Segmenting-and-Clustering-Neighborhoods-in-Toronto-3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Segmenting and Clustering Neighborhoods in Toronto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Install & Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching package metadata .............\n",
      "Solving package specifications: .\n",
      "\n",
      "# All requested packages already installed.\n",
      "# packages in environment at /opt/conda/envs/DSX-Python35:\n",
      "#\n",
      "folium                    0.5.0                      py_0    conda-forge\n"
     ]
    }
   ],
   "source": [
    "!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "#!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "\n",
    "import requests # library to handle requests\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "\n",
    "import folium # map rendering library\n",
    "\n",
    "\n",
    "\n",
    "import types\n",
    "from botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "print('Libraries imported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ## 2. Read html using BeautifulSoup library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M';\n",
    "html = requests.get(url).text\n",
    "soup = BeautifulSoup(html,'lxml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the table\n",
    "postal_codes_table = soup.find('table',{'class':'wikitable sortable'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract td's only\n",
    "table_cells = postal_codes_table.find_all('td')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group each 3 cells together\n",
    "N = 3\n",
    "table_rows = [table_cells[n:n+N] for n in range(0, len(table_cells), N)]\n",
    "postal_code = []\n",
    "borough = []\n",
    "neighborhood = []\n",
    "for row in table_rows:\n",
    "    postal_code_text = row[0].text\n",
    "    borough_text = row[1].text\n",
    "    neighborhood_text = row[2].text.rstrip('\\n')\n",
    "    if borough_text != 'Not assigned':\n",
    "        postal_code.append(postal_code_text)\n",
    "        borough.append(borough_text)\n",
    "        neighborhood.append(neighborhood_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Create & Explore Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Heights</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode           Borough      Neighborhood\n",
       "0        M3A        North York         Parkwoods\n",
       "1        M4A        North York  Victoria Village\n",
       "2        M5A  Downtown Toronto      Harbourfront\n",
       "3        M5A  Downtown Toronto       Regent Park\n",
       "4        M6A        North York  Lawrence Heights"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df['PostalCode'] = postal_code\n",
    "df['Borough'] = borough\n",
    "df['Neighborhood'] = neighborhood\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</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"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group neighborhoods of the same postal code\n",
    "df = df.groupby(['PostalCode','Borough']).aggregate(lambda x : ', '.join(x)).reset_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront, Regent Park</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode           Borough               Neighborhood\n",
       "53        M5A  Downtown Toronto  Harbourfront, Regent Park"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check M5A \n",
    "df.loc[df['PostalCode'] == 'M5A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill 'Not assigned' neighborhood to the proper borough\n",
    "df.loc[df['Neighborhood'] == 'Not assigned', 'Neighborhood'] = df['Borough']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>Queen's Park</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode       Borough  Neighborhood\n",
       "85        M7A  Queen's Park  Queen's Park"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check M5A \n",
    "df.loc[df['PostalCode'] == 'M7A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df shape\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insert Geospatial Coordinates CSV file to the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code   Latitude  Longitude\n",
       "0         M1B  43.806686 -79.194353\n",
       "1         M1C  43.784535 -79.160497\n",
       "2         M1E  43.763573 -79.188711\n",
       "3         M1G  43.770992 -79.216917\n",
       "4         M1H  43.773136 -79.239476"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share your notebook.\n",
    "client_1f2743066d6b4a08862baaf50b794f6d = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id='DaADxXD1xYXv4gPZuq9gM2enGDBpOTc_pftcgowbyk-C',\n",
    "    ibm_auth_endpoint=\"https://iam.bluemix.net/oidc/token\",\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')\n",
    "\n",
    "body = client_1f2743066d6b4a08862baaf50b794f6d.get_object(Bucket='mydatascienceproject-donotdelete-pr-4no7swxe9h5vwt',Key='Geospatial_Coordinates.csv')['Body']\n",
    "# add missing __iter__ method, so pandas accepts body as file-like object\n",
    "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
    "\n",
    "df_data_1 = pd.read_csv(body)\n",
    "df_data_1.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform Left Join to the data frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    }\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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode      Borough                            Neighborhood   Latitude  \\\n",
       "0        M1B  Scarborough                          Rouge, Malvern  43.806686   \n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union  43.784535   \n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill  43.763573   \n",
       "3        M1G  Scarborough                                  Woburn  43.770992   \n",
       "4        M1H  Scarborough                               Cedarbrae  43.773136   \n",
       "\n",
       "   Longitude  \n",
       "0 -79.194353  \n",
       "1 -79.160497  \n",
       "2 -79.188711  \n",
       "3 -79.216917  \n",
       "4 -79.239476  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_data_1.rename(columns={'Postal Code':'PostalCode'},inplace=True)\n",
    "df_data_1.head()\n",
    "df = pd.merge(df, df_data_1, on='PostalCode', how='left')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "      <th>57</th>\n",
       "      <td>M5G</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Central Bay Street</td>\n",
       "      <td>43.657952</td>\n",
       "      <td>-79.387383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode           Borough        Neighborhood   Latitude  Longitude\n",
       "57        M5G  Downtown Toronto  Central Bay Street  43.657952 -79.387383"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check M5G \n",
    "df.loc[df['PostalCode'] == 'M5G']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's create a map of Toronto with neighborhoods superimposed on top."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "first, let's work with only borough that containts the name Toronto "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>M4E</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.676357</td>\n",
       "      <td>-79.293031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>M4K</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Danforth West, Riverdale</td>\n",
       "      <td>43.679557</td>\n",
       "      <td>-79.352188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>M4L</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Beaches West, India Bazaar</td>\n",
       "      <td>43.668999</td>\n",
       "      <td>-79.315572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43</td>\n",
       "      <td>M4M</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Studio District</td>\n",
       "      <td>43.659526</td>\n",
       "      <td>-79.340923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44</td>\n",
       "      <td>M4N</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Lawrence Park</td>\n",
       "      <td>43.728020</td>\n",
       "      <td>-79.388790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index PostalCode          Borough                    Neighborhood  \\\n",
       "0     37        M4E     East Toronto                     The Beaches   \n",
       "1     41        M4K     East Toronto    The Danforth West, Riverdale   \n",
       "2     42        M4L     East Toronto  The Beaches West, India Bazaar   \n",
       "3     43        M4M     East Toronto                 Studio District   \n",
       "4     44        M4N  Central Toronto                   Lawrence Park   \n",
       "\n",
       "    Latitude  Longitude  \n",
       "0  43.676357 -79.293031  \n",
       "1  43.679557 -79.352188  \n",
       "2  43.668999 -79.315572  \n",
       "3  43.659526 -79.340923  \n",
       "4  43.728020 -79.388790  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Borough'].str.contains(\"Toronto\")].reset_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Toronto are 43.653963, -79.387207.\n"
     ]
    }
   ],
   "source": [
    "address = 'Toronto, Canada'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"toronto_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Toronto are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
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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 0x7f1eaafc02b0>"
      ]
     },
     "execution_count": 18,
     "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='#3186c0',\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 start utilizing the Foursquare API to explore the neighborhoods and segment them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Explore Neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Foursquare Credentials and Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_ID = 'B2CWZ3L4M04KBQOBSR0JDVTDGVCDFFW0EVYKBBZL5JVVXWFJ' # your Foursquare ID\n",
    "CLIENT_SECRET = 'WNG3ODIQUSFPIQ0DEJGDJJT5U0TSJA1KOCARDPQSVR3IPDKC' # your Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's explore the first neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Beaches'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0, 'Neighborhood']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### get latitude and longitude of the neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of The Beaches are 43.67635739999999, -79.2930312.\n"
     ]
    }
   ],
   "source": [
    "neighborhood_latitude = df.loc[0, 'Latitude'] # neighborhood latitude value\n",
    "neighborhood_longitude = df.loc[0, 'Longitude'] # neighborhood longitude value\n",
    "\n",
    "neighborhood_name = df.loc[0, '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": [
    "#### Now, let's get the top 100 venues that are in Marble Hill within a radius of 500 meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "LIMIT = 100 # limit of number of venues returned by Foursquare API\n",
    "\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",
    "\n",
    "# get resullt\n",
    "results = requests.get(url).json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'meta': {'code': 200, 'requestId': '5ca4b54cdd57977ce652ec2e'},\n",
       " 'response': {'groups': [{'items': [{'reasons': {'count': 0,\n",
       "       'items': [{'reasonName': 'globalInteractionReason',\n",
       "         'summary': 'This spot is popular',\n",
       "         'type': 'general'}]},\n",
       "      'referralId': 'e-0-4ad4c062f964a52011f820e3-0',\n",
       "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/food_grocery_',\n",
       "          'suffix': '.png'},\n",
       "         'id': '50aa9e744b90af0d42d5de0e',\n",
       "         'name': 'Health Food Store',\n",
       "         'pluralName': 'Health Food Stores',\n",
       "         'primary': True,\n",
       "         'shortName': 'Health Food Store'}],\n",
       "       'id': '4ad4c062f964a52011f820e3',\n",
       "       'location': {'address': '125 Southwood Dr',\n",
       "        'cc': 'CA',\n",
       "        'city': 'Toronto',\n",
       "        'country': 'Canada',\n",
       "        'distance': 471,\n",
       "        'formattedAddress': ['125 Southwood Dr',\n",
       "         'Toronto ON M4E 0B8',\n",
       "         'Canada'],\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.678879,\n",
       "          'lng': -79.297734}],\n",
       "        'lat': 43.678879,\n",
       "        'lng': -79.297734,\n",
       "        'postalCode': 'M4E 0B8',\n",
       "        'state': 'ON'},\n",
       "       'name': 'The Big Carrot Natural Food Market',\n",
       "       'photos': {'count': 0, 'groups': []},\n",
       "       'venuePage': {'id': '75150878'}}},\n",
       "     {'reasons': {'count': 0,\n",
       "       'items': [{'reasonName': 'globalInteractionReason',\n",
       "         'summary': 'This spot is popular',\n",
       "         'type': 'general'}]},\n",
       "      'referralId': 'e-0-4b8daea1f964a520480833e3-1',\n",
       "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/nightlife/pub_',\n",
       "          'suffix': '.png'},\n",
       "         'id': '4bf58dd8d48988d11b941735',\n",
       "         'name': 'Pub',\n",
       "         'pluralName': 'Pubs',\n",
       "         'primary': True,\n",
       "         'shortName': 'Pub'}],\n",
       "       'id': '4b8daea1f964a520480833e3',\n",
       "       'location': {'address': '676 Kingston Rd.',\n",
       "        'cc': 'CA',\n",
       "        'city': 'Toronto',\n",
       "        'country': 'Canada',\n",
       "        'crossStreet': 'at Main St.',\n",
       "        'distance': 460,\n",
       "        'formattedAddress': ['676 Kingston Rd. (at Main St.)',\n",
       "         'Toronto ON M4E 1R4',\n",
       "         'Canada'],\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.679181434941015,\n",
       "          'lng': -79.29721535878515}],\n",
       "        'lat': 43.679181434941015,\n",
       "        'lng': -79.29721535878515,\n",
       "        'postalCode': 'M4E 1R4',\n",
       "        'state': 'ON'},\n",
       "       'name': 'Grover Pub and Grub',\n",
       "       'photos': {'count': 0, 'groups': []}}},\n",
       "     {'reasons': {'count': 0,\n",
       "       'items': [{'reasonName': 'globalInteractionReason',\n",
       "         'summary': 'This spot is popular',\n",
       "         'type': 'general'}]},\n",
       "      'referralId': 'e-0-4e77e3861f6ecf8d3648300c-2',\n",
       "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',\n",
       "          'suffix': '.png'},\n",
       "         'id': '4bf58dd8d48988d1e0931735',\n",
       "         'name': 'Coffee Shop',\n",
       "         'pluralName': 'Coffee Shops',\n",
       "         'primary': True,\n",
       "         'shortName': 'Coffee Shop'}],\n",
       "       'id': '4e77e3861f6ecf8d3648300c',\n",
       "       'location': {'address': '637 Kingston Rd.',\n",
       "        'cc': 'CA',\n",
       "        'city': 'Toronto',\n",
       "        'country': 'Canada',\n",
       "        'crossStreet': 'at Main St.',\n",
       "        'distance': 486,\n",
       "        'formattedAddress': ['637 Kingston Rd. (at Main St.)',\n",
       "         'Toronto ON M4E 1R3',\n",
       "         'Canada'],\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.67879837444001,\n",
       "          'lng': -79.2980449760153}],\n",
       "        'lat': 43.67879837444001,\n",
