Capsone Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # library to handle data in a vectorized manner\n",
    "import pandas as pd # library for data analsysis\n",
    "import requests # Library for web scraping\n",
    "\n",
    "print('Libraries imported.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BeautifulSoup  & csv imported.\n"
     ]
    }
   ],
   "source": [
    "# To run this, you can install BeautifulSoup\n",
    "# https://pypi.python.org/pypi/beautifulsoup4\n",
    "\n",
    "# Or download the file\n",
    "# http://beautiful-soup-4\n",
    "# and unzip it in the same directory as this file\n",
    "import requests\n",
    "from urllib.request import urlopen\n",
    "from bs4 import BeautifulSoup\n",
    "import ssl\n",
    "import csv\n",
    "\n",
    "print('BeautifulSoup  & csv imported.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SSL certificate errors ignored.\n"
     ]
    }
   ],
   "source": [
    "# Ignore SSL certificate errors\n",
    "ctx = ssl.create_default_context()\n",
    "ctx.check_hostname = False\n",
    "ctx.verify_mode = ssl.CERT_NONE\n",
    "\n",
    "print('SSL certificate errors ignored.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "soup ready\n"
     ]
    }
   ],
   "source": [
    "source = requests.get('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M').text\n",
    "\n",
    "soup = BeautifulSoup(source, 'lxml')\n",
    "\n",
    "#print(soup.prettify())\n",
    "print('soup ready')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = soup.find('table',{'class':'wikitable sortable'})\n",
    "#table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_rows = table.find_all('tr')\n",
    "\n",
    "#table_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  PostalCode           Borough     Neighbourhood\n",
      "1        M1A      Not assigned      Not assigned\n",
      "2        M2A      Not assigned      Not assigned\n",
      "3        M3A        North York         Parkwoods\n",
      "4        M4A        North York  Victoria Village\n",
      "5        M5A  Downtown Toronto      Harbourfront\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "for row in table_rows:\n",
    "    data.append([t.text.strip() for t in row.find_all('td')])\n",
    "\n",
    "df = pd.DataFrame(data, columns=['PostalCode', 'Borough', 'Neighbourhood'])\n",
    "df = df[~df['PostalCode'].isnull()]  # to filter out bad rows\n",
    "\n",
    "print(df.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Transformed into DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 287 entries, 1 to 287\n",
      "Data columns (total 3 columns):\n",
      "PostalCode       287 non-null object\n",
      "Borough          287 non-null object\n",
      "Neighbourhood    287 non-null object\n",
      "dtypes: object(3)\n",
      "memory usage: 9.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(287, 3)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode           Borough     Neighbourhood\n",
       "3        M3A        North York         Parkwoods\n",
       "4        M4A        North York  Victoria Village\n",
       "5        M5A  Downtown Toronto      Harbourfront\n",
       "6        M6A        North York  Lawrence Heights\n",
       "7        M6A        North York    Lawrence Manor"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[df['Borough']==\"Not assigned\"].index,axis=0, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'PostalCode', 'Borough', 'Neighbourhood'], dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.reset_index()\n",
    "df1.head()\n",
    "df1.columns\n",
    "df1.drop('index', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "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>PostalCode</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode           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"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2 \n",
    "(using the Geospatial_Coordiante.csv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: geopy in /opt/conda/envs/Python36/lib/python3.6/site-packages (1.18.1)\n",
      "Requirement already satisfied: geographiclib<2,>=1.49 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geopy) (1.49)\n"
     ]
    }
   ],
   "source": [
    "!pip install geopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Postal Code</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": [
       "  Postal Code   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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = pd.read_csv(\"https://cocl.us/Geospatial_data\") \n",
    "# Preview the first 5 lines of the loaded data \n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2.rename(columns={'Postal Code': 'PostalCode'}, inplace=True)\n",
    "#data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "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>PostalCode</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>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>1</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>2</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge Hill</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode      Borough   Neighbourhood   Latitude  Longitude\n",
       "0        M1B  Scarborough           Rouge  43.806686 -79.194353\n",
       "1        M1B  Scarborough         Malvern  43.806686 -79.194353\n",
       "2        M1C  Scarborough  Highland Creek  43.784535 -79.160497\n",
       "3        M1C  Scarborough      Rouge Hill  43.784535 -79.160497\n",
       "4        M1C  Scarborough      Port Union  43.784535 -79.160497"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(df1, data2, how='inner', on=None, left_on=None, right_on=None,\n",
    "         left_index=False, right_index=False, sort=True,\n",
    "         suffixes=('_x', '_y'), copy=True, indicator=False,\n",
    "         validate=None)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3\n",
    "analyzing Borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your credentails:\n",
      "CLIENT_ID: VO3CR5T34VIYBYGSOK4QWJ30HTRNX4N3YLRODMXLYK4CRWTH\n",
      "CLIENT_SECRET:INL4034YUC0HEUYMEV40OAHSSOIMFOHZO5XWDNS5I3YVSAHX\n"
     ]
    }
   ],
   "source": [
    "CLIENT_ID = 'VO3CR5T34VIYBYGSOK4QWJ30HTRNX4N3YLRODMXLYK4CRWTH' # your Foursquare ID\n",
    "CLIENT_SECRET = 'INL4034YUC0HEUYMEV40OAHSSOIMFOHZO5XWDNS5I3YVSAHX' # your Foursquare Secret\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": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Scarborough'"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[0, 'Borough']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of Scarborough are 43.806686299999996, -79.19435340000001.\n"
     ]
    }
   ],
   "source": [
    "Borough_latitude = data.loc[0, 'Latitude'] # Borough latitude value\n",
    "Borough_longitude = data.loc[0, 'Longitude'] # Borough longitude value\n",
    "\n",
    "Borough_name = data.loc[0, 'Borough'] # Borough name\n",
    "\n",
    "print('Latitude and longitude values of {} are {}, {}.'.format(Borough_name, \n",
    "                                                               Borough_latitude, \n",
    "                                                               Borough_longitude))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'meta': {'code': 200, 'requestId': '5e512fad1a4b0a001b3e523e'},\n",
       " 'response': {'warning': {'text': \"There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area.\"},\n",
       "  'headerLocation': 'Malvern',\n",
       "  'headerFullLocation': 'Malvern, Toronto',\n",
       "  'headerLocationGranularity': 'neighborhood',\n",
       "  'totalResults': 0,\n",
       "  'suggestedBounds': {'ne': {'lat': 43.806704300018,\n",
       "    'lng': -79.19432850472293},\n",
       "   'sw': {'lat': 43.80666829998199, 'lng': -79.19437829527709}},\n",
       "  'groups': [{'type': 'Recommended Places',\n",
       "    'name': 'recommended',\n",
       "    'items': []}]}}"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type your answer here\n",
    "LIMIT = 100 # limit of number of venues returned by Foursquare API\n",
    "\n",
    "radius = 2 # define radius\n",
    "\n",
    " # create 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",
    "    Borough_latitude, \n",
    "    Borough_longitude, \n",
    "    radius, \n",
    "    LIMIT)\n",
    "url # display UR\n",
    "\n",
    "results = requests.get(url).json()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'groups'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-103-745f8c248222>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvenues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'response'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'groups'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'items'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mnearby_venues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson_normalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvenues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'groups'"
     ]
    }
   ],
   "source": [
    "venues = results['response']['groups'][0]['items']\n",
    "nearby_venues = json_normalize(venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that extracts the category of the town\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": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'nearby_venues' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-153-8d3c8155afff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'{} venues were returned by Foursquare.'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnearby_venues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'nearby_venues' is not defined"
     ]
    }
   ],
   "source": [
    "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=100):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "\n",
    "    # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "East York\n",
      "East York\n",
      "East York\n",
      "East Toronto\n",
      "East York\n",
      "East York\n",
      "East York\n",
      "East Toronto\n",
      "East Toronto\n",
      "East Toronto\n",
      "East Toronto\n",
      "East Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "North York\n",
      "North York\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "York\n",
      "York\n",
      "Downtown Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "York\n",
      "York\n",
      "York\n",
      "York\n",
      "York\n",
      "York\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "Downtown Toronto\n",
      "Mississauga\n",
      "East Toronto\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Queen's Park\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "York\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n"
     ]
    }
   ],
   "source": [
    "Toronto_venues = getNearbyVenues(names= data['Borough'],\n",
    "                                   latitudes=data['Latitude'],\n",
    "                                   longitudes=data['Longitude']\n",
    "                                  )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let's check the size of the resulting dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(262, 7)\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>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood 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>Scarborough</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>McCowan Park</td>\n",
       "      <td>43.745089</td>\n",
       "      <td>-79.239336</td>\n",
       "      <td>Playground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.799525</td>\n",
