Exploring Toronto(1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h1 align=center><font size = 5>Read and Clean Neighbourhood Data for Toronto</font></h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /opt/conda/envs/Python36\n",
      "\n",
      "  added / updated specs: \n",
      "    - geopy\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    geographiclib-1.50         |             py_0          34 KB  conda-forge\n",
      "    geopy-1.21.0               |             py_0          58 KB  conda-forge\n",
      "    ca-certificates-2019.11.28 |       hecc5488_0         145 KB  conda-forge\n",
      "    certifi-2019.11.28         |           py36_0         149 KB  conda-forge\n",
      "    openssl-1.1.1d             |       h516909a_0         2.1 MB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         2.5 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "    geographiclib:   1.50-py_0         conda-forge\n",
      "    geopy:           1.21.0-py_0       conda-forge\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    ca-certificates: 2019.11.27-0                  --> 2019.11.28-hecc5488_0 conda-forge\n",
      "    certifi:         2019.11.28-py36_0             --> 2019.11.28-py36_0     conda-forge\n",
      "\n",
      "The following packages will be DOWNGRADED:\n",
      "\n",
      "    openssl:         1.1.1d-h7b6447c_3             --> 1.1.1d-h516909a_0     conda-forge\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "geographiclib-1.50   | 34 KB     | ##################################### | 100% \n",
      "geopy-1.21.0         | 58 KB     | ##################################### | 100% \n",
      "ca-certificates-2019 | 145 KB    | ##################################### | 100% \n",
      "certifi-2019.11.28   | 149 KB    | ##################################### | 100% \n",
      "openssl-1.1.1d       | 2.1 MB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /opt/conda/envs/Python36\n",
      "\n",
      "  added / updated specs: \n",
      "    - folium=0.5.0\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    branca-0.3.1               |             py_0          25 KB  conda-forge\n",
      "    folium-0.5.0               |             py_0          45 KB  conda-forge\n",
      "    vincent-0.4.4              |             py_1          28 KB  conda-forge\n",
      "    altair-4.0.1               |             py_0         575 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         673 KB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "    altair:  4.0.1-py_0 conda-forge\n",
      "    branca:  0.3.1-py_0 conda-forge\n",
      "    folium:  0.5.0-py_0 conda-forge\n",
      "    vincent: 0.4.4-py_1 conda-forge\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "branca-0.3.1         | 25 KB     | ##################################### | 100% \n",
      "folium-0.5.0         | 45 KB     | ##################################### | 100% \n",
      "vincent-0.4.4        | 28 KB     | ##################################### | 100% \n",
      "altair-4.0.1         | 575 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # library to handle data in a vectorized manner\n",
    "\n",
    "import pandas as pd # library for data analsysis\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "import json # library to handle JSON files\n",
    "\n",
    "!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "\n",
    "import requests # library to handle requests\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "import folium # map rendering library\n",
    "\n",
    "print('Libraries imported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read contents of url using urllib library and transfer contents on a pandas dataframe with read_html <br>\n",
    "Only keep rows for which __Borough__ is NOT \"Not assigned\"  \n",
    "If __Neighbourhood__ is \"Not assigned\" set it to name of Borough\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M9A</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>Queen's Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Malvern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M3B</td>\n",
       "      <td>North York</td>\n",
       "      <td>Don Mills North</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode           Borough     Neighbourhood\n",
       "0      M3A        North York         Parkwoods\n",
       "1      M4A        North York  Victoria Village\n",
       "2      M5A  Downtown Toronto      Harbourfront\n",
       "3      M6A        North York  Lawrence Heights\n",
       "4      M6A        North York    Lawrence Manor\n",
       "5      M7A  Downtown Toronto      Queen's Park\n",
       "6      M9A      Queen's Park      Queen's Park\n",
       "7      M1B       Scarborough             Rouge\n",
       "8      M1B       Scarborough           Malvern\n",
