Toronto.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import needed libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import lxml.html as lh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we will be analyzing neighborhoods in Toronto. First, we will need the data! Fortunately, Wikipedia has a list of postal codes that we can use.  \n",
    "Let's load it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the URL we want to scrape\n",
    "url='https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\n",
    "\n",
    "#Create a handle, page, to handle the contents of the website\n",
    "page = requests.get(url)\n",
    "\n",
    "#Store the contents of the website under doc\n",
    "doc = lh.fromstring(page.content)\n",
    "\n",
    "#Parse data that are stored between <tr>..</tr> of HTML\n",
    "tr_elements = doc.xpath('//tr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the length of the first 12 rows --> this is for sanity check, all rows have to have the same width\n",
    "[len(T) for T in tr_elements[:12]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:\"Postcode\"\n",
      "2:\"Borough\"\n",
      "3:\"Neighbourhood\n",
      "\"\n"
     ]
    }
   ],
   "source": [
    "# Let's parse the first rows as header\n",
    "tr_elements = doc.xpath('//tr')\n",
    "\n",
    "#Create empty list\n",
    "col=[]\n",
    "i=0\n",
    "\n",
    "#For each row, store each first element (header) and an empty list\n",
    "for t in tr_elements[0]:\n",
    "    i+=1\n",
    "    name=t.text_content()\n",
    "    print( '%d:\"%s\"'%(i,name))\n",
    "    col.append((name,[]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since our first row is the header, data is stored on the second row onwards\n",
    "for j in range(1,len(tr_elements)):\n",
    "    #T is our j'th row\n",
    "    T=tr_elements[j]\n",
    "    \n",
    "    #If row is not of size 3, the //tr data is not from our table \n",
    "    if len(T)!=3:\n",
    "        break\n",
    "    \n",
    "    #i is the index of our column\n",
    "    i=0\n",
    "    \n",
    "    #Iterate through each element of the row\n",
    "    for t in T.iterchildren():\n",
    "        data=t.text_content() \n",
    "        #Check if row is empty\n",
    "        if i>0:\n",
    "        #Convert any numerical value to integers\n",
    "            try:\n",
    "                data=int(data)\n",
    "            except:\n",
    "                pass\n",
    "        #Append the data to the empty list of the i'th column\n",
    "        col[i][1].append(data)\n",
    "        #Increment i for the next column\n",
    "        i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[287, 287, 287]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just to be sure, let's check the length of each column --> they should be the same\n",
    "[len(C) for (title,C) in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood\\n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M1A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M2A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront\\n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode           Borough     Neighbourhood\\n\n",
       "0      M1A      Not assigned      Not assigned\\n\n",
       "1      M2A      Not assigned      Not assigned\\n\n",
       "2      M3A        North York         Parkwoods\\n\n",
       "3      M4A        North York  Victoria Village\\n\n",
       "4      M5A  Downtown Toronto      Harbourfront\\n"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we are ready to create our pandas dataframe\n",
    "Dict={title:column for (title,column) in col}\n",
    "df=pd.DataFrame(Dict)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, we have the data! However, notice the 'Not assigned' values and the \\n in Neighbourhood column.   \n",
    "We have to fix these. Let's wrangle with the data a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's fix the \\n issue first; replace the \\n with empty string and rename the column\n",
    "df['Neighbourhood\\n'] = df['Neighbourhood\\n'].str.replace(r'\\n', '')\n",
    "df.rename(columns={\"Postcode\": \"PostalCode\",\"Neighbourhood\\n\": \"Neighborhood\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of our dataframe is:  (103, 3)\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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>East Birchmount Park, Ionview, Kennedy Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Clairlea, Golden Mile, Oakridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffcrest, Cliffside, Scarborough Village West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>M1N</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Birch Cliff, Cliffside West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>M1P</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Dorset Park, Scarborough Town Centre, Wexford ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>M1R</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Maryvale, Wexford</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode      Borough                                       Neighborhood\n",
       "0         M1B  Scarborough                                     Rouge, Malvern\n",
       "2         M1C  Scarborough             Highland Creek, Rouge Hill, Port Union\n",
       "5         M1E  Scarborough                  Guildwood, Morningside, West Hill\n",
       "8         M1G  Scarborough                                             Woburn\n",
       "9         M1H  Scarborough                                          Cedarbrae\n",
       "10        M1J  Scarborough                                Scarborough Village\n",
       "11        M1K  Scarborough        East Birchmount Park, Ionview, Kennedy Park\n",
       "14        M1L  Scarborough                    Clairlea, Golden Mile, Oakridge\n",
       "17        M1M  Scarborough    Cliffcrest, Cliffside, Scarborough Village West\n",
       "20        M1N  Scarborough                        Birch Cliff, Cliffside West\n",
       "22        M1P  Scarborough  Dorset Park, Scarborough Town Centre, Wexford ...\n",
       "25        M1R  Scarborough                                  Maryvale, Wexford"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's handle the 'Not assigned' values\n",
    "df = df[df.Borough != 'Not assigned']\n",
    "\n",
    "# We removed all the 'Not assigned' values from the Borough col\n",
    "# Now, let's turn our attention to concatenating the neighborhoods based on borough\n",
    "\n",
    "# first, group by borough\n",
    "df_grouped = df.groupby(['PostalCode'])['Neighborhood'].apply(', '.join).reset_index()\n",
    "df = df_grouped.merge(df, on = 'PostalCode') # join the grouped df with the regular one\n",
    "df.drop_duplicates(subset= \"PostalCode\", inplace=True) # remove duplicate postal codes from joined df\n",
    "\n",
    "# second, let's remove the unnecessary column and rename our headers\n",
    "df = df.drop(columns = 'Neighborhood_y')\n",
    "df.rename(columns={\"Postcode\": \"PostalCode\",\"Neighborhood_x\": \"Neighborhood\"}, inplace=True)\n",
    "\n",
    "# Now let's handle to 'Not assigned' in Neighborhood column\n",
    "df.loc[(df.Neighborhood == 'Not assigned'),'Neighborhood'] = df.loc[(df.Neighborhood == 'Not assigned'),'Borough']\n",
    "\n",
    "# Last, rearange column order \n",
    "cols = ['PostalCode', 'Borough', 'Neighborhood']\n",
    "df = df[cols]\n",
    "\n",
    "# Now, let's get the shape of our dataframe and print out the first 12 rows\n",
    "print('The shape of our dataframe is: ',df.shape)\n",
    "df.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataframe looks good! Let's now add the coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>East Birchmount Park, Ionview, Kennedy Park</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Clairlea, Golden Mile, Oakridge</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffcrest, Cliffside, Scarborough Village West</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>M1N</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Birch Cliff, Cliffside West</td>\n",
       "      <td>43.692657</td>\n",
       "      <td>-79.264848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>M1P</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Dorset Park, Scarborough Town Centre, Wexford ...</td>\n",
       "      <td>43.757410</td>\n",
       "      <td>-79.273304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>M1R</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Maryvale, Wexford</td>\n",
       "      <td>43.750072</td>\n",
       "      <td>-79.295849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode      Borough                                       Neighborhood  \\\n",
       "0         M1B  Scarborough                                     Rouge, Malvern   \n",
       "1         M1C  Scarborough             Highland Creek, Rouge Hill, Port Union   \n",
       "2         M1E  Scarborough                  Guildwood, Morningside, West Hill   \n",
       "3         M1G  Scarborough                                             Woburn   \n",
       "4         M1H  Scarborough                                          Cedarbrae   \n",
       "5         M1J  Scarborough                                Scarborough Village   \n",
       "6         M1K  Scarborough        East Birchmount Park, Ionview, Kennedy Park   \n",
       "7         M1L  Scarborough                    Clairlea, Golden Mile, Oakridge   \n",
       "8         M1M  Scarborough    Cliffcrest, Cliffside, Scarborough Village West   \n",
       "9         M1N  Scarborough                        Birch Cliff, Cliffside West   \n",
       "10        M1P  Scarborough  Dorset Park, Scarborough Town Centre, Wexford ...   \n",
       "11        M1R  Scarborough                                  Maryvale, Wexford   \n",
       "\n",
       "     Latitude  Longitude  \n",
       "0   43.806686 -79.194353  \n",
       "1   43.784535 -79.160497  \n",
       "2   43.763573 -79.188711  \n",
       "3   43.770992 -79.216917  \n",
       "4   43.773136 -79.239476  \n",
       "5   43.744734 -79.239476  \n",
       "6   43.727929 -79.262029  \n",
       "7   43.711112 -79.284577  \n",
