Capstone_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Battle of Neighborhoods (Week 1 & Week 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Discussion and Background of the Business Problem:\n",
    "\t\tBusiness Problem: What is the prospect of opening a gym while avoiding too much competition in Auckland, New Zealand?\n",
    "\n",
    "Auckland, with its population being accounted for 30 percent of the population of New Zealand, is the most populated city in New Zealand. As it is the most populated city in New Zealand, the risk is also high to open up a new business. Since opening a gym isn’t as demanding and risky as opening a restaurant, I’ve decided to determine the best location to open up a new gym in Auckland.\n",
    "There are many popular gyms located in Auckland: Les Mills, Jetts, City Fitness, and more. The goal of this project is to find the optimal location for a new venue to open up by using various data mining techniques and machine learning algorithm such as K means clustering. We can achieve this by drawing location data of all the venues in Auckland by using online resources, such as Google maps/Foursquare, and determine the location where a new gym can be situated.\n",
    "\n",
    "\n",
    "### Target Audience\n",
    "\n",
    "The target audience that can be mainly benefited from this analysis is:\n",
    "1. a businessman who is looking to open up a new gym \n",
    "2. anyone in general who's interested in data science\n",
    "3. someone who wants to see where gyms are clustered in Auckland. \n",
    "\n",
    "Our target location will be a balance between the proximity to other venues and its residents.\n",
    "\n",
    "p.s this type of analysis can be applied to any the other types of businesses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#declare all the libraries required\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import folium\n",
    "from geopy.geocoders import Nominatim\n",
    "from bs4 import BeautifulSoup\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "from sklearn.cluster import KMeans\n",
    "from folium import plugins\n",
    "from folium.plugins import HeatMap\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data gathering/preparation\n",
    "\n",
    "Summary: The geolocation of the districts were obtained through google maps and the population data was obtained through the wikipeida page(https://en.wikipedia.org/wiki/Auckland_Council#Wards_and_local_boards). The rest, the venues,venue categories, and venue geolocations were obtained through Foursquare.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaned html 3 times till i've reached the table I needed\n",
    "source = requests.get('https://en.wikipedia.org/wiki/Auckland_Council#Wards_and_local_boards').text\n",
    "soup = BeautifulSoup(source, 'html5lib')\n",
    "soup2 = BeautifulSoup(str(list(soup.findAll('table',{\"class\": \"wikitable\"}))))\n",
    "soup3 = BeautifulSoup(str(list(soup2.findAll('td')[84:])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Extract Ward and its population from the wikipedia page and obtain latitude,longitude from google maps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ward</th>\n",
       "      <th>Population</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albany</td>\n",
       "      <td>107,500</td>\n",
       "      <td>-36.717481</td>\n",
       "      <td>174.749155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albany</td>\n",
       "      <td>67,800</td>\n",
       "      <td>-36.726563</td>\n",
       "      <td>174.698022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>111,600</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>64,000</td>\n",
       "      <td>-36.907962</td>\n",
       "      <td>174.689443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Franklin</td>\n",
       "      <td>76,400</td>\n",
       "      <td>-37.200700</td>\n",
       "      <td>174.904000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Howick</td>\n",
       "      <td>153,400</td>\n",
       "      <td>-36.911300</td>\n",
       "      <td>174.871000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Manukau</td>\n",
       "      <td>81,600</td>\n",
       "      <td>-36.988080</td>\n",
       "      <td>174.754931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Manukau</td>\n",
       "      <td>89,600</td>\n",
       "      <td>-36.967400</td>\n",
       "      <td>174.859000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Manurewa-Papakura</td>\n",
       "      <td>96,200</td>\n",
       "      <td>-37.025200</td>\n",
       "      <td>174.893000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Manurewa-Papakura</td>\n",
       "      <td>56,600</td>\n",
       "      <td>-37.062400</td>\n",
       "      <td>174.942000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Maungakiekie-Tāmaki</td>\n",
       "      <td>81,300</td>\n",
       "      <td>-36.943300</td>\n",
       "      <td>174.894000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>North Shore</td>\n",
       "      <td>63,500</td>\n",
       "      <td>-36.789300</td>\n",
       "      <td>174.772000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>North Shore</td>\n",
       "      <td>96,100</td>\n",
       "      <td>-36.786010</td>\n",
       "      <td>174.704150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Orākei</td>\n",
       "      <td>93,000</td>\n",
       "      <td>-36.855900</td>\n",
       "      <td>174.814000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Rodney</td>\n",
       "      <td>66,700</td>\n",
       "      <td>-36.903990</td>\n",
       "      <td>174.930160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Waitakere</td>\n",
       "      <td>124,400</td>\n",
       "      <td>-36.896460</td>\n",
       "      <td>174.606500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Waitakere</td>\n",
       "      <td>55,000</td>\n",
       "      <td>-36.850700</td>\n",
       "      <td>174.542000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Waitematā and Gulf</td>\n",
       "      <td>1,020</td>\n",
       "      <td>-36.311860</td>\n",
       "      <td>175.496490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Waitematā and Gulf</td>\n",
       "      <td>9,880</td>\n",
       "      <td>-36.801080</td>\n",
       "      <td>175.065290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Waitematā and Gulf</td>\n",
       "      <td>113,800</td>\n",
       "      <td>-36.845110</td>\n",
       "      <td>174.750270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Whau</td>\n",
       "      <td>86,300</td>\n",
       "      <td>-36.898640</td>\n",
       "      <td>174.697060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Ward Population    Latitude   Longitude\n",
       "0                Albany    107,500  -36.717481  174.749155\n",
       "1                Albany     67,800  -36.726563  174.698022\n",
       "2   Albert-Eden-Roskill    111,600  -36.904393  174.740919\n",
       "3   Albert-Eden-Roskill     64,000  -36.907962  174.689443\n",
       "4              Franklin     76,400  -37.200700  174.904000\n",
       "5                Howick    153,400  -36.911300  174.871000\n",
       "6               Manukau     81,600  -36.988080  174.754931\n",
       "7               Manukau     89,600  -36.967400  174.859000\n",
       "8     Manurewa-Papakura     96,200  -37.025200  174.893000\n",
       "9     Manurewa-Papakura     56,600  -37.062400  174.942000\n",
       "10  Maungakiekie-Tāmaki     81,300  -36.943300  174.894000\n",
       "11          North Shore     63,500  -36.789300  174.772000\n",
       "12          North Shore     96,100  -36.786010  174.704150\n",
       "13               Orākei     93,000  -36.855900  174.814000\n",
       "14               Rodney     66,700  -36.903990  174.930160\n",
       "15            Waitakere    124,400  -36.896460  174.606500\n",
       "16            Waitakere     55,000  -36.850700  174.542000\n",
       "17   Waitematā and Gulf      1,020  -36.311860  175.496490\n",
       "18   Waitematā and Gulf      9,880  -36.801080  175.065290\n",
       "19   Waitematā and Gulf    113,800  -36.845110  174.750270\n",
       "20                 Whau     86,300  -36.898640  174.697060"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = []\n",
    "for table_cell in soup3.findAll('td'):\n",
    "    try:\n",
    "\n",