       "        'lng': -79.2980449760153,\n",
       "        'postalCode': 'M4E 1R3',\n",
       "        'state': 'ON'},\n",
       "       'name': 'Starbucks',\n",
       "       'photos': {'count': 0, 'groups': []}}},\n",
       "     {'reasons': {'count': 0,\n",
       "       'items': [{'reasonName': 'globalInteractionReason',\n",
       "         'summary': 'This spot is popular',\n",
       "         'type': 'general'}]},\n",
       "      'referralId': 'e-0-4df91c4bae60f95f82229ad5-3',\n",
       "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/parks_outdoors/neighborhood_',\n",
       "          'suffix': '.png'},\n",
       "         'id': '4f2a25ac4b909258e854f55f',\n",
       "         'name': 'Neighborhood',\n",
       "         'pluralName': 'Neighborhoods',\n",
       "         'primary': True,\n",
       "         'shortName': 'Neighborhood'}],\n",
       "       'id': '4df91c4bae60f95f82229ad5',\n",
       "       'location': {'cc': 'CA',\n",
       "        'city': 'Toronto',\n",
       "        'country': 'Canada',\n",
       "        'distance': 468,\n",
       "        'formattedAddress': ['Toronto ON', 'Canada'],\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.68056321147582,\n",
       "          'lng': -79.2928688743688}],\n",
       "        'lat': 43.68056321147582,\n",
       "        'lng': -79.2928688743688,\n",
       "        'state': 'ON'},\n",
       "       'name': 'Upper Beaches',\n",
       "       'photos': {'count': 0, 'groups': []}}}],\n",
       "    'name': 'recommended',\n",
       "    'type': 'Recommended Places'}],\n",
       "  'headerFullLocation': 'The Beaches, Toronto',\n",
       "  'headerLocation': 'The Beaches',\n",
       "  'headerLocationGranularity': 'neighborhood',\n",
       "  'suggestedBounds': {'ne': {'lat': 43.680857404499996,\n",
       "    'lng': -79.28682091449052},\n",
       "   'sw': {'lat': 43.67185739549999, 'lng': -79.29924148550948}},\n",
       "  'totalResults': 4}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "#### Now let's clean the json and structure it into a *pandas* dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Big Carrot Natural Food Market</td>\n",
       "      <td>Health Food Store</td>\n",
       "      <td>43.678879</td>\n",
       "      <td>-79.297734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Grover Pub and Grub</td>\n",
       "      <td>Pub</td>\n",
       "      <td>43.679181</td>\n",
       "      <td>-79.297215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>43.678798</td>\n",
       "      <td>-79.298045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Upper Beaches</td>\n",
       "      <td>Neighborhood</td>\n",
       "      <td>43.680563</td>\n",
       "      <td>-79.292869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 name         categories        lat        lng\n",
       "0  The Big Carrot Natural Food Market  Health Food Store  43.678879 -79.297734\n",
       "1                 Grover Pub and Grub                Pub  43.679181 -79.297215\n",
       "2                           Starbucks        Coffee Shop  43.678798 -79.298045\n",
       "3                       Upper Beaches       Neighborhood  43.680563 -79.292869"
      ]
     },
     "execution_count": 25,
     "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": "markdown",
   "metadata": {},
   "source": [
    "### Let's create a function to repeat the same process to all the neighborhoods in Toronto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "#### Run the above function on each neighborhood and create a new dataframe called *toronto_venues*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Beaches\n",
      "The Danforth West, Riverdale\n",
      "The Beaches West, India Bazaar\n",
      "Studio District\n",
      "Lawrence Park\n",
      "Davisville North\n",
      "North Toronto West\n",
      "Davisville\n",
      "Moore Park, Summerhill East\n",
      "Deer Park, Forest Hill SE, Rathnelly, South Hill, Summerhill West\n",
      "Rosedale\n",
      "Cabbagetown, St. James Town\n",
      "Church and Wellesley\n",
      "Harbourfront, Regent Park\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",
      "Roselawn\n",
      "Forest Hill North, Forest Hill West\n",
      "The Annex, North Midtown, Yorkville\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",
      "Dovercourt Village, Dufferin\n",
      "Little Portugal, Trinity\n",
      "Brockton, Exhibition Place, Parkdale Village\n",
      "High Park, The Junction South\n",
      "Parkdale, Roncesvalles\n",
      "Runnymede, Swansea\n",
      "Business Reply Mail Processing Centre 969 Eastern\n"
     ]
    }
   ],
   "source": [
    "toronto_venues = getNearbyVenues(names=df['Neighborhood'],\n",
    "                                   latitudes=df['Latitude'],\n",
    "                                   longitudes=df['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.676357</td>\n",
       "      <td>-79.293031</td>\n",
       "      <td>The Big Carrot Natural Food Market</td>\n",
       "      <td>43.678879</td>\n",
       "      <td>-79.297734</td>\n",
       "      <td>Health Food Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.676357</td>\n",
       "      <td>-79.293031</td>\n",
       "      <td>Grover Pub and Grub</td>\n",
       "      <td>43.679181</td>\n",
       "      <td>-79.297215</td>\n",
       "      <td>Pub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.676357</td>\n",
       "      <td>-79.293031</td>\n",
       "      <td>Starbucks</td>\n",
       "      <td>43.678798</td>\n",
       "      <td>-79.298045</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.676357</td>\n",
       "      <td>-79.293031</td>\n",
       "      <td>Upper Beaches</td>\n",
       "      <td>43.680563</td>\n",
       "      <td>-79.292869</td>\n",
       "      <td>Neighborhood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Danforth West, Riverdale</td>\n",
       "      <td>43.679557</td>\n",
       "      <td>-79.352188</td>\n",
       "      <td>Pantheon</td>\n",
       "      <td>43.677621</td>\n",
       "      <td>-79.351434</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Neighborhood  Neighborhood Latitude  \\\n",
       "0                   The Beaches              43.676357   \n",
       "1                   The Beaches              43.676357   \n",
       "2                   The Beaches              43.676357   \n",
       "3                   The Beaches              43.676357   \n",
       "4  The Danforth West, Riverdale              43.679557   \n",
       "\n",
       "   Neighborhood Longitude                               Venue  Venue Latitude  \\\n",
       "0              -79.293031  The Big Carrot Natural Food Market       43.678879   \n",
       "1              -79.293031                 Grover Pub and Grub       43.679181   \n",
       "2              -79.293031                           Starbucks       43.678798   \n",
       "3              -79.293031                       Upper Beaches       43.680563   \n",
       "4              -79.352188                            Pantheon       43.677621   \n",
       "\n",
       "   Venue Longitude     Venue Category  \n",
       "0       -79.297734  Health Food Store  \n",
       "1       -79.297215                Pub  \n",
       "2       -79.298045        Coffee Shop  \n",
       "3       -79.292869       Neighborhood  \n",
       "4       -79.351434   Greek Restaurant  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_venues.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's check how many venues were returned for each neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighborhood\n",
       "Adelaide, King, Richmond                                                                                      100\n",
       "Berczy Park                                                                                                    57\n",
       "Brockton, Exhibition Place, Parkdale Village                                                                   23\n",
       "Business Reply Mail Processing Centre 969 Eastern                                                              17\n",
       "CN Tower, Bathurst Quay, Island airport, Harbourfront West, King and Spadina, Railway Lands, South Niagara     14\n",
       "Cabbagetown, St. James Town                                                                                    43\n",
       "Central Bay Street                                                                                             81\n",
       "Chinatown, Grange Park, Kensington Market                                                                     100\n",
       "Christie                                                                                                       15\n",
       "Church and Wellesley                                                                                           89\n",
       "Commerce Court, Victoria Hotel                                                                                100\n",
       "Davisville                                                                                                     39\n",
       "Davisville North                                                                                                7\n",
       "Deer Park, Forest Hill SE, Rathnelly, South Hill, Summerhill West                                              15\n",
       "Design Exchange, Toronto Dominion Centre                                                                      100\n",
       "Dovercourt Village, Dufferin                                                                                   20\n",
       "First Canadian Place, Underground city                                                                        100\n",
       "Forest Hill North, Forest Hill West                                                                             5\n",
       "Harbord, University of Toronto                                                                                 34\n",
       "Harbourfront East, Toronto Islands, Union Station                                                             100\n",
       "Harbourfront, Regent Park                                                                                      48\n",
       "High Park, The Junction South                                                                                  24\n",
       "Lawrence Park                                                                                                   3\n",
       "Little Portugal, Trinity                                                                                       65\n",
       "Moore Park, Summerhill East                                                                                     1\n",
       "North Toronto West                                                                                             20\n",
       "Parkdale, Roncesvalles                                                                                         16\n",
       "Rosedale                                                                                                        4\n",
       "Roselawn                                                                                                        1\n",
       "Runnymede, Swansea                                                                                             40\n",
       "Ryerson, Garden District                                                                                      100\n",
       "St. James Town                                                                                                100\n",
       "Stn A PO Boxes 25 The Esplanade                                                                                96\n",
       "Studio District                                                                                                40\n",
       "The Annex, North Midtown, Yorkville                                                                            25\n",
       "The Beaches                                                                                                     4\n",
       "The Beaches West, India Bazaar                                                                                 20\n",
       "The Danforth West, Riverdale                                                                                   43\n",
       "Name: Venue, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_venues.groupby('Neighborhood').count()['Venue']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's find out how many unique categories can be curated from all the returned venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 240 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} uniques categories.'.format(len(toronto_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Analyze Each Neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Venue Category_Accessories Store</th>\n",
       "      <th>Venue Category_Adult Boutique</th>\n",
       "      <th>Venue Category_Afghan Restaurant</th>\n",
       "      <th>Venue Category_Airport</th>\n",
       "      <th>Venue Category_Airport Food Court</th>\n",
       "      <th>Venue Category_Airport Gate</th>\n",
       "      <th>Venue Category_Airport Lounge</th>\n",
       "      <th>Venue Category_Airport Service</th>\n",
       "      <th>Venue Category_Airport Terminal</th>\n",
       "      <th>...</th>\n",
       "      <th>Venue Category_Toy / Game Store</th>\n",
       "      <th>Venue Category_Trail</th>\n",
       "      <th>Venue Category_Train Station</th>\n",
       "      <th>Venue Category_Transportation Service</th>\n",
       "      <th>Venue Category_Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Venue Category_Video Game Store</th>\n",
       "      <th>Venue Category_Vietnamese Restaurant</th>\n",
       "      <th>Venue Category_Wine Bar</th>\n",
       "      <th>Venue Category_Wings Joint</th>\n",
       "      <th>Venue Category_Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Beaches</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Beaches</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Beaches</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Beaches</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Danforth West, Riverdale</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 × 241 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Neighborhood  Venue Category_Accessories Store  \\\n",
       "0                   The Beaches                                 0   \n",
       "1                   The Beaches                                 0   \n",
       "2                   The Beaches                                 0   \n",
       "3                   The Beaches                                 0   \n",
       "4  The Danforth West, Riverdale                                 0   \n",
       "\n",
       "   Venue Category_Adult Boutique  Venue Category_Afghan Restaurant  \\\n",
       "0                              0                                 0   \n",
       "1                              0                                 0   \n",
       "2                              0                                 0   \n",
       "3                              0                                 0   \n",
       "4                              0                                 0   \n",
       "\n",
       "   Venue Category_Airport  Venue Category_Airport Food Court  \\\n",