       "8   43.716316 -79.239476  \n",
       "9   43.692657 -79.264848  \n",
       "10  43.757410 -79.273304  \n",
       "11  43.750072 -79.295849  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have our coordinates stored in a .csv; let's store the data in a pandas df\n",
    "coord = pd.read_csv('Geospatial_Coordinates.csv')\n",
    "\n",
    "# We can merge our two dfs based on postal code\n",
    "coord.rename(columns = {'Postal Code': 'PostalCode'}, inplace = True)\n",
    "df = df.merge(coord, on = 'PostalCode') # join the grouped df with the regular one\n",
    "\n",
    "# Let's see our df\n",
    "df.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are done with the data wrangling for now, so let the fun begin!  \n",
    "Let's explore the city of Toronto a little bit. We will use Foursquare API to get venues data that we can map and cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, let's load additional libraries\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "import folium # map rendering library\n",
    "\n",
    "import json # library to handle JSON files\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "import requests # library to handle requests\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have all the necessary libraries, we can locate Toronto's coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Toronto are 43.653963, -79.387207.\n"
     ]
    }
   ],
   "source": [
    "address = 'Toronto'\n",
    "# let's use geolocator to find the coordinates of Toronto\n",
    "geolocator = Nominatim(user_agent=\"ca_exp\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Toronto are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the map of Toronto now, with our boroughs superimposed on the map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1dfc72c5a88>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Toronto using latitude and longitude values\n",
    "map_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood in zip(df['Latitude'], df['Longitude'], df['Borough'], df['Neighborhood']):\n",
    "    label = '{}; {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_toronto)  \n",
    "    \n",
    "map_toronto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we are going to use Foursquare API to explore venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your credentails:\n",
      "CLIENT_ID: SYGGTJ5AS5QEBE0VA5XIOVWRN0MROFX5LI5C5UGSKHNTS3DA\n",
      "CLIENT_SECRET:HMKAHE0SZNZXRGRJKSHAPCLV52CRLDPBNCRJBBPFUAMODJ0S\n"
     ]
    }
   ],
   "source": [
    "CLIENT_ID = 'SYGGTJ5AS5QEBE0VA5XIOVWRN0MROFX5LI5C5UGSKHNTS3DA' # your Foursquare ID\n",
    "CLIENT_SECRET = 'HMKAHE0SZNZXRGRJKSHAPCLV52CRLDPBNCRJBBPFUAMODJ0S' # your Foursquare Secret\n",
    "VERSION = '20191216' # Foursquare API version\n",
    "\n",
    "print('Your credentails:')\n",
    "print('CLIENT_ID: ' + CLIENT_ID)\n",
    "print('CLIENT_SECRET:' + CLIENT_SECRET)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore the first borough in our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M4W</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>43.679563</td>\n",
       "      <td>-79.377529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M4X</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Cabbagetown, St. James Town</td>\n",
       "      <td>43.667967</td>\n",
       "      <td>-79.367675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M4Y</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Church and Wellesley</td>\n",
       "      <td>43.665860</td>\n",
       "      <td>-79.383160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "      <td>43.654260</td>\n",
       "      <td>-79.360636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M5B</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Ryerson, Garden District</td>\n",
       "      <td>43.657162</td>\n",
       "      <td>-79.378937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>M5C</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>St. James Town</td>\n",
       "      <td>43.651494</td>\n",
       "      <td>-79.375418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>M5E</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>43.644771</td>\n",
       "      <td>-79.373306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>M5G</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Central Bay Street</td>\n",
       "      <td>43.657952</td>\n",
       "      <td>-79.387383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>M5H</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Adelaide, King, Richmond</td>\n",
       "      <td>43.650571</td>\n",
       "      <td>-79.384568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>M5J</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront East, Toronto Islands, Union Station</td>\n",
       "      <td>43.640816</td>\n",
       "      <td>-79.381752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>M5K</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Design Exchange, Toronto Dominion Centre</td>\n",
       "      <td>43.647177</td>\n",
       "      <td>-79.381576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>M5L</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Commerce Court, Victoria Hotel</td>\n",
       "      <td>43.648198</td>\n",
       "      <td>-79.379817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>M5S</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbord, University of Toronto</td>\n",
       "      <td>43.662696</td>\n",
       "      <td>-79.400049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>M5T</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Chinatown, Grange Park, Kensington Market</td>\n",
       "      <td>43.653206</td>\n",
       "      <td>-79.400049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>M5V</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>CN Tower, Bathurst Quay, Island airport, Harbo...</td>\n",
       "      <td>43.628947</td>\n",
       "      <td>-79.394420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>M5W</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Stn A PO Boxes 25 The Esplanade</td>\n",
       "      <td>43.646435</td>\n",
       "      <td>-79.374846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>M5X</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>First Canadian Place, Underground city</td>\n",
       "      <td>43.648429</td>\n",
       "      <td>-79.382280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>M6G</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Christie</td>\n",
       "      <td>43.669542</td>\n",
       "      <td>-79.422564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>M9A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>43.667856</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode           Borough  \\\n",
       "0         M4W  Downtown Toronto   \n",
       "1         M4X  Downtown Toronto   \n",
       "2         M4Y  Downtown Toronto   \n",
       "3         M5A  Downtown Toronto   \n",
       "4         M5B  Downtown Toronto   \n",
       "5         M5C  Downtown Toronto   \n",
       "6         M5E  Downtown Toronto   \n",
       "7         M5G  Downtown Toronto   \n",
       "8         M5H  Downtown Toronto   \n",
       "9         M5J  Downtown Toronto   \n",
       "10        M5K  Downtown Toronto   \n",
       "11        M5L  Downtown Toronto   \n",
       "12        M5S  Downtown Toronto   \n",
       "13        M5T  Downtown Toronto   \n",
       "14        M5V  Downtown Toronto   \n",
       "15        M5W  Downtown Toronto   \n",
       "16        M5X  Downtown Toronto   \n",
       "17        M6G  Downtown Toronto   \n",
       "18        M9A  Downtown Toronto   \n",
       "\n",
       "                                         Neighborhood   Latitude  Longitude  \n",
       "0                                            Rosedale  43.679563 -79.377529  \n",
       "1                         Cabbagetown, St. James Town  43.667967 -79.367675  \n",
       "2                                Church and Wellesley  43.665860 -79.383160  \n",
       "3                                        Harbourfront  43.654260 -79.360636  \n",
       "4                            Ryerson, Garden District  43.657162 -79.378937  \n",
       "5                                      St. James Town  43.651494 -79.375418  \n",
       "6                                         Berczy Park  43.644771 -79.373306  \n",
       "7                                  Central Bay Street  43.657952 -79.387383  \n",
       "8                            Adelaide, King, Richmond  43.650571 -79.384568  \n",
       "9   Harbourfront East, Toronto Islands, Union Station  43.640816 -79.381752  \n",
       "10           Design Exchange, Toronto Dominion Centre  43.647177 -79.381576  \n",
       "11                     Commerce Court, Victoria Hotel  43.648198 -79.379817  \n",
       "12                     Harbord, University of Toronto  43.662696 -79.400049  \n",
       "13          Chinatown, Grange Park, Kensington Market  43.653206 -79.400049  \n",
       "14  CN Tower, Bathurst Quay, Island airport, Harbo...  43.628947 -79.394420  \n",
       "15                    Stn A PO Boxes 25 The Esplanade  43.646435 -79.374846  \n",
       "16             First Canadian Place, Underground city  43.648429 -79.382280  \n",
       "17                                           Christie  43.669542 -79.422564  \n",
       "18                                       Queen's Park  43.667856 -79.532242  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_data = df[df['Borough'] == 'Downtown Toronto'].reset_index(drop=True)\n",
    "dt_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Downtown Toronto are 43.6563221, -79.3809161.\n"
     ]
    }
   ],
   "source": [
    "address = 'Downtown Toronto'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"ca_exp\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Downtown Toronto are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's map all the neighborhoods in Downtown Toronto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
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        circle_marker_a144d6dd4b164ab29bdec8585c9e6a83.bindPopup(popup_d6308bb9101341819d19de5720405b67)
        ;