       "      <td>-79.318389</td>\n",
       "      <td>Mr Congee Chinese Cuisine 龍粥記</td>\n",
       "      <td>43.798879</td>\n",
       "      <td>-79.318335</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.799525</td>\n",
       "      <td>-79.318389</td>\n",
       "      <td>Price Chopper</td>\n",
       "      <td>43.799445</td>\n",
       "      <td>-79.318563</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.799525</td>\n",
       "      <td>-79.318389</td>\n",
       "      <td>Subway</td>\n",
       "      <td>43.798671</td>\n",
       "      <td>-79.318475</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.799525</td>\n",
       "      <td>-79.318389</td>\n",
       "      <td>Shoppers Drug Mart</td>\n",
       "      <td>43.799966</td>\n",
       "      <td>-79.317985</td>\n",
       "      <td>Pharmacy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  Neighborhood Latitude  Neighborhood Longitude  \\\n",
       "0  Scarborough              43.744734              -79.239476   \n",
       "1  Scarborough              43.799525              -79.318389   \n",
       "2  Scarborough              43.799525              -79.318389   \n",
       "3  Scarborough              43.799525              -79.318389   \n",
       "4  Scarborough              43.799525              -79.318389   \n",
       "\n",
       "                           Venue  Venue Latitude  Venue Longitude  \\\n",
       "0                   McCowan Park       43.745089       -79.239336   \n",
       "1  Mr Congee Chinese Cuisine 龍粥記       43.798879       -79.318335   \n",
       "2                  Price Chopper       43.799445       -79.318563   \n",
       "3                         Subway       43.798671       -79.318475   \n",
       "4             Shoppers Drug Mart       43.799966       -79.317985   \n",
       "\n",
       "       Venue Category  \n",
       "0          Playground  \n",
       "1  Chinese Restaurant  \n",
       "2       Grocery Store  \n",
       "3      Sandwich Place  \n",
       "4            Pharmacy  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(Toronto_venues.shape)\n",
    "Toronto_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central Toronto</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Downtown Toronto</th>\n",
       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Toronto</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East York</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Etobicoke</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississauga</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North York</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarborough</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Toronto</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Neighborhood Latitude  Neighborhood Longitude  Venue  \\\n",
       "Neighborhood                                                             \n",
       "Central Toronto                      24                      24     24   \n",
       "Downtown Toronto                    164                     164    164   \n",
       "East Toronto                          1                       1      1   \n",
       "East York                             5                       5      5   \n",
       "Etobicoke                             4                       4      4   \n",
       "Mississauga                           1                       1      1   \n",
       "North York                           33                      33     33   \n",
       "Scarborough                          10                      10     10   \n",
       "West Toronto                         20                      20     20   \n",
       "\n",
       "                  Venue Latitude  Venue Longitude  Venue Category  \n",
       "Neighborhood                                                       \n",
       "Central Toronto               24               24              24  \n",
       "Downtown Toronto             164              164             164  \n",
       "East Toronto                   1                1               1  \n",
       "East York                      5                5               5  \n",
       "Etobicoke                      4                4               4  \n",
       "Mississauga                    1                1               1  \n",
       "North York                    33               33              33  \n",
       "Scarborough                   10               10              10  \n",
       "West Toronto                  20               20              20  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_venues.groupby('Neighborhood').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's find out how many unique categories can be curated from all the returned venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 78 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} uniques categories.'.format(len(Toronto_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Each Neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        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>Neighborhood</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Baseball Field</th>\n",
       "      <th>Beer Store</th>\n",
       "      <th>Bookstore</th>\n",
       "      <th>...</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Taco Place</th>\n",
       "      <th>Tea Room</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Theater</th>\n",
       "      <th>Thrift / Vintage Store</th>\n",
       "      <th>Toy / Game Store</th>\n",
       "      <th>Trail</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  American Restaurant  Art Gallery  Asian Restaurant  Bakery  \\\n",
       "0  Scarborough                    0            0                 0       0   \n",
       "1  Scarborough                    0            0                 0       0   \n",
       "2  Scarborough                    0            0                 0       0   \n",
       "3  Scarborough                    0            0                 0       0   \n",
       "4  Scarborough                    0            0                 0       0   \n",
       "\n",
       "   Bank  Bar  Baseball Field  Beer Store  Bookstore  ...  Sushi Restaurant  \\\n",
       "0     0    0               0           0          0  ...                 0   \n",
       "1     0    0               0           0          0  ...                 0   \n",
       "2     0    0               0           0          0  ...                 0   \n",
       "3     0    0               0           0          0  ...                 0   \n",
       "4     0    0               0           0          0  ...                 0   \n",
       "\n",
       "   Taco Place  Tea Room  Thai Restaurant  Theater  Thrift / Vintage Store  \\\n",
       "0           0         0                0        0                       0   \n",
       "1           0         0                0        0                       0   \n",
       "2           0         0                0        0                       0   \n",
       "3           0         0                0        0                       0   \n",
       "4           0         0                0        0                       0   \n",
       "\n",
       "   Toy / Game Store  Trail  Vegetarian / Vegan Restaurant  Yoga Studio  \n",
       "0                 0      0                              0            0  \n",
       "1                 0      0                              0            0  \n",
       "2                 0      0                              0            0  \n",
       "3                 0      0                              0            0  \n",
       "4                 0      0                              0            0  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "Toronto_onehot = pd.get_dummies(Toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "Toronto_onehot['Neighborhood'] = Toronto_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [Toronto_onehot.columns[-1]] + list(Toronto_onehot.columns[:-1])\n",
    "Toronto_onehot = Toronto_onehot[fixed_columns]\n",
    "\n",
    "Toronto_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(262, 79)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_onehot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>Neighborhood</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Baseball Field</th>\n",
       "      <th>Beer Store</th>\n",
       "      <th>Bookstore</th>\n",
       "      <th>...</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Taco Place</th>\n",
       "      <th>Tea Room</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Theater</th>\n",
       "      <th>Thrift / Vintage Store</th>\n",
       "      <th>Toy / Game Store</th>\n",
       "      <th>Trail</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>0.02439</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02439</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.02439</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030488</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East Toronto</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>East York</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mississauga</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>North York</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>West Toronto</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighborhood  American Restaurant  Art Gallery  Asian Restaurant  \\\n",
       "0   Central Toronto             0.000000      0.00000               0.0   \n",
       "1  Downtown Toronto             0.012195      0.02439               0.0   \n",
       "2      East Toronto             0.000000      0.00000               0.0   \n",
       "3         East York             0.000000      0.00000               0.0   \n",
       "4         Etobicoke             0.000000      0.00000               0.0   \n",
       "5       Mississauga             0.000000      0.00000               0.0   \n",
       "6        North York             0.000000      0.00000               0.0   \n",
       "7       Scarborough             0.000000      0.00000               0.0   \n",
       "8      West Toronto             0.000000      0.00000               0.1   \n",
       "\n",
       "    Bakery      Bank      Bar  Baseball Field  Beer Store  Bookstore  ...  \\\n",
       "0  0.00000  0.000000  0.00000        0.000000         0.0   0.041667  ...   \n",
       "1  0.02439  0.000000  0.02439        0.000000         0.0   0.012195  ...   \n",
       "2  0.00000  0.000000  0.00000        0.000000         0.0   0.000000  ...   \n",
       "3  0.00000  0.000000  0.00000        0.000000         0.0   0.000000  ...   \n",
       "4  0.00000  0.000000  0.00000        0.000000         0.0   0.000000  ...   \n",
       "5  0.00000  0.000000  0.00000        0.000000         0.0   0.000000  ...   \n",
       "6  0.00000  0.090909  0.00000        0.030303         0.0   0.000000  ...   \n",
       "7  0.00000  0.000000  0.00000        0.000000         0.0   0.000000  ...   \n",
       "8  0.00000  0.000000  0.10000        0.000000         0.1   0.000000  ...   \n",
       "\n",
       "   Sushi Restaurant  Taco Place  Tea Room  Thai Restaurant   Theater  \\\n",
       "0          0.041667    0.000000  0.000000         0.000000  0.000000   \n",
       "1          0.030488    0.018293  0.018293         0.012195  0.000000   \n",