       "9      M3B        North York   Don Mills North"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import urllib.request\n",
    "\n",
    "url = \"https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\"\n",
    "content = urllib.request.urlopen(url).read()\n",
    "df_list = pd.read_html(content)\n",
    "df_t = df_list[0]\n",
    "df_n= df_t[df_t.Borough != 'Not assigned']\n",
    "df = df_n.reset_index(drop=True)\n",
    "#df_n.head(10)\n",
    "\n",
    "# iterate over rows and replace \"Not assigned\" Neighbourhood with Borough\n",
    "for index in range(len(df)):\n",
    "    if (df.loc[index, \"Neighbourhood\"] == 'Not assigned'): \n",
    "        df.loc[index,\"Neighbourhood\"] = df.loc[index, \"Borough\"]\n",
    "        \n",
    "df.head(10)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get latitude and longitude for PostalCodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode   Latitude  Longitude\n",
       "0      M1B  43.806686 -79.194353\n",
       "1      M1C  43.784535 -79.160497\n",
       "2      M1E  43.763573 -79.188711\n",
       "3      M1G  43.770992 -79.216917\n",
       "4      M1H  43.773136 -79.239476"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reaad from CSV file:\n",
    "import pandas as pd\n",
    "\n",
    "url = \"http://cocl.us/Geospatial_data/Geospatial_Coordinates.csv\"\n",
    "df_codes = pd.read_csv(url)\n",
    "df_codes.rename(columns = {'Postal Code': 'Postcode'}, inplace = True) # Rename Postal Code to Postcode\n",
    "df_codes.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add latitide and longitude columns to data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.753259</td>\n",
       "      <td>-79.329656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "      <td>43.654260</td>\n",
       "      <td>-79.360636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Heights</td>\n",
       "      <td>43.718518</td>\n",
       "      <td>-79.464763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor</td>\n",
       "      <td>43.718518</td>\n",
       "      <td>-79.464763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>43.662301</td>\n",
       "      <td>-79.389494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M9A</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>43.667856</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M3B</td>\n",
       "      <td>North York</td>\n",
       "      <td>Don Mills North</td>\n",
       "      <td>43.745906</td>\n",
       "      <td>-79.352188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode           Borough     Neighbourhood   Latitude  Longitude\n",
       "0      M3A        North York         Parkwoods  43.753259 -79.329656\n",
       "1      M4A        North York  Victoria Village  43.725882 -79.315572\n",
       "2      M5A  Downtown Toronto      Harbourfront  43.654260 -79.360636\n",
       "3      M6A        North York  Lawrence Heights  43.718518 -79.464763\n",
       "4      M6A        North York    Lawrence Manor  43.718518 -79.464763\n",
       "5      M7A  Downtown Toronto      Queen's Park  43.662301 -79.389494\n",
       "6      M9A      Queen's Park      Queen's Park  43.667856 -79.532242\n",
       "7      M1B       Scarborough             Rouge  43.806686 -79.194353\n",
       "8      M1B       Scarborough           Malvern  43.806686 -79.194353\n",
       "9      M3B        North York   Don Mills North  43.745906 -79.352188"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfn = pd.merge(df, df_codes, on ='Postcode')\n",
    "dfn.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfn.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's narrow down to boroughs that have the word Toronto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "      <td>43.654260</td>\n",
       "      <td>-79.360636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>43.662301</td>\n",
       "      <td>-79.389494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5B</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Ryerson</td>\n",
       "      <td>43.657162</td>\n",
       "      <td>-79.378937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M5B</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Garden District</td>\n",
       "      <td>43.657162</td>\n",
       "      <td>-79.378937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5C</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>St. James Town</td>\n",
       "      <td>43.651494</td>\n",
       "      <td>-79.375418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M4E</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.676357</td>\n",
       "      <td>-79.293031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M5E</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>43.644771</td>\n",