       "0                       0                                  0   \n",
       "1                       0                                  0   \n",
       "2                       0                                  0   \n",
       "3                       0                                  0   \n",
       "4                       0                                  0   \n",
       "\n",
       "   Venue Category_Airport Gate  Venue Category_Airport Lounge  \\\n",
       "0                            0                              0   \n",
       "1                            0                              0   \n",
       "2                            0                              0   \n",
       "3                            0                              0   \n",
       "4                            0                              0   \n",
       "\n",
       "   Venue Category_Airport Service  Venue Category_Airport Terminal  \\\n",
       "0                               0                                0   \n",
       "1                               0                                0   \n",
       "2                               0                                0   \n",
       "3                               0                                0   \n",
       "4                               0                                0   \n",
       "\n",
       "              ...              Venue Category_Toy / Game Store  \\\n",
       "0             ...                                            0   \n",
       "1             ...                                            0   \n",
       "2             ...                                            0   \n",
       "3             ...                                            0   \n",
       "4             ...                                            0   \n",
       "\n",
       "   Venue Category_Trail  Venue Category_Train Station  \\\n",
       "0                     0                             0   \n",
       "1                     0                             0   \n",
       "2                     0                             0   \n",
       "3                     0                             0   \n",
       "4                     0                             0   \n",
       "\n",
       "   Venue Category_Transportation Service  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Venue Category_Vegetarian / Vegan Restaurant  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "\n",
       "   Venue Category_Video Game Store  Venue Category_Vietnamese Restaurant  \\\n",
       "0                                0                                     0   \n",
       "1                                0                                     0   \n",
       "2                                0                                     0   \n",
       "3                                0                                     0   \n",
       "4                                0                                     0   \n",
       "\n",
       "   Venue Category_Wine Bar  Venue Category_Wings Joint  \\\n",
       "0                        0                           0   \n",
       "1                        0                           0   \n",
       "2                        0                           0   \n",
       "3                        0                           0   \n",
       "4                        0                           0   \n",
       "\n",
       "   Venue Category_Yoga Studio  \n",
       "0                           0  \n",
       "1                           0  \n",
       "2                           0  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 241 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "toronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']])\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "toronto_onehot['Neighborhood'] = toronto_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [toronto_onehot.columns[-1]] + list(toronto_onehot.columns[:-1])\n",
    "toronto_onehot = toronto_onehot[fixed_columns]\n",
    "\n",
    "toronto_onehot.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1709, 241)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new data frame shape\n",
    "toronto_onehot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Venue Category_Accessories Store</th>\n",
       "      <th>Venue Category_Adult Boutique</th>\n",
       "      <th>Venue Category_Afghan Restaurant</th>\n",
       "      <th>Venue Category_Airport</th>\n",
       "      <th>Venue Category_Airport Food Court</th>\n",
       "      <th>Venue Category_Airport Gate</th>\n",
       "      <th>Venue Category_Airport Lounge</th>\n",
       "      <th>Venue Category_Airport Service</th>\n",
       "      <th>Venue Category_Airport Terminal</th>\n",
       "      <th>...</th>\n",
       "      <th>Venue Category_Toy / Game Store</th>\n",
       "      <th>Venue Category_Trail</th>\n",
       "      <th>Venue Category_Train Station</th>\n",
       "      <th>Venue Category_Transportation Service</th>\n",
       "      <th>Venue Category_Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Venue Category_Video Game Store</th>\n",
       "      <th>Venue Category_Vietnamese Restaurant</th>\n",
       "      <th>Venue Category_Wine Bar</th>\n",
       "      <th>Venue Category_Wings Joint</th>\n",
       "      <th>Venue Category_Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelaide, King, Richmond</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brockton, Exhibition Place, Parkdale Village</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Business Reply Mail Processing Centre 969 Eastern</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CN Tower, Bathurst Quay, Island airport, Harbo...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cabbagetown, St. James Town</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Central Bay Street</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012346</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012346</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chinatown, Grange Park, Kensington Market</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Christie</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Church and Wellesley</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.011236</td>\n",
       "      <td>0.011236</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011236</td>\n",
       "      <td>0.011236</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011236</td>\n",
       "      <td>0.022472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Commerce Court, Victoria Hotel</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Davisville</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Davisville North</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Deer Park, Forest Hill SE, Rathnelly, South Hi...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Design Exchange, Toronto Dominion Centre</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dovercourt Village, Dufferin</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>First Canadian Place, Underground city</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Forest Hill North, Forest Hill West</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Harbord, University of Toronto</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Harbourfront East, Toronto Islands, Union Station</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Harbourfront, Regent Park</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>High Park, The Junction South</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Lawrence Park</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Little Portugal, Trinity</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Moore Park, Summerhill East</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>North Toronto West</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Parkdale, Roncesvalles</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Rosedale</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Roselawn</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Runnymede, Swansea</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Ryerson, Garden District</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>St. James Town</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Stn A PO Boxes 25 The Esplanade</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010417</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Studio District</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>The Annex, North Midtown, Yorkville</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>The Beaches</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>The Beaches West, India Bazaar</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>The Danforth West, Riverdale</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38 rows × 241 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Neighborhood  \\\n",
       "0                            Adelaide, King, Richmond   \n",
       "1                                         Berczy Park   \n",
       "2        Brockton, Exhibition Place, Parkdale Village   \n",
       "3   Business Reply Mail Processing Centre 969 Eastern   \n",
       "4   CN Tower, Bathurst Quay, Island airport, Harbo...   \n",
       "5                         Cabbagetown, St. James Town   \n",
       "6                                  Central Bay Street   \n",
       "7           Chinatown, Grange Park, Kensington Market   \n",
       "8                                            Christie   \n",
       "9                                Church and Wellesley   \n",
       "10                     Commerce Court, Victoria Hotel   \n",
       "11                                         Davisville   \n",
       "12                                   Davisville North   \n",
       "13  Deer Park, Forest Hill SE, Rathnelly, South Hi...   \n",
       "14           Design Exchange, Toronto Dominion Centre   \n",
       "15                       Dovercourt Village, Dufferin   \n",
       "16             First Canadian Place, Underground city   \n",
       "17                Forest Hill North, Forest Hill West   \n",
       "18                     Harbord, University of Toronto   \n",
       "19  Harbourfront East, Toronto Islands, Union Station   \n",
       "20                          Harbourfront, Regent Park   \n",
       "21                      High Park, The Junction South   \n",
       "22                                      Lawrence Park   \n",
       "23                           Little Portugal, Trinity   \n",
       "24                        Moore Park, Summerhill East   \n",
       "25                                 North Toronto West   \n",
       "26                             Parkdale, Roncesvalles   \n",
       "27                                           Rosedale   \n",
       "28                                           Roselawn   \n",
       "29                                 Runnymede, Swansea   \n",
       "30                           Ryerson, Garden District   \n",
       "31                                     St. James Town   \n",
       "32                    Stn A PO Boxes 25 The Esplanade   \n",
       "33                                    Studio District   \n",
       "34                The Annex, North Midtown, Yorkville   \n",
       "35                                        The Beaches   \n",
       "36                     The Beaches West, India Bazaar   \n",
       "37                       The Danforth West, Riverdale   \n",
       "\n",
       "    Venue Category_Accessories Store  Venue Category_Adult Boutique  \\\n",
       "0                               0.01                       0.000000   \n",
       "1                               0.00                       0.000000   \n",
       "2                               0.00                       0.000000   \n",
       "3                               0.00                       0.000000   \n",
       "4                               0.00                       0.000000   \n",
       "5                               0.00                       0.000000   \n",
       "6                               0.00                       0.000000   \n",
       "7                               0.00                       0.000000   \n",
       "8                               0.00                       0.000000   \n",
       "9                               0.00                       0.011236   \n",
       "10                              0.00                       0.000000   \n",
       "11                              0.00                       0.000000   \n",
       "12                              0.00                       0.000000   \n",
       "13                              0.00                       0.000000   \n",
       "14                              0.00                       0.000000   \n",
       "15                              0.00                       0.000000   \n",
       "16                              0.00                       0.000000   \n",
       "17                              0.00                       0.000000   \n",
       "18                              0.00                       0.000000   \n",
       "19                              0.00                       0.000000   \n",
       "20                              0.00                       0.000000   \n",
       "21                              0.00                       0.000000   \n",
       "22                              0.00                       0.000000   \n",
       "23                              0.00                       0.000000   \n",
       "24                              0.00                       0.000000   \n",
       "25                              0.00                       0.000000   \n",
       "26                              0.00                       0.000000   \n",
       "27                              0.00                       0.000000   \n",
       "28                              0.00                       0.000000   \n",
       "29                              0.00                       0.000000   \n",
       "30                              0.00                       0.000000   \n",
       "31                              0.01                       0.000000   \n",
       "32                              0.00                       0.000000   \n",
       "33                              0.00                       0.000000   \n",
       "34                              0.00                       0.000000   \n",
       "35                              0.00                       0.000000   \n",
       "36                              0.00                       0.000000   \n",
       "37                              0.00                       0.000000   \n",
       "\n",
       "    Venue Category_Afghan Restaurant  Venue Category_Airport  \\\n",
       "0                           0.000000                0.000000   \n",
       "1                           0.000000                0.000000   \n",
       "2                           0.000000                0.000000   \n",
       "3                           0.000000                0.000000   \n",
       "4                           0.000000                0.071429   \n",
       "5                           0.000000                0.000000   \n",
       "6                           0.000000                0.000000   \n",
       "7                           0.000000                0.000000   \n",