    "        value.append(table_cell.text)\n",
    "    except:\n",
    "        None\n",
    "value = value[:(len(value))-24]\n",
    "value = [i.replace('\\n','') for i in value]\n",
    "population = []\n",
    "ward = []\n",
    "for n in range(len(value)):\n",
    "    if 0 < len(value[n]) < 21 and 'Board' not in value[n]:\n",
    "        count = n-1\n",
    "        try:\n",
    "            if int(value[n][0]) != int:\n",
    "                population.append(value[n])\n",
    "        except ValueError:\n",
    "            ward.append(value[n])\n",
    "    elif 'Board' in value[n]:\n",
    "        if 'Board' in value[count]:\n",
    "            ward.append(ward[len(ward)-1])\n",
    "# used https://www.latlong.net to find coordinates\n",
    "lat = ['-36.717481','-36.726563','-36.904393','-36.907962','-37.200700','-36.911300','-36.988080','-36.967400','-37.025200','-37.062400','-36.943300','-36.789300','-36.786010','-36.855900','-36.903990','-36.896460','-36.850700','-36.311860','-36.801080','-36.845110','-36.898640']\n",
    "long = ['174.749155','174.698022','174.740919','174.689443','174.904000','174.871000','174.754931','174.859000','174.893000','174.942000','174.894000','174.772000','174.704150','174.814000','174.930160','174.606500','174.542000','175.496490','175.065290','174.750270','174.697060']\n",
    "\n",
    "\n",
    "df = pd.DataFrame({'Ward':pd.Series(ward),'Population':pd.Series(population),'Latitude':lat,'Longitude':long})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Needs to convert Lat,Long into floats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-36.717481, -36.726563, -36.904393, -36.907962, -37.2007  ,\n",
       "        -36.9113  , -36.98808 , -36.9674  , -37.0252  , -37.0624  ,\n",
       "        -36.9433  , -36.7893  , -36.78601 , -36.8559  , -36.90399 ,\n",
       "        -36.89646 , -36.8507  , -36.31186 , -36.80108 , -36.84511 ,\n",
       "        -36.89864 ],\n",
       "       [174.749155, 174.698022, 174.740919, 174.689443, 174.904   ,\n",
       "        174.871   , 174.754931, 174.859   , 174.893   , 174.942   ,\n",
       "        174.894   , 174.772   , 174.70415 , 174.814   , 174.93016 ,\n",
       "        174.6065  , 174.542   , 175.49649 , 175.06529 , 174.75027 ,\n",
       "        174.69706 ]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_lat = [float(x) for x in df['Latitude']]\n",
    "float_lon = [float(x) for x in df['Longitude']]\n",
    "np.array([float_lat,float_lon])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3. using geolocator, make folium map with marker cluster to see where each ward(or area) sits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Auckland: -36.852095, 174.7631803.\n"
     ]
    },
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            var html_ae02972954ff45928b7b5dd79729e62a = $(`<div id="html_ae02972954ff45928b7b5dd79729e62a" style="width: 100.0%; height: 100.0%;">Orākei</div>`)[0];
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            var html_68e73bdc33334a10ac620a5262c71262 = $(`<div id="html_68e73bdc33334a10ac620a5262c71262" style="width: 100.0%; height: 100.0%;">Waitakere</div>`)[0];
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        var popup_81d61f159fd2400d8f3101c208c905b5 = L.popup({"maxWidth": "100%"});

        
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        var popup_68e2a38526cd4a7c994857175d3722da = L.popup({"maxWidth": "100%"});

        
            var html_56b44768611c484f830730f3770235eb = $(`<div id="html_56b44768611c484f830730f3770235eb" style="width: 100.0%; height: 100.0%;">Whau</div>`)[0];
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x165e372fc18>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address = 'Auckland, New Zealand'\n",
    "geolocator = Nominatim(user_agent=\"auckland_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "Latitude = location.latitude\n",
    "Longitude = location.longitude\n",
    "print('The geograpical coordinate of Auckland: {}, {}.'.format(Latitude, Longitude))\n",
    "\n",
    "# create map of New York using latitude and longitude values\n",
    "map_Auckland = folium.Map(location=[Latitude, Longitude], zoom_start=10)\n",
    "float_lat = [float(x) for x in df['Latitude']]\n",
    "float_lon = [float(x) for x in df['Longitude']]\n",
    "\n",
    "label = list(df['Ward'])\n",
    "plugins.MarkerCluster(\n",
    "    np.array([float_lat,float_lon]).T,\n",
    "    popups=label\n",
    "    ).add_to(map_Auckland)  \n",
    "    \n",
    "map_Auckland"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4. Using foursquare, we can obtain each venues' geolocation.\n",
    "\n",
    "    I've, however, realized that Foursquare doesn't contain enough information about gym locations. \n",
    "    Hence, I had added more data using google maps to enhance the accuracy of the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_ID = 'YOUR_ID' #Foursquare ID\n",
    "CLIENT_SECRET = 'YOUR_SECRET' #Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version\n",
    "\n",
    "def getNearbyVenues(names, latitudes, longitudes, radius=4000):\n",
    "    LIMIT = 300\n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "        add_gym_name = ['Miada Gym','The Peoples Gym','JustWorkout New Lynn - 24 hour gym','F45 Training New Lynn','Jetts Mount Roskill','City Fitness (Three Kings)','Snap Fitness Mount Roskill 24/7','Tony Martins Gym',\n",
    "                        'Health & Sports Fitness Club','AUT Gym City Campus','Jetts Auckland CBD','BodyTech Gym','Lifestyle Fitness - Scene','Anytime Fitness (Onehunga)',\n",
    "                       'Gym Tonic Auckland','CityFitness (New Market)','Jetts Remuera','Bodi HQ','Club VO2 Limited','YMCA - Ellerslie']\n",
    "        add_gym_lat = ['-36.8956127','-36.908379','-36.9148985','-36.8920433','-36.9045356','-36.9147613','-36.9111928','-36.9168886','-36.873919','-36.8520362','-36.8506273','-36.8630003','-36.8496639','-36.9072642','-36.878701','-36.8730207','-36.8828274','-36.8944274','-36.8964866','-36.898364']\n",
    "        add_gym_long = ['174.6846088','174.6736224','174.6592887','174.6915611','174.7189411','174.6757126','174.7247775','174.7318157','174.7328456','174.755267','174.7557774','174.758125','174.7538503','174.6757126','174.7472007','174.7682606','174.7761202','174.7855447','174.7886775','174.8106263']\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            add_gym_name[v], \n",
    "            add_gym_lat[v], \n",
    "            add_gym_long[v],  \n",
    "            'Gym') for v in range(len(add_gym_name))])\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Area', \n",
    "                  'Area_Latitude', \n",
    "                  'Area_Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue_Latitude', \n",
    "                  'Venue_Longitude', \n",
    "                  'Venue_Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Albany\n",
      "Albany\n",
      "Albert-Eden-Roskill\n",
      "Albert-Eden-Roskill\n",
      "Franklin\n",
      "Howick\n",
      "Manukau\n",
      "Manukau\n",
      "Manurewa-Papakura\n",
      "Manurewa-Papakura\n",
      "Maungakiekie-Tāmaki\n",
      "North Shore\n",
      "North Shore\n",
      "Orākei\n",
      "Rodney\n",
      "Waitakere\n",
      "Waitakere\n",
      "Waitematā and Gulf\n",
      "Waitematā and Gulf\n",
      "Waitematā and Gulf\n",
      "Whau\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>Area</th>\n",
       "      <th>Area_Latitude</th>\n",
       "      <th>Area_Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue_Latitude</th>\n",
       "      <th>Venue_Longitude</th>\n",
       "      <th>Venue_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albany</td>\n",
       "      <td>-36.717481</td>\n",
       "      <td>174.749155</td>\n",
       "      <td>Deep Creek Brewing Company</td>\n",
       "      <td>-36.7137</td>\n",
       "      <td>174.748</td>\n",
       "      <td>Brewery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albany</td>\n",
       "      <td>-36.717481</td>\n",
       "      <td>174.749155</td>\n",
       "      <td>Browns Bay Beach</td>\n",
       "      <td>-36.7143</td>\n",
       "      <td>174.75</td>\n",
       "      <td>Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albany</td>\n",
       "      <td>-36.717481</td>\n",
       "      <td>174.749155</td>\n",
       "      <td>La Tropezienne French Café</td>\n",
       "      <td>-36.7139</td>\n",