        
    
    
            var circle_marker_21a14b2d830a4e4791145674fc9b8b84 = L.circleMarker(
                [43.6481985, -79.37981690000001],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fb4f4f9aa6ff4badb73ccf1a73043ae5);
        
    
        var popup_0cfd464a3d32498baac0923d044f5a20 = L.popup({"maxWidth": "100%"});

        
            var html_6ccce1b5c4b2453bbe62164b2bb64fb3 = $(`<div id="html_6ccce1b5c4b2453bbe62164b2bb64fb3" style="width: 100.0%; height: 100.0%;">Commerce Court, Victoria Hotel</div>`)[0];
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        circle_marker_21a14b2d830a4e4791145674fc9b8b84.bindPopup(popup_0cfd464a3d32498baac0923d044f5a20)
        ;

        
    
    
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                [43.6626956, -79.4000493],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fb4f4f9aa6ff4badb73ccf1a73043ae5);
        
    
        var popup_434e1aac8f4e4cd991beabf7ce8c4932 = L.popup({"maxWidth": "100%"});

        
            var html_8d6d114ecd5545fc892dee0563d32c97 = $(`<div id="html_8d6d114ecd5545fc892dee0563d32c97" style="width: 100.0%; height: 100.0%;">Harbord, University of Toronto</div>`)[0];
            popup_434e1aac8f4e4cd991beabf7ce8c4932.setContent(html_8d6d114ecd5545fc892dee0563d32c97);
        

        circle_marker_8e4f8baf657142c2a9bb2cc1ba274079.bindPopup(popup_434e1aac8f4e4cd991beabf7ce8c4932)
        ;

        
    
    
            var circle_marker_7f261fd972d8494b9af69bada39c1c08 = L.circleMarker(
                [43.6532057, -79.4000493],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fb4f4f9aa6ff4badb73ccf1a73043ae5);
        
    
        var popup_fdc0bf2cd9ea44e09824fab949edf0e6 = L.popup({"maxWidth": "100%"});

        
            var html_dbdc249a71ed46b19351d3fd1e5de3b4 = $(`<div id="html_dbdc249a71ed46b19351d3fd1e5de3b4" style="width: 100.0%; height: 100.0%;">Chinatown, Grange Park, Kensington Market</div>`)[0];
            popup_fdc0bf2cd9ea44e09824fab949edf0e6.setContent(html_dbdc249a71ed46b19351d3fd1e5de3b4);
        

        circle_marker_7f261fd972d8494b9af69bada39c1c08.bindPopup(popup_fdc0bf2cd9ea44e09824fab949edf0e6)
        ;

        
    
    
            var circle_marker_7d66816638704683b60bc6cc7b83a89f = L.circleMarker(
                [43.6289467, -79.3944199],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fb4f4f9aa6ff4badb73ccf1a73043ae5);
        
    
        var popup_9e8a6f7341c94c4a99cd68f51706dcfa = L.popup({"maxWidth": "100%"});

        
            var html_ebbe086e10c54c58b5c4afafce247b0b = $(`<div id="html_ebbe086e10c54c58b5c4afafce247b0b" style="width: 100.0%; height: 100.0%;">CN Tower, Bathurst Quay, Island airport, Harbourfront West, King and Spadina, Railway Lands, South Niagara</div>`)[0];
            popup_9e8a6f7341c94c4a99cd68f51706dcfa.setContent(html_ebbe086e10c54c58b5c4afafce247b0b);
        

        circle_marker_7d66816638704683b60bc6cc7b83a89f.bindPopup(popup_9e8a6f7341c94c4a99cd68f51706dcfa)
        ;

        
    
    
            var circle_marker_b405a19f833143e4b623160b4be84798 = L.circleMarker(
                [43.6464352, -79.37484599999999],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fb4f4f9aa6ff4badb73ccf1a73043ae5);
        
    
        var popup_5a84384ba0b84239b3f8068af1f3ba63 = L.popup({"maxWidth": "100%"});

        
            var html_1d690c92abfe45f89930547cffd8d2cd = $(`<div id="html_1d690c92abfe45f89930547cffd8d2cd" style="width: 100.0%; height: 100.0%;">Stn A PO Boxes 25 The Esplanade</div>`)[0];
            popup_5a84384ba0b84239b3f8068af1f3ba63.setContent(html_1d690c92abfe45f89930547cffd8d2cd);
        

        circle_marker_b405a19f833143e4b623160b4be84798.bindPopup(popup_5a84384ba0b84239b3f8068af1f3ba63)
        ;

        
    
    
            var circle_marker_c883b112741a4a42aaf775640e19bfae = L.circleMarker(
                [43.6484292, -79.3822802],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fb4f4f9aa6ff4badb73ccf1a73043ae5);
        
    
        var popup_aa720583e2c641b8bc1b2cf78d62570b = L.popup({"maxWidth": "100%"});

        
            var html_d67e3c8be2554f8eb08e1379c1021fb0 = $(`<div id="html_d67e3c8be2554f8eb08e1379c1021fb0" style="width: 100.0%; height: 100.0%;">First Canadian Place, Underground city</div>`)[0];
            popup_aa720583e2c641b8bc1b2cf78d62570b.setContent(html_d67e3c8be2554f8eb08e1379c1021fb0);
        

        circle_marker_c883b112741a4a42aaf775640e19bfae.bindPopup(popup_aa720583e2c641b8bc1b2cf78d62570b)
        ;

