Capsone Project3.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": 87,
   "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": 87,
     "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": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=50):\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 = ['Borough', \n",
    "                  'Borough Latitude', \n",
    "                  'Borough Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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",
      "(80, 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>Borough</th>\n",
       "      <th>Borough Latitude</th>\n",
       "      <th>Borough 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>Price Chopper</td>\n",
       "      <td>43.799445</td>\n",
       "      <td>-79.318563</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North York</td>\n",
       "      <td>43.803762</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>New York Fries</td>\n",
       "      <td>43.803664</td>\n",
       "      <td>-79.363905</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>43.704324</td>\n",
       "      <td>-79.388790</td>\n",
       "      <td>Jules Cafe Patisserie</td>\n",
       "      <td>43.704138</td>\n",
       "      <td>-79.388413</td>\n",
       "      <td>Dessert Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>43.704324</td>\n",
       "      <td>-79.388790</td>\n",
       "      <td>Thobors Boulangerie Patisserie Café</td>\n",
       "      <td>43.704514</td>\n",
       "      <td>-79.388616</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Borough  Borough Latitude  Borough Longitude  \\\n",
       "0      Scarborough         43.744734         -79.239476   \n",
       "1      Scarborough         43.799525         -79.318389   \n",
       "2       North York         43.803762         -79.363452   \n",
       "3  Central Toronto         43.704324         -79.388790   \n",
       "4  Central Toronto         43.704324         -79.388790   \n",
       "\n",
       "                                 Venue  Venue Latitude  Venue Longitude  \\\n",
       "0                         McCowan Park       43.745089       -79.239336   \n",
       "1                        Price Chopper       43.799445       -79.318563   \n",
       "2                       New York Fries       43.803664       -79.363905   \n",
       "3                Jules Cafe Patisserie       43.704138       -79.388413   \n",
       "4  Thobors Boulangerie Patisserie Café       43.704514       -79.388616   \n",
       "\n",
       "         Venue Category  \n",
       "0            Playground  \n",
       "1         Grocery Store  \n",
       "2  Fast Food Restaurant  \n",
       "3          Dessert Shop  \n",
       "4                  Café  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Borough_venues = getNearbyVenues(names=data['Borough'],\n",
    "                                   latitudes=data['Latitude'],\n",
    "                                   longitudes=data['Longitude']\n",
    "                                  )\n",
    "print(Borough_venues.shape)\n",
    "Borough_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "Borough_grouped = Borough_venues.groupby('Borough').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /opt/conda/envs/Python36\n",
      "\n",
      "  added / updated specs: \n",
      "    - folium=0.5.0\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    vincent-0.4.4              |             py_1          28 KB  conda-forge\n",
      "    certifi-2019.11.28         |           py36_0         149 KB  conda-forge\n",
      "    altair-4.0.1               |             py_0         575 KB  conda-forge\n",
      "    openssl-1.1.1d             |       h516909a_0         2.1 MB  conda-forge\n",
      "    branca-0.4.0               |             py_0          26 KB  conda-forge\n",
      "    ca-certificates-2019.11.28 |       hecc5488_0         145 KB  conda-forge\n",
      "    folium-0.5.0               |             py_0          45 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         3.0 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "    altair:          4.0.1-py_0        conda-forge\n",
      "    branca:          0.4.0-py_0        conda-forge\n",
      "    folium:          0.5.0-py_0        conda-forge\n",
      "    vincent:         0.4.4-py_1        conda-forge\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    ca-certificates: 2019.11.27-0                  --> 2019.11.28-hecc5488_0 conda-forge\n",
      "    certifi:         2019.11.28-py36_0             --> 2019.11.28-py36_0     conda-forge\n",
      "\n",
      "The following packages will be DOWNGRADED:\n",
      "\n",
      "    openssl:         1.1.1d-h7b6447c_3             --> 1.1.1d-h516909a_0     conda-forge\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "vincent-0.4.4        | 28 KB     | ##################################### | 100% \n",
      "certifi-2019.11.28   | 149 KB    | ##################################### | 100% \n",
      "altair-4.0.1         | 575 KB    | ##################################### | 100% \n",
      "openssl-1.1.1d       | 2.1 MB    | ##################################### | 100% \n",
      "branca-0.4.0         | 26 KB     | ##################################### | 100% \n",
      "ca-certificates-2019 | 145 KB    | ##################################### | 100% \n",
      "folium-0.5.0         | 45 KB     | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (4) found smaller than n_clusters (5). Possibly due to duplicate points in X.\n",
