td>\n",
       "      <td>Mtr Hung Hom Station Platform 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category                 Cinema Name   Latitude   Longitude  \\\n",
       "609   Metro Station  Broadway Circuit - The ONE  22.297150  114.172230   \n",
       "610   Metro Station  Broadway Circuit - The ONE  22.304787  114.171664   \n",
       "611   Metro Station  Broadway Circuit - The ONE  22.295573  114.173652   \n",
       "2180  Metro Station                 LUX Theatre  22.305477  114.188624   \n",
       "2181  Metro Station                 LUX Theatre  22.309115  114.182668   \n",
       "2182  Metro Station                 LUX Theatre  22.303110  114.181630   \n",
       "2183  Metro Station                 LUX Theatre  22.303085  114.181160   \n",
       "\n",
       "                                        Name  \n",
       "609       MTR Tsim Sha Tsui Station (港鐵尖沙咀站)  \n",
       "610               MTR Jordan Station (港鐵佐敦站)  \n",
       "611   MTR East Tsim Sha Tsui Station (港鐵尖東站)  \n",
       "2180             MTR Whampoa Station (港鐵黃埔站)  \n",
       "2181         MTR Ho Man Tin Station (港鐵何文田站)  \n",
       "2182         Mtr Hung Hom Station Platform 2  \n",
       "2183         Mtr Hung Hom Station Platform 4  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_over_2 = df_venues_count[df_venues_count['Metro Station'] > 2].index.tolist()\n",
    "df_venues[(df_venues['Cinema Name'].isin(metro_over_2)) & (df_venues.Category == 'Metro Station')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Venue 'Mtr Hung Hom Station Platform 4' is duplicated and should be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues.loc[2182, 'Name'] = 'MTR Hung Hom Station'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues.drop(index=2183, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-construct the dataframe again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Category</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Food</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinema Name</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>AMC Pacific Place</th>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadway Circuit - CINEMATHEQUE</th>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadway Circuit - CYBERPORT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadway Circuit - HOLLYWOOD</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadway Circuit - KINGSWOOD GINZA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Category                            Arts & Entertainment  Bus Stop  Food  \\\n",
       "Cinema Name                                                                \n",
       "AMC Pacific Place                                    8.0      13.0   8.0   \n",
       "Broadway Circuit - CINEMATHEQUE                      3.0      25.0  13.0   \n",
       "Broadway Circuit - CYBERPORT                         0.0       2.0   1.0   \n",
       "Broadway Circuit - HOLLYWOOD                         1.0       7.0   0.0   \n",
       "Broadway Circuit - KINGSWOOD GINZA                   0.0       3.0   0.0   \n",
       "\n",
       "Category                            Metro Station  Shop & Service  \n",
       "Cinema Name                                                        \n",
       "AMC Pacific Place                             1.0            21.0  \n",
       "Broadway Circuit - CINEMATHEQUE               1.0            25.0  \n",
       "Broadway Circuit - CYBERPORT                  0.0             3.0  \n",
       "Broadway Circuit - HOLLYWOOD                  1.0             6.0  \n",
       "Broadway Circuit - KINGSWOOD GINZA            0.0             2.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues_count = df_venues.groupby(['Cinema Name','Category'], as_index=False).count()\n",
    "df_venues_count.drop(columns=['Latitude','Longitude'], inplace=True)\n",
    "df_venues_count.rename(columns={'Name':'Count'}, inplace=True)\n",
    "df_venues_count = df_venues_count.pivot(index='Cinema Name', columns='Category', values='Count').fillna(0)\n",
    "df_venues_count.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of other variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5602a58>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(2, 2, figsize=(10, 10))\n",
    "sns.distplot(df_venues_count['Arts & Entertainment'] , color=\"skyblue\", ax=axes[0, 0], kde=False)\n",
    "sns.distplot(df_venues_count['Bus Stop'] , color=\"olive\", ax=axes[0, 1], kde=False)\n",
    "sns.distplot(df_venues_count['Food'] , color=\"gold\", ax=axes[1, 0], kde=False)\n",
    "sns.distplot(df_venues_count['Shop & Service'] , color=\"teal\", ax=axes[1, 1], kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of other variables are quite similar. Now check their **Pearson Correlation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Category</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Food</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</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>Arts &amp; Entertainment</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.494525</td>\n",