       "      <td>-79.373306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M5G</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Central Bay Street</td>\n",
       "      <td>43.657952</td>\n",
       "      <td>-79.387383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M6G</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Christie</td>\n",
       "      <td>43.669542</td>\n",
       "      <td>-79.422564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M5H</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>43.650571</td>\n",
       "      <td>-79.384568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode           Borough       Neighbourhood   Latitude  Longitude\n",
       "0      M5A  Downtown Toronto        Harbourfront  43.654260 -79.360636\n",
       "1      M7A  Downtown Toronto        Queen's Park  43.662301 -79.389494\n",
       "2      M5B  Downtown Toronto             Ryerson  43.657162 -79.378937\n",
       "3      M5B  Downtown Toronto     Garden District  43.657162 -79.378937\n",
       "4      M5C  Downtown Toronto      St. James Town  43.651494 -79.375418\n",
       "5      M4E      East Toronto         The Beaches  43.676357 -79.293031\n",
       "6      M5E  Downtown Toronto         Berczy Park  43.644771 -79.373306\n",
       "7      M5G  Downtown Toronto  Central Bay Street  43.657952 -79.387383\n",
       "8      M6G  Downtown Toronto            Christie  43.669542 -79.422564\n",
       "9      M5H  Downtown Toronto            Adelaide  43.650571 -79.384568"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_nbhd = dfn[dfn['Borough'].str.contains('Toronto')]  \n",
    "toronto_nbhd.reset_index(inplace = True, drop = True)\n",
    "toronto_nbhd.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print number of neighbourhoods having the word \"Toronto\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74, 5)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_nbhd.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now lets analyse the Downtown Toronto neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Toronto are 43.653963, -79.387207.\n"
     ]
    }
   ],
   "source": [
    "address = 'Toronto, Canada'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"Toronto_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Toronto are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a map of Toronto with neighborhoods superimposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "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 0x7f18b0263390>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Toronto using latitude and longitude values\n",
    "map_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood in zip(toronto_nbhd['Latitude'], toronto_nbhd['Longitude'], toronto_nbhd['Borough'], toronto_nbhd['Neighbourhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_toronto)  \n",
    "\n",
    "map_toronto\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we will use the Foursquare API to explore neighborhoods and segment them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Denine Foursquare Credentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your credentails:\n",
      "CLIENT_ID: OHO02Z4Z1LC5E2ATUXJQH1S2RJVI4OLQ1Z2RLB2U01V2J0RX\n",
      "CLIENT_SECRET:CG4FDCUJ5CGAE3ZR550L4BFYVUICFVGIHRVNUPXZSZU4I43K\n"
     ]
    }
   ],
   "source": [
    "CLIENT_ID = 'OHO02Z4Z1LC5E2ATUXJQH1S2RJVI4OLQ1Z2RLB2U01V2J0RX' \n",
    "CLIENT_SECRET = 'CG4FDCUJ5CGAE3ZR550L4BFYVUICFVGIHRVNUPXZSZU4I43K' \n",
    "VERSION = '20180605' # Foursquare API version\n",
    "\n",
    "print('Your credentails:')\n",
    "print('CLIENT_ID: ' + CLIENT_ID)\n",
    "print('CLIENT_SECRET:' + CLIENT_SECRET)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let;s explore first neighborhood in our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Harbourfront'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_nbhd.loc[0,'Neighbourhood']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the neighborhood's latitude and longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of Harbourfront are 43.6542599, -79.3606359.\n"
     ]
    }
   ],
   "source": [
    "neighborhood_latitude = toronto_nbhd.loc[0, 'Latitude'] # neighborhood latitude value\n",
    "neighborhood_longitude = toronto_nbhd.loc[0, 'Longitude'] # neighborhood longitude value\n",
    "\n",
    "neighborhood_name = toronto_nbhd.loc[0, 'Neighbourhood'] # neighborhood name\n",
    "\n",
    "print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n",
    "                                                               neighborhood_latitude, \n",
    "                                                               neighborhood_longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's get the top 100 venues in Harbourfront "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat = neighborhood_latitude\n",
    "lng = neighborhood_longitude\n",
    "radius = 500\n",