       "8                           0.000000                0.000000   \n",
       "9                           0.011236                0.000000   \n",
       "10                          0.000000                0.000000   \n",
       "11                          0.000000                0.000000   \n",
       "12                          0.000000                0.000000   \n",
       "13                          0.000000                0.000000   \n",
       "14                          0.000000                0.000000   \n",
       "15                          0.000000                0.000000   \n",
       "16                          0.000000                0.000000   \n",
       "17                          0.000000                0.000000   \n",
       "18                          0.000000                0.000000   \n",
       "19                          0.000000                0.000000   \n",
       "20                          0.000000                0.000000   \n",
       "21                          0.000000                0.000000   \n",
       "22                          0.000000                0.000000   \n",
       "23                          0.000000                0.000000   \n",
       "24                          0.000000                0.000000   \n",
       "25                          0.000000                0.000000   \n",
       "26                          0.000000                0.000000   \n",
       "27                          0.000000                0.000000   \n",
       "28                          0.000000                0.000000   \n",
       "29                          0.000000                0.000000   \n",
       "30                          0.000000                0.000000   \n",
       "31                          0.000000                0.000000   \n",
       "32                          0.000000                0.000000   \n",
       "33                          0.000000                0.000000   \n",
       "34                          0.000000                0.000000   \n",
       "35                          0.000000                0.000000   \n",
       "36                          0.000000                0.000000   \n",
       "37                          0.000000                0.000000   \n",
       "\n",
       "    Venue Category_Airport Food Court  Venue Category_Airport Gate  \\\n",
       "0                            0.000000                     0.000000   \n",
       "1                            0.000000                     0.000000   \n",
       "2                            0.000000                     0.000000   \n",
       "3                            0.000000                     0.000000   \n",
       "4                            0.071429                     0.071429   \n",
       "5                            0.000000                     0.000000   \n",
       "6                            0.000000                     0.000000   \n",
       "7                            0.000000                     0.000000   \n",
       "8                            0.000000                     0.000000   \n",
       "9                            0.000000                     0.000000   \n",
       "10                           0.000000                     0.000000   \n",
       "11                           0.000000                     0.000000   \n",
       "12                           0.000000                     0.000000   \n",
       "13                           0.000000                     0.000000   \n",
       "14                           0.000000                     0.000000   \n",
       "15                           0.000000                     0.000000   \n",
       "16                           0.000000                     0.000000   \n",
       "17                           0.000000                     0.000000   \n",
       "18                           0.000000                     0.000000   \n",
       "19                           0.000000                     0.000000   \n",
       "20                           0.000000                     0.000000   \n",
       "21                           0.000000                     0.000000   \n",
       "22                           0.000000                     0.000000   \n",
       "23                           0.000000                     0.000000   \n",
       "24                           0.000000                     0.000000   \n",
       "25                           0.000000                     0.000000   \n",
       "26                           0.000000                     0.000000   \n",
       "27                           0.000000                     0.000000   \n",
       "28                           0.000000                     0.000000   \n",
       "29                           0.000000                     0.000000   \n",
       "30                           0.000000                     0.000000   \n",
       "31                           0.000000                     0.000000   \n",
       "32                           0.000000                     0.000000   \n",
       "33                           0.000000                     0.000000   \n",
       "34                           0.000000                     0.000000   \n",
       "35                           0.000000                     0.000000   \n",
       "36                           0.000000                     0.000000   \n",
       "37                           0.000000                     0.000000   \n",
       "\n",
       "    Venue Category_Airport Lounge  Venue Category_Airport Service  \\\n",
       "0                        0.000000                        0.000000   \n",
       "1                        0.000000                        0.000000   \n",
       "2                        0.000000                        0.000000   \n",
       "3                        0.000000                        0.000000   \n",
       "4                        0.142857                        0.142857   \n",
       "5                        0.000000                        0.000000   \n",
       "6                        0.000000                        0.000000   \n",
       "7                        0.000000                        0.000000   \n",
       "8                        0.000000                        0.000000   \n",
       "9                        0.000000                        0.000000   \n",
       "10                       0.000000                        0.000000   \n",
       "11                       0.000000                        0.000000   \n",
       "12                       0.000000                        0.000000   \n",
       "13                       0.000000                        0.000000   \n",
       "14                       0.000000                        0.000000   \n",
       "15                       0.000000                        0.000000   \n",
       "16                       0.000000                        0.000000   \n",
       "17                       0.000000                        0.000000   \n",
       "18                       0.000000                        0.000000   \n",
       "19                       0.000000                        0.000000   \n",
       "20                       0.000000                        0.000000   \n",
       "21                       0.000000                        0.000000   \n",
       "22                       0.000000                        0.000000   \n",
       "23                       0.000000                        0.000000   \n",
       "24                       0.000000                        0.000000   \n",
       "25                       0.000000                        0.000000   \n",
       "26                       0.000000                        0.000000   \n",
       "27                       0.000000                        0.000000   \n",
       "28                       0.000000                        0.000000   \n",
       "29                       0.000000                        0.000000   \n",
       "30                       0.000000                        0.000000   \n",
       "31                       0.000000                        0.000000   \n",
       "32                       0.000000                        0.000000   \n",
       "33                       0.000000                        0.000000   \n",
       "34                       0.000000                        0.000000   \n",
       "35                       0.000000                        0.000000   \n",
       "36                       0.000000                        0.000000   \n",
       "37                       0.000000                        0.000000   \n",
       "\n",
       "    Venue Category_Airport Terminal             ...              \\\n",
       "0                          0.000000             ...               \n",
       "1                          0.000000             ...               \n",
       "2                          0.000000             ...               \n",
       "3                          0.000000             ...               \n",
       "4                          0.142857             ...               \n",
       "5                          0.000000             ...               \n",
       "6                          0.000000             ...               \n",
       "7                          0.000000             ...               \n",
       "8                          0.000000             ...               \n",
       "9                          0.000000             ...               \n",
       "10                         0.000000             ...               \n",
       "11                         0.000000             ...               \n",
       "12                         0.000000             ...               \n",
       "13                         0.000000             ...               \n",
       "14                         0.000000             ...               \n",
       "15                         0.000000             ...               \n",
       "16                         0.000000             ...               \n",
       "17                         0.000000             ...               \n",
       "18                         0.000000             ...               \n",
       "19                         0.000000             ...               \n",
       "20                         0.000000             ...               \n",
       "21                         0.000000             ...               \n",
       "22                         0.000000             ...               \n",
       "23                         0.000000             ...               \n",
       "24                         0.000000             ...               \n",
       "25                         0.000000             ...               \n",
       "26                         0.000000             ...               \n",
       "27                         0.000000             ...               \n",
       "28                         0.000000             ...               \n",
       "29                         0.000000             ...               \n",
       "30                         0.000000             ...               \n",
       "31                         0.000000             ...               \n",
       "32                         0.000000             ...               \n",
       "33                         0.000000             ...               \n",
       "34                         0.000000             ...               \n",
       "35                         0.000000             ...               \n",
       "36                         0.000000             ...               \n",
       "37                         0.000000             ...               \n",
       "\n",
       "    Venue Category_Toy / Game Store  Venue Category_Trail  \\\n",
       "0                          0.000000              0.000000   \n",
       "1                          0.000000              0.000000   \n",
       "2                          0.000000              0.000000   \n",
       "3                          0.000000              0.000000   \n",
       "4                          0.000000              0.000000   \n",
       "5                          0.000000              0.000000   \n",
       "6                          0.000000              0.000000   \n",
       "7                          0.000000              0.000000   \n",
       "8                          0.000000              0.000000   \n",
       "9                          0.000000              0.000000   \n",
       "10                         0.000000              0.000000   \n",
       "11                         0.025641              0.000000   \n",
       "12                         0.000000              0.000000   \n",
       "13                         0.000000              0.000000   \n",
       "14                         0.000000              0.000000   \n",
       "15                         0.000000              0.000000   \n",
       "16                         0.000000              0.000000   \n",
       "17                         0.000000              0.200000   \n",
       "18                         0.000000              0.000000   \n",
       "19                         0.000000              0.000000   \n",
       "20                         0.000000              0.000000   \n",
       "21                         0.000000              0.000000   \n",
       "22                         0.000000              0.000000   \n",
       "23                         0.000000              0.000000   \n",
       "24                         0.000000              0.000000   \n",
       "25                         0.000000              0.000000   \n",
       "26                         0.000000              0.000000   \n",
       "27                         0.000000              0.250000   \n",
       "28                         0.000000              0.000000   \n",
       "29                         0.000000              0.000000   \n",
       "30                         0.010000              0.000000   \n",
       "31                         0.000000              0.000000   \n",
       "32                         0.000000              0.000000   \n",
       "33                         0.000000              0.000000   \n",
       "34                         0.000000              0.000000   \n",
       "35                         0.000000              0.000000   \n",
       "36                         0.000000              0.000000   \n",
       "37                         0.000000              0.023256   \n",
       "\n",
       "    Venue Category_Train Station  Venue Category_Transportation Service  \\\n",
       "0                           0.00                               0.000000   \n",
       "1                           0.00                               0.000000   \n",
       "2                           0.00                               0.000000   \n",
       "3                           0.00                               0.000000   \n",
       "4                           0.00                               0.000000   \n",
       "5                           0.00                               0.000000   \n",
       "6                           0.00                               0.000000   \n",
       "7                           0.00                               0.000000   \n",
       "8                           0.00                               0.000000   \n",
       "9                           0.00                               0.000000   \n",