       "      <td>174.748</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albany</td>\n",
       "      <td>-36.717481</td>\n",
       "      <td>174.749155</td>\n",
       "      <td>Hatz Donburi</td>\n",
       "      <td>-36.7139</td>\n",
       "      <td>174.748</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albany</td>\n",
       "      <td>-36.717481</td>\n",
       "      <td>174.749155</td>\n",
       "      <td>Browns Bay</td>\n",
       "      <td>-36.7164</td>\n",
       "      <td>174.748</td>\n",
       "      <td>Neighborhood</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Area Area_Latitude Area_Longitude                       Venue  \\\n",
       "0  Albany    -36.717481     174.749155  Deep Creek Brewing Company   \n",
       "1  Albany    -36.717481     174.749155            Browns Bay Beach   \n",
       "2  Albany    -36.717481     174.749155  La Tropezienne French Café   \n",
       "3  Albany    -36.717481     174.749155                Hatz Donburi   \n",
       "4  Albany    -36.717481     174.749155                  Browns Bay   \n",
       "\n",
       "  Venue_Latitude Venue_Longitude       Venue_Category  \n",
       "0       -36.7137         174.748              Brewery  \n",
       "1       -36.7143          174.75                Beach  \n",
       "2       -36.7139         174.748                 Café  \n",
       "3       -36.7139         174.748  Japanese Restaurant  \n",
       "4       -36.7164         174.748         Neighborhood  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auckland_venues = getNearbyVenues(names=df['Ward'],latitudes=df['Latitude'],longitudes=df['Longitude'])\n",
    "auckland_venues.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Exploratory Data Analysis\n",
    "\n",
    "#### 3.1 First, we will map out gyms per area(or ward).\n",
    "##### filter out categories that contain 'gym'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Area_Latitude</th>\n",
       "      <th>Area_Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue_Latitude</th>\n",
       "      <th>Venue_Longitude</th>\n",
       "      <th>Venue_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albany</td>\n",
       "      <td>-36.717481</td>\n",
       "      <td>174.749155</td>\n",
       "      <td>Millennium Institute</td>\n",
       "      <td>-36.7419</td>\n",
       "      <td>174.733</td>\n",
       "      <td>Gym Pool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "      <td>Jetts 24 Hour Fitness St Lukes</td>\n",
       "      <td>-36.8853</td>\n",
       "      <td>174.731</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "      <td>Clip 'N Climb</td>\n",
       "      <td>-36.8879</td>\n",
       "      <td>174.747</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "      <td>Jetts Mount Roskill</td>\n",
       "      <td>-36.9045356</td>\n",
       "      <td>174.7189411</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "      <td>Snap Fitness Mount Roskill 24/7</td>\n",
       "      <td>-36.9111928</td>\n",
       "      <td>174.7247775</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "      <td>Tony Martins Gym</td>\n",
       "      <td>-36.9168886</td>\n",
       "      <td>174.7318157</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "      <td>Health &amp; Sports Fitness Club</td>\n",
       "      <td>-36.873919</td>\n",
       "      <td>174.7328456</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "      <td>Gym Tonic Auckland</td>\n",
       "      <td>-36.878701</td>\n",
       "      <td>174.7472007</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "      <td>Jetts Remuera</td>\n",
       "      <td>-36.8828274</td>\n",
       "      <td>174.7761202</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "      <td>Bodi HQ</td>\n",
       "      <td>-36.8944274</td>\n",
       "      <td>174.7855447</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.904393</td>\n",
       "      <td>174.740919</td>\n",
       "      <td>Club VO2 Limited</td>\n",
       "      <td>-36.8964866</td>\n",
       "      <td>174.7886775</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.907962</td>\n",
       "      <td>174.689443</td>\n",
       "      <td>Les Mills New Lynn</td>\n",
       "      <td>-36.9102</td>\n",
       "      <td>174.684</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.907962</td>\n",
       "      <td>174.689443</td>\n",
       "      <td>The Peoples Gym</td>\n",
       "      <td>-36.908379</td>\n",
       "      <td>174.6736224</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.907962</td>\n",
       "      <td>174.689443</td>\n",
       "      <td>JustWorkout New Lynn - 24 hour gym</td>\n",
       "      <td>-36.9148985</td>\n",
       "      <td>174.6592887</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.907962</td>\n",
       "      <td>174.689443</td>\n",
       "      <td>City Fitness (Three Kings)</td>\n",
       "      <td>-36.9147613</td>\n",
       "      <td>174.6757126</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>-36.907962</td>\n",
       "      <td>174.689443</td>\n",
       "      <td>Anytime Fitness (Onehunga)</td>\n",
       "      <td>-36.9072642</td>\n",
       "      <td>174.6757126</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Franklin</td>\n",
       "      <td>-37.200700</td>\n",
       "      <td>174.904000</td>\n",
       "      <td>Counties Fitness</td>\n",
       "      <td>-37.208</td>\n",
       "      <td>174.909</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Howick</td>\n",
       "      <td>-36.911300</td>\n",
       "      <td>174.871000</td>\n",
       "      <td>Extreme Edge Rock Climbing</td>\n",
       "      <td>-36.8911</td>\n",
       "      <td>174.851</td>\n",
       "      <td>Climbing Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Howick</td>\n",
       "      <td>-36.911300</td>\n",
       "      <td>174.871000</td>\n",
       "      <td>Lagoon Leisure and Fitness - YMCA</td>\n",
       "      <td>-36.9014</td>\n",
       "      <td>174.853</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Howick</td>\n",
       "      <td>-36.911300</td>\n",
       "      <td>174.871000</td>\n",
       "      <td>jetts Gym</td>\n",
       "      <td>-36.9374</td>\n",
       "      <td>174.875</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Howick</td>\n",
       "      <td>-36.911300</td>\n",
       "      <td>174.871000</td>\n",
       "      <td>Jetts 24/7</td>\n",
       "      <td>-36.8996</td>\n",
       "      <td>174.904</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>North Shore</td>\n",
       "      <td>-36.786010</td>\n",
       "      <td>174.704150</td>\n",
       "      <td>North Shore Leisure</td>\n",
       "      <td>-36.7818</td>\n",
       "      <td>174.724</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Orākei</td>\n",
       "      <td>-36.855900</td>\n",
       "      <td>174.814000</td>\n",
       "      <td>Parnell Baths</td>\n",
       "      <td>-36.8492</td>\n",
       "      <td>174.791</td>\n",
       "      <td>Gym Pool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Orākei</td>\n",
       "      <td>-36.855900</td>\n",
       "      <td>174.814000</td>\n",
       "      <td>Next Generation</td>\n",
       "      <td>-36.8539</td>\n",
       "      <td>174.773</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Orākei</td>\n",
       "      <td>-36.855900</td>\n",
       "      <td>174.814000</td>\n",
       "      <td>YMCA - Ellerslie</td>\n",
       "      <td>-36.898364</td>\n",
       "      <td>174.8106263</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Waitakere</td>\n",
       "      <td>-36.896460</td>\n",
       "      <td>174.606500</td>\n",
       "      <td>Extreme Edge Indoor Rock Climbing</td>\n",
       "      <td>-36.908</td>\n",
       "      <td>174.638</td>\n",
       "      <td>Climbing Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Waitakere</td>\n",
       "      <td>-36.896460</td>\n",
       "      <td>174.606500</td>\n",
       "      <td>West Wave Gym</td>\n",
       "      <td>-36.8758</td>\n",
       "      <td>174.627</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Waitematā and Gulf</td>\n",
       "      <td>-36.845110</td>\n",
       "      <td>174.750270</td>\n",
       "      <td>Les Mills Auckland City</td>\n",
       "      <td>-36.8484</td>\n",
       "      <td>174.758</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Waitematā and Gulf</td>\n",