    "LIMIT= 100\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'meta': {'code': 200, 'requestId': '5e435f2ec546f3001b87391b'},\n",
       " 'response': {'suggestedFilters': {'header': 'Tap to show:',\n",
       "   'filters': [{'name': 'Open now', 'key': 'openNow'}]},\n",
       "  'headerLocation': 'Corktown',\n",
       "  'headerFullLocation': 'Corktown, Toronto',\n",
       "  'headerLocationGranularity': 'neighborhood',\n",
       "  'totalResults': 46,\n",
       "  'suggestedBounds': {'ne': {'lat': 43.6587599045, 'lng': -79.3544279001486},\n",
       "   'sw': {'lat': 43.6497598955, 'lng': -79.36684389985142}},\n",
       "  'groups': [{'type': 'Recommended Places',\n",
       "    'name': 'recommended',\n",
       "    'items': [{'reasons': {'count': 0,\n",
       "       'items': [{'summary': 'This spot is popular',\n",
       "         'type': 'general',\n",
       "         'reasonName': 'globalInteractionReason'}]},\n",
       "      'venue': {'id': '54ea41ad498e9a11e9e13308',\n",
       "       'name': 'Roselle Desserts',\n",
       "       'location': {'address': '362 King St E',\n",
       "        'crossStreet': 'Trinity St',\n",
       "        'lat': 43.653446723052674,\n",
       "        'lng': -79.3620167174383,\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.653446723052674,\n",
       "          'lng': -79.3620167174383}],\n",
       "        'distance': 143,\n",
       "        'postalCode': 'M5A 1K9',\n",
       "        'cc': 'CA',\n",
       "        'city': 'Toronto',\n",
       "        'state': 'ON',\n",
       "        'country': 'Canada',\n",
       "        'formattedAddress': ['362 King St E (Trinity St)',\n",
       "         'Toronto ON M5A 1K9',\n",
       "         'Canada']},\n",
       "       'categories': [{'id': '4bf58dd8d48988d16a941735',\n",
       "         'name': 'Bakery',\n",
       "         'pluralName': 'Bakeries',\n",
       "         'shortName': 'Bakery',\n",
       "         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/bakery_',\n",
       "          'suffix': '.png'},\n",
       "         'primary': True}],\n",
       "       'photos': {'count': 0, 'groups': []}},\n",
       "      'referralId': 'e-0-54ea41ad498e9a11e9e13308-0'},\n",
       "     {'reasons': {'count': 0,\n",
       "       'items': [{'summary': 'This spot is popular',\n",
       "         'type': 'general',\n",
       "         'reasonName': 'globalInteractionReason'}]},\n",
       "      'venue': {'id': '53b8466a498e83df908c3f21',\n",
       "       'name': 'Tandem Coffee',\n",
       "       'location': {'address': '368 King St E',\n",
       "        'crossStreet': 'at Trinity St',\n",
       "        'lat': 43.65355870959944,\n",
       "        'lng': -79.36180945913513,\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.65355870959944,\n",
       "          'lng': -79.36180945913513}],\n",
       "        'distance': 122,\n",
       "        'cc': 'CA',\n",
       "        'city': 'Toronto',\n",
       "        'state': 'ON',\n",
       "        'country': 'Canada',\n",
       "        'formattedAddress': ['368 King St E (at Trinity St)',\n",
       "         'Toronto ON',\n",
       "         'Canada']},\n",
       "       'categories': [{'id': '4bf58dd8d48988d1e0931735',\n",
       "         'name': 'Coffee Shop',\n",
       "         'pluralName': 'Coffee Shops',\n",
       "         'shortName': 'Coffee Shop',\n",
       "         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',\n",
       "          'suffix': '.png'},\n",
       "         'primary': True}],\n",
       "       'photos': {'count': 0, 'groups': []}},\n",
       "      'referralId': 'e-0-53b8466a498e83df908c3f21-1'},\n",
       "     {'reasons': {'count': 0,\n",
       "       'items': [{'summary': 'This spot is popular',\n",
       "         'type': 'general',\n",
       "         'reasonName': 'globalInteractionReason'}]},\n",
       "      'venue': {'id': '574c229e498ebb5c6b257902',\n",
       "       'name': 'Cooper Koo Family YMCA',\n",
       "       'location': {'address': '461 Cherry St.',\n",
       "        'lat': 43.65319052672638,\n",
       "        'lng': -79.35794700053884,\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.65319052672638,\n",
       "          'lng': -79.35794700053884}],\n",
       "        'distance': 247,\n",
       "        'postalCode': 'M5A 1H1',\n",
       "        'cc': 'CA',\n",
       "        'city': 'Toronto',\n",
       "        'state': 'ON',\n",
       "        'country': 'Canada',\n",
       "        'formattedAddress': ['461 Cherry St.',\n",
       "         'Toronto ON M5A 1H1',\n",
       "         'Canada']},\n",
       "       'categories': [{'id': '4bf58dd8d48988d175941735',\n",
       "         'name': 'Gym / Fitness Center',\n",
       "         'pluralName': 'Gyms or Fitness Centers',\n",