       "10                          0.00                               0.000000   \n",
       "11                          0.00                               0.025641   \n",
       "12                          0.00                               0.000000   \n",
       "13                          0.00                               0.000000   \n",
       "14                          0.01                               0.000000   \n",
       "15                          0.00                               0.000000   \n",
       "16                          0.01                               0.000000   \n",
       "17                          0.00                               0.000000   \n",
       "18                          0.00                               0.000000   \n",
       "19                          0.01                               0.000000   \n",
       "20                          0.00                               0.000000   \n",
       "21                          0.00                               0.000000   \n",
       "22                          0.00                               0.000000   \n",
       "23                          0.00                               0.000000   \n",
       "24                          0.00                               0.000000   \n",
       "25                          0.00                               0.000000   \n",
       "26                          0.00                               0.000000   \n",
       "27                          0.00                               0.000000   \n",
       "28                          0.00                               0.000000   \n",
       "29                          0.00                               0.000000   \n",
       "30                          0.00                               0.000000   \n",
       "31                          0.00                               0.000000   \n",
       "32                          0.00                               0.000000   \n",
       "33                          0.00                               0.000000   \n",
       "34                          0.00                               0.000000   \n",
       "35                          0.00                               0.000000   \n",
       "36                          0.00                               0.000000   \n",
       "37                          0.00                               0.000000   \n",
       "\n",
       "    Venue Category_Vegetarian / Vegan Restaurant  \\\n",
       "0                                       0.010000   \n",
       "1                                       0.017544   \n",
       "2                                       0.000000   \n",
       "3                                       0.000000   \n",
       "4                                       0.000000   \n",
       "5                                       0.000000   \n",
       "6                                       0.012346   \n",
       "7                                       0.050000   \n",
       "8                                       0.000000   \n",
       "9                                       0.000000   \n",
       "10                                      0.010000   \n",
       "11                                      0.000000   \n",
       "12                                      0.000000   \n",
       "13                                      0.000000   \n",
       "14                                      0.010000   \n",
       "15                                      0.000000   \n",
       "16                                      0.010000   \n",
       "17                                      0.000000   \n",
       "18                                      0.000000   \n",
       "19                                      0.010000   \n",
       "20                                      0.000000   \n",
       "21                                      0.000000   \n",
       "22                                      0.000000   \n",
       "23                                      0.015385   \n",
       "24                                      0.000000   \n",
       "25                                      0.000000   \n",
       "26                                      0.000000   \n",
       "27                                      0.000000   \n",
       "28                                      0.000000   \n",
       "29                                      0.025000   \n",
       "30                                      0.010000   \n",
       "31                                      0.010000   \n",
       "32                                      0.010417   \n",
       "33                                      0.000000   \n",
       "34                                      0.040000   \n",
       "35                                      0.000000   \n",
       "36                                      0.000000   \n",
       "37                                      0.000000   \n",
       "\n",
       "    Venue Category_Video Game Store  Venue Category_Vietnamese Restaurant  \\\n",
       "0                          0.000000                              0.000000   \n",
       "1                          0.000000                              0.000000   \n",
       "2                          0.000000                              0.000000   \n",
       "3                          0.000000                              0.000000   \n",
       "4                          0.000000                              0.000000   \n",
       "5                          0.000000                              0.000000   \n",
       "6                          0.000000                              0.000000   \n",
       "7                          0.000000                              0.050000   \n",
       "8                          0.000000                              0.000000   \n",
       "9                          0.011236                              0.011236   \n",
       "10                         0.000000                              0.000000   \n",
       "11                         0.000000                              0.000000   \n",
       "12                         0.000000                              0.000000   \n",
       "13                         0.000000                              0.066667   \n",
       "14                         0.000000                              0.000000   \n",
       "15                         0.000000                              0.000000   \n",
       "16                         0.000000                              0.000000   \n",
       "17                         0.000000                              0.000000   \n",
       "18                         0.029412                              0.000000   \n",
       "19                         0.000000                              0.000000   \n",
       "20                         0.000000                              0.000000   \n",
       "21                         0.000000                              0.000000   \n",
       "22                         0.000000                              0.000000   \n",
       "23                         0.000000                              0.030769   \n",
       "24                         0.000000                              0.000000   \n",
       "25                         0.000000                              0.000000   \n",
       "26                         0.000000                              0.000000   \n",
       "27                         0.000000                              0.000000   \n",
       "28                         0.000000                              0.000000   \n",
       "29                         0.000000                              0.000000   \n",
       "30                         0.010000                              0.010000   \n",
       "31                         0.000000                              0.000000   \n",
       "32                         0.000000                              0.000000   \n",
       "33                         0.000000                              0.000000   \n",
       "34                         0.000000                              0.000000   \n",
       "35                         0.000000                              0.000000   \n",
       "36                         0.000000                              0.000000   \n",
       "37                         0.000000                              0.000000   \n",
       "\n",
       "    Venue Category_Wine Bar  Venue Category_Wings Joint  \\\n",
       "0                  0.010000                    0.000000   \n",
       "1                  0.000000                    0.000000   \n",
       "2                  0.000000                    0.000000   \n",
       "3                  0.000000                    0.000000   \n",
       "4                  0.000000                    0.000000   \n",
       "5                  0.000000                    0.000000   \n",
       "6                  0.012346                    0.000000   \n",
       "7                  0.010000                    0.000000   \n",
       "8                  0.000000                    0.000000   \n",
       "9                  0.000000                    0.011236   \n",
       "10                 0.010000                    0.000000   \n",
       "11                 0.000000                    0.000000   \n",
       "12                 0.000000                    0.000000   \n",
       "13                 0.000000                    0.000000   \n",
       "14                 0.010000                    0.000000   \n",
       "15                 0.000000                    0.000000   \n",
       "16                 0.010000                    0.000000   \n",
       "17                 0.000000                    0.000000   \n",
       "18                 0.000000                    0.000000   \n",
       "19                 0.010000                    0.000000   \n",
       "20                 0.000000                    0.000000   \n",
       "21                 0.000000                    0.000000   \n",
       "22                 0.000000                    0.000000   \n",
       "23                 0.015385                    0.000000   \n",
       "24                 0.000000                    0.000000   \n",
       "25                 0.000000                    0.000000   \n",
       "26                 0.000000                    0.000000   \n",
       "27                 0.000000                    0.000000   \n",
       "28                 0.000000                    0.000000   \n",
       "29                 0.000000                    0.000000   \n",
       "30                 0.010000                    0.000000   \n",
       "31                 0.000000                    0.000000   \n",
       "32                 0.000000                    0.000000   \n",
       "33                 0.000000                    0.000000   \n",
       "34                 0.000000                    0.000000   \n",
       "35                 0.000000                    0.000000   \n",
       "36                 0.000000                    0.000000   \n",
       "37                 0.000000                    0.000000   \n",
       "\n",
       "    Venue Category_Yoga Studio  \n",
       "0                     0.000000  \n",
       "1                     0.000000  \n",
       "2                     0.000000  \n",
       "3                     0.058824  \n",
       "4                     0.000000  \n",
       "5                     0.000000  \n",
       "6                     0.012346  \n",
       "7                     0.000000  \n",
       "8                     0.000000  \n",
       "9                     0.022472  \n",
       "10                    0.000000  \n",
       "11                    0.000000  \n",
       "12                    0.000000  \n",
       "13                    0.000000  \n",
       "14                    0.000000  \n",
       "15                    0.000000  \n",
       "16                    0.000000  \n",
       "17                    0.000000  \n",
       "18                    0.000000  \n",
       "19                    0.000000  \n",
       "20                    0.000000  \n",
       "21                    0.000000  \n",
       "22                    0.000000  \n",
       "23                    0.015385  \n",
       "24                    0.000000  \n",
       "25                    0.050000  \n",
       "26                    0.000000  \n",
       "27                    0.000000  \n",
       "28                    0.000000  \n",
       "29                    0.000000  \n",
       "30                    0.000000  \n",
       "31                    0.000000  \n",
       "32                    0.000000  \n",
       "33                    0.025000  \n",
       "34                    0.000000  \n",
       "35                    0.000000  \n",
       "36                    0.000000  \n",
       "37                    0.023256  \n",
       "\n",
       "[38 rows x 241 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_grouped = toronto_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "toronto_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's confirm the new size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 241)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_grouped.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's print each neighborhood along with the top 5 most common venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Adelaide, King, Richmond----\n",
      "                            venue  freq\n",
      "0      Venue Category_Coffee Shop  0.06\n",
      "1       Venue Category_Steakhouse  0.04\n",
      "2             Venue Category_Café  0.04\n",
      "3  Venue Category_Thai Restaurant  0.04\n",
      "4     Venue Category_Burger Joint  0.03\n",
      "\n",
      "\n",
      "----Berczy Park----\n",
      "                               venue  freq\n",
      "0         Venue Category_Coffee Shop  0.07\n",
      "1        Venue Category_Cocktail Bar  0.05\n",
      "2      Venue Category_Farmers Market  0.04\n",
      "3          Venue Category_Restaurant  0.04\n",
      "4  Venue Category_Seafood Restaurant  0.04\n",
      "\n",
      "\n",
      "----Brockton, Exhibition Place, Parkdale Village----\n",
      "                                 venue  freq\n",
      "0        Venue Category_Breakfast Spot  0.09\n",
      "1             Venue Category_Nightclub  0.09\n",
      "2           Venue Category_Coffee Shop  0.09\n",
      "3                  Venue Category_Café  0.09\n",
      "4  Venue Category_Gym / Fitness Center  0.04\n",
      "\n",
      "\n",
      "----Business Reply Mail Processing Centre 969 Eastern----\n",
      "                               venue  freq\n",
      "0  Venue Category_Light Rail Station  0.12\n",
      "1         Venue Category_Yoga Studio  0.06\n",
      "2       Venue Category_Auto Workshop  0.06\n",
      "3                Venue Category_Park  0.06\n",
      "4          Venue Category_Comic Shop  0.06\n",
      "\n",
      "\n",
      "----CN Tower, Bathurst Quay, Island airport, Harbourfront West, King and Spadina, Railway Lands, South Niagara----\n",
      "                             venue  freq\n",
      "0   Venue Category_Airport Service  0.14\n",
      "1  Venue Category_Airport Terminal  0.14\n",
      "2    Venue Category_Airport Lounge  0.14\n",
      "3          Venue Category_Boutique  0.07\n",
      "4   Venue Category_Harbor / Marina  0.07\n",
      "\n",
      "\n",
      "----Cabbagetown, St. James Town----\n",
      "                               venue  freq\n",