       "2          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "3          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "4          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "5          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "6          0.060606    0.000000  0.000000         0.000000  0.090909   \n",
       "7          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "8          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "\n",
       "   Thrift / Vintage Store  Toy / Game Store  Trail  \\\n",
       "0                0.000000          0.041667    0.0   \n",
       "1                0.018293          0.000000    0.0   \n",
       "2                0.000000          0.000000    1.0   \n",
       "3                0.000000          0.000000    0.0   \n",
       "4                0.000000          0.000000    0.0   \n",
       "5                0.000000          0.000000    0.0   \n",
       "6                0.000000          0.000000    0.0   \n",
       "7                0.000000          0.000000    0.0   \n",
       "8                0.000000          0.000000    0.0   \n",
       "\n",
       "   Vegetarian / Vegan Restaurant  Yoga Studio  \n",
       "0                       0.000000          0.0  \n",
       "1                       0.018293          0.0  \n",
       "2                       0.000000          0.0  \n",
       "3                       0.000000          0.0  \n",
       "4                       0.000000          0.0  \n",
       "5                       0.000000          0.0  \n",
       "6                       0.000000          0.0  \n",
       "7                       0.000000          0.0  \n",
       "8                       0.000000          0.1  \n",
       "\n",
       "[9 rows x 79 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_grouped = Toronto_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "Toronto_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Let's confirm the new size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 79)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_grouped.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's print each neighborhood along with the top 5 most common venues\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Central Toronto----\n",
      "                    venue  freq\n",
      "0            Liquor Store  0.21\n",
      "1             Supermarket  0.21\n",
      "2            Dessert Shop  0.08\n",
      "3                    Park  0.08\n",
      "4  Furniture / Home Store  0.08\n",
      "\n",
      "\n",
      "----Downtown Toronto----\n",
      "                   venue  freq\n",
      "0            Coffee Shop  0.13\n",
      "1                   Café  0.05\n",
      "2  Performing Arts Venue  0.05\n",
      "3                    Gym  0.04\n",
      "4          Deli / Bodega  0.04\n",
      "\n",
      "\n",
      "----East Toronto----\n",
      "                 venue  freq\n",
      "0                Trail   1.0\n",
      "1  American Restaurant   0.0\n",
      "2   Mac & Cheese Joint   0.0\n",
      "3                 Pool   0.0\n",
      "4           Playground   0.0\n",
      "\n",
      "\n",
      "----East York----\n",
      "                 venue  freq\n",
      "0          Coffee Shop   0.2\n",
      "1  Housing Development   0.2\n",
      "2    Indian Restaurant   0.2\n",
      "3       Sandwich Place   0.2\n",
      "4  Sporting Goods Shop   0.2\n",
      "\n",
      "\n",
      "----Etobicoke----\n",
      "                  venue  freq\n",
      "0          Dance Studio  0.50\n",
      "1           Coffee Shop  0.25\n",
      "2           Pizza Place  0.25\n",
      "3  Gym / Fitness Center  0.00\n",
      "4                   Pub  0.00\n",
      "\n",
      "\n",
      "----Mississauga----\n",
      "                 venue  freq\n",
      "0          Coffee Shop   1.0\n",
      "1  American Restaurant   0.0\n",
      "2            Nightclub   0.0\n",
      "3                  Pub   0.0\n",
      "4                 Pool   0.0\n",
      "\n",
      "\n",
      "----North York----\n",
      "                  venue  freq\n",
      "0           Coffee Shop  0.15\n",
      "1         Deli / Bodega  0.09\n",
      "2                  Bank  0.09\n",
      "3    Italian Restaurant  0.09\n",
      "4  Fast Food Restaurant  0.09\n",
      "\n",
      "\n",
      "----Scarborough----\n",
      "                  venue  freq\n",
      "0              Pharmacy   0.1\n",
      "1  Fast Food Restaurant   0.1\n",
      "2           Coffee Shop   0.1\n",
      "3         Grocery Store   0.1\n",
      "4        Sandwich Place   0.1\n",
      "\n",
      "\n",
      "----West Toronto----\n",
      "                 venue  freq\n",
      "0          Yoga Studio   0.1\n",
      "1           Beer Store   0.1\n",
      "2  Japanese Restaurant   0.1\n",
      "3   Mac & Cheese Joint   0.1\n",
      "4          Pizza Place   0.1\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "for hood in Toronto_grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = Toronto_grouped[Toronto_grouped['Neighborhood'] == hood].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": [
    "### Let's put that into a *pandas* dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let's create the new dataframe and display the top 10 venues for each neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "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>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Bowling Alley</td>\n",
       "      <td>Bookstore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Trail</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Dessert Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>East York</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Housing Development</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Diner</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0   Central Toronto           Supermarket          Liquor Store   \n",
       "1  Downtown Toronto           Coffee Shop                  Café   \n",
       "2      East Toronto                 Trail           Yoga Studio   \n",
       "3         East York           Coffee Shop   Sporting Goods Shop   \n",
       "4         Etobicoke          Dance Studio           Coffee Shop   \n",
       "\n",
       "    3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0  Furniture / Home Store          Dessert Shop                  Park   \n",
       "1   Performing Arts Venue                   Gym         Deli / Bodega   \n",
       "2                   Diner           Coffee Shop           College Gym   \n",
       "3          Sandwich Place   Housing Development     Indian Restaurant   \n",
       "4             Pizza Place                 Diner           College Gym   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0    Italian Restaurant           Coffee Shop    Seafood Restaurant   \n",
       "1      Sushi Restaurant           Art Gallery          Concert Hall   \n",
       "2          Concert Hall        Cosmetics Shop          Dance Studio   \n",
       "3           College Gym          Concert Hall        Cosmetics Shop   \n",
       "4          Concert Hall        Cosmetics Shop         Deli / Bodega   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0         Bowling Alley              Bookstore  \n",
       "1                Bakery           Cocktail Bar  \n",
       "2         Deli / Bodega           Dessert Shop  \n",
       "3          Dance Studio          Deli / Bodega  \n",
       "4          Dessert Shop            Yoga Studio  "
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = Toronto_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(Toronto_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(Toronto_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster Neighborhoods\n",
    "\n",
    "Run k-means to cluster the neighborhood into 5 clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Baseball Field</th>\n",
       "      <th>Beer Store</th>\n",
       "      <th>Bookstore</th>\n",
       "      <th>Bowling Alley</th>\n",
       "      <th>...</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Taco Place</th>\n",
       "      <th>Tea Room</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Theater</th>\n",
       "      <th>Thrift / Vintage Store</th>\n",
       "      <th>Toy / Game Store</th>\n",
       "      <th>Trail</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   American Restaurant  Art Gallery  Asian Restaurant   Bakery      Bank  \\\n",
       "0             0.000000      0.00000               0.0  0.00000  0.000000   \n",
       "1             0.012195      0.02439               0.0  0.02439  0.000000   \n",
       "2             0.000000      0.00000               0.0  0.00000  0.000000   \n",
       "3             0.000000      0.00000               0.0  0.00000  0.000000   \n",
       "4             0.000000      0.00000               0.0  0.00000  0.000000   \n",
       "5             0.000000      0.00000               0.0  0.00000  0.000000   \n",
       "6             0.000000      0.00000               0.0  0.00000  0.090909   \n",
       "7             0.000000      0.00000               0.0  0.00000  0.000000   \n",
       "8             0.000000      0.00000               0.1  0.00000  0.000000   \n",
       "\n",
       "       Bar  Baseball Field  Beer Store  Bookstore  Bowling Alley  ...  \\\n",
       "0  0.00000        0.000000         0.0   0.041667       0.041667  ...   \n",
       "1  0.02439        0.000000         0.0   0.012195       0.000000  ...   \n",
       "2  0.00000        0.000000         0.0   0.000000       0.000000  ...   \n",
       "3  0.00000        0.000000         0.0   0.000000       0.000000  ...   \n",
       "4  0.00000        0.000000         0.0   0.000000       0.000000  ...   \n",
       "5  0.00000        0.000000         0.0   0.000000       0.000000  ...   \n",
       "6  0.00000        0.030303         0.0   0.000000       0.000000  ...   \n",
       "7  0.00000        0.000000         0.0   0.000000       0.000000  ...   \n",
       "8  0.10000        0.000000         0.1   0.000000       0.000000  ...   \n",
       "\n",
       "   Sushi Restaurant  Taco Place  Tea Room  Thai Restaurant   Theater  \\\n",
       "0          0.041667    0.000000  0.000000         0.000000  0.000000   \n",
       "1          0.030488    0.018293  0.018293         0.012195  0.000000   \n",
       "2          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "3          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "4          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "5          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "6          0.060606    0.000000  0.000000         0.000000  0.090909   \n",
       "7          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "8          0.000000    0.000000  0.000000         0.000000  0.000000   \n",
       "\n",
       "   Thrift / Vintage Store  Toy / Game Store  Trail  \\\n",
       "0                0.000000          0.041667    0.0   \n",
       "1                0.018293          0.000000    0.0   \n",
       "2                0.000000          0.000000    1.0   \n",
       "3                0.000000          0.000000    0.0   \n",
       "4                0.000000          0.000000    0.0   \n",
       "5                0.000000          0.000000    0.0   \n",
       "6                0.000000          0.000000    0.0   \n",
       "7                0.000000          0.000000    0.0   \n",
       "8                0.000000          0.000000    0.0   \n",
       "\n",
       "   Vegetarian / Vegan Restaurant  Yoga Studio  \n",
       "0                       0.000000          0.0  \n",