       "      <td>-36.845110</td>\n",
       "      <td>174.750270</td>\n",
       "      <td>Les Mills</td>\n",
       "      <td>-36.8445</td>\n",
       "      <td>174.771</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Waitematā and Gulf</td>\n",
       "      <td>-36.845110</td>\n",
       "      <td>174.750270</td>\n",
       "      <td>AUT Gym City Campus</td>\n",
       "      <td>-36.8520362</td>\n",
       "      <td>174.755267</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Waitematā and Gulf</td>\n",
       "      <td>-36.845110</td>\n",
       "      <td>174.750270</td>\n",
       "      <td>Jetts Auckland CBD</td>\n",
       "      <td>-36.8506273</td>\n",
       "      <td>174.7557774</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Waitematā and Gulf</td>\n",
       "      <td>-36.845110</td>\n",
       "      <td>174.750270</td>\n",
       "      <td>BodyTech Gym</td>\n",
       "      <td>-36.8630003</td>\n",
       "      <td>174.758125</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Waitematā and Gulf</td>\n",
       "      <td>-36.845110</td>\n",
       "      <td>174.750270</td>\n",
       "      <td>Lifestyle Fitness - Scene</td>\n",
       "      <td>-36.8496639</td>\n",
       "      <td>174.7538503</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Waitematā and Gulf</td>\n",
       "      <td>-36.845110</td>\n",
       "      <td>174.750270</td>\n",
       "      <td>CityFitness (New Market)</td>\n",
       "      <td>-36.8730207</td>\n",
       "      <td>174.7682606</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Whau</td>\n",
       "      <td>-36.898640</td>\n",
       "      <td>174.697060</td>\n",
       "      <td>Jetts 24/7 Fitness New Lynn</td>\n",
       "      <td>-36.9056</td>\n",
       "      <td>174.697</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Whau</td>\n",
       "      <td>-36.898640</td>\n",
       "      <td>174.697060</td>\n",
       "      <td>Miada Gym</td>\n",
       "      <td>-36.8956127</td>\n",
       "      <td>174.6846088</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Whau</td>\n",
       "      <td>-36.898640</td>\n",
       "      <td>174.697060</td>\n",
       "      <td>F45 Training New Lynn</td>\n",
       "      <td>-36.8920433</td>\n",
       "      <td>174.6915611</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Area Area_Latitude Area_Longitude  \\\n",
       "0                Albany    -36.717481     174.749155   \n",
       "1   Albert-Eden-Roskill    -36.904393     174.740919   \n",
       "2   Albert-Eden-Roskill    -36.904393     174.740919   \n",
       "3   Albert-Eden-Roskill    -36.904393     174.740919   \n",
       "4   Albert-Eden-Roskill    -36.904393     174.740919   \n",
       "5   Albert-Eden-Roskill    -36.904393     174.740919   \n",
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       "7   Albert-Eden-Roskill    -36.904393     174.740919   \n",
       "8   Albert-Eden-Roskill    -36.904393     174.740919   \n",
       "9   Albert-Eden-Roskill    -36.904393     174.740919   \n",
       "10  Albert-Eden-Roskill    -36.904393     174.740919   \n",
       "11  Albert-Eden-Roskill    -36.907962     174.689443   \n",
       "12  Albert-Eden-Roskill    -36.907962     174.689443   \n",
       "13  Albert-Eden-Roskill    -36.907962     174.689443   \n",
       "14  Albert-Eden-Roskill    -36.907962     174.689443   \n",
       "15  Albert-Eden-Roskill    -36.907962     174.689443   \n",
       "16             Franklin    -37.200700     174.904000   \n",
       "17               Howick    -36.911300     174.871000   \n",
       "18               Howick    -36.911300     174.871000   \n",
       "19               Howick    -36.911300     174.871000   \n",
       "20               Howick    -36.911300     174.871000   \n",
       "21          North Shore    -36.786010     174.704150   \n",
       "22               Orākei    -36.855900     174.814000   \n",
       "23               Orākei    -36.855900     174.814000   \n",
       "24               Orākei    -36.855900     174.814000   \n",
       "25            Waitakere    -36.896460     174.606500   \n",
       "26            Waitakere    -36.896460     174.606500   \n",
       "27   Waitematā and Gulf    -36.845110     174.750270   \n",
       "28   Waitematā and Gulf    -36.845110     174.750270   \n",
       "29   Waitematā and Gulf    -36.845110     174.750270   \n",
       "30   Waitematā and Gulf    -36.845110     174.750270   \n",
       "31   Waitematā and Gulf    -36.845110     174.750270   \n",
       "32   Waitematā and Gulf    -36.845110     174.750270   \n",
       "33   Waitematā and Gulf    -36.845110     174.750270   \n",
       "34                 Whau    -36.898640     174.697060   \n",
       "35                 Whau    -36.898640     174.697060   \n",
       "36                 Whau    -36.898640     174.697060   \n",
       "\n",
       "                                 Venue Venue_Latitude Venue_Longitude  \\\n",
       "0                 Millennium Institute       -36.7419         174.733   \n",
       "1       Jetts 24 Hour Fitness St Lukes       -36.8853         174.731   \n",
       "2                        Clip 'N Climb       -36.8879         174.747   \n",
       "3                  Jetts Mount Roskill    -36.9045356     174.7189411   \n",
       "4      Snap Fitness Mount Roskill 24/7    -36.9111928     174.7247775   \n",
       "5                     Tony Martins Gym    -36.9168886     174.7318157   \n",
       "6         Health & Sports Fitness Club     -36.873919     174.7328456   \n",
       "7                   Gym Tonic Auckland     -36.878701     174.7472007   \n",
       "8                        Jetts Remuera    -36.8828274     174.7761202   \n",
       "9                              Bodi HQ    -36.8944274     174.7855447   \n",
       "10                    Club VO2 Limited    -36.8964866     174.7886775   \n",
       "11                  Les Mills New Lynn       -36.9102         174.684   \n",
       "12                     The Peoples Gym     -36.908379     174.6736224   \n",
       "13  JustWorkout New Lynn - 24 hour gym    -36.9148985     174.6592887   \n",
       "14          City Fitness (Three Kings)    -36.9147613     174.6757126   \n",
       "15          Anytime Fitness (Onehunga)    -36.9072642     174.6757126   \n",
       "16                    Counties Fitness        -37.208         174.909   \n",
       "17          Extreme Edge Rock Climbing       -36.8911         174.851   \n",
       "18   Lagoon Leisure and Fitness - YMCA       -36.9014         174.853   \n",
       "19                           jetts Gym       -36.9374         174.875   \n",
       "20                          Jetts 24/7       -36.8996         174.904   \n",
       "21                 North Shore Leisure       -36.7818         174.724   \n",
       "22                       Parnell Baths       -36.8492         174.791   \n",
       "23                     Next Generation       -36.8539         174.773   \n",
       "24                    YMCA - Ellerslie     -36.898364     174.8106263   \n",
       "25   Extreme Edge Indoor Rock Climbing        -36.908         174.638   \n",
       "26                       West Wave Gym       -36.8758         174.627   \n",
       "27             Les Mills Auckland City       -36.8484         174.758   \n",
       "28                           Les Mills       -36.8445         174.771   \n",
       "29                 AUT Gym City Campus    -36.8520362      174.755267   \n",
       "30                  Jetts Auckland CBD    -36.8506273     174.7557774   \n",
       "31                        BodyTech Gym    -36.8630003      174.758125   \n",
       "32           Lifestyle Fitness - Scene    -36.8496639     174.7538503   \n",
       "33            CityFitness (New Market)    -36.8730207     174.7682606   \n",
       "34         Jetts 24/7 Fitness New Lynn       -36.9056         174.697   \n",
       "35                           Miada Gym    -36.8956127     174.6846088   \n",
       "36               F45 Training New Lynn    -36.8920433     174.6915611   \n",
       "\n",
       "          Venue_Category  \n",
       "0               Gym Pool  \n",
       "1                    Gym  \n",
       "2                    Gym  \n",
       "3                    Gym  \n",
       "4                    Gym  \n",
       "5                    Gym  \n",
       "6                    Gym  \n",