        
    
    
            var circle_marker_c3e14ddd73b0479db2e9c85e049907bd = L.circleMarker(
                [43.669542, -79.4225637],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fb4f4f9aa6ff4badb73ccf1a73043ae5);
        
    
        var popup_4fa248f1517b40ae8d1d9b05586bb8a5 = L.popup({"maxWidth": "100%"});

        
            var html_ed20bcc8e0e1423fb12d8182cac57372 = $(`<div id="html_ed20bcc8e0e1423fb12d8182cac57372" style="width: 100.0%; height: 100.0%;">Christie</div>`)[0];
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        circle_marker_c3e14ddd73b0479db2e9c85e049907bd.bindPopup(popup_4fa248f1517b40ae8d1d9b05586bb8a5)
        ;

        
    
    
            var circle_marker_aee891372270466c9ed5bf51c91fc11e = L.circleMarker(
                [43.6678556, -79.53224240000002],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fb4f4f9aa6ff4badb73ccf1a73043ae5);
        
    
        var popup_20d71ec959104393bb39dde5a5ee87b5 = L.popup({"maxWidth": "100%"});

        
            var html_a468206c3981471ca43172c162313ae9 = $(`<div id="html_a468206c3981471ca43172c162313ae9" style="width: 100.0%; height: 100.0%;">Queen&#39;s Park</div>`)[0];
            popup_20d71ec959104393bb39dde5a5ee87b5.setContent(html_a468206c3981471ca43172c162313ae9);
        

        circle_marker_aee891372270466c9ed5bf51c91fc11e.bindPopup(popup_20d71ec959104393bb39dde5a5ee87b5)
        ;

        
    