       "1                       0.018293          0.0  \n",
       "2                       0.000000          0.0  \n",
       "3                       0.000000          0.0  \n",
       "4                       0.000000          0.0  \n",
       "5                       0.000000          0.0  \n",
       "6                       0.000000          0.0  \n",
       "7                       0.000000          0.0  \n",
       "8                       0.000000          0.1  \n",
       "\n",
       "[9 rows x 78 columns]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "Toronto_grouped_clustering = Toronto_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(Toronto_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] \n",
    "Toronto_grouped_clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rouge</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Bowling Alley</td>\n",
       "      <td>Bookstore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malvern</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Highland Creek</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>2.0</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Trail</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Dessert Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rouge Hill</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>4.0</td>\n",
       "      <td>East York</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Housing Development</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Port Union</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Diner</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guildwood</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mississauga</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morningside</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North York</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Hot Dog Joint</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Hill</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woburn</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "      <td>1.0</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Record Shop</td>\n",
       "      <td>Mac &amp; Cheese Joint</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cedarbrae</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarborough Village</th>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Birchmount Park</th>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ionview</th>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kennedy Park</th>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clairlea</th>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golden Mile</th>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oakridge</th>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cliffcrest</th>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cliffside</th>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarborough Village West</th>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Birch Cliff</th>\n",
       "      <td>M1N</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.692657</td>\n",
       "      <td>-79.264848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cliffside West</th>\n",
       "      <td>M1N</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.692657</td>\n",
       "      <td>-79.264848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dorset Park</th>\n",
       "      <td>M1P</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.757410</td>\n",
       "      <td>-79.273304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarborough Town Centre</th>\n",
       "      <td>M1P</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.757410</td>\n",
       "      <td>-79.273304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wexford Heights</th>\n",
       "      <td>M1P</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.757410</td>\n",
       "      <td>-79.273304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryvale</th>\n",
       "      <td>M1R</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.750072</td>\n",
       "      <td>-79.295849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wexford</th>\n",
       "      <td>M1R</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.750072</td>\n",
       "      <td>-79.295849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agincourt</th>\n",
       "      <td>M1S</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.794200</td>\n",
       "      <td>-79.262029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clarks Corners</th>\n",
       "      <td>M1T</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.781638</td>\n",
       "      <td>-79.304302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sullivan</th>\n",
       "      <td>M1T</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.781638</td>\n",
       "      <td>-79.304302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royal York South West</th>\n",
       "      <td>M8Z</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.628841</td>\n",
       "      <td>-79.520999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South of Bloor</th>\n",
       "      <td>M8Z</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.628841</td>\n",
       "      <td>-79.520999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not assigned</th>\n",
       "      <td>M9A</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>43.667856</td>\n",
       "      <td>-79.532242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloverdale</th>\n",
       "      <td>M9B</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.650943</td>\n",
       "      <td>-79.554724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Islington</th>\n",
       "      <td>M9B</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.650943</td>\n",
       "      <td>-79.554724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Martin Grove</th>\n",
       "      <td>M9B</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.650943</td>\n",
       "      <td>-79.554724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princess Gardens</th>\n",
       "      <td>M9B</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.650943</td>\n",
       "      <td>-79.554724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Deane Park</th>\n",
       "      <td>M9B</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.650943</td>\n",
       "      <td>-79.554724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bloordale Gardens</th>\n",
       "      <td>M9C</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.643515</td>\n",
       "      <td>-79.577201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eringate</th>\n",
       "      <td>M9C</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.643515</td>\n",
       "      <td>-79.577201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Markland Wood</th>\n",
       "      <td>M9C</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.643515</td>\n",
       "      <td>-79.577201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old Burnhamthorpe</th>\n",
       "      <td>M9C</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.643515</td>\n",
       "      <td>-79.577201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humber Summit</th>\n",
       "      <td>M9L</td>\n",
       "      <td>North York</td>\n",
       "      <td>43.756303</td>\n",
       "      <td>-79.565963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emery</th>\n",
       "      <td>M9M</td>\n",
       "      <td>North York</td>\n",
       "      <td>43.724766</td>\n",
       "      <td>-79.532242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humberlea</th>\n",
       "      <td>M9M</td>\n",
       "      <td>North York</td>\n",
       "      <td>43.724766</td>\n",
       "      <td>-79.532242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weston</th>\n",
       "      <td>M9N</td>\n",
       "      <td>York</td>\n",
       "      <td>43.706876</td>\n",
       "      <td>-79.518188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Westmount</th>\n",
       "      <td>M9P</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.696319</td>\n",
       "      <td>-79.532242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kingsview Village</th>\n",
       "      <td>M9R</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.688905</td>\n",
       "      <td>-79.554724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Martin Grove Gardens</th>\n",
       "      <td>M9R</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.688905</td>\n",
       "      <td>-79.554724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richview Gardens</th>\n",
       "      <td>M9R</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.688905</td>\n",
       "      <td>-79.554724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Phillips</th>\n",
       "      <td>M9R</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.688905</td>\n",
       "      <td>-79.554724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albion Gardens</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beaumond Heights</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humbergate</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamestown</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mount Olive</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silverstone</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Steeles</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thistletown</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwest</th>\n",
       "      <td>M9W</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>43.706748</td>\n",
       "      <td>-79.594054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         PostalCode       Borough   Latitude  Longitude  \\\n",
       "Neighbourhood                                                             \n",
       "Rouge                           M1B   Scarborough  43.806686 -79.194353   \n",
       "Malvern                         M1B   Scarborough  43.806686 -79.194353   \n",
       "Highland Creek                  M1C   Scarborough  43.784535 -79.160497   \n",
       "Rouge Hill                      M1C   Scarborough  43.784535 -79.160497   \n",
       "Port Union                      M1C   Scarborough  43.784535 -79.160497   \n",
       "Guildwood                       M1E   Scarborough  43.763573 -79.188711   \n",
       "Morningside                     M1E   Scarborough  43.763573 -79.188711   \n",
       "West Hill                       M1E   Scarborough  43.763573 -79.188711   \n",
       "Woburn                          M1G   Scarborough  43.770992 -79.216917   \n",
       "Cedarbrae                       M1H   Scarborough  43.773136 -79.239476   \n",
       "Scarborough Village             M1J   Scarborough  43.744734 -79.239476   \n",
       "East Birchmount Park            M1K   Scarborough  43.727929 -79.262029   \n",
       "Ionview                         M1K   Scarborough  43.727929 -79.262029   \n",
       "Kennedy Park                    M1K   Scarborough  43.727929 -79.262029   \n",
       "Clairlea                        M1L   Scarborough  43.711112 -79.284577   \n",
       "Golden Mile                     M1L   Scarborough  43.711112 -79.284577   \n",
       "Oakridge                        M1L   Scarborough  43.711112 -79.284577   \n",
       "Cliffcrest                      M1M   Scarborough  43.716316 -79.239476   \n",
       "Cliffside                       M1M   Scarborough  43.716316 -79.239476   \n",