       "7                    Gym  \n",
       "8                    Gym  \n",
       "9                    Gym  \n",
       "10                   Gym  \n",
       "11                   Gym  \n",
       "12                   Gym  \n",
       "13                   Gym  \n",
       "14                   Gym  \n",
       "15                   Gym  \n",
       "16                   Gym  \n",
       "17          Climbing Gym  \n",
       "18  Gym / Fitness Center  \n",
       "19                   Gym  \n",
       "20  Gym / Fitness Center  \n",
       "21                   Gym  \n",
       "22              Gym Pool  \n",
       "23  Gym / Fitness Center  \n",
       "24                   Gym  \n",
       "25          Climbing Gym  \n",
       "26                   Gym  \n",
       "27                   Gym  \n",
       "28                   Gym  \n",
       "29                   Gym  \n",
       "30                   Gym  \n",
       "31                   Gym  \n",
       "32                   Gym  \n",
       "33                   Gym  \n",
       "34                   Gym  \n",
       "35                   Gym  \n",
       "36                   Gym  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gym = auckland_venues[auckland_venues['Venue_Category']\\\n",
    "                                                          .str.contains('Gym')].reset_index(drop=True)\n",
    "drop_index = []\n",
    "unique_val = list(df_gym.Venue.unique())\n",
    "euc = []\n",
    "indexes = []\n",
    "for n in range(len(unique_val)):\n",
    "    for i in range(len(df_gym)):\n",
    "        if unique_val[n] == df_gym['Venue'][i]:\n",
    "            i_euc = np.linalg.norm(float(df_gym['Area_Latitude'][i]) - float(df_gym['Venue_Latitude'][i])) + np.linalg.norm(float(df_gym['Area_Longitude'][i]) - float(df_gym['Venue_Longitude'][i]))\n",
    "            euc.append(i_euc)\n",
    "            indexes.append(i)\n",
    "    indexes.pop(euc.index(min(euc)))\n",
    "    df_gym.drop(df_gym.index[indexes],inplace=True)\n",
    "    df_gym = df_gym.reset_index(drop=True)\n",
    "    euc.clear()\n",
    "    indexes.clear()\n",
    "df_gym"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the map, we can see that most of the gyms are clustered around New Lynn/Roskill/CBD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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        var popup_41ed83a91a4f4040b7d0d7d59c024f65 = L.popup({"maxWidth": "100%"});

        
            var html_9bc57292583844bb802569a8487645eb = $(`<div id="html_9bc57292583844bb802569a8487645eb" style="width: 100.0%; height: 100.0%;">Les Mills Waitematā and Gulf</div>`)[0];
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            var html_a6fd41d864844b179087b599e617a6f7 = $(`<div id="html_a6fd41d864844b179087b599e617a6f7" style="width: 100.0%; height: 100.0%;">AUT Gym City Campus Waitematā and Gulf</div>`)[0];
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        circle_marker_7457d5ca42014c35ac4ea971456e9613.bindPopup(popup_59e762010ac848728086cf0c8b3d661f)
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                [-36.90563311184323, 174.6969041666845],
                {"bubblingMouseEvents": true, "color": "#ff5f30", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff5f30", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 7, "stroke": true, "weight": 3}
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                [-36.8956127, 174.6846088],
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            var circle_marker_2dd7f19b3a09484789762ff7f29e2dec = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#ff5f30", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff5f30", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 7, "stroke": true, "weight": 3}
            ).addTo(map_58668d4d4b1c401fafdf4bdce1c223c4);
        
    
        var popup_004de545eb8f42ff9b85534130ad81b2 = L.popup({"maxWidth": "100%"});

        
            var html_d0b6aa3d912a407589ad4fa4a3fbe07f = $(`<div id="html_d0b6aa3d912a407589ad4fa4a3fbe07f" style="width: 100.0%; height: 100.0%;">F45 Training New Lynn Whau</div>`)[0];
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x165e3895320>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map\n",
    "map_clusters = folium.Map(location=[Latitude, Longitude], zoom_start=11,tiles=\"openstreetmap\", \n",
    "                            attr=\"<a href=https://github.com/python-visualization/folium/>Folium</a>\")\n",
    "ward_list = list(df_gym.Area.unique())\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(len(ward_list))\n",
    "ys = [i + x + (i*x)**2 for i in range(len(ward_list))]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "markers_colors = []\n",
    "for lat, lon, poi, area in zip(df_gym['Venue_Latitude'], df_gym['Venue_Longitude'], df_gym['Venue'], df_gym['Area']):\n",
    "\n",
    "    label = folium.Popup(str(poi) + ' ' + str(area), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=7,\n",
    "        popup=label,\n",
    "        color=rainbow[ward_list.index(area)-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[ward_list.index(area)-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the map, we can see that New Lynn-Avondale area are also highly populated. CBD, or the city center, however, doesn't (probably because a lot of people would live elsewhere and going to the city for work.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x165e38469e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = [x.replace(',','') for x in df['Population']]\n",
    "pop = [float(x) for x in temp]\n",
    "max_amount = max(pop)\n",
    "\n",
    "auckland_population_density = folium.Map(location=[Latitude, Longitude], zoom_start=10)\n",
    "hm = HeatMap( list(zip(df.Latitude.values.astype(float), df.Longitude.values.astype(float), pop)),\n",
    "                   min_opacity=0.8,\n",
    "                   max_val=max_amount,\n",
    "                   radius=30, blur=20, \n",
    "                   max_zoom=1\n",
    "                 )\n",
    "\n",
    "\n",
    "auckland_population_density.add_child(hm)\n",
    "auckland_population_density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Again, this is the heatmap version of the gym clusters in auckland. We can, again, conclude that most of the gyms are located in or near west auckland"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x165e42b5588>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = list(df_gym['Venue'].groupby(df_gym['Area']).size())\n",
    "gym_counts = []\n",
    "for i in range(len(temp)):\n",
    "    for n in range(temp[i]):\n",
    "        gym_counts.append(temp[i])\n",
    "\n",
    "max_amount = max(gym_counts)\n",
    "auckland_gym_density = folium.Map(location=[Latitude, Longitude], zoom_start=11)\n",
    "\n",
    "hm = HeatMap( list(zip(df_gym.Venue_Latitude.values.astype(float), df_gym.Venue_Longitude.values.astype(float), gym_counts)),\n",
    "                   min_opacity=0.9,\n",
    "                   max_val=max_amount,\n",
    "                   radius=30, blur=30, \n",
    "                   max_zoom=1\n",
    "                 )\n",
    "\n",
    "auckland_gym_density.add_child(hm)\n",
    "auckland_gym_density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the bar plot, we can narrow it down to top 3 areas with most gyms:\n",
    "1. Albert-Eden-Roskill (West Auckland)\n",
    "2. Waitemata and Gulf (CBD)\n",
    "3. Howick (East Auckland)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "df_gym_per_ward = df_gym['Venue'].groupby(df_gym['Area']).size().to_frame().reset_index()\n",
    "bar = sns.barplot(x='Area', y='Venue', data=df_gym_per_ward)\n",
    "\n",
    "bar.set_ylabel(\"Total Number of Gyms\", fontsize=14)\n",
    "bar.set_xlabel(\"Area\", fontsize=14)\n",
    "bar.set_xticklabels(bar.get_xticklabels(),rotation=40)\n",
    "bar.set_title(\"Number of Gyms per Area\", fontsize=14)\n",
    "#plt.savefig(\"No_of_Rest_as_Top_Venues.png\", dpi=240)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The barplot of the population vs. Area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Daniel\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py:887: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  result = concat(values, axis=self.axis)\n"
     ]
    }
   ],
   "source": [
    "result = df['Population'].groupby(df['Ward']).apply(pd.DataFrame)\n",