      "0         Venue Category_Coffee Shop  0.09\n",
      "1          Venue Category_Restaurant  0.07\n",
      "2         Venue Category_Pizza Place  0.05\n",
      "3                Venue Category_Café  0.05\n",
      "4  Venue Category_Italian Restaurant  0.05\n",
      "\n",
      "\n",
      "----Central Bay Street----\n",
      "                               venue  freq\n",
      "0         Venue Category_Coffee Shop  0.16\n",
      "1  Venue Category_Italian Restaurant  0.05\n",
      "2                 Venue Category_Bar  0.04\n",
      "3                Venue Category_Café  0.04\n",
      "4     Venue Category_Bubble Tea Shop  0.04\n",
      "\n",
      "\n",
      "----Chinatown, Grange Park, Kensington Market----\n",
      "                                          venue  freq\n",
      "0                           Venue Category_Café  0.07\n",
      "1                            Venue Category_Bar  0.06\n",
      "2          Venue Category_Vietnamese Restaurant  0.05\n",
      "3  Venue Category_Vegetarian / Vegan Restaurant  0.05\n",
      "4                    Venue Category_Coffee Shop  0.04\n",
      "\n",
      "\n",
      "----Christie----\n",
      "                               venue  freq\n",
      "0                Venue Category_Café  0.20\n",
      "1       Venue Category_Grocery Store  0.20\n",
      "2                Venue Category_Park  0.13\n",
      "3  Venue Category_Italian Restaurant  0.07\n",
      "4          Venue Category_Restaurant  0.07\n",
      "\n",
      "\n",
      "----Church and Wellesley----\n",
      "                                venue  freq\n",
      "0  Venue Category_Japanese Restaurant  0.07\n",
      "1          Venue Category_Coffee Shop  0.07\n",
      "2              Venue Category_Gay Bar  0.04\n",
      "3     Venue Category_Sushi Restaurant  0.04\n",
      "4           Venue Category_Restaurant  0.03\n",
      "\n",
      "\n",
      "----Commerce Court, Victoria Hotel----\n",
      "                                venue  freq\n",
      "0          Venue Category_Coffee Shop  0.12\n",
      "1                Venue Category_Hotel  0.06\n",
      "2                 Venue Category_Café  0.06\n",
      "3  Venue Category_American Restaurant  0.04\n",
      "4           Venue Category_Restaurant  0.04\n",
      "\n",
      "\n",
      "----Davisville----\n",
      "                               venue  freq\n",
      "0        Venue Category_Dessert Shop  0.10\n",
      "1      Venue Category_Sandwich Place  0.08\n",
      "2         Venue Category_Pizza Place  0.08\n",
      "3    Venue Category_Sushi Restaurant  0.05\n",
      "4  Venue Category_Italian Restaurant  0.05\n",
      "\n",
      "\n",
      "----Davisville North----\n",
      "                           venue  freq\n",
      "0             Venue Category_Gym  0.14\n",
      "1  Venue Category_Breakfast Spot  0.14\n",
      "2    Venue Category_Burger Joint  0.14\n",
      "3            Venue Category_Park  0.14\n",
      "4  Venue Category_Sandwich Place  0.14\n",
      "\n",
      "\n",
      "----Deer Park, Forest Hill SE, Rathnelly, South Hill, Summerhill West----\n",
      "                             venue  freq\n",
      "0       Venue Category_Coffee Shop  0.13\n",
      "1               Venue Category_Pub  0.13\n",
      "2       Venue Category_Pizza Place  0.07\n",
      "3        Venue Category_Sports Bar  0.07\n",
      "4  Venue Category_Sushi Restaurant  0.07\n",
      "\n",
      "\n",
      "----Design Exchange, Toronto Dominion Centre----\n",
      "                                venue  freq\n",
      "0          Venue Category_Coffee Shop  0.12\n",
      "1                 Venue Category_Café  0.08\n",
      "2                Venue Category_Hotel  0.07\n",
      "3           Venue Category_Restaurant  0.04\n",
      "4  Venue Category_American Restaurant  0.04\n",
      "\n",
      "\n",
      "----Dovercourt Village, Dufferin----\n",
      "                           venue  freq\n",
      "0     Venue Category_Supermarket  0.10\n",
      "1        Venue Category_Pharmacy  0.10\n",
      "2  Venue Category_Discount Store  0.10\n",
      "3          Venue Category_Bakery  0.10\n",
      "4            Venue Category_Pool  0.05\n",
      "\n",
      "\n",
      "----First Canadian Place, Underground city----\n",
      "                                venue  freq\n",
      "0          Venue Category_Coffee Shop  0.09\n",
      "1                 Venue Category_Café  0.07\n",
      "2                Venue Category_Hotel  0.06\n",
      "3           Venue Category_Steakhouse  0.04\n",
      "4  Venue Category_American Restaurant  0.03\n",
      "\n",
      "\n",
      "----Forest Hill North, Forest Hill West----\n",
      "                             venue  freq\n",
      "0     Venue Category_Jewelry Store   0.2\n",
      "1             Venue Category_Trail   0.2\n",
      "2          Venue Category_Bus Line   0.2\n",
      "3              Venue Category_Park   0.2\n",
      "4  Venue Category_Sushi Restaurant   0.2\n",
      "\n",
      "\n",
      "----Harbord, University of Toronto----\n",
      "                                venue  freq\n",
      "0                 Venue Category_Café  0.15\n",
      "1               Venue Category_Bakery  0.06\n",
      "2                  Venue Category_Bar  0.06\n",
      "3  Venue Category_Japanese Restaurant  0.06\n",
      "4            Venue Category_Bookstore  0.06\n",
      "\n",
      "\n",
      "----Harbourfront East, Toronto Islands, Union Station----\n",
      "                               venue  freq\n",
      "0         Venue Category_Coffee Shop  0.13\n",
      "1            Venue Category_Aquarium  0.05\n",
      "2               Venue Category_Hotel  0.05\n",
      "3  Venue Category_Italian Restaurant  0.04\n",
      "4                Venue Category_Café  0.04\n",
      "\n",
      "\n",
      "----Harbourfront, Regent Park----\n",
      "                        venue  freq\n",
      "0  Venue Category_Coffee Shop  0.17\n",
      "1         Venue Category_Park  0.06\n",
      "2       Venue Category_Bakery  0.06\n",
      "3         Venue Category_Café  0.06\n",
      "4          Venue Category_Pub  0.06\n",
      "\n",
      "\n",
      "----High Park, The Junction South----\n",
      "                                   venue  freq\n",
      "0      Venue Category_Mexican Restaurant  0.08\n",
      "1                     Venue Category_Bar  0.08\n",
      "2                    Venue Category_Café  0.08\n",
      "3  Venue Category_Furniture / Home Store  0.04\n",
      "4     Venue Category_Arts & Crafts Store  0.04\n",
      "\n",
      "\n",
      "----Lawrence Park----\n",
      "                              venue  freq\n",
      "0               Venue Category_Park  0.33\n",
      "1        Venue Category_Swim School  0.33\n",
      "2           Venue Category_Bus Line  0.33\n",
      "3  Venue Category_Accessories Store  0.00\n",
      "4       Venue Category_Neighborhood  0.00\n",
      "\n",
      "\n",
      "----Little Portugal, Trinity----\n",
      "                             venue  freq\n",
      "0               Venue Category_Bar  0.14\n",
      "1       Venue Category_Coffee Shop  0.05\n",
      "2  Venue Category_Asian Restaurant  0.05\n",
      "3        Venue Category_Restaurant  0.03\n",
      "4       Venue Category_Men's Store  0.03\n",
      "\n",
      "\n",
      "----Moore Park, Summerhill East----\n",
      "                                     venue  freq\n",
      "0                Venue Category_Playground   1.0\n",
      "1            Venue Category_Adult Boutique   0.0\n",
      "2  Venue Category_Mediterranean Restaurant   0.0\n",
      "3               Venue Category_Men's Store   0.0\n",
      "4        Venue Category_Mexican Restaurant   0.0\n",
      "\n",
      "\n",
      "----North Toronto West----\n",
      "                                venue  freq\n",
      "0       Venue Category_Clothing Store  0.15\n",
      "1  Venue Category_Sporting Goods Shop  0.10\n",
      "2          Venue Category_Coffee Shop  0.10\n",
      "3          Venue Category_Yoga Studio  0.05\n",
      "4       Venue Category_Cosmetics Shop  0.05\n",
      "\n",
      "\n",
      "----Parkdale, Roncesvalles----\n",
      "                                        venue  freq\n",
      "0               Venue Category_Breakfast Spot  0.12\n",
      "1                    Venue Category_Gift Shop  0.12\n",
      "2                Venue Category_Movie Theater  0.06\n",
      "3  Venue Category_Eastern European Restaurant  0.06\n",
      "4                  Venue Category_Coffee Shop  0.06\n",
      "\n",
      "\n",
      "----Rosedale----\n",
      "                           venue  freq\n",
      "0            Venue Category_Park  0.50\n",
      "1      Venue Category_Playground  0.25\n",
      "2           Venue Category_Trail  0.25\n",
      "3     Venue Category_Music Venue  0.00\n",
      "4  Venue Category_Medical Center  0.00\n",
      "\n",
      "\n",
      "----Roselawn----\n",
      "                               venue  freq\n",
      "0              Venue Category_Garden   1.0\n",
      "1   Venue Category_Accessories Store   0.0\n",
      "2      Venue Category_Medical Center   0.0\n",
      "3         Venue Category_Men's Store   0.0\n",
      "4  Venue Category_Mexican Restaurant   0.0\n",
      "\n",
      "\n",
      "----Runnymede, Swansea----\n",
      "                               venue  freq\n",
      "0         Venue Category_Coffee Shop  0.10\n",
      "1                Venue Category_Café  0.08\n",
      "2         Venue Category_Pizza Place  0.05\n",
      "3  Venue Category_Italian Restaurant  0.05\n",
      "4    Venue Category_Sushi Restaurant  0.05\n",
      "\n",
      "\n",
      "----Ryerson, Garden District----\n",
      "                                      venue  freq\n",
      "0                Venue Category_Coffee Shop  0.08\n",
      "1             Venue Category_Clothing Store  0.07\n",
      "2             Venue Category_Cosmetics Shop  0.04\n",
      "3                       Venue Category_Café  0.04\n",
      "4  Venue Category_Middle Eastern Restaurant  0.03\n",
      "\n",
      "\n",
      "----St. James Town----\n",
      "                           venue  freq\n",
      "0     Venue Category_Coffee Shop  0.07\n",
      "1      Venue Category_Restaurant  0.06\n",
      "2            Venue Category_Café  0.05\n",
      "3           Venue Category_Hotel  0.05\n",
      "4  Venue Category_Breakfast Spot  0.04\n",
      "\n",
      "\n",
      "----Stn A PO Boxes 25 The Esplanade----\n",
      "                                 venue  freq\n",
      "0           Venue Category_Coffee Shop  0.10\n",
      "1                  Venue Category_Café  0.04\n",
      "2            Venue Category_Restaurant  0.04\n",
      "3  Venue Category_Fast Food Restaurant  0.03\n",
      "4          Venue Category_Cocktail Bar  0.03\n",
      "\n",
      "\n",
      "----Studio District----\n",
      "                                venue  freq\n",
      "0                 Venue Category_Café  0.10\n",
      "1          Venue Category_Coffee Shop  0.08\n",
      "2            Venue Category_Gastropub  0.05\n",
      "3   Venue Category_Italian Restaurant  0.05\n",
      "4  Venue Category_American Restaurant  0.05\n",
      "\n",
      "\n",
      "----The Annex, North Midtown, Yorkville----\n",
      "                           venue  freq\n",
      "0  Venue Category_Sandwich Place  0.12\n",
      "1     Venue Category_Coffee Shop  0.12\n",
      "2            Venue Category_Café  0.12\n",
      "3     Venue Category_Pizza Place  0.08\n",
      "4            Venue Category_Park  0.04\n",
      "\n",
      "\n",
      "----The Beaches----\n",
      "                               venue  freq\n",
      "0         Venue Category_Coffee Shop  0.25\n",
      "1   Venue Category_Health Food Store  0.25\n",
      "2                 Venue Category_Pub  0.25\n",
      "3        Venue Category_Neighborhood  0.25\n",
      "4  Venue Category_Mexican Restaurant  0.00\n",
      "\n",
      "\n",
      "----The Beaches West, India Bazaar----\n",
      "                              venue  freq\n",
      "0               Venue Category_Park  0.10\n",
      "1        Venue Category_Pizza Place  0.05\n",
      "2  Venue Category_Fish & Chips Shop  0.05\n",
      "3     Venue Category_Sandwich Place  0.05\n",
      "4       Venue Category_Liquor Store  0.05\n",
      "\n",
      "\n",
      "----The Danforth West, Riverdale----\n",
      "                               venue  freq\n",
      "0    Venue Category_Greek Restaurant  0.21\n",
      "1         Venue Category_Coffee Shop  0.09\n",
      "2      Venue Category_Ice Cream Shop  0.07\n",
      "3  Venue Category_Italian Restaurant  0.07\n",
      "4   Venue Category_Indian Restaurant  0.02\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "for hood in toronto_grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = toronto_grouped[toronto_grouped['Neighborhood'] == hood].T.reset_index()\n",
    "    temp.columns = ['venue','freq']\n",
    "    temp = temp.iloc[1:]\n",
    "    temp['freq'] = temp['freq'].astype(float)\n",
    "    temp = temp.round({'freq': 2})\n",
    "    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's put that into a *pandas* dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's write a function to sort the venues in descending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the new dataframe and display the top 10 venues for each neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelaide, King, Richmond</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Steakhouse</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Thai Restaurant</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Gym</td>\n",
       "      <td>Venue Category_Burger Joint</td>\n",
       "      <td>Venue Category_Hotel</td>\n",
       "      <td>Venue Category_American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Cocktail Bar</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Steakhouse</td>\n",
       "      <td>Venue Category_Pub</td>\n",
       "      <td>Venue Category_Cheese Shop</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Seafood Restaurant</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brockton, Exhibition Place, Parkdale Village</td>\n",
       "      <td>Venue Category_Breakfast Spot</td>\n",
       "      <td>Venue Category_Nightclub</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Caribbean Restaurant</td>\n",
       "      <td>Venue Category_Stadium</td>\n",
       "      <td>Venue Category_Furniture / Home Store</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Climbing Gym</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Business Reply Mail Processing Centre 969 Eastern</td>\n",
       "      <td>Venue Category_Light Rail Station</td>\n",
       "      <td>Venue Category_Yoga Studio</td>\n",
       "      <td>Venue Category_Garden</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Burrito Place</td>\n",
       "      <td>Venue Category_Brewery</td>\n",
       "      <td>Venue Category_Skate Park</td>\n",
       "      <td>Venue Category_Smoke Shop</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CN Tower, Bathurst Quay, Island airport, Harbo...</td>\n",
       "      <td>Venue Category_Airport Terminal</td>\n",
       "      <td>Venue Category_Airport Service</td>\n",
       "      <td>Venue Category_Airport Lounge</td>\n",