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1dfc8ac3248>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Downtown Toronto using latitude and longitude values\n",
    "map_dt = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, label in zip(dt_data['Latitude'], dt_data['Longitude'], dt_data['Neighborhood']):\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_dt)  \n",
    "    \n",
    "map_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's explore 'Church and Wellesley' more in depth using Foursquare API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of Church and Wellesley are 43.6658599, -79.38315990000001.\n"
     ]
    }
   ],
   "source": [
    "# First, let's filter the data\n",
    "neighborhood_latitude = dt_data.loc[2, 'Latitude'] # neighborhood latitude value\n",
    "neighborhood_longitude = dt_data.loc[2, 'Longitude'] # neighborhood longitude value\n",
    "\n",
    "neighborhood_name = dt_data.loc[2, 'Neighborhood'] # neighborhood name\n",
    "\n",
    "print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n",
    "                                                               neighborhood_latitude, \n",
    "                                                               neighborhood_longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's get the top 100 venues that are in Marble Hill within a radius of 500 meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "LIMIT = 100 # limit of number of venues returned by Foursquare API\n",
    "radius = 500 # 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",
    "    neighborhood_latitude, \n",
    "    neighborhood_longitude, \n",
    "    radius, \n",
    "    LIMIT)\n",
    "url # display URL\n",
    "\n",
    "# Store the results\n",
    "results = requests.get(url).json()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the foursquare API, all the relevant information is in the 'items'. We will create a function to get the category types out of the json and then continue the analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that extracts the category of the venue\n",
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's clean the json and store it in pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Storm Crow Manor</td>\n",
       "      <td>Theme Restaurant</td>\n",
       "      <td>43.666840</td>\n",
       "      <td>-79.381593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>DanceLifeX Centre</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>43.666956</td>\n",
       "      <td>-79.385297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Smith</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>43.666927</td>\n",
       "      <td>-79.381421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>The Alley</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>43.665922</td>\n",
       "      <td>-79.385567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Sansotei Ramen 三草亭</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>43.666735</td>\n",
       "      <td>-79.385353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name        categories        lat        lng\n",
       "0    Storm Crow Manor  Theme Restaurant  43.666840 -79.381593\n",
       "1   DanceLifeX Centre      Dance Studio  43.666956 -79.385297\n",
       "2               Smith    Breakfast Spot  43.666927 -79.381421\n",
       "3           The Alley   Bubble Tea Shop  43.665922 -79.385567\n",
       "4  Sansotei Ramen 三草亭  Ramen Restaurant  43.666735 -79.385353"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues = results['response']['groups'][0]['items']\n",
    "    \n",
    "nearby_venues = json_normalize(venues) # flatten JSON\n",
    "\n",
    "# filter columns\n",
    "filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
    "nearby_venues =nearby_venues.loc[:, filtered_columns]\n",
    "\n",
    "# filter the category for each row\n",
    "nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n",
    "\n",
    "# clean columns\n",
    "nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n",
    "\n",
    "nearby_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83 venues were returned by Foursquare.\n"
     ]
    }
   ],
   "source": [
    "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we got venues data from one neighborhood, let's scale it up by getting venues data from all neighborhoods in Downtown Toronto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following function will get venues data from all neighborhoods:\n",
    "\n",
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\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": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rosedale\n",
      "Cabbagetown, St. James Town\n",
      "Church and Wellesley\n",
      "Harbourfront\n",
      "Ryerson, Garden District\n",
      "St. James Town\n",
      "Berczy Park\n",
      "Central Bay Street\n",
      "Adelaide, King, Richmond\n",
      "Harbourfront East, Toronto Islands, Union Station\n",
      "Design Exchange, Toronto Dominion Centre\n",
      "Commerce Court, Victoria Hotel\n",
      "Harbord, University of Toronto\n",
      "Chinatown, Grange Park, Kensington Market\n",
      "CN Tower, Bathurst Quay, Island airport, Harbourfront West, King and Spadina, Railway Lands, South Niagara\n",
      "Stn A PO Boxes 25 The Esplanade\n",
      "First Canadian Place, Underground city\n",
      "Christie\n",
      "Queen's Park\n"
     ]
    }
   ],
   "source": [
    "# Let's pass the function to our dataframe\n",
    "\n",
    "dt_venues = getNearbyVenues(names=dt_data['Neighborhood'],\n",
    "                                   latitudes=dt_data['Latitude'],\n",
    "                                   longitudes=dt_data['Longitude']\n",
    "                                  )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the size of the resulting dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1279, 7)\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>0</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>43.679563</td>\n",
       "      <td>-79.377529</td>\n",
       "      <td>Rosedale Park</td>\n",
       "      <td>43.682328</td>\n",
       "      <td>-79.378934</td>\n",
       "      <td>Playground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>43.679563</td>\n",
       "      <td>-79.377529</td>\n",
       "      <td>Whitney Park</td>\n",
       "      <td>43.682036</td>\n",
       "      <td>-79.373788</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>43.679563</td>\n",
       "      <td>-79.377529</td>\n",
       "      <td>Alex Murray Parkette</td>\n",
       "      <td>43.678300</td>\n",
       "      <td>-79.382773</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>43.679563</td>\n",
       "      <td>-79.377529</td>\n",
       "      <td>Milkman's Lane</td>\n",
       "      <td>43.676352</td>\n",
       "      <td>-79.373842</td>\n",
       "      <td>Trail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Cabbagetown, St. James Town</td>\n",
       "      <td>43.667967</td>\n",
       "      <td>-79.367675</td>\n",
       "      <td>Cranberries</td>\n",
       "      <td>43.667843</td>\n",
       "      <td>-79.369407</td>\n",
       "      <td>Diner</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Neighborhood  Neighborhood Latitude  Neighborhood Longitude  \\\n",
       "0                     Rosedale              43.679563              -79.377529   \n",
       "1                     Rosedale              43.679563              -79.377529   \n",
       "2                     Rosedale              43.679563              -79.377529   \n",
       "3                     Rosedale              43.679563              -79.377529   \n",
       "4  Cabbagetown, St. James Town              43.667967              -79.367675   \n",
       "\n",
       "                  Venue  Venue Latitude  Venue Longitude Venue Category  \n",
       "0         Rosedale Park       43.682328       -79.378934     Playground  \n",
       "1          Whitney Park       43.682036       -79.373788           Park  \n",
       "2  Alex Murray Parkette       43.678300       -79.382773           Park  \n",
       "3        Milkman's Lane       43.676352       -79.373842          Trail  \n",
       "4           Cranberries       43.667843       -79.369407          Diner  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dt_venues.shape)\n",
    "dt_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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",
       "      <td>Adelaide, King, Richmond</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CN Tower, Bathurst Quay, Island airport, Harbourfront West, King and Spadina, Railway Lands, South Niagara</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cabbagetown, St. James Town</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Central Bay Street</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Chinatown, Grange Park, Kensington Market</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Christie</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Church and Wellesley</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Commerce Court, Victoria Hotel</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Design Exchange, Toronto Dominion Centre</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>First Canadian Place, Underground city</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Harbord, University of Toronto</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Harbourfront</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Harbourfront East, Toronto Islands, Union Station</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rosedale</td>\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",
       "      <td>Ryerson, Garden District</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>St. James Town</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Stn A PO Boxes 25 The Esplanade</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Neighborhood Latitude  \\\n",
       "Neighborhood                                                                \n",
       "Adelaide, King, Richmond                                              100   \n",
       "Berczy Park                                                            55   \n",
       "CN Tower, Bathurst Quay, Island airport, Harbou...                     15   \n",
       "Cabbagetown, St. James Town                                            43   \n",
       "Central Bay Street                                                     84   \n",
       "Chinatown, Grange Park, Kensington Market                              94   \n",
       "Christie                                                               17   \n",
       "Church and Wellesley                                                   83   \n",
       "Commerce Court, Victoria Hotel                                        100   \n",
       "Design Exchange, Toronto Dominion Centre                              100   \n",
       "First Canadian Place, Underground city                                100   \n",
       "Harbord, University of Toronto                                         38   \n",
       "Harbourfront                                                           50   \n",
       "Harbourfront East, Toronto Islands, Union Station                     100   \n",
       "Rosedale                                                                4   \n",
       "Ryerson, Garden District                                              100   \n",
       "St. James Town                                                        100   \n",
       "Stn A PO Boxes 25 The Esplanade                                        96   \n",
       "\n",
       "                                                    Neighborhood Longitude  \\\n",
       "Neighborhood                                                                 \n",
       "Adelaide, King, Richmond                                               100   \n",
       "Berczy Park                                                             55   \n",
       "CN Tower, Bathurst Quay, Island airport, Harbou...                      15   \n",
       "Cabbagetown, St. James Town                                             43   \n",