    "result = result.T.fillna('0')\n",
    "pop_sum = []\n",
    "for i in range(len(result)):\n",
    "    result[i] = [x.replace(',','') for x in result[i]]\n",
    "    result[i] = [float(x) for x in result[i]]\n",
    "    pop_sum.append(sum(result[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index_pop = list(result.index)\n",
    "zipped_pop = list(zip(index_pop,pop_sum))\n",
    "sum_pop_per_ward = pd.DataFrame(zipped_pop,columns=['Area','Population'])\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "bar = sns.barplot(x='Area', y='Population', data=sum_pop_per_ward)\n",
    "bar.set_ylabel(\"Population per Area\", fontsize=14)\n",
    "bar.set_xlabel(\"Area\", fontsize=14)\n",
    "bar.set_xticklabels(bar.get_xticklabels(),rotation=40)\n",
    "bar.set_title(\"Population\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The barplot of venues vs. area\n",
    "\n",
    "#### This is to see if other venues (restaurants, grocery stores etc) are also clustered around the same area. We do not want a gym where it's too far off from the grocery store and such.\n",
    "\n",
    "#### It seems that the West Auckland (Albert-Eden-Roskil) has them all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Daniel\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py:887: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  result = concat(values, axis=self.axis)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "venue_per_area = auckland_venues['Venue'].groupby(auckland_venues['Area']).apply(pd.DataFrame)\n",
    "# df_gym_per_ward = df_gym_per_ward.columns.groupby(df['Ward']).apply(pd.DataFrame)\n",
    "venue_per_area = venue_per_area.T.fillna('')\n",
    "ven_count =[]\n",
    "for i in range(len(venue_per_area)):\n",
    "    count = 0\n",
    "    for n in range(len(venue_per_area.iloc[i])):\n",
    "        if venue_per_area.iloc[i][n] != '':\n",
    "            count += 1\n",
    "    ven_count.append(count)\n",
    "index_ven = list(venue_per_area.index)\n",
    "zipped_ven = list(zip(index_ven,ven_count))\n",
    "sum_ven_per_area = pd.DataFrame(zipped_ven,columns=['Area','Venues'])\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "# venue_per_area = venue_per_area['Venue'].groupby(venue_per_area['Area']).size().to_frame().reset_index()\n",
    "bar = sns.barplot(x='Area', y='Venues', data=sum_ven_per_area)\n",
    "\n",
    "bar.set_ylabel(\"Total Number of Venues\", fontsize=14)\n",
    "bar.set_xlabel(\"Area\", fontsize=14)\n",
    "bar.set_xticklabels(bar.get_xticklabels(),rotation=40)\n",
    "bar.set_title(\"Number of venues per Area\", fontsize=14)\n",
    "#plt.savefig(\"No_of_Rest_as_Top_Venues.png\", dpi=240)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will use K means clustering method to cluster venues into areas. \n",
    "\n",
    "    Since my goal now was to see where other venues are clustered, I've excluded the gym from the venue categories. I've realized that simply doing heatmaps or barcharts would not work on such big categories of data. It had worked with finding clusters for one category, but it seemed that more categories -> more time consuming to do it manually. This is where machine learning such as K means come in handy and be useful."
   ]
  },
  {
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>African Restaurant</th>\n",
       "      <th>Airport</th>\n",
       "      <th>Airport Lounge</th>\n",
       "      <th>Airport Service</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Aquarium</th>\n",
       "      <th>Arcade</th>\n",
       "      <th>Argentinian Restaurant</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>...</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Video Store</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Vineyard</th>\n",
       "      <th>Water Park</th>\n",
       "      <th>Waterfront</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Winery</th>\n",
       "      <th>Zoo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albany</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00565</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00565</td>\n",
       "      <td>0.005650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00565</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Franklin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Howick</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.022989</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manukau</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018519</td>\n",
       "      <td>0.046296</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>0.009259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 175 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Area  African Restaurant   Airport  Airport Lounge  \\\n",
       "0               Albany                 0.0  0.000000        0.000000   \n",
       "1  Albert-Eden-Roskill                 0.0  0.000000        0.000000   \n",
       "2             Franklin                 0.0  0.000000        0.000000   \n",
       "3               Howick                 0.0  0.000000        0.000000   \n",
       "4              Manukau                 0.0  0.018519        0.046296   \n",
       "\n",
       "   Airport Service  American Restaurant  Aquarium    Arcade  \\\n",
       "0         0.000000             0.007299       0.0  0.000000   \n",
       "1         0.000000             0.005650       0.0  0.000000   \n",
       "2         0.000000             0.000000       0.0  0.000000   \n",
       "3         0.000000             0.000000       0.0  0.011494   \n",
       "4         0.009259             0.009259       0.0  0.000000   \n",
       "\n",
       "   Argentinian Restaurant  Art Gallery  ...  Vegetarian / Vegan Restaurant  \\\n",
       "0                     0.0      0.00000  ...                       0.000000   \n",
       "1                     0.0      0.00565  ...                       0.011299   \n",
       "2                     0.0      0.00000  ...                       0.000000   \n",
       "3                     0.0      0.00000  ...                       0.000000   \n",
       "4                     0.0      0.00000  ...                       0.000000   \n",
       "\n",
       "   Video Game Store  Video Store  Vietnamese Restaurant  Vineyard  Water Park  \\\n",
       "0               0.0      0.00000               0.000000       0.0         0.0   \n",
       "1               0.0      0.00565               0.005650       0.0         0.0   \n",
       "2               0.0      0.00000               0.000000       0.0         0.0   \n",
       "3               0.0      0.00000               0.022989       0.0         0.0   \n",
       "4               0.0      0.00000               0.027778       0.0         0.0   \n",
       "\n",
       "   Waterfront  Wine Bar    Winery       Zoo  \n",
       "0         0.0   0.00000  0.000000  0.000000  \n",
       "1         0.0   0.00565  0.000000  0.000000  \n",
       "2         0.0   0.00000  0.000000  0.000000  \n",
       "3         0.0   0.00000  0.000000  0.000000  \n",
       "4         0.0   0.00000  0.009259  0.009259  \n",
       "\n",
       "[5 rows x 175 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding venues per area\n",
    "other_venues = auckland_venues[~auckland_venues['Venue_Category']\\\n",
    "                                                          .str.contains('Gym')].reset_index(drop=True)\n",
    "ven_onehot = pd.get_dummies(other_venues[['Venue_Category']], prefix=\"\", prefix_sep=\"\")\n",
    "ven_onehot['Area'] = other_venues['Area']\n",
    "fixed = [ven_onehot.columns[-1]] + list(ven_onehot.columns[:-1])\n",
    "ven_onehot = ven_onehot[fixed]\n",
    "venues_Grouped = ven_onehot.groupby('Area').mean().reset_index()\n",
    "venues_Grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Restrict top venues to 5 in this case and see what venues had most frequently showed up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tAlbany\n",
      "                  Venue  Freq\n",
      "0                  Café  0.16\n",
      "1  Fast Food Restaurant  0.07\n",
      "2                   Bar  0.05\n",
      "3           Coffee Shop  0.04\n",
      "4                  Park  0.04\n",
      "\n",
      "\n",
      "\tAlbert-Eden-Roskill\n",
      "                Venue  Freq\n",
      "0                Café  0.16\n",
      "1         Supermarket  0.05\n",
      "2  Chinese Restaurant  0.05\n",