       "      <td>Venue Category_Boat or Ferry</td>\n",
       "      <td>Venue Category_Airport Gate</td>\n",
       "      <td>Venue Category_Sculpture Garden</td>\n",
       "      <td>Venue Category_Plane</td>\n",
       "      <td>Venue Category_Airport Food Court</td>\n",
       "      <td>Venue Category_Airport</td>\n",
       "      <td>Venue Category_Harbor / Marina</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Neighborhood  \\\n",
       "0                           Adelaide, King, Richmond   \n",
       "1                                        Berczy Park   \n",
       "2       Brockton, Exhibition Place, Parkdale Village   \n",
       "3  Business Reply Mail Processing Centre 969 Eastern   \n",
       "4  CN Tower, Bathurst Quay, Island airport, Harbo...   \n",
       "\n",
       "               1st Most Common Venue           2nd Most Common Venue  \\\n",
       "0         Venue Category_Coffee Shop       Venue Category_Steakhouse   \n",
       "1         Venue Category_Coffee Shop     Venue Category_Cocktail Bar   \n",
       "2      Venue Category_Breakfast Spot        Venue Category_Nightclub   \n",
       "3  Venue Category_Light Rail Station      Venue Category_Yoga Studio   \n",
       "4    Venue Category_Airport Terminal  Venue Category_Airport Service   \n",
       "\n",
       "           3rd Most Common Venue           4th Most Common Venue  \\\n",
       "0            Venue Category_Café  Venue Category_Thai Restaurant   \n",
       "1          Venue Category_Bakery       Venue Category_Steakhouse   \n",
       "2            Venue Category_Café      Venue Category_Coffee Shop   \n",
       "3          Venue Category_Garden      Venue Category_Pizza Place   \n",
       "4  Venue Category_Airport Lounge    Venue Category_Boat or Ferry   \n",
       "\n",
       "                 5th Most Common Venue            6th Most Common Venue  \\\n",
       "0                Venue Category_Bakery        Venue Category_Restaurant   \n",
       "1                   Venue Category_Pub       Venue Category_Cheese Shop   \n",
       "2  Venue Category_Caribbean Restaurant           Venue Category_Stadium   \n",
       "3            Venue Category_Restaurant     Venue Category_Burrito Place   \n",
       "4          Venue Category_Airport Gate  Venue Category_Sculpture Garden   \n",
       "\n",
       "                   7th Most Common Venue              8th Most Common Venue  \\\n",
       "0                     Venue Category_Gym        Venue Category_Burger Joint   \n",
       "1          Venue Category_Farmers Market          Venue Category_Restaurant   \n",
       "2  Venue Category_Furniture / Home Store  Venue Category_Italian Restaurant   \n",
       "3                 Venue Category_Brewery          Venue Category_Skate Park   \n",
       "4                   Venue Category_Plane  Venue Category_Airport Food Court   \n",
       "\n",
       "               9th Most Common Venue              10th Most Common Venue  \n",
       "0               Venue Category_Hotel  Venue Category_American Restaurant  \n",
       "1  Venue Category_Seafood Restaurant                 Venue Category_Café  \n",
       "2        Venue Category_Climbing Gym           Venue Category_Restaurant  \n",
       "3          Venue Category_Smoke Shop       Venue Category_Farmers Market  \n",
       "4             Venue Category_Airport      Venue Category_Harbor / Marina  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(toronto_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Cluster Neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run *k*-means to cluster the neighborhood into 5 clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "toronto_grouped_clustering = toronto_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "add clustering labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)"
   ]
  },
  {
   "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": 40,
   "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>index</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",
       "      <th>33</th>\n",
       "      <td>78</td>\n",
       "      <td>M6K</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Brockton, Exhibition Place, Parkdale Village</td>\n",
       "      <td>43.636847</td>\n",
       "      <td>-79.428191</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Breakfast Spot</td>\n",
       "      <td>Venue Category_Nightclub</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Caribbean Restaurant</td>\n",
       "      <td>Venue Category_Stadium</td>\n",
       "      <td>Venue Category_Furniture / Home Store</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Climbing Gym</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>82</td>\n",
       "      <td>M6P</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>High Park, The Junction South</td>\n",
       "      <td>43.661608</td>\n",
       "      <td>-79.464763</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Mexican Restaurant</td>\n",
       "      <td>Venue Category_Bar</td>\n",
       "      <td>Venue Category_Flea Market</td>\n",
       "      <td>Venue Category_Arts &amp; Crafts Store</td>\n",
       "      <td>Venue Category_Music Venue</td>\n",
       "      <td>Venue Category_Diner</td>\n",
       "      <td>Venue Category_Cajun / Creole Restaurant</td>\n",
       "      <td>Venue Category_Sandwich Place</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>83</td>\n",
       "      <td>M6R</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Parkdale, Roncesvalles</td>\n",
       "      <td>43.648960</td>\n",
       "      <td>-79.456325</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Breakfast Spot</td>\n",
       "      <td>Venue Category_Gift Shop</td>\n",
       "      <td>Venue Category_Bookstore</td>\n",
       "      <td>Venue Category_Dessert Shop</td>\n",
       "      <td>Venue Category_Movie Theater</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Bank</td>\n",
       "      <td>Venue Category_Dog Run</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>84</td>\n",
       "      <td>M6S</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Runnymede, Swansea</td>\n",
       "      <td>43.651571</td>\n",
       "      <td>-79.484450</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_Sushi Restaurant</td>\n",
       "      <td>Venue Category_Food &amp; Drink Shop</td>\n",
       "      <td>Venue Category_Pub</td>\n",
       "      <td>Venue Category_Indie Movie Theater</td>\n",
       "      <td>Venue Category_Latin American Restaurant</td>\n",
       "      <td>Venue Category_Dessert Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>87</td>\n",
       "      <td>M7Y</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Business Reply Mail Processing Centre 969 Eastern</td>\n",
       "      <td>43.662744</td>\n",
       "      <td>-79.321558</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Light Rail Station</td>\n",
       "      <td>Venue Category_Yoga Studio</td>\n",
       "      <td>Venue Category_Garden</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Burrito Place</td>\n",
       "      <td>Venue Category_Brewery</td>\n",
       "      <td>Venue Category_Skate Park</td>\n",
       "      <td>Venue Category_Smoke Shop</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index PostalCode       Borough  \\\n",
       "33     78        M6K  West Toronto   \n",
       "34     82        M6P  West Toronto   \n",
       "35     83        M6R  West Toronto   \n",
       "36     84        M6S  West Toronto   \n",
       "37     87        M7Y  East Toronto   \n",
       "\n",
       "                                         Neighborhood   Latitude  Longitude  \\\n",
       "33       Brockton, Exhibition Place, Parkdale Village  43.636847 -79.428191   \n",
       "34                      High Park, The Junction South  43.661608 -79.464763   \n",
       "35                             Parkdale, Roncesvalles  43.648960 -79.456325   \n",
       "36                                 Runnymede, Swansea  43.651571 -79.484450   \n",
       "37  Business Reply Mail Processing Centre 969 Eastern  43.662744 -79.321558   \n",
       "\n",
       "    Cluster Labels              1st Most Common Venue  \\\n",
       "33               0      Venue Category_Breakfast Spot   \n",
       "34               0                Venue Category_Café   \n",
       "35               0      Venue Category_Breakfast Spot   \n",
       "36               0         Venue Category_Coffee Shop   \n",
       "37               0  Venue Category_Light Rail Station   \n",
       "\n",
       "                2nd Most Common Venue              3rd Most Common Venue  \\\n",
       "33           Venue Category_Nightclub                Venue Category_Café   \n",
       "34  Venue Category_Mexican Restaurant                 Venue Category_Bar   \n",
       "35           Venue Category_Gift Shop           Venue Category_Bookstore   \n",
       "36                Venue Category_Café  Venue Category_Italian Restaurant   \n",
       "37         Venue Category_Yoga Studio              Venue Category_Garden   \n",
       "\n",
       "          4th Most Common Venue                5th Most Common Venue  \\\n",
       "33   Venue Category_Coffee Shop  Venue Category_Caribbean Restaurant   \n",
       "34   Venue Category_Flea Market   Venue Category_Arts & Crafts Store   \n",
       "35  Venue Category_Dessert Shop         Venue Category_Movie Theater   \n",
       "36   Venue Category_Pizza Place      Venue Category_Sushi Restaurant   \n",
       "37   Venue Category_Pizza Place            Venue Category_Restaurant   \n",
       "\n",
       "               6th Most Common Venue                  7th Most Common Venue  \\\n",
       "33            Venue Category_Stadium  Venue Category_Furniture / Home Store   \n",
       "34        Venue Category_Music Venue                   Venue Category_Diner   \n",
       "35         Venue Category_Restaurant                    Venue Category_Bank   \n",
       "36  Venue Category_Food & Drink Shop                     Venue Category_Pub   \n",
       "37      Venue Category_Burrito Place                 Venue Category_Brewery   \n",
       "\n",
       "                       8th Most Common Venue  \\\n",
       "33         Venue Category_Italian Restaurant   \n",
       "34  Venue Category_Cajun / Creole Restaurant   \n",
       "35                    Venue Category_Dog Run   \n",
       "36        Venue Category_Indie Movie Theater   \n",
       "37                 Venue Category_Skate Park   \n",
       "\n",
       "                       9th Most Common Venue  \\\n",
       "33               Venue Category_Climbing Gym   \n",
       "34             Venue Category_Sandwich Place   \n",
       "35         Venue Category_Italian Restaurant   \n",
       "36  Venue Category_Latin American Restaurant   \n",
       "37                 Venue Category_Smoke Shop   \n",
       "\n",
       "                 10th Most Common Venue  \n",
       "33            Venue Category_Restaurant  \n",
       "34  Venue Category_Fast Food Restaurant  \n",
       "35                   Venue Category_Bar  \n",
       "36          Venue Category_Dessert Shop  \n",
       "37        Venue Category_Farmers Market  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "toronto_merged = df\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "toronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "\n",
    "toronto_merged.tail() # check the last columns!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "clusters sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster Labels\n",
       "0    33\n",
       "1     1\n",
       "2     1\n",
       "3     2\n",
       "4     1\n",
       "Name: index, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.groupby('Cluster Labels').count()['index']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's visualize the resulting clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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                var html_faaccdefa7024e058d008d6e2aa15b8c = $('<div id="html_faaccdefa7024e058d008d6e2aa15b8c" style="width: 100.0%; height: 100.0%;">High Park, The Junction South Cluster 0</div>')[0];
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            var circle_marker_7422c361a6b048aaa3597e3402d284f1 = L.circleMarker(
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                {
  "bubblingMouseEvents": true,
  "color": "#ff0000",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#ff0000",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
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            circle_marker_7422c361a6b048aaa3597e3402d284f1.bindPopup(popup_12abbacf5088432e90ea10d6bf0b90ff);

            
        
    
            var circle_marker_3a6815a42f684e7192dba3d3f290fb76 = L.circleMarker(
                [43.6515706,-79.4844499],
                {
  "bubblingMouseEvents": true,
  "color": "#ff0000",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#ff0000",
  "fillOpacity": 0.7,
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  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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            var popup_0dc94ba0e33b4185b68387dee64efa94 = L.popup({maxWidth: '300'});

            
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                {
  "bubblingMouseEvents": true,
  "color": "#ff0000",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#ff0000",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
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                ).addTo(map_b1f6499a232548c7a1152ff0e5ad23c4);
            
    
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                var html_b094fad18cf94e8a972e60cbf4ebea07 = $('<div id="html_b094fad18cf94e8a972e60cbf4ebea07" style="width: 100.0%; height: 100.0%;">Business Reply Mail Processing Centre 969 Eastern Cluster 0</div>')[0];
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f1eaae44208>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map\n",
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Neighborhood'], toronto_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Examine Clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you can examine each cluster and determine the discriminating venue categories that distinguish each cluster. Based on the defining categories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</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",
       "      <th>0</th>\n",
       "      <td>M4E</td>\n",
       "      <td>-79.293031</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Health Food Store</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Pub</td>\n",
       "      <td>Venue Category_Neighborhood</td>\n",
       "      <td>Venue Category_Coworking Space</td>\n",
       "      <td>Venue Category_Cosmetics Shop</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "      <td>Venue Category_Falafel Restaurant</td>\n",
       "      <td>Venue Category_Event Space</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4K</td>\n",
       "      <td>-79.352188</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Greek Restaurant</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Ice Cream Shop</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Yoga Studio</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_Bookstore</td>\n",
       "      <td>Venue Category_Brewery</td>\n",