       "Central Bay Street                                                      84   \n",
       "Chinatown, Grange Park, Kensington Market                               94   \n",
       "Christie                                                                17   \n",
       "Church and Wellesley                                                    83   \n",
       "Commerce Court, Victoria Hotel                                         100   \n",
       "Design Exchange, Toronto Dominion Centre                               100   \n",
       "First Canadian Place, Underground city                                 100   \n",
       "Harbord, University of Toronto                                          38   \n",
       "Harbourfront                                                            50   \n",
       "Harbourfront East, Toronto Islands, Union Station                      100   \n",
       "Rosedale                                                                 4   \n",
       "Ryerson, Garden District                                               100   \n",
       "St. James Town                                                         100   \n",
       "Stn A PO Boxes 25 The Esplanade                                         96   \n",
       "\n",
       "                                                    Venue  Venue Latitude  \\\n",
       "Neighborhood                                                                \n",
       "Adelaide, King, Richmond                              100             100   \n",
       "Berczy Park                                            55              55   \n",
       "CN Tower, Bathurst Quay, Island airport, Harbou...     15              15   \n",
       "Cabbagetown, St. James Town                            43              43   \n",
       "Central Bay Street                                     84              84   \n",
       "Chinatown, Grange Park, Kensington Market              94              94   \n",
       "Christie                                               17              17   \n",
       "Church and Wellesley                                   83              83   \n",
       "Commerce Court, Victoria Hotel                        100             100   \n",
       "Design Exchange, Toronto Dominion Centre              100             100   \n",
       "First Canadian Place, Underground city                100             100   \n",
       "Harbord, University of Toronto                         38              38   \n",
       "Harbourfront                                           50              50   \n",
       "Harbourfront East, Toronto Islands, Union Station     100             100   \n",
       "Rosedale                                                4               4   \n",
       "Ryerson, Garden District                              100             100   \n",
       "St. James Town                                        100             100   \n",
       "Stn A PO Boxes 25 The Esplanade                        96              96   \n",
       "\n",
       "                                                    Venue Longitude  \\\n",
       "Neighborhood                                                          \n",
       "Adelaide, King, Richmond                                        100   \n",
       "Berczy Park                                                      55   \n",
       "CN Tower, Bathurst Quay, Island airport, Harbou...               15   \n",
       "Cabbagetown, St. James Town                                      43   \n",
       "Central Bay Street                                               84   \n",
       "Chinatown, Grange Park, Kensington Market                        94   \n",
       "Christie                                                         17   \n",
       "Church and Wellesley                                             83   \n",
       "Commerce Court, Victoria Hotel                                  100   \n",
       "Design Exchange, Toronto Dominion Centre                        100   \n",
       "First Canadian Place, Underground city                          100   \n",
       "Harbord, University of Toronto                                   38   \n",
       "Harbourfront                                                     50   \n",
       "Harbourfront East, Toronto Islands, Union Station               100   \n",
       "Rosedale                                                          4   \n",
       "Ryerson, Garden District                                        100   \n",
       "St. James Town                                                  100   \n",
       "Stn A PO Boxes 25 The Esplanade                                  96   \n",
       "\n",
       "                                                    Venue Category  \n",
       "Neighborhood                                                        \n",
       "Adelaide, King, Richmond                                       100  \n",
       "Berczy Park                                                     55  \n",
       "CN Tower, Bathurst Quay, Island airport, Harbou...              15  \n",
       "Cabbagetown, St. James Town                                     43  \n",
       "Central Bay Street                                              84  \n",
       "Chinatown, Grange Park, Kensington Market                       94  \n",
       "Christie                                                        17  \n",
       "Church and Wellesley                                            83  \n",
       "Commerce Court, Victoria Hotel                                 100  \n",
       "Design Exchange, Toronto Dominion Centre                       100  \n",
       "First Canadian Place, Underground city                         100  \n",
       "Harbord, University of Toronto                                  38  \n",
       "Harbourfront                                                    50  \n",
       "Harbourfront East, Toronto Islands, Union Station              100  \n",
       "Rosedale                                                         4  \n",
       "Ryerson, Garden District                                       100  \n",
       "St. James Town                                                 100  \n",
       "Stn A PO Boxes 25 The Esplanade                                 96  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Amount of venues returned per Neighborhood\n",
    "dt_venues.groupby('Neighborhood').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 205 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "# Amount of unique venues in the borough\n",
    "print('There are {} uniques categories.'.format(len(dt_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have made some descriptive analysis, we can start looking into clustering the neighborhood.  \n",
    "First, we need to one-hot encode the categorical variables, then we can set up a clustering using k-means method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Afghan Restaurant</th>\n",
       "      <th>Airport</th>\n",
       "      <th>Airport Food Court</th>\n",
       "      <th>Airport Gate</th>\n",
       "      <th>Airport Lounge</th>\n",
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       "      <th>Toy / Game Store</th>\n",
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       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Wings Joint</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>Rosedale</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>4</td>\n",
       "      <td>Cabbagetown, St. James Town</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 × 205 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Neighborhood  Afghan Restaurant  Airport  \\\n",
       "0                     Rosedale                  0        0   \n",
       "1                     Rosedale                  0        0   \n",
       "2                     Rosedale                  0        0   \n",
       "3                     Rosedale                  0        0   \n",
       "4  Cabbagetown, St. James Town                  0        0   \n",
       "\n",
       "   Airport Food Court  Airport Gate  Airport Lounge  Airport Service  \\\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",
       "   Airport Terminal  American Restaurant  Antique Shop  ...  \\\n",
       "0                 0                    0             0  ...   \n",
       "1                 0                    0             0  ...   \n",
       "2                 0                    0             0  ...   \n",
       "3                 0                    0             0  ...   \n",
       "4                 0                    0             0  ...   \n",
       "\n",
       "   Thrift / Vintage Store  Toy / Game Store  Trail  Train Station  \\\n",
       "0                       0                 0      0              0   \n",
       "1                       0                 0      0              0   \n",
       "2                       0                 0      0              0   \n",
       "3                       0                 0      1              0   \n",
       "4                       0                 0      0              0   \n",
       "\n",
       "   Vegetarian / Vegan Restaurant  Video Game Store  Vietnamese Restaurant  \\\n",
       "0                              0                 0                      0   \n",
       "1                              0                 0                      0   \n",
       "2                              0                 0                      0   \n",
       "3                              0                 0                      0   \n",
       "4                              0                 0                      0   \n",
       "\n",
       "   Wine Bar  Wings Joint  Yoga Studio  \n",
       "0         0            0            0  \n",
       "1         0            0            0  \n",
       "2         0            0            0  \n",
       "3         0            0            0  \n",
       "4         0            0            0  \n",
       "\n",
       "[5 rows x 205 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "dt_onehot = pd.get_dummies(dt_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "dt_onehot['Neighborhood'] = dt_venues['Neighborhood'] \n",
    "dt_onehot = dt_onehot[ ['Neighborhood'] + [ col for col in dt_onehot.columns if col != 'Neighborhood' ] ]\n",
    "dt_onehot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's group the data by neigborhood and by taking the mean of the frequency of occurancy of a venue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_grouped = dt_onehot.groupby('Neighborhood').mean().reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print each neighborhood out along with the top 5 venues categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Adelaide, King, Richmond----\n",
      "             venue  freq\n",
      "0      Coffee Shop  0.07\n",
      "1             Café  0.05\n",
      "2  Thai Restaurant  0.04\n",
      "3       Steakhouse  0.04\n",
      "4      Salad Place  0.03\n",
      "\n",
      "\n",
      "----Berczy Park----\n",
      "         venue  freq\n",
      "0  Coffee Shop  0.09\n",
      "1       Bakery  0.04\n",
      "2   Steakhouse  0.04\n",
      "3         Café  0.04\n",
      "4  Cheese Shop  0.04\n",
      "\n",
      "\n",
      "----CN Tower, Bathurst Quay, Island airport, Harbourfront West, King and Spadina, Railway Lands, South Niagara----\n",
      "                venue  freq\n",
      "0      Airport Lounge  0.13\n",
      "1     Airport Service  0.13\n",
      "2    Airport Terminal  0.13\n",
      "3             Airport  0.07\n",
      "4  Airport Food Court  0.07\n",
      "\n",
      "\n",
      "----Cabbagetown, St. James Town----\n",
      "         venue  freq\n",
      "0  Coffee Shop  0.09\n",
      "1   Restaurant  0.07\n",
      "2         Café  0.05\n",
      "3  Pizza Place  0.05\n",
      "4       Bakery  0.05\n",
      "\n",
      "\n",
      "----Central Bay Street----\n",
      "                venue  freq\n",
      "0         Coffee Shop  0.14\n",
      "1      Sandwich Place  0.05\n",
      "2  Italian Restaurant  0.05\n",
      "3        Burger Joint  0.04\n",
      "4      Ice Cream Shop  0.04\n",