      "3       Grocery Store  0.04\n",
      "4                 Bar  0.04\n",
      "\n",
      "\n",
      "\tFranklin\n",
      "              Venue  Freq\n",
      "0              Café  0.33\n",
      "1       Supermarket  0.22\n",
      "2  Department Store  0.11\n",
      "3      Burger Joint  0.11\n",
      "4         Racetrack  0.11\n",
      "\n",
      "\n",
      "\tHowick\n",
      "              Venue  Freq\n",
      "0      Burger Joint  0.07\n",
      "1              Café  0.07\n",
      "2     Grocery Store  0.06\n",
      "3  Sushi Restaurant  0.05\n",
      "4              Park  0.05\n",
      "\n",
      "\n",
      "\tManukau\n",
      "                  Venue  Freq\n",
      "0                  Café  0.10\n",
      "1  Fast Food Restaurant  0.10\n",
      "2                 Hotel  0.06\n",
      "3        Airport Lounge  0.05\n",
      "4         Grocery Store  0.04\n",
      "\n",
      "\n",
      "\tManurewa-Papakura\n",
      "                  Venue  Freq\n",
      "0                  Café  0.13\n",
      "1  Fast Food Restaurant  0.12\n",
      "2         Grocery Store  0.10\n",
      "3                  Park  0.06\n",
      "4           Supermarket  0.06\n",
      "\n",
      "\n",
      "\tMaungakiekie-Tāmaki\n",
      "                  Venue  Freq\n",
      "0                  Café  0.09\n",
      "1         Grocery Store  0.05\n",
      "2  Fast Food Restaurant  0.05\n",
      "3      Department Store  0.05\n",
      "4                Market  0.04\n",
      "\n",
      "\n",
      "\tNorth Shore\n",
      "                  Venue  Freq\n",
      "0                  Café  0.14\n",
      "1                  Park  0.07\n",
      "2  Fast Food Restaurant  0.04\n",
      "3          Burger Joint  0.04\n",
      "4     Electronics Store  0.04\n",
      "\n",
      "\n",
      "\tOrākei\n",
      "                Venue  Freq\n",
      "0                Café  0.15\n",
      "1                Park  0.05\n",
      "2               Beach  0.04\n",
      "3  Italian Restaurant  0.03\n",
      "4         Pizza Place  0.03\n",
      "\n",
      "\n",
      "\tRodney\n",
      "                  Venue  Freq\n",
      "0         Grocery Store  0.10\n",
      "1                  Café  0.05\n",
      "2  Fast Food Restaurant  0.05\n",
      "3                 Beach  0.04\n",
      "4            Restaurant  0.03\n",
      "\n",
      "\n",
      "\tWaitakere\n",
      "           Venue  Freq\n",
      "0  Train Station  0.10\n",
      "1          Trail  0.06\n",
      "2           Café  0.06\n",
      "3           Park  0.06\n",
      "4  Grocery Store  0.06\n",
      "\n",
      "\n",
      "\tWaitematā and Gulf\n",
      "                 Venue  Freq\n",
      "0                 Café  0.12\n",
      "1           Restaurant  0.06\n",
      "2                  Bar  0.05\n",
      "3  Japanese Restaurant  0.04\n",
      "4                Hotel  0.04\n",
      "\n",
      "\n",
      "\tWhau\n",
      "                  Venue  Freq\n",
      "0                  Café  0.14\n",
      "1  Fast Food Restaurant  0.06\n",
      "2           Coffee Shop  0.05\n",
      "3           Supermarket  0.05\n",
      "4     Indian Restaurant  0.05\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "num_top_venues = 5\n",
    "\n",
    "for places in venues_Grouped['Area']:\n",
    "    print(\"\\t\"+places)\n",
    "    temp = venues_Grouped[venues_Grouped['Area'] == places].T.reset_index()\n",
    "    temp.columns = ['Venue','Freq']\n",
    "    temp = temp.iloc[1:]\n",
    "    temp['Freq'] = temp['Freq'].astype(float)\n",
    "    temp = temp.round({'Freq': 2})\n",
    "    print(temp.sort_values('Freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Does this confirm that Kiwis love their coffee?\n",
    "\n",
    "    Cafe seems to top most areas, which isn't surprising. Supermarket is the second most frequent. This is probably most convenient for the gym goers to have nearby.."
   ]
  },
  {
   "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>Area</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albany</td>\n",
       "      <td>Café</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Park</td>\n",
       "      <td>Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>Café</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Franklin</td>\n",
       "      <td>Café</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Howick</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Café</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Park</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manukau</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Airport Lounge</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Area 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "1               Albany                  Café  Fast Food Restaurant   \n",
       "2  Albert-Eden-Roskill                  Café           Supermarket   \n",
       "3             Franklin                  Café           Supermarket   \n",
       "4               Howick          Burger Joint                  Café   \n",
       "5              Manukau  Fast Food Restaurant                  Café   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \n",
       "1                   Bar                  Park                 Beach  \n",
       "2    Chinese Restaurant                   Bar         Grocery Store  \n",
       "3          Burger Joint      Department Store  Fast Food Restaurant  \n",
       "4         Grocery Store                  Park      Sushi Restaurant  \n",
       "5                 Hotel        Airport Lounge         Grocery Store  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Define a Function to select the top n rows\n",
    "\n",
    "def return_most_common_venues(row, num_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "num_venues = 5\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Area']\n",
    "for ind in np.arange(num_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "top_venues = pd.DataFrame(columns=columns)\n",
    "top_venues['Area'] = venues_Grouped['Area']\n",
    "\n",
    "for ind in np.arange(venues_Grouped.shape[0]):\n",
    "    top_venues.iloc[ind, 1:] = return_most_common_venues(venues_Grouped.iloc[ind, :], num_venues)\n",
    "\n",
    "    \n",
    "top_venues.index = np.arange(1, len(top_venues)+1)\n",
    "top_venues.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check the 5 Cluster labels : [0 0 2 1 4]\n"
     ]
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "venues_clustering = venues_Grouped.drop('Area', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(venues_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "print (\"Check the 5 Cluster labels :\",  kmeans.labels_[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_venues.insert(0, 'Cluster Label', kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Daniel\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py:887: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  result = concat(values, axis=self.axis)\n"
     ]
    }
   ],
   "source": [
    "lat_consolidate = df['Latitude'].groupby(df['Ward']).apply(pd.DataFrame)\n",
    "lat_consolidate = lat_consolidate.T.fillna('0')\n",
    "lat = []\n",
    "for i in range(len(lat_consolidate)):\n",
    "    lat_consolidate.iloc[i] = [float(x) for x in lat_consolidate.iloc[i]]\n",
    "    summed = []\n",
    "    for n in range(len(lat_consolidate.iloc[i])):\n",
    "        if(lat_consolidate.iloc[i][n] != 0):\n",
    "            summed.append(lat_consolidate.iloc[i][n])\n",
    "    lat.append(min(summed))\n",
    "long_consolidate = df['Longitude'].groupby(df['Ward']).apply(pd.DataFrame)\n",
    "long_consolidate = long_consolidate.T.fillna('0')\n",
    "long = []\n",
    "for i in range(len(long_consolidate)):\n",
    "    long_consolidate.iloc[i] = [float(x) for x in long_consolidate.iloc[i]]\n",
    "    summed = []\n",
    "    for n in range(len(long_consolidate.iloc[i])):\n",
    "        if(long_consolidate.iloc[i][n] != 0):\n",
    "            summed.append(long_consolidate.iloc[i][n])\n",
    "    long.append(min(summed))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_consolidate = sum_pop_per_ward\n",
    "df_consolidate['Latitude'] = lat\n",
    "df_consolidate['Longitude'] = long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .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>Area</th>\n",