       "         'shortName': 'Gym / Fitness',\n",
       "         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/building/gym_',\n",
       "          'suffix': '.png'},\n",
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       "        'lng': -79.357538,\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.650495,\n",
       "          'lng': -79.357538}],\n",
       "        'distance': 487,\n",
       "        'cc': 'CA',\n",
       "        'country': 'Canada',\n",
       "        'formattedAddress': ['Canada']},\n",
       "       'categories': [{'id': '4bf58dd8d48988d116951735',\n",
       "         'name': 'Antique Shop',\n",
       "         'pluralName': 'Antique Shops',\n",
       "         'shortName': 'Antiques',\n",
       "         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/antique_',\n",
       "          'suffix': '.png'},\n",
       "         'primary': True}],\n",
       "       'photos': {'count': 0, 'groups': []}},\n",
       "      'referralId': 'e-0-579cc5f2498e0fe8397deebb-45'}]}]}}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = requests.get(url).json()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that extracts the category of the venue\n",
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Roselle Desserts</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>43.653447</td>\n",
       "      <td>-79.362017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tandem Coffee</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>43.653559</td>\n",
       "      <td>-79.361809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cooper Koo Family YMCA</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>43.653191</td>\n",
       "      <td>-79.357947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Body Blitz Spa East</td>\n",
       "      <td>Spa</td>\n",
       "      <td>43.654735</td>\n",
       "      <td>-79.359874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Impact Kitchen</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>43.656369</td>\n",
       "      <td>-79.356980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name            categories        lat        lng\n",
       "0        Roselle Desserts                Bakery  43.653447 -79.362017\n",
       "1           Tandem Coffee           Coffee Shop  43.653559 -79.361809\n",
       "2  Cooper Koo Family YMCA  Gym / Fitness Center  43.653191 -79.357947\n",
       "3     Body Blitz Spa East                   Spa  43.654735 -79.359874\n",
       "4          Impact Kitchen            Restaurant  43.656369 -79.356980"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues = results['response']['groups'][0]['items']\n",
    "    \n",
    "nearby_venues = json_normalize(venues) # flatten JSON\n",
    "\n",
    "# filter columns\n",
    "filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
    "nearby_venues =nearby_venues.loc[:, filtered_columns]\n",
    "\n",
    "# filter the category for each row\n",
    "nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n",
    "\n",
    "# clean columns\n",
    "nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n",
    "\n",
    "nearby_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46 venues were returned by Foursquare.\n"
     ]
    }
   ],
   "source": [
    "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of unique categires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nearby_venues['categories'].nunique()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of venues for each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "categories\n",
       "Antique Shop             1\n",
       "Bakery                   3\n",
       "Bank                     1\n",
       "Beer Store               1\n",
       "Breakfast Spot           2\n",
       "Café                     3\n",
       "Chocolate Shop           1\n",
       "Coffee Shop              8\n",
       "Cosmetics Shop           1\n",
       "Dessert Shop             1\n",
       "Electronics Store        1\n",
       "Event Space              1\n",
       "Farmers Market           1\n",
       "French Restaurant        1\n",
       "Gym / Fitness Center     1\n",
       "Health Food Store        1\n",
       "Historic Site            1\n",
       "Hotel                    1\n",
       "Ice Cream Shop           1\n",
       "Mexican Restaurant       2\n",
       "Park                     3\n",
       "Performing Arts Venue    1\n",
       "Pub                      3\n",
       "Restaurant               2\n",
       "Shoe Store               1\n",
       "Spa                      1\n",
       "Theater                  1\n",
       "Yoga Studio              1\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nearby_venues.groupby(\"categories\")['name'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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