       "Scarborough Village West        M1M   Scarborough  43.716316 -79.239476   \n",
       "Birch Cliff                     M1N   Scarborough  43.692657 -79.264848   \n",
       "Cliffside West                  M1N   Scarborough  43.692657 -79.264848   \n",
       "Dorset Park                     M1P   Scarborough  43.757410 -79.273304   \n",
       "Scarborough Town Centre         M1P   Scarborough  43.757410 -79.273304   \n",
       "Wexford Heights                 M1P   Scarborough  43.757410 -79.273304   \n",
       "Maryvale                        M1R   Scarborough  43.750072 -79.295849   \n",
       "Wexford                         M1R   Scarborough  43.750072 -79.295849   \n",
       "Agincourt                       M1S   Scarborough  43.794200 -79.262029   \n",
       "Clarks Corners                  M1T   Scarborough  43.781638 -79.304302   \n",
       "Sullivan                        M1T   Scarborough  43.781638 -79.304302   \n",
       "...                             ...           ...        ...        ...   \n",
       "Royal York South West           M8Z     Etobicoke  43.628841 -79.520999   \n",
       "South of Bloor                  M8Z     Etobicoke  43.628841 -79.520999   \n",
       "Not assigned                    M9A  Queen's Park  43.667856 -79.532242   \n",
       "Cloverdale                      M9B     Etobicoke  43.650943 -79.554724   \n",
       "Islington                       M9B     Etobicoke  43.650943 -79.554724   \n",
       "Martin Grove                    M9B     Etobicoke  43.650943 -79.554724   \n",
       "Princess Gardens                M9B     Etobicoke  43.650943 -79.554724   \n",
       "West Deane Park                 M9B     Etobicoke  43.650943 -79.554724   \n",
       "Bloordale Gardens               M9C     Etobicoke  43.643515 -79.577201   \n",
       "Eringate                        M9C     Etobicoke  43.643515 -79.577201   \n",
       "Markland Wood                   M9C     Etobicoke  43.643515 -79.577201   \n",
       "Old Burnhamthorpe               M9C     Etobicoke  43.643515 -79.577201   \n",
       "Humber Summit                   M9L    North York  43.756303 -79.565963   \n",
       "Emery                           M9M    North York  43.724766 -79.532242   \n",
       "Humberlea                       M9M    North York  43.724766 -79.532242   \n",
       "Weston                          M9N          York  43.706876 -79.518188   \n",
       "Westmount                       M9P     Etobicoke  43.696319 -79.532242   \n",
       "Kingsview Village               M9R     Etobicoke  43.688905 -79.554724   \n",
       "Martin Grove Gardens            M9R     Etobicoke  43.688905 -79.554724   \n",
       "Richview Gardens                M9R     Etobicoke  43.688905 -79.554724   \n",
       "St. Phillips                    M9R     Etobicoke  43.688905 -79.554724   \n",
       "Albion Gardens                  M9V     Etobicoke  43.739416 -79.588437   \n",
       "Beaumond Heights                M9V     Etobicoke  43.739416 -79.588437   \n",
       "Humbergate                      M9V     Etobicoke  43.739416 -79.588437   \n",
       "Jamestown                       M9V     Etobicoke  43.739416 -79.588437   \n",
       "Mount Olive                     M9V     Etobicoke  43.739416 -79.588437   \n",
       "Silverstone                     M9V     Etobicoke  43.739416 -79.588437   \n",
       "South Steeles                   M9V     Etobicoke  43.739416 -79.588437   \n",
       "Thistletown                     M9V     Etobicoke  43.739416 -79.588437   \n",
       "Northwest                       M9W     Etobicoke  43.706748 -79.594054   \n",
       "\n",
       "                          Cluster Labels      Neighborhood  \\\n",
       "Neighbourhood                                                \n",
       "Rouge                                1.0   Central Toronto   \n",
       "Malvern                              1.0  Downtown Toronto   \n",
       "Highland Creek                       2.0      East Toronto   \n",
       "Rouge Hill                           4.0         East York   \n",
       "Port Union                           3.0         Etobicoke   \n",
       "Guildwood                            0.0       Mississauga   \n",
       "Morningside                          1.0        North York   \n",
       "West Hill                            1.0       Scarborough   \n",
       "Woburn                               1.0      West Toronto   \n",
       "Cedarbrae                            NaN               NaN   \n",
       "Scarborough Village                  NaN               NaN   \n",
       "East Birchmount Park                 NaN               NaN   \n",
       "Ionview                              NaN               NaN   \n",
       "Kennedy Park                         NaN               NaN   \n",
       "Clairlea                             NaN               NaN   \n",
       "Golden Mile                          NaN               NaN   \n",
       "Oakridge                             NaN               NaN   \n",
       "Cliffcrest                           NaN               NaN   \n",
       "Cliffside                            NaN               NaN   \n",
       "Scarborough Village West             NaN               NaN   \n",
       "Birch Cliff                          NaN               NaN   \n",
       "Cliffside West                       NaN               NaN   \n",
       "Dorset Park                          NaN               NaN   \n",
       "Scarborough Town Centre              NaN               NaN   \n",
       "Wexford Heights                      NaN               NaN   \n",
       "Maryvale                             NaN               NaN   \n",
       "Wexford                              NaN               NaN   \n",
       "Agincourt                            NaN               NaN   \n",
       "Clarks Corners                       NaN               NaN   \n",
       "Sullivan                             NaN               NaN   \n",
       "...                                  ...               ...   \n",
       "Royal York South West                NaN               NaN   \n",
       "South of Bloor                       NaN               NaN   \n",
       "Not assigned                         NaN               NaN   \n",
       "Cloverdale                           NaN               NaN   \n",
       "Islington                            NaN               NaN   \n",
       "Martin Grove                         NaN               NaN   \n",
       "Princess Gardens                     NaN               NaN   \n",
       "West Deane Park                      NaN               NaN   \n",
       "Bloordale Gardens                    NaN               NaN   \n",
       "Eringate                             NaN               NaN   \n",
       "Markland Wood                        NaN               NaN   \n",
       "Old Burnhamthorpe                    NaN               NaN   \n",
       "Humber Summit                        NaN               NaN   \n",
       "Emery                                NaN               NaN   \n",
       "Humberlea                            NaN               NaN   \n",
       "Weston                               NaN               NaN   \n",
       "Westmount                            NaN               NaN   \n",
       "Kingsview Village                    NaN               NaN   \n",
       "Martin Grove Gardens                 NaN               NaN   \n",
       "Richview Gardens                     NaN               NaN   \n",
       "St. Phillips                         NaN               NaN   \n",
       "Albion Gardens                       NaN               NaN   \n",
       "Beaumond Heights                     NaN               NaN   \n",
       "Humbergate                           NaN               NaN   \n",
       "Jamestown                            NaN               NaN   \n",
       "Mount Olive                          NaN               NaN   \n",
       "Silverstone                          NaN               NaN   \n",
       "South Steeles                        NaN               NaN   \n",
       "Thistletown                          NaN               NaN   \n",
       "Northwest                            NaN               NaN   \n",
       "\n",
       "                         1st Most Common Venue 2nd Most Common Venue  \\\n",
       "Neighbourhood                                                          \n",
       "Rouge                              Supermarket          Liquor Store   \n",
       "Malvern                            Coffee Shop                  Café   \n",
       "Highland Creek                           Trail           Yoga Studio   \n",
       "Rouge Hill                         Coffee Shop   Sporting Goods Shop   \n",
       "Port Union                        Dance Studio           Coffee Shop   \n",
       "Guildwood                          Coffee Shop     Electronics Store   \n",
       "Morningside                        Coffee Shop   Fried Chicken Joint   \n",
       "West Hill                        Grocery Store     Electronics Store   \n",
       "Woburn                             Yoga Studio            Beer Store   \n",
       "Cedarbrae                                  NaN                   NaN   \n",
       "Scarborough Village                        NaN                   NaN   \n",
       "East Birchmount Park                       NaN                   NaN   \n",
       "Ionview                                    NaN                   NaN   \n",
       "Kennedy Park                               NaN                   NaN   \n",
       "Clairlea                                   NaN                   NaN   \n",
       "Golden Mile                                NaN                   NaN   \n",
       "Oakridge                                   NaN                   NaN   \n",
       "Cliffcrest                                 NaN                   NaN   \n",
       "Cliffside                                  NaN                   NaN   \n",
       "Scarborough Village West                   NaN                   NaN   \n",
       "Birch Cliff                                NaN                   NaN   \n",
       "Cliffside West                             NaN                   NaN   \n",
       "Dorset Park                                NaN                   NaN   \n",
       "Scarborough Town Centre                    NaN                   NaN   \n",
       "Wexford Heights                            NaN                   NaN   \n",
       "Maryvale                                   NaN                   NaN   \n",
       "Wexford                                    NaN                   NaN   \n",
       "Agincourt                                  NaN                   NaN   \n",
       "Clarks Corners                             NaN                   NaN   \n",
       "Sullivan                                   NaN                   NaN   \n",
       "...                                        ...                   ...   \n",
       "Royal York South West                      NaN                   NaN   \n",
       "South of Bloor                             NaN                   NaN   \n",
       "Not assigned                               NaN                   NaN   \n",
       "Cloverdale                                 NaN                   NaN   \n",
       "Islington                                  NaN                   NaN   \n",
       "Martin Grove                               NaN                   NaN   \n",
       "Princess Gardens                           NaN                   NaN   \n",