       "      <th>Population</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Label</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albany</td>\n",
       "      <td>107500.0</td>\n",
       "      <td>-36.726563</td>\n",
       "      <td>174.698022</td>\n",
       "      <td>0</td>\n",
       "      <td>Café</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Park</td>\n",
       "      <td>Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albert-Eden-Roskill</td>\n",
       "      <td>67800.0</td>\n",
       "      <td>-36.907962</td>\n",
       "      <td>174.689443</td>\n",
       "      <td>0</td>\n",
       "      <td>Café</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Franklin</td>\n",
       "      <td>111600.0</td>\n",
       "      <td>-37.200700</td>\n",
       "      <td>174.904000</td>\n",
       "      <td>2</td>\n",
       "      <td>Café</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Howick</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>-36.911300</td>\n",
       "      <td>174.871000</td>\n",
       "      <td>1</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Café</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Park</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manukau</td>\n",
       "      <td>76400.0</td>\n",
       "      <td>-36.988080</td>\n",
       "      <td>174.754931</td>\n",
       "      <td>4</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Airport Lounge</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Area  Population   Latitude   Longitude  Cluster Label  \\\n",
       "0               Albany    107500.0 -36.726563  174.698022              0   \n",
       "1  Albert-Eden-Roskill     67800.0 -36.907962  174.689443              0   \n",
       "2             Franklin    111600.0 -37.200700  174.904000              2   \n",
       "3               Howick     64000.0 -36.911300  174.871000              1   \n",
       "4              Manukau     76400.0 -36.988080  174.754931              4   \n",
       "\n",
       "  1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "0                  Café  Fast Food Restaurant                   Bar   \n",
       "1                  Café           Supermarket    Chinese Restaurant   \n",
       "2                  Café           Supermarket          Burger Joint   \n",
       "3          Burger Joint                  Café         Grocery Store   \n",
       "4  Fast Food Restaurant                  Café                 Hotel   \n",
       "\n",
       "  4th Most Common Venue 5th Most Common Venue  \n",
       "0                  Park                 Beach  \n",
       "1                   Bar         Grocery Store  \n",
       "2      Department Store  Fast Food Restaurant  \n",
       "3                  Park      Sushi Restaurant  \n",
       "4        Airport Lounge         Grocery Store  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged = df_consolidate\n",
    "\n",
    "#merge them all\n",
    "df_merged = df_merged.join \\\n",
    "                                        (top_venues.set_index('Area'), on='Area')\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    I've applied gym counts to the size of clusters. Thus, each color cluster areas with similar venues and the size emphasizes where most gyms are located within those areas. From Albany(north) to New Lynn(west) are clustered together as red; meaning they possess similar venues. As most gyms are clustered in west auckland, it has the most emphasis here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x165e3df6a58>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map\n",
    "map_clusters = folium.Map(location=[Latitude, Longitude], zoom_start=11,tiles=\"openstreetmap\", \n",
    "                            attr=\"<a href=https://github.com/python-visualization/folium/>Folium</a>\")\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(df_merged.Latitude, df_merged.Longitude, df_merged.Area, df_merged['Cluster Label']):\n",
    "    radius = 1.5\n",
    "    if poi in list(df_gym_per_ward['Area']):\n",
    "        radius=int(df_gym_per_ward[df_gym_per_ward['Area']==poi]['Venue'])*1.5\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    This is another one to show emphasis on the number of venues per area with the cluster. It seems that lots are going on from north to west auckland. Although not much on the east."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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            var html_c19b47db6d044cbda974d1bcbfa67e76 = $(`<div id="html_c19b47db6d044cbda974d1bcbfa67e76" style="width: 100.0%; height: 100.0%;">Orākei Cluster 0</div>`)[0];
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            var html_8ce146393a1146219cfc4f55a4ecb651 = $(`<div id="html_8ce146393a1146219cfc4f55a4ecb651" style="width: 100.0%; height: 100.0%;">Rodney Cluster 1</div>`)[0];
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        var popup_89d35d79d9394fcd87f57803f1ae2e78 = L.popup({"maxWidth": "100%"});

        
            var html_45e190ebd24349508140ee175bbe37cc = $(`<div id="html_45e190ebd24349508140ee175bbe37cc" style="width: 100.0%; height: 100.0%;">Waitakere Cluster 3</div>`)[0];
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            var html_dd75f15438544827925e695f067ac76b = $(`<div id="html_dd75f15438544827925e695f067ac76b" style="width: 100.0%; height: 100.0%;">Waitematā and Gulf Cluster 0</div>`)[0];
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                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 12.0, "stroke": true, "weight": 3}
            ).addTo(map_3f9aae842d6e4169bac67e25cfdb9188);
        
    
        var popup_b9ce49c85aba4c1d938c0bb50fd0a27e = L.popup({"maxWidth": "100%"});

        
            var html_28bacdbb86a848e1b9ad9ddb05c04290 = $(`<div id="html_28bacdbb86a848e1b9ad9ddb05c04290" style="width: 100.0%; height: 100.0%;">Whau Cluster 0</div>`)[0];
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x165e3df6e80>"
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     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map\n",
    "map_clusters = folium.Map(location=[Latitude, Longitude], zoom_start=11,tiles=\"openstreetmap\", \n",
    "                            attr=\"<a href=https://github.com/python-visualization/folium/>Folium</a>\")\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(df_merged['Latitude'], df_merged['Longitude'], df_merged['Area'], df_merged['Cluster Label']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=int(sum_ven_per_area[sum_ven_per_area['Area']==poi]['Venues'])*0.1,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
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    "# 4. The Discussion & The Conclusion\n",
    "\n",
    "## The Discussion of the result\n",
    "- It seems that lots of businesses/venues are located near west auckland. The Auckland CBD is also the case. Therefore, the competition is high in this area. We want to avoid competitions.\n",
    "- It also seems that west auckland possess most of the auckland population. This isn't really the case for the CBD. The crowd factor is especially high for the gym. We want a location near the population.\n",
    "\n",
    "## The Conclusion\n",
    "According to this analysis, the west auckland is very hot for business. It not only possess most venues, but it also has the most people. However, there are also lots of big names(Les Mills)/local gyms in this area. This means that unless your gym is exceptionally better than other gyms, you won't make it far. I would recommend building one near the East as its population is second highest after the west.\n",
    "\n",
    "At the end of the day, all gyms are really the same. People go there to sweat/lose calories. If your goal is to make kiwis more fit, I'd recommend building where it's less accessible. East auckland not only offers the best chance of survival for the business, but it also needs more establishments.\n",
    "\n",
    "## Future Development\n",
    "\n",
    "I've used K means clustering for this assignment, but other algorithms such as DBSCAN could've been used and give interesting results. Also, the property data(price) could've been added for further analysis. using geojson to further divide each areas and do clustering would've given even more accurate results.\n",
    "\n",
    "Thank you for your time!\n",
    "\n",
    "reach me at: daniel.jnw.lee@gmail.com"
   ]
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