      "\n",
      "\n",
      "----Chinatown, Grange Park, Kensington Market----\n",
      "                   venue  freq\n",
      "0                   Café  0.07\n",
      "1                    Bar  0.05\n",
      "2  Vietnamese Restaurant  0.04\n",
      "3     Chinese Restaurant  0.04\n",
      "4     Mexican Restaurant  0.04\n",
      "\n",
      "\n",
      "----Christie----\n",
      "               venue  freq\n",
      "0               Café  0.18\n",
      "1      Grocery Store  0.18\n",
      "2               Park  0.12\n",
      "3  Convenience Store  0.06\n",
      "4              Diner  0.06\n",
      "\n",
      "\n",
      "----Church and Wellesley----\n",
      "                 venue  freq\n",
      "0          Coffee Shop  0.06\n",
      "1     Sushi Restaurant  0.06\n",
      "2  Japanese Restaurant  0.05\n",
      "3              Gay Bar  0.05\n",
      "4           Restaurant  0.04\n",
      "\n",
      "\n",
      "----Commerce Court, Victoria Hotel----\n",
      "                 venue  freq\n",
      "0          Coffee Shop  0.10\n",
      "1                 Café  0.07\n",
      "2                Hotel  0.06\n",
      "3           Restaurant  0.05\n",
      "4  American Restaurant  0.04\n",
      "\n",
      "\n",
      "----Design Exchange, Toronto Dominion Centre----\n",
      "         venue  freq\n",
      "0  Coffee Shop  0.13\n",
      "1         Café  0.08\n",
      "2        Hotel  0.07\n",
      "3   Restaurant  0.05\n",
      "4          Bar  0.04\n",
      "\n",
      "\n",
      "----First Canadian Place, Underground city----\n",
      "         venue  freq\n",
      "0  Coffee Shop  0.09\n",
      "1         Café  0.07\n",
      "2   Steakhouse  0.04\n",
      "3   Restaurant  0.04\n",
      "4        Hotel  0.04\n",
      "\n",
      "\n",
      "----Harbord, University of Toronto----\n",
      "                venue  freq\n",
      "0                Café  0.16\n",
      "1  Italian Restaurant  0.05\n",
      "2      Sandwich Place  0.05\n",
      "3           Bookstore  0.05\n",
      "4          Restaurant  0.05\n",
      "\n",
      "\n",
      "----Harbourfront----\n",
      "         venue  freq\n",
      "0  Coffee Shop  0.14\n",
      "1         Park  0.08\n",
      "2       Bakery  0.06\n",
      "3          Pub  0.06\n",
      "4   Restaurant  0.04\n",
      "\n",
      "\n",
      "----Harbourfront East, Toronto Islands, Union Station----\n",
      "                venue  freq\n",
      "0         Coffee Shop  0.13\n",
      "1            Aquarium  0.05\n",
      "2               Hotel  0.04\n",
      "3                Café  0.04\n",
      "4  Italian Restaurant  0.03\n",
      "\n",
      "\n",
      "----Rosedale----\n",
      "               venue  freq\n",
      "0               Park  0.50\n",
      "1         Playground  0.25\n",
      "2              Trail  0.25\n",
      "3  Afghan Restaurant  0.00\n",
      "4             Museum  0.00\n",
      "\n",
      "\n",
      "----Ryerson, Garden District----\n",
      "                       venue  freq\n",
      "0                Coffee Shop  0.09\n",
      "1             Clothing Store  0.07\n",
      "2             Cosmetics Shop  0.04\n",
      "3  Middle Eastern Restaurant  0.03\n",
      "4       Fast Food Restaurant  0.03\n",
      "\n",
      "\n",
      "----St. James Town----\n",
      "            venue  freq\n",
      "0            Café  0.06\n",
      "1     Coffee Shop  0.06\n",
      "2      Restaurant  0.05\n",
      "3           Hotel  0.04\n",
      "4  Clothing Store  0.04\n",
      "\n",
      "\n",
      "----Stn A PO Boxes 25 The Esplanade----\n",
      "                 venue  freq\n",
      "0          Coffee Shop  0.10\n",
      "1                 Café  0.04\n",
      "2           Restaurant  0.04\n",
      "3                Hotel  0.03\n",
      "4  Japanese Restaurant  0.03\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "for hood in dt_grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = dt_grouped[dt_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 this into a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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",
       "      <td>0</td>\n",
       "      <td>Adelaide, King, Richmond</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>Cheese Shop</td>\n",
       "      <td>Beer Bar</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Café</td>\n",
       "      <td>French Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>CN Tower, Bathurst Quay, Island airport, Harbo...</td>\n",
       "      <td>Airport Lounge</td>\n",
       "      <td>Airport Service</td>\n",
       "      <td>Airport Terminal</td>\n",
       "      <td>Harbor / Marina</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Boutique</td>\n",
       "      <td>Boat or Ferry</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Sculpture Garden</td>\n",
       "      <td>Airport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Cabbagetown, St. James Town</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Flower Shop</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Park</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Central Bay Street</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>Spa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Neighborhood 1st Most Common Venue  \\\n",
       "0                           Adelaide, King, Richmond           Coffee Shop   \n",
       "1                                        Berczy Park           Coffee Shop   \n",
       "2  CN Tower, Bathurst Quay, Island airport, Harbo...        Airport Lounge   \n",
       "3                        Cabbagetown, St. James Town           Coffee Shop   \n",
       "4                                 Central Bay Street           Coffee Shop   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0                  Café            Steakhouse       Thai Restaurant   \n",
       "1          Cocktail Bar           Cheese Shop              Beer Bar   \n",
       "2       Airport Service      Airport Terminal       Harbor / Marina   \n",
       "3            Restaurant                   Pub           Flower Shop   \n",
       "4    Italian Restaurant        Sandwich Place          Burger Joint   \n",
       "\n",
       "  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "0                Bakery            Restaurant          Burger Joint   \n",
       "1        Farmers Market    Seafood Restaurant                Bakery   \n",
       "2                   Bar              Boutique         Boat or Ferry   \n",
       "3    Italian Restaurant                  Café                Bakery   \n",
       "4        Ice Cream Shop                  Café   Japanese Restaurant   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "0           Salad Place      Sushi Restaurant       Asian Restaurant  \n",
       "1            Steakhouse                  Café      French Restaurant  \n",
       "2   Rental Car Location      Sculpture Garden                Airport  \n",
       "3           Pizza Place                  Park         Breakfast Spot  \n",
       "4   Fried Chicken Joint           Salad Place                    Spa  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to sort venues in descending\n",
    "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]\n",
    "\n",
    "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'] = dt_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(dt_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(dt_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run the k-means clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, 0, 0, 3, 2, 0, 0, 0])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "dt_grouped_clustering = dt_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(dt_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] "
   ]
  },
  {
   "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": 151,
   "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>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</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",
       "      <td>0</td>\n",
       "      <td>M4W</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>43.679563</td>\n",
       "      <td>-79.377529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Trail</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Dumpling Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Doner Restaurant</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M4X</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Cabbagetown, St. James Town</td>\n",
       "      <td>43.667967</td>\n",
       "      <td>-79.367675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Flower Shop</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Park</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M4Y</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Church and Wellesley</td>\n",
       "      <td>43.665860</td>\n",
       "      <td>-79.383160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Gay Bar</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Men's Store</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "      <td>43.654260</td>\n",
       "      <td>-79.360636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Café</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M5B</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Ryerson, Garden District</td>\n",
       "      <td>43.657162</td>\n",
       "      <td>-79.378937</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Bookstore</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode           Borough                 Neighborhood   Latitude  \\\n",
       "0        M4W  Downtown Toronto                     Rosedale  43.679563   \n",
       "1        M4X  Downtown Toronto  Cabbagetown, St. James Town  43.667967   \n",
       "2        M4Y  Downtown Toronto         Church and Wellesley  43.665860   \n",
       "3        M5A  Downtown Toronto                 Harbourfront  43.654260   \n",
       "4        M5B  Downtown Toronto     Ryerson, Garden District  43.657162   \n",
       "\n",
       "   Longitude  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0 -79.377529             1.0                  Park            Playground   \n",
       "1 -79.367675             0.0           Coffee Shop            Restaurant   \n",
       "2 -79.383160             0.0           Coffee Shop      Sushi Restaurant   \n",
       "3 -79.360636             0.0           Coffee Shop                  Park   \n",
       "4 -79.378937             0.0           Coffee Shop        Clothing Store   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue      5th Most Common Venue  \\\n",
       "0                 Trail         Deli / Bodega          Electronics Store   \n",
       "1                   Pub           Flower Shop         Italian Restaurant   \n",
       "2   Japanese Restaurant               Gay Bar                 Restaurant   \n",
       "3                Bakery                   Pub                       Café   \n",
       "4        Cosmetics Shop                  Café  Middle Eastern Restaurant   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue     8th Most Common Venue  \\\n",
       "0   Dumpling Restaurant            Donut Shop          Doner Restaurant   \n",
       "1                  Café                Bakery               Pizza Place   \n",
       "2             Gastropub           Men's Store  Mediterranean Restaurant   \n",
       "3            Restaurant    Mexican Restaurant            Breakfast Spot   \n",
       "4  Fast Food Restaurant       Bubble Tea Shop                     Diner   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0               Dog Run         Discount Store  \n",
       "1                  Park         Breakfast Spot  \n",
       "2                 Hotel                    Gym  \n",
       "3               Theater         Ice Cream Shop  \n",
       "4             Bookstore            Pizza Place  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "\n",
    "dt_merged = dt_data\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "dt_merged = dt_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "dt_merged.head() # check the last columns!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's examine our clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</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",