       "West Deane Park                            NaN                   NaN   \n",
       "Bloordale Gardens                          NaN                   NaN   \n",
       "Eringate                                   NaN                   NaN   \n",
       "Markland Wood                              NaN                   NaN   \n",
       "Old Burnhamthorpe                          NaN                   NaN   \n",
       "Humber Summit                              NaN                   NaN   \n",
       "Emery                                      NaN                   NaN   \n",
       "Humberlea                                  NaN                   NaN   \n",
       "Weston                                     NaN                   NaN   \n",
       "Westmount                                  NaN                   NaN   \n",
       "Kingsview Village                          NaN                   NaN   \n",
       "Martin Grove Gardens                       NaN                   NaN   \n",
       "Richview Gardens                           NaN                   NaN   \n",
       "St. Phillips                               NaN                   NaN   \n",
       "Albion Gardens                             NaN                   NaN   \n",
       "Beaumond Heights                           NaN                   NaN   \n",
       "Humbergate                                 NaN                   NaN   \n",
       "Jamestown                                  NaN                   NaN   \n",
       "Mount Olive                                NaN                   NaN   \n",
       "Silverstone                                NaN                   NaN   \n",
       "South Steeles                              NaN                   NaN   \n",
       "Thistletown                                NaN                   NaN   \n",
       "Northwest                                  NaN                   NaN   \n",
       "\n",
       "                           3rd Most Common Venue 4th Most Common Venue  \\\n",
       "Neighbourhood                                                            \n",
       "Rouge                     Furniture / Home Store          Dessert Shop   \n",
       "Malvern                    Performing Arts Venue                   Gym   \n",
       "Highland Creek                             Diner           Coffee Shop   \n",
       "Rouge Hill                        Sandwich Place   Housing Development   \n",
       "Port Union                           Pizza Place                 Diner   \n",
       "Guildwood                            College Gym          Concert Hall   \n",
       "Morningside                              Theater                  Bank   \n",
       "West Hill                     Chinese Restaurant              Pharmacy   \n",
       "Woburn                               Coffee Shop           Record Shop   \n",
       "Cedarbrae                                    NaN                   NaN   \n",
       "Scarborough Village                          NaN                   NaN   \n",
       "East Birchmount Park                         NaN                   NaN   \n",
       "Ionview                                      NaN                   NaN   \n",
       "Kennedy Park                                 NaN                   NaN   \n",
       "Clairlea                                     NaN                   NaN   \n",
       "Golden Mile                                  NaN                   NaN   \n",
       "Oakridge                                     NaN                   NaN   \n",
       "Cliffcrest                                   NaN                   NaN   \n",
       "Cliffside                                    NaN                   NaN   \n",
       "Scarborough Village West                     NaN                   NaN   \n",
       "Birch Cliff                                  NaN                   NaN   \n",
       "Cliffside West                               NaN                   NaN   \n",
       "Dorset Park                                  NaN                   NaN   \n",
       "Scarborough Town Centre                      NaN                   NaN   \n",
       "Wexford Heights                              NaN                   NaN   \n",
       "Maryvale                                     NaN                   NaN   \n",
       "Wexford                                      NaN                   NaN   \n",
       "Agincourt                                    NaN                   NaN   \n",
       "Clarks Corners                               NaN                   NaN   \n",
       "Sullivan                                     NaN                   NaN   \n",
       "...                                          ...                   ...   \n",
       "Royal York South West                        NaN                   NaN   \n",
       "South of Bloor                               NaN                   NaN   \n",
       "Not assigned                                 NaN                   NaN   \n",
       "Cloverdale                                   NaN                   NaN   \n",
       "Islington                                    NaN                   NaN   \n",
       "Martin Grove                                 NaN                   NaN   \n",
       "Princess Gardens                             NaN                   NaN   \n",
       "West Deane Park                              NaN                   NaN   \n",
       "Bloordale Gardens                            NaN                   NaN   \n",
       "Eringate                                     NaN                   NaN   \n",
       "Markland Wood                                NaN                   NaN   \n",
       "Old Burnhamthorpe                            NaN                   NaN   \n",
       "Humber Summit                                NaN                   NaN   \n",
       "Emery                                        NaN                   NaN   \n",
       "Humberlea                                    NaN                   NaN   \n",
       "Weston                                       NaN                   NaN   \n",
       "Westmount                                    NaN                   NaN   \n",
       "Kingsview Village                            NaN                   NaN   \n",
       "Martin Grove Gardens                         NaN                   NaN   \n",
       "Richview Gardens                             NaN                   NaN   \n",
       "St. Phillips                                 NaN                   NaN   \n",
       "Albion Gardens                               NaN                   NaN   \n",
       "Beaumond Heights                             NaN                   NaN   \n",
       "Humbergate                                   NaN                   NaN   \n",
       "Jamestown                                    NaN                   NaN   \n",
       "Mount Olive                                  NaN                   NaN   \n",
       "Silverstone                                  NaN                   NaN   \n",
       "South Steeles                                NaN                   NaN   \n",
       "Thistletown                                  NaN                   NaN   \n",
       "Northwest                                    NaN                   NaN   \n",
       "\n",
       "                         5th Most Common Venue 6th Most Common Venue  \\\n",
       "Neighbourhood                                                          \n",
       "Rouge                                     Park    Italian Restaurant   \n",
       "Malvern                          Deli / Bodega      Sushi Restaurant   \n",
       "Highland Creek                     College Gym          Concert Hall   \n",
       "Rouge Hill                   Indian Restaurant           College Gym   \n",
       "Port Union                         College Gym          Concert Hall   \n",
       "Guildwood                       Cosmetics Shop          Dance Studio   \n",
       "Morningside               Fast Food Restaurant         Deli / Bodega   \n",
       "West Hill                       Sandwich Place        Cosmetics Shop   \n",
       "Woburn                      Mac & Cheese Joint   Japanese Restaurant   \n",
       "Cedarbrae                                  NaN                   NaN   \n",
       "Scarborough Village                        NaN                   NaN   \n",
       "East Birchmount Park                       NaN                   NaN   \n",
       "Ionview                                    NaN                   NaN   \n",
       "Kennedy Park                               NaN                   NaN   \n",
       "Clairlea                                   NaN                   NaN   \n",
       "Golden Mile                                NaN                   NaN   \n",
       "Oakridge                                   NaN                   NaN   \n",
       "Cliffcrest                                 NaN                   NaN   \n",
       "Cliffside                                  NaN                   NaN   \n",
       "Scarborough Village West                   NaN                   NaN   \n",
       "Birch Cliff                                NaN                   NaN   \n",
       "Cliffside West                             NaN                   NaN   \n",
       "Dorset Park                                NaN                   NaN   \n",
       "Scarborough Town Centre                    NaN                   NaN   \n",
       "Wexford Heights                            NaN                   NaN   \n",
       "Maryvale                                   NaN                   NaN   \n",
       "Wexford                                    NaN                   NaN   \n",
       "Agincourt                                  NaN                   NaN   \n",
       "Clarks Corners                             NaN                   NaN   \n",
       "Sullivan                                   NaN                   NaN   \n",
       "...                                        ...                   ...   \n",
       "Royal York South West                      NaN                   NaN   \n",
       "South of Bloor                             NaN                   NaN   \n",
       "Not assigned                               NaN                   NaN   \n",
       "Cloverdale                                 NaN                   NaN   \n",
       "Islington                                  NaN                   NaN   \n",
       "Martin Grove                               NaN                   NaN   \n",
       "Princess Gardens                           NaN                   NaN   \n",
       "West Deane Park                            NaN                   NaN   \n",
       "Bloordale Gardens                          NaN                   NaN   \n",
       "Eringate                                   NaN                   NaN   \n",
       "Markland Wood                              NaN                   NaN   \n",
       "Old Burnhamthorpe                          NaN                   NaN   \n",
       "Humber Summit                              NaN                   NaN   \n",
       "Emery                                      NaN                   NaN   \n",
       "Humberlea                                  NaN                   NaN   \n",
       "Weston                                     NaN                   NaN   \n",
       "Westmount                                  NaN                   NaN   \n",
       "Kingsview Village                          NaN                   NaN   \n",