       "      <td>0.414387</td>\n",
       "      <td>0.389271</td>\n",
       "      <td>0.506590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bus Stop</th>\n",
       "      <td>0.494525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.893873</td>\n",
       "      <td>0.563799</td>\n",
       "      <td>0.896388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food</th>\n",
       "      <td>0.414387</td>\n",
       "      <td>0.893873</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.583749</td>\n",
       "      <td>0.872533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metro Station</th>\n",
       "      <td>0.389271</td>\n",
       "      <td>0.563799</td>\n",
       "      <td>0.583749</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <td>0.506590</td>\n",
       "      <td>0.896388</td>\n",
       "      <td>0.872533</td>\n",
       "      <td>0.499546</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Category              Arts & Entertainment  Bus Stop      Food  Metro Station  \\\n",
       "Category                                                                        \n",
       "Arts & Entertainment              1.000000  0.494525  0.414387       0.389271   \n",
       "Bus Stop                          0.494525  1.000000  0.893873       0.563799   \n",
       "Food                              0.414387  0.893873  1.000000       0.583749   \n",
       "Metro Station                     0.389271  0.563799  0.583749       1.000000   \n",
       "Shop & Service                    0.506590  0.896388  0.872533       0.499546   \n",
       "\n",
       "Category              Shop & Service  \n",
       "Category                              \n",
       "Arts & Entertainment        0.506590  \n",
       "Bus Stop                    0.896388  \n",
       "Food                        0.872533  \n",
       "Metro Station               0.499546  \n",
       "Shop & Service              1.000000  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues_count.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that 'Bus Stop', 'Shop & Service' and 'Food' category are highly correlated.  \n",
    "Find **P-Value** of the variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By convention, when the p-value is:\n",
    "- < 0.001 we say there is strong evidence that the correlation is significant,\n",
    "- < 0.05; there is moderate evidence that the correlation is significant,\n",
    "- < 0.1; there is weak evidence that the correlation is significant, and\n",
    "- is >  0.1; there is no evidence that the correlation is significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_value_data = []\n",
    "for left in df_venues_count.columns:\n",
    "    p_values = [left]\n",
    "    for right in df_venues_count.columns:        \n",
    "        pearson_coef, p_value = stats.pearsonr(df_venues_count[left], df_venues_count[right])\n",
    "        if(p_value < 0.001):\n",
    "            p_values.append('strong')\n",
    "        elif(p_value < 0.05):\n",
    "            p_values.append('moderate')\n",
    "        elif(p_value < 0.1):\n",
    "            p_values.append('weak')\n",
    "        else:\n",
    "            p_values.append('no')            \n",
    "    p_value_data.append(p_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_p_values = pd.DataFrame(p_value_data, columns=['Category'] + df_venues_count.columns.tolist())"
   ]
  },
  {
   "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>Category</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Food</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arts &amp; Entertainment</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>moderate</td>\n",
       "      <td>moderate</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Food</td>\n",
       "      <td>moderate</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Metro Station</td>\n",
       "      <td>moderate</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Category Arts & Entertainment Bus Stop      Food Metro Station  \\\n",
       "0  Arts & Entertainment               strong   strong  moderate      moderate   \n",
       "1              Bus Stop               strong   strong    strong        strong   \n",
       "2                  Food             moderate   strong    strong        strong   \n",
       "3         Metro Station             moderate   strong    strong        strong   \n",
       "4        Shop & Service               strong   strong    strong        strong   \n",
       "\n",
       "  Shop & Service  \n",
       "0         strong  \n",
       "1         strong  \n",
       "2         strong  \n",
       "3         strong  \n",