      "  return_n_iter=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0, 3, 0, 2], dtype=int32)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "import folium # map rendering library\n",
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(Borough_grouped)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length of values does not match length of index",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-126-fdf77439ac0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# add clustering labels\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[0mBorough_venues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Cluster Labels'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkmeans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_\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;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36minsert\u001b[0;34m(self, loc, column, value, allow_duplicates)\u001b[0m\n\u001b[1;32m   3469\u001b[0m         \"\"\"\n\u001b[1;32m   3470\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\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[0m\n\u001b[0;32m-> 3471\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbroadcast\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   3472\u001b[0m         self._data.insert(loc, column, value,\n\u001b[1;32m   3473\u001b[0m                           allow_duplicates=allow_duplicates)\n",
      "\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value, broadcast)\u001b[0m\n\u001b[1;32m   3628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3629\u001b[0m             \u001b[0;31m# turn me into an ndarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3630\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msanitize_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   3631\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\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[1;32m   3632\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\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\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36msanitize_index\u001b[0;34m(data, index, copy)\u001b[0m\n\u001b[1;32m    517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    518\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\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;32m--> 519\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Length of values does not match length of index'\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    520\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    521\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCIndexClass\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Length of values does not match length of index"
     ]
    }
   ],
   "source": [
    "# add clustering labels\n",
    "Borough_venues.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "\n",
    "\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "data_merged = data.join(Borough_venues.set_index('Borough'), on='Borough')\n",
    "\n",
    "data_merged.head() # check the last columns!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-150-1c9520ecaf96>, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-150-1c9520ecaf96>\"\u001b[0;36m, line \u001b[0;32m7\u001b[0m\n\u001b[0;31m    map_clusters = folium.Map(location=[Borough Latitude, Borough longitude], zoom_start=11)\u001b[0m\n\u001b[0m                                                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "\n",
    "# create map\n",
    "map_clusters = folium.Map(location=[Borough Latitude, Borough 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",
    "\n",
    "map_clusters\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      43.806686\n",
       "1      43.806686\n",
       "2      43.784535\n",
       "3      43.784535\n",
       "4      43.784535\n",
       "5      43.763573\n",
       "6      43.763573\n",
       "7      43.763573\n",
       "8      43.770992\n",
       "9      43.773136\n",
       "10     43.744734\n",
       "11     43.727929\n",
       "12     43.727929\n",
       "13     43.727929\n",
       "14     43.711112\n",
       "15     43.711112\n",
       "16     43.711112\n",
       "17     43.716316\n",
       "18     43.716316\n",
       "19     43.716316\n",
       "20     43.692657\n",
       "21     43.692657\n",
       "22     43.757410\n",
       "23     43.757410\n",
       "24     43.757410\n",
       "25     43.750072\n",
       "26     43.750072\n",
       "27     43.794200\n",
       "28     43.781638\n",
       "29     43.781638\n",
       "         ...    \n",
       "180    43.628841\n",
       "181    43.628841\n",
       "182    43.667856\n",
       "183    43.650943\n",
       "184    43.650943\n",
       "185    43.650943\n",
       "186    43.650943\n",
       "187    43.650943\n",
       "188    43.643515\n",
       "189    43.643515\n",
       "190    43.643515\n",
       "191    43.643515\n",
       "192    43.756303\n",
       "193    43.724766\n",
       "194    43.724766\n",
       "195    43.706876\n",
       "196    43.696319\n",
       "197    43.688905\n",
       "198    43.688905\n",
       "199    43.688905\n",
       "200    43.688905\n",
       "201    43.739416\n",
       "202    43.739416\n",
       "203    43.739416\n",
       "204    43.739416\n",
       "205    43.739416\n",
       "206    43.739416\n",
       "207    43.739416\n",
       "208    43.739416\n",
       "209    43.706748\n",
       "Name: Latitude, Length: 210, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(data['Latitude'], data['Longitude'], data['Borough']):\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",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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