       "Martin Grove Gardens                       NaN                   NaN   \n",
       "Richview Gardens                           NaN                   NaN   \n",
       "St. Phillips                               NaN                   NaN   \n",
       "Albion Gardens                             NaN                   NaN   \n",
       "Beaumond Heights                           NaN                   NaN   \n",
       "Humbergate                                 NaN                   NaN   \n",
       "Jamestown                                  NaN                   NaN   \n",
       "Mount Olive                                NaN                   NaN   \n",
       "Silverstone                                NaN                   NaN   \n",
       "South Steeles                              NaN                   NaN   \n",
       "Thistletown                                NaN                   NaN   \n",
       "Northwest                                  NaN                   NaN   \n",
       "\n",
       "                         7th Most Common Venue 8th Most Common Venue  \\\n",
       "Neighbourhood                                                          \n",
       "Rouge                              Coffee Shop    Seafood Restaurant   \n",
       "Malvern                            Art Gallery          Concert Hall   \n",
       "Highland Creek                  Cosmetics Shop          Dance Studio   \n",
       "Rouge Hill                        Concert Hall        Cosmetics Shop   \n",
       "Port Union                      Cosmetics Shop         Deli / Bodega   \n",
       "Guildwood                        Deli / Bodega          Dessert Shop   \n",
       "Morningside                 Italian Restaurant        Clothing Store   \n",
       "West Hill                      Bubble Tea Shop            Playground   \n",
       "Woburn                                 Brewery           Pizza Place   \n",
       "Cedarbrae                                  NaN                   NaN   \n",
       "Scarborough Village                        NaN                   NaN   \n",
       "East Birchmount Park                       NaN                   NaN   \n",
       "Ionview                                    NaN                   NaN   \n",
       "Kennedy Park                               NaN                   NaN   \n",
       "Clairlea                                   NaN                   NaN   \n",
       "Golden Mile                                NaN                   NaN   \n",
       "Oakridge                                   NaN                   NaN   \n",
       "Cliffcrest                                 NaN                   NaN   \n",
       "Cliffside                                  NaN                   NaN   \n",
       "Scarborough Village West                   NaN                   NaN   \n",
       "Birch Cliff                                NaN                   NaN   \n",
       "Cliffside West                             NaN                   NaN   \n",
       "Dorset Park                                NaN                   NaN   \n",
       "Scarborough Town Centre                    NaN                   NaN   \n",
       "Wexford Heights                            NaN                   NaN   \n",
       "Maryvale                                   NaN                   NaN   \n",
       "Wexford                                    NaN                   NaN   \n",
       "Agincourt                                  NaN                   NaN   \n",
       "Clarks Corners                             NaN                   NaN   \n",
       "Sullivan                                   NaN                   NaN   \n",
       "...                                        ...                   ...   \n",
       "Royal York South West                      NaN                   NaN   \n",
       "South of Bloor                             NaN                   NaN   \n",
       "Not assigned                               NaN                   NaN   \n",
       "Cloverdale                                 NaN                   NaN   \n",
       "Islington                                  NaN                   NaN   \n",
       "Martin Grove                               NaN                   NaN   \n",
       "Princess Gardens                           NaN                   NaN   \n",
       "West Deane Park                            NaN                   NaN   \n",
       "Bloordale Gardens                          NaN                   NaN   \n",
       "Eringate                                   NaN                   NaN   \n",
       "Markland Wood                              NaN                   NaN   \n",
       "Old Burnhamthorpe                          NaN                   NaN   \n",
       "Humber Summit                              NaN                   NaN   \n",
       "Emery                                      NaN                   NaN   \n",
       "Humberlea                                  NaN                   NaN   \n",
       "Weston                                     NaN                   NaN   \n",
       "Westmount                                  NaN                   NaN   \n",
       "Kingsview Village                          NaN                   NaN   \n",
       "Martin Grove Gardens                       NaN                   NaN   \n",
       "Richview Gardens                           NaN                   NaN   \n",
       "St. Phillips                               NaN                   NaN   \n",
       "Albion Gardens                             NaN                   NaN   \n",
       "Beaumond Heights                           NaN                   NaN   \n",
       "Humbergate                                 NaN                   NaN   \n",
       "Jamestown                                  NaN                   NaN   \n",
       "Mount Olive                                NaN                   NaN   \n",
       "Silverstone                                NaN                   NaN   \n",
       "South Steeles                              NaN                   NaN   \n",
       "Thistletown                                NaN                   NaN   \n",
       "Northwest                                  NaN                   NaN   \n",
       "\n",
       "                         9th Most Common Venue 10th Most Common Venue  \n",
       "Neighbourhood                                                          \n",
       "Rouge                            Bowling Alley              Bookstore  \n",
       "Malvern                                 Bakery           Cocktail Bar  \n",
       "Highland Creek                   Deli / Bodega           Dessert Shop  \n",
       "Rouge Hill                        Dance Studio          Deli / Bodega  \n",
       "Port Union                        Dessert Shop            Yoga Studio  \n",
       "Guildwood                                Diner            Yoga Studio  \n",
       "Morningside                      Hot Dog Joint             Restaurant  \n",
       "West Hill                          Coffee Shop   Fast Food Restaurant  \n",
       "Woburn                        Asian Restaurant                    Bar  \n",
       "Cedarbrae                                  NaN                    NaN  \n",
       "Scarborough Village                        NaN                    NaN  \n",
       "East Birchmount Park                       NaN                    NaN  \n",
       "Ionview                                    NaN                    NaN  \n",
       "Kennedy Park                               NaN                    NaN  \n",
       "Clairlea                                   NaN                    NaN  \n",
       "Golden Mile                                NaN                    NaN  \n",
       "Oakridge                                   NaN                    NaN  \n",
       "Cliffcrest                                 NaN                    NaN  \n",
       "Cliffside                                  NaN                    NaN  \n",
       "Scarborough Village West                   NaN                    NaN  \n",
       "Birch Cliff                                NaN                    NaN  \n",
       "Cliffside West                             NaN                    NaN  \n",
       "Dorset Park                                NaN                    NaN  \n",
       "Scarborough Town Centre                    NaN                    NaN  \n",
       "Wexford Heights                            NaN                    NaN  \n",
       "Maryvale                                   NaN                    NaN  \n",
       "Wexford                                    NaN                    NaN  \n",
       "Agincourt                                  NaN                    NaN  \n",
       "Clarks Corners                             NaN                    NaN  \n",
       "Sullivan                                   NaN                    NaN  \n",
       "...                                        ...                    ...  \n",
       "Royal York South West                      NaN                    NaN  \n",
       "South of Bloor                             NaN                    NaN  \n",
       "Not assigned                               NaN                    NaN  \n",
       "Cloverdale                                 NaN                    NaN  \n",
       "Islington                                  NaN                    NaN  \n",
       "Martin Grove                               NaN                    NaN  \n",
       "Princess Gardens                           NaN                    NaN  \n",
       "West Deane Park                            NaN                    NaN  \n",
       "Bloordale Gardens                          NaN                    NaN  \n",
       "Eringate                                   NaN                    NaN  \n",
       "Markland Wood                              NaN                    NaN  \n",
       "Old Burnhamthorpe                          NaN                    NaN  \n",
       "Humber Summit                              NaN                    NaN  \n",
       "Emery                                      NaN                    NaN  \n",
       "Humberlea                                  NaN                    NaN  \n",
       "Weston                                     NaN                    NaN  \n",
       "Westmount                                  NaN                    NaN  \n",
       "Kingsview Village                          NaN                    NaN  \n",
       "Martin Grove Gardens                       NaN                    NaN  \n",
       "Richview Gardens                           NaN                    NaN  \n",
       "St. Phillips                               NaN                    NaN  \n",
       "Albion Gardens                             NaN                    NaN  \n",
       "Beaumond Heights                           NaN                    NaN  \n",
       "Humbergate                                 NaN                    NaN  \n",
       "Jamestown                                  NaN                    NaN  \n",
       "Mount Olive                                NaN                    NaN  \n",
       "Silverstone                                NaN                    NaN  \n",
       "South Steeles                              NaN                    NaN  \n",
       "Thistletown                                NaN                    NaN  \n",
       "Northwest                                  NaN                    NaN  \n",
       "\n",