       "      <td>1</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Flower Shop</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Park</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Gay Bar</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Men's Store</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Café</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Bookstore</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Beer Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>Cheese Shop</td>\n",
       "      <td>Beer Bar</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Café</td>\n",
       "      <td>French Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>Spa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Aquarium</td>\n",
       "      <td>Café</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Scenic Lookout</td>\n",
       "      <td>Baseball Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Beer Bar</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Art Gallery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Gastropub</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Borough  Cluster Labels 1st Most Common Venue  \\\n",
       "1   Downtown Toronto             0.0           Coffee Shop   \n",
       "2   Downtown Toronto             0.0           Coffee Shop   \n",
       "3   Downtown Toronto             0.0           Coffee Shop   \n",
       "4   Downtown Toronto             0.0           Coffee Shop   \n",
       "5   Downtown Toronto             0.0           Coffee Shop   \n",
       "6   Downtown Toronto             0.0           Coffee Shop   \n",
       "7   Downtown Toronto             0.0           Coffee Shop   \n",
       "8   Downtown Toronto             0.0           Coffee Shop   \n",
       "9   Downtown Toronto             0.0           Coffee Shop   \n",
       "10  Downtown Toronto             0.0           Coffee Shop   \n",
       "11  Downtown Toronto             0.0           Coffee Shop   \n",
       "15  Downtown Toronto             0.0           Coffee Shop   \n",
       "16  Downtown Toronto             0.0           Coffee Shop   \n",
       "\n",
       "   2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "1             Restaurant                   Pub           Flower Shop   \n",
       "2       Sushi Restaurant   Japanese Restaurant               Gay Bar   \n",
       "3                   Park                Bakery                   Pub   \n",
       "4         Clothing Store        Cosmetics Shop                  Café   \n",
       "5                   Café            Restaurant                 Hotel   \n",
       "6           Cocktail Bar           Cheese Shop              Beer Bar   \n",
       "7     Italian Restaurant        Sandwich Place          Burger Joint   \n",
       "8                   Café            Steakhouse       Thai Restaurant   \n",
       "9               Aquarium                  Café                 Hotel   \n",
       "10                  Café                 Hotel            Restaurant   \n",
       "11                  Café                 Hotel            Restaurant   \n",
       "15            Restaurant                  Café                 Hotel   \n",
       "16                  Café            Restaurant            Steakhouse   \n",
       "\n",
       "        5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "1          Italian Restaurant                  Café                Bakery   \n",
       "2                  Restaurant             Gastropub           Men's Store   \n",
       "3                        Café            Restaurant    Mexican Restaurant   \n",
       "4   Middle Eastern Restaurant  Fast Food Restaurant       Bubble Tea Shop   \n",
       "5              Clothing Store    Italian Restaurant   American Restaurant   \n",
       "6              Farmers Market    Seafood Restaurant                Bakery   \n",
       "7              Ice Cream Shop                  Café   Japanese Restaurant   \n",
       "8                      Bakery            Restaurant          Burger Joint   \n",
       "9                  Restaurant   Fried Chicken Joint               Brewery   \n",
       "10                        Bar         Deli / Bodega             Gastropub   \n",
       "11        American Restaurant                Bakery                   Gym   \n",
       "15                   Beer Bar    Seafood Restaurant   Japanese Restaurant   \n",
       "16                      Hotel                Bakery   American Restaurant   \n",
       "\n",
       "       8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "1                Pizza Place                  Park         Breakfast Spot  \n",
       "2   Mediterranean Restaurant                 Hotel                    Gym  \n",
       "3             Breakfast Spot               Theater         Ice Cream Shop  \n",
       "4                      Diner             Bookstore            Pizza Place  \n",
       "5             Breakfast Spot                 Diner               Beer Bar  \n",
       "6                 Steakhouse                  Café      French Restaurant  \n",
       "7        Fried Chicken Joint           Salad Place                    Spa  \n",
       "8                Salad Place      Sushi Restaurant       Asian Restaurant  \n",
       "9         Italian Restaurant        Scenic Lookout       Baseball Stadium  \n",
       "10        Seafood Restaurant                Bakery    American Restaurant  \n",
       "11                Steakhouse    Italian Restaurant          Deli / Bodega  \n",
       "15        Italian Restaurant                  Park            Art Gallery  \n",
       "16              Burger Joint         Deli / Bodega              Gastropub  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_merged.loc[dt_merged['Cluster Labels'] == 0, dt_merged.columns[[1] + list(range(5, dt_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like our first cluster is characterized by having many coffee shops and restaurants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>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",
       "      <td>0</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Trail</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Dumpling Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Doner Restaurant</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Borough  Cluster Labels 1st Most Common Venue  \\\n",
       "0  Downtown Toronto             1.0                  Park   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0            Playground                 Trail         Deli / Bodega   \n",
       "\n",
       "  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "0     Electronics Store   Dumpling Restaurant            Donut Shop   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "0      Doner Restaurant               Dog Run         Discount Store  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_merged.loc[dt_merged['Cluster Labels'] == 1, dt_merged.columns[[1] + list(range(5, dt_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our second one has park and playground as most common venue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</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",
       "      <td>17</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Park</td>\n",
       "      <td>Baby Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Nightclub</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
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       "             Borough  Cluster Labels 1st Most Common Venue  \\\n",
       "17  Downtown Toronto             2.0         Grocery Store   \n",
       "\n",
       "   2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "17                  Café                  Park            Baby Store   \n",
       "\n",
       "   5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "17           Coffee Shop     Convenience Store             Nightclub   \n",
       "\n",
       "   8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "17                 Diner    Athletics & Sports             Restaurant  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_merged.loc[dt_merged['Cluster Labels'] == 2, dt_merged.columns[[1] + list(range(5, dt_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third cluster characterizes a more residential area with the most prominent place being a grocery store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
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       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
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       "      <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",
       "      <td>12</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bookstore</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Dumpling Restaurant</td>\n",
       "      <td>Vegetarian / Vegan Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
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       "             Borough  Cluster Labels 1st Most Common Venue  \\\n",
       "12  Downtown Toronto             3.0                  Café   \n",
       "13  Downtown Toronto             3.0                  Café   \n",
       "\n",
       "   2nd Most Common Venue 3rd Most Common Venue  4th Most Common Venue  \\\n",
       "12             Bookstore               Theater    Japanese Restaurant   \n",
       "13                   Bar    Mexican Restaurant  Vietnamese Restaurant   \n",
       "\n",
       "   5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "12                   Bar                Bakery            Restaurant   \n",
       "13           Coffee Shop                Bakery    Chinese Restaurant   \n",
       "\n",
       "   8th Most Common Venue          9th Most Common Venue 10th Most Common Venue  \n",
       "12        Sandwich Place             Italian Restaurant       Sushi Restaurant  \n",
       "13   Dumpling Restaurant  Vegetarian / Vegan Restaurant             Donut Shop  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
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    }
   ],
   "source": [
    "dt_merged.loc[dt_merged['Cluster Labels'] == 3, dt_merged.columns[[1] + list(range(5, dt_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fourth cluster is similar to the first one, although café places usually sell food next to the coffee.  \n",
    "We could assume that the neighborhoods in this cluster are smaller in population or less crowded in office hours, since there is no need for coffee houses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>14</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Airport Lounge</td>\n",
       "      <td>Airport Service</td>\n",
       "      <td>Airport Terminal</td>\n",
       "      <td>Harbor / Marina</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Boutique</td>\n",
       "      <td>Boat or Ferry</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Sculpture Garden</td>\n",
       "      <td>Airport</td>\n",
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       "             Borough  Cluster Labels 1st Most Common Venue  \\\n",
       "14  Downtown Toronto             4.0        Airport Lounge   \n",
       "\n",
       "   2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "14       Airport Service      Airport Terminal       Harbor / Marina   \n",
       "\n",
       "   5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "14                   Bar              Boutique         Boat or Ferry   \n",
       "\n",
       "   8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "14   Rental Car Location      Sculpture Garden                Airport  "
      ]
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    "dt_merged.loc[dt_merged['Cluster Labels'] == 4, dt_merged.columns[[1] + list(range(5, dt_merged.shape[1]))]]"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our last cluster clearly shows a neighborhood that contains an airport."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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