       "4         strong  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_p_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation between 'Bus Stop', 'Food', 'Metro Station' and 'Shop & Service' are statistically significant, and the coefficient of > 0.5 shows that the relationship is positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Broadway Circuit - MONGKOK</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Broadway Circuit - The ONE</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grand Ocean</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Grand Cinema</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UA IMAX @ Airport</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name  Rating\n",
       "0  Broadway Circuit - MONGKOK     4.5\n",
       "1  Broadway Circuit - The ONE     4.5\n",
       "2                 Grand Ocean     4.3\n",
       "3            The Grand Cinema     3.4\n",
       "4           AMC Pacific Place     2.3\n",
       "5           UA IMAX @ Airport     1.5"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boss_favorite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/jupyterlab/conda\n",
      "\n",
      "  added / updated specs: \n",
      "    - folium=0.5\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    folium-0.5.0               |             py_0          45 KB  conda-forge\n",
      "    conda-4.5.11               |           py36_0         625 KB  conda-forge\n",
      "    certifi-2018.8.24          |        py36_1001         139 KB  conda-forge\n",
      "    vincent-0.4.4              |             py_1          28 KB  conda-forge\n",
      "    branca-0.3.0               |             py_0          24 KB  conda-forge\n",
      "    altair-2.2.2               |           py36_1         461 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         1.3 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "    altair:          2.2.2-py36_1      conda-forge\n",
      "    branca:          0.3.0-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: 2018.03.07-0                  --> 2018.8.24-ha4d7672_0 conda-forge\n",
      "    certifi:         2018.8.24-py36_1              --> 2018.8.24-py36_1001  conda-forge\n",
      "    conda:           4.5.11-py36_0                 --> 4.5.11-py36_0        conda-forge\n",
      "    openssl:         1.0.2p-h14c3975_0             --> 1.0.2p-h470a237_0    conda-forge\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "folium-0.5.0         | 45 KB     | ##################################### | 100% \n",
      "conda-4.5.11         | 625 KB    | ##################################### | 100% \n",
      "certifi-2018.8.24    | 139 KB    | ##################################### | 100% \n",
      "vincent-0.4.4        | 28 KB     | ##################################### | 100% \n",
      "branca-0.3.0         | 24 KB     | ##################################### | 100% \n",
      "altair-2.2.2         | 461 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: / "
     ]
    }
   ],
   "source": [
    "!conda install -c conda-forge folium=0.5 --yes\n",
    "import folium\n",
    "\n",
    "print('Folium installed and imported!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hk_coords = getLatLng('Hong Kong')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the location of cinemas, target location and stakeholder's favorite cineams on the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hk_map = folium.Map(location=hk_coords, zoom_start=12, tiles='Stamen Toner')\n",
    "\n",
    "cinemas_fg = folium.FeatureGroup()\n",
    "targets_fg = folium.FeatureGroup()\n",
    "\n",
    "for(location, address, latitude, longitude) in df_possible_locations.itertuples(index=False):\n",
    "    targets_fg.add_child(\n",
    "        folium.features.CircleMarker(\n",
    "            location=(latitude, longitude),\n",
    "            popup=location,\n",
    "            radius=5,\n",
    "            fill=True,\n",
    "            color='yellow',\n",
    "            fill_opacity=1.\n",
    "        )\n",
    "    )\n",
    "\n",
    "boss_ratings = df_boss_favorite.set_index('Name')    \n",
    "name_list = boss_ratings.index.tolist()\n",
    "\n",
    "for (name, address, latitude, longitude ) in df_cinemas.itertuples(index=False):    \n",
    "    \n",
    "    color = 'blue'        \n",
    "    popup = name\n",
    "    \n",
    "    if( name in name_list ):\n",
    "        color = 'red'    \n",
    "        popup = '{} - Rating: {}'.format(name, boss_ratings.loc[name,'Rating'])\n",
    "        \n",
    "    cinemas_fg.add_child(        \n",
    "        folium.features.CircleMarker(\n",
    "            location=(latitude, longitude),\n",
    "            popup=popup,\n",
    "            radius=5,\n",
    "            fill=True,\n",
    "            color=color,\n",
    "            fill_opacity=1.\n",
    "        )\n",
    "    )\n",
    "    \n",
    "hk_map.add_child(cinemas_fg)\n",
    "hk_map.add_child(targets_fg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of Hong Kong cinemas (blue circle) and stakeholder's favorite cinemas (red circle) location are built near main road, and centralized in urban area of Hong Kong. \n",