       "[210 rows x 16 columns]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "Toronto_merged = data\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "Toronto_merged = Toronto_merged.join(neighborhoods_venues_sorted)\n",
    "\n",
    "Toronto_merged.set_index(\"Neighbourhood\") # check the last columns!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "can't assign to function call (<ipython-input-305-2c3f15e5044f>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-305-2c3f15e5044f>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    Toronto_merged =Toronto_merged.set_index('Neighbourhood'), on='Neighbourhood'\u001b[0m\n\u001b[0m                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to function call\n"
     ]
    }
   ],
   "source": [
    "Toronto_merged = Toronto_merged.set_index('Neighbourhood'), on='Neighbourhood'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'Cluster_Labels'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-307-79836afa6ff4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mToronto_merged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Cluster_Labels'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mToronto_merged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCluster_Labels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5065\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5066\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5067\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5069\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'Cluster_Labels'"
     ]
    }
   ],
   "source": [
    "Toronto_merged['Cluster_Labels'] = Toronto_merged.Cluster_Labels.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally, let's visualize the resulting clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot convert the series to <class 'float'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-296-c2a64f22ad7a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# create map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmap_clusters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfolium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlatitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlongitude\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzoom_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# set color scheme for the clusters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkclusters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/folium/map.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, location, width, height, left, top, position, tiles, API_key, max_zoom, min_zoom, zoom_start, world_copy_jump, no_wrap, attr, min_lat, max_lat, min_lon, max_lon, max_bounds, detect_retina, crs, control_scale, prefer_canvas, no_touch, disable_3d, subdomains, png_enabled)\u001b[0m\n\u001b[1;32m    167\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzoom_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin_zoom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_validate_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzoom_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzoom_start\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/folium/utilities.py\u001b[0m in \u001b[0;36m_validate_location\u001b[0;34m(location)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_validate_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;34m\"\"\"Validates and formats location values before setting.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0m_isnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m         raise ValueError('Location values cannot contain NaNs, '\n\u001b[1;32m     32\u001b[0m                          'got {!r}'.format(location))\n",
      "\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/folium/utilities.py\u001b[0m in \u001b[0;36m_isnan\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_isnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0;34m\"\"\"Check if there are NaNs values in the iterable.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_flatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/folium/utilities.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_isnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0;34m\"\"\"Check if there are NaNs values in the iterable.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_flatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     91\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m         raise TypeError(\"cannot convert the series to \"\n\u001b[0;32m---> 93\u001b[0;31m                         \"{0}\".format(str(converter)))\n\u001b[0m\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"__{name}__\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: cannot convert the series to <class 'float'>"
     ]
    }
   ],
   "source": [
    "# create map\n",
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\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(Toronto_merged['Latitude'], Toronto_merged['Longitude'], Toronto_merged['Neighborhood'], Toronto_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Examine Clusters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "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>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mississauga</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels Neighborhood 1st Most Common Venue  \\\n",
       "5  Scarborough             0.0  Mississauga           Coffee Shop   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "5     Electronics Store           College Gym          Concert Hall   \n",
       "\n",
       "  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "5        Cosmetics Shop          Dance Studio         Deli / Bodega   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "5          Dessert Shop                 Diner            Yoga Studio  "
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_merged.loc[Toronto_merged['Cluster Labels'] == 0, Toronto_merged.columns[[1] + list(range(5, Toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "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>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Bowling Alley</td>\n",
       "      <td>Bookstore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North York</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Hot Dog Joint</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Record Shop</td>\n",
       "      <td>Mac &amp; Cheese Joint</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels      Neighborhood 1st Most Common Venue  \\\n",
       "0  Scarborough             1.0   Central Toronto           Supermarket   \n",
       "1  Scarborough             1.0  Downtown Toronto           Coffee Shop   \n",
       "6  Scarborough             1.0        North York           Coffee Shop   \n",
       "7  Scarborough             1.0       Scarborough         Grocery Store   \n",
       "8  Scarborough             1.0      West Toronto           Yoga Studio   \n",
       "\n",
       "  2nd Most Common Venue   3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0          Liquor Store  Furniture / Home Store          Dessert Shop   \n",
       "1                  Café   Performing Arts Venue                   Gym   \n",
       "6   Fried Chicken Joint                 Theater                  Bank   \n",
       "7     Electronics Store      Chinese Restaurant              Pharmacy   \n",
       "8            Beer Store             Coffee Shop           Record Shop   \n",
       "\n",
       "  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "0                  Park    Italian Restaurant           Coffee Shop   \n",
       "1         Deli / Bodega      Sushi Restaurant           Art Gallery   \n",
       "6  Fast Food Restaurant         Deli / Bodega    Italian Restaurant   \n",
       "7        Sandwich Place        Cosmetics Shop       Bubble Tea Shop   \n",
       "8    Mac & Cheese Joint   Japanese Restaurant               Brewery   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "0    Seafood Restaurant         Bowling Alley              Bookstore  \n",
       "1          Concert Hall                Bakery           Cocktail Bar  \n",
       "6        Clothing Store         Hot Dog Joint             Restaurant  \n",
       "7            Playground           Coffee Shop   Fast Food Restaurant  \n",
       "8           Pizza Place      Asian Restaurant                    Bar  "
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_merged.loc[Toronto_merged['Cluster Labels'] == 1, Toronto_merged.columns[[1] + list(range(5, Toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "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>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>2.0</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Trail</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Dessert Shop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels  Neighborhood 1st Most Common Venue  \\\n",
       "2  Scarborough             2.0  East Toronto                 Trail   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "2           Yoga Studio                 Diner           Coffee Shop   \n",
       "\n",
       "  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "2           College Gym          Concert Hall        Cosmetics Shop   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "2          Dance Studio         Deli / Bodega           Dessert Shop  "
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_merged.loc[Toronto_merged['Cluster Labels'] == 2, Toronto_merged.columns[[1] + list(range(5, Toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "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>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Diner</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels Neighborhood 1st Most Common Venue  \\\n",
       "4  Scarborough             3.0    Etobicoke          Dance Studio   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "4           Coffee Shop           Pizza Place                 Diner   \n",
       "\n",
       "  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "4           College Gym          Concert Hall        Cosmetics Shop   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "4         Deli / Bodega          Dessert Shop            Yoga Studio  "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_merged.loc[Toronto_merged['Cluster Labels'] == 3, Toronto_merged.columns[[1] + list(range(5, Toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "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>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>4.0</td>\n",
       "      <td>East York</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Housing Development</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>College Gym</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels Neighborhood 1st Most Common Venue  \\\n",
       "3  Scarborough             4.0    East York           Coffee Shop   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "3   Sporting Goods Shop        Sandwich Place   Housing Development   \n",
       "\n",
       "  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "3     Indian Restaurant           College Gym          Concert Hall   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "3        Cosmetics Shop          Dance Studio          Deli / Bodega  "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_merged.loc[Toronto_merged['Cluster Labels'] == 4, Toronto_merged.columns[[1] + list(range(5, Toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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