    "The target locations (yellow circle) of new cinema are not near to main road."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's use __Content-Based__ or __Item-Item recommendation systems__. In this case, I am going to try to figure out the boss's favorite new cinema location by counting number of nearby venues and ratings given."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize the values of venues dataframe by using MinMaxScaler method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "venues_normalized = scaler.fit_transform(df_venues_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues_normalized = pd.DataFrame(\n",
    "    venues_normalized,\n",
    "    index=df_venues_count.index,\n",
    "    columns=df_venues_count.columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues_normalized.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge the data with boss's favorite list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "boss_rating_table = pd.merge(\n",
    "    df_boss_favorite,\n",
    "    df_venues_normalized,\n",
    "    how='inner',\n",
    "    left_on='Name',\n",
    "    right_index=True\n",
    ")\n",
    "boss_rating_table.drop(['Name','Rating'], axis=1, inplace=True)\n",
    "boss_rating_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dot product to get the weight of rating on each category according to boss's favorite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "boss_profile = boss_rating_table.transpose().dot(df_boss_favorite['Rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "boss_profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize the values of target venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_targets_normalized = pd.DataFrame(\n",
    "    scaler.transform(df_target_venues_count),\n",
    "    index=df_target_venues_count.index,\n",
    "    columns=df_target_venues_count.columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_targets_normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results section where you discuss the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the boss's profile and the complete list of cinemas and their venues count in hand, I am going to take the weighted average of every lcoation based on the profile and recommend the top location that most satisfy it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_recommend = (df_targets_normalized*boss_profile).sum(axis=1)/boss_profile.sum()\n",
    "df_recommend = df_recommend.reset_index(name='Rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_possible_locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.merge(\n",
    "    df_possible_locations,\n",
    "    df_recommend,\n",
    "    left_on='Location',\n",
    "    right_on='Location'\n",
    ")\n",
    "df_final.sort_values('Rating', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('I should recommend the location \"{}\" of address \"{}\" to the stackholder'.format(df_final.iat[0,0], df_final.iat[0,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is reasonable. Location \"L5\" has the most number of venues in category \"Bus Stop\", \"Food\", \"Metro Station\" and \"Shop & Service\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_target_venues_count.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, these categories are most concerned by the stakeholder according to profile rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "boss_profile.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, Location \"L5\" should be recommeded to the stakeholder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Discussion section where you discuss any observations you noted and any recommendations you can make based on the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of venues of 5 target locations are actually below the average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues_count.mean().to_frame(name='Average Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_target_venues_count.mean().to_frame('Average Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I should contact local commercial property agents to find more suitable locations. Moreover, FourSquare is not popular in Hong Kong, the data maybe out-dated or unreliable, the report should gather more data from other location data source such as Google Place API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion section where you conclude the report."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stakeholder's problem is resolved. Stakeholder wants to find the best place to build a new cinema in Hong Kong, and the factors of \"best location\" is based on the number of venues in eating, shopping, transportation category around the location. Stakeholder also provide his favorite list of cinema to further explain what the \"best location\" is. Content-based filtering machine learning technique is the most suitable method to resolve the problem. It combines stakeholder's preference and cinema profile to make the recommendation result.\n",
    "\n",
    "The 5 target locations of new cinema may not be a good choices. As the weighting matrix is developed, I can quickly pick other locations and make the recommendation again.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}