       "      <td>Venue Category_Bubble Tea Shop</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M4L</td>\n",
       "      <td>-79.315572</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Park</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "      <td>Venue Category_Ice Cream Shop</td>\n",
       "      <td>Venue Category_Burger Joint</td>\n",
       "      <td>Venue Category_Liquor Store</td>\n",
       "      <td>Venue Category_Burrito Place</td>\n",
       "      <td>Venue Category_Fish &amp; Chips Shop</td>\n",
       "      <td>Venue Category_Sandwich Place</td>\n",
       "      <td>Venue Category_Steakhouse</td>\n",
       "      <td>Venue Category_Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4M</td>\n",
       "      <td>-79.340923</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Gastropub</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_American Restaurant</td>\n",
       "      <td>Venue Category_Yoga Studio</td>\n",
       "      <td>Venue Category_Park</td>\n",
       "      <td>Venue Category_Brewery</td>\n",
       "      <td>Venue Category_Seafood Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M4P</td>\n",
       "      <td>-79.390197</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Burger Joint</td>\n",
       "      <td>Venue Category_Sandwich Place</td>\n",
       "      <td>Venue Category_Breakfast Spot</td>\n",
       "      <td>Venue Category_Gym</td>\n",
       "      <td>Venue Category_Park</td>\n",
       "      <td>Venue Category_Hotel</td>\n",
       "      <td>Venue Category_Food &amp; Drink Shop</td>\n",
       "      <td>Venue Category_Yoga Studio</td>\n",
       "      <td>Venue Category_Donut Shop</td>\n",
       "      <td>Venue Category_Dumpling Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M4R</td>\n",
       "      <td>-79.405678</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Clothing Store</td>\n",
       "      <td>Venue Category_Sporting Goods Shop</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Yoga Studio</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "      <td>Venue Category_Mexican Restaurant</td>\n",
       "      <td>Venue Category_Diner</td>\n",
       "      <td>Venue Category_Dessert Shop</td>\n",
       "      <td>Venue Category_Cosmetics Shop</td>\n",
       "      <td>Venue Category_Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M4S</td>\n",
       "      <td>-79.388790</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Dessert Shop</td>\n",
       "      <td>Venue Category_Sandwich Place</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Thai Restaurant</td>\n",
       "      <td>Venue Category_Sushi Restaurant</td>\n",
       "      <td>Venue Category_Flower Shop</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M4V</td>\n",
       "      <td>-79.400049</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Pub</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_American Restaurant</td>\n",
       "      <td>Venue Category_Convenience Store</td>\n",
       "      <td>Venue Category_Medical Center</td>\n",
       "      <td>Venue Category_Sports Bar</td>\n",
       "      <td>Venue Category_Bagel Shop</td>\n",
       "      <td>Venue Category_Supermarket</td>\n",
       "      <td>Venue Category_Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M4X</td>\n",
       "      <td>-79.367675</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Pub</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Beer Store</td>\n",
       "      <td>Venue Category_Bank</td>\n",
       "      <td>Venue Category_Diner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M4Y</td>\n",
       "      <td>-79.383160</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Japanese Restaurant</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Sushi Restaurant</td>\n",
       "      <td>Venue Category_Gay Bar</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Burger Joint</td>\n",
       "      <td>Venue Category_Pub</td>\n",
       "      <td>Venue Category_Bubble Tea Shop</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Men's Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M5A</td>\n",
       "      <td>-79.360636</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Pub</td>\n",
       "      <td>Venue Category_Park</td>\n",
       "      <td>Venue Category_Mexican Restaurant</td>\n",
       "      <td>Venue Category_Breakfast Spot</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Theater</td>\n",
       "      <td>Venue Category_Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M5B</td>\n",
       "      <td>-79.378937</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Clothing Store</td>\n",
       "      <td>Venue Category_Cosmetics Shop</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Middle Eastern Restaurant</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Tea Room</td>\n",
       "      <td>Venue Category_Bar</td>\n",
       "      <td>Venue Category_Ramen Restaurant</td>\n",
       "      <td>Venue Category_Bubble Tea Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M5C</td>\n",
       "      <td>-79.375418</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Hotel</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Breakfast Spot</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Cosmetics Shop</td>\n",
       "      <td>Venue Category_Clothing Store</td>\n",
       "      <td>Venue Category_Gastropub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>M5E</td>\n",
       "      <td>-79.373306</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Cocktail Bar</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Steakhouse</td>\n",
       "      <td>Venue Category_Pub</td>\n",
       "      <td>Venue Category_Cheese Shop</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Seafood Restaurant</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M5G</td>\n",
       "      <td>-79.387383</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Sandwich Place</td>\n",
       "      <td>Venue Category_Burger Joint</td>\n",
       "      <td>Venue Category_Bar</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Bubble Tea Shop</td>\n",
       "      <td>Venue Category_Ice Cream Shop</td>\n",
       "      <td>Venue Category_Indian Restaurant</td>\n",
       "      <td>Venue Category_Japanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M5H</td>\n",
       "      <td>-79.384568</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Steakhouse</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Thai Restaurant</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Gym</td>\n",
       "      <td>Venue Category_Burger Joint</td>\n",
       "      <td>Venue Category_Hotel</td>\n",
       "      <td>Venue Category_American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>M5J</td>\n",
       "      <td>-79.381752</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Aquarium</td>\n",
       "      <td>Venue Category_Hotel</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Fried Chicken Joint</td>\n",
       "      <td>Venue Category_Brewery</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_Scenic Lookout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>M5K</td>\n",
       "      <td>-79.381576</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Hotel</td>\n",
       "      <td>Venue Category_American Restaurant</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Seafood Restaurant</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Gastropub</td>\n",
       "      <td>Venue Category_Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>M5L</td>\n",
       "      <td>-79.379817</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Hotel</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_American Restaurant</td>\n",
       "      <td>Venue Category_Gastropub</td>\n",
       "      <td>Venue Category_Gym</td>\n",
       "      <td>Venue Category_Deli / Bodega</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>M5R</td>\n",
       "      <td>-79.405678</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Sandwich Place</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_BBQ Joint</td>\n",
       "      <td>Venue Category_Indian Restaurant</td>\n",
       "      <td>Venue Category_Jewish Restaurant</td>\n",
       "      <td>Venue Category_Flower Shop</td>\n",
       "      <td>Venue Category_Liquor Store</td>\n",
       "      <td>Venue Category_Cosmetics Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>M5S</td>\n",
       "      <td>-79.400049</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Japanese Restaurant</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Bar</td>\n",
       "      <td>Venue Category_Bookstore</td>\n",
       "      <td>Venue Category_Beer Bar</td>\n",
       "      <td>Venue Category_Beer Store</td>\n",
       "      <td>Venue Category_Noodle House</td>\n",
       "      <td>Venue Category_Sandwich Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>M5T</td>\n",
       "      <td>-79.400049</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Bar</td>\n",
       "      <td>Venue Category_Vietnamese Restaurant</td>\n",
       "      <td>Venue Category_Vegetarian / Vegan Restaurant</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Mexican Restaurant</td>\n",
       "      <td>Venue Category_Chinese Restaurant</td>\n",
       "      <td>Venue Category_Cocktail Bar</td>\n",
       "      <td>Venue Category_Burger Joint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>M5V</td>\n",
       "      <td>-79.394420</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Airport Terminal</td>\n",
       "      <td>Venue Category_Airport Service</td>\n",
       "      <td>Venue Category_Airport Lounge</td>\n",
       "      <td>Venue Category_Boat or Ferry</td>\n",
       "      <td>Venue Category_Airport Gate</td>\n",
       "      <td>Venue Category_Sculpture Garden</td>\n",
       "      <td>Venue Category_Plane</td>\n",
       "      <td>Venue Category_Airport Food Court</td>\n",
       "      <td>Venue Category_Airport</td>\n",
       "      <td>Venue Category_Harbor / Marina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>M5W</td>\n",
       "      <td>-79.374846</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "      <td>Venue Category_Seafood Restaurant</td>\n",
       "      <td>Venue Category_Hotel</td>\n",
       "      <td>Venue Category_Pub</td>\n",
       "      <td>Venue Category_Cocktail Bar</td>\n",
       "      <td>Venue Category_Art Gallery</td>\n",
       "      <td>Venue Category_Beer Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>M5X</td>\n",
       "      <td>-79.382280</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Hotel</td>\n",
       "      <td>Venue Category_Steakhouse</td>\n",
       "      <td>Venue Category_Gastropub</td>\n",
       "      <td>Venue Category_Bar</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Burger Joint</td>\n",
       "      <td>Venue Category_American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>M6G</td>\n",
       "      <td>-79.422564</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Grocery Store</td>\n",
       "      <td>Venue Category_Park</td>\n",
       "      <td>Venue Category_Convenience Store</td>\n",
       "      <td>Venue Category_Nightclub</td>\n",
       "      <td>Venue Category_Baby Store</td>\n",
       "      <td>Venue Category_Diner</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>M6H</td>\n",
       "      <td>-79.442259</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Discount Store</td>\n",
       "      <td>Venue Category_Pharmacy</td>\n",
       "      <td>Venue Category_Supermarket</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_Liquor Store</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "      <td>Venue Category_Middle Eastern Restaurant</td>\n",
       "      <td>Venue Category_Music Venue</td>\n",
       "      <td>Venue Category_Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>M6J</td>\n",
       "      <td>-79.419750</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Bar</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Asian Restaurant</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Cocktail Bar</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_New American Restaurant</td>\n",
       "      <td>Venue Category_Bakery</td>\n",
       "      <td>Venue Category_Men's Store</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>M6K</td>\n",
       "      <td>-79.428191</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Breakfast Spot</td>\n",
       "      <td>Venue Category_Nightclub</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Caribbean Restaurant</td>\n",
       "      <td>Venue Category_Stadium</td>\n",
       "      <td>Venue Category_Furniture / Home Store</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Climbing Gym</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>M6P</td>\n",
       "      <td>-79.464763</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Mexican Restaurant</td>\n",
       "      <td>Venue Category_Bar</td>\n",
       "      <td>Venue Category_Flea Market</td>\n",
       "      <td>Venue Category_Arts &amp; Crafts Store</td>\n",
       "      <td>Venue Category_Music Venue</td>\n",
       "      <td>Venue Category_Diner</td>\n",
       "      <td>Venue Category_Cajun / Creole Restaurant</td>\n",
       "      <td>Venue Category_Sandwich Place</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>M6R</td>\n",
       "      <td>-79.456325</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Breakfast Spot</td>\n",
       "      <td>Venue Category_Gift Shop</td>\n",
       "      <td>Venue Category_Bookstore</td>\n",
       "      <td>Venue Category_Dessert Shop</td>\n",
       "      <td>Venue Category_Movie Theater</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Bank</td>\n",
       "      <td>Venue Category_Dog Run</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>M6S</td>\n",
       "      <td>-79.484450</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Coffee Shop</td>\n",
       "      <td>Venue Category_Café</td>\n",
       "      <td>Venue Category_Italian Restaurant</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_Sushi Restaurant</td>\n",
       "      <td>Venue Category_Food &amp; Drink Shop</td>\n",
       "      <td>Venue Category_Pub</td>\n",
       "      <td>Venue Category_Indie Movie Theater</td>\n",
       "      <td>Venue Category_Latin American Restaurant</td>\n",
       "      <td>Venue Category_Dessert Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>M7Y</td>\n",
       "      <td>-79.321558</td>\n",
       "      <td>0</td>\n",
       "      <td>Venue Category_Light Rail Station</td>\n",
       "      <td>Venue Category_Yoga Studio</td>\n",
       "      <td>Venue Category_Garden</td>\n",
       "      <td>Venue Category_Pizza Place</td>\n",
       "      <td>Venue Category_Restaurant</td>\n",
       "      <td>Venue Category_Burrito Place</td>\n",
       "      <td>Venue Category_Brewery</td>\n",
       "      <td>Venue Category_Skate Park</td>\n",
       "      <td>Venue Category_Smoke Shop</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode  Longitude  Cluster Labels               1st Most Common Venue  \\\n",
       "0         M4E -79.293031               0    Venue Category_Health Food Store   \n",
       "1         M4K -79.352188               0     Venue Category_Greek Restaurant   \n",
       "2         M4L -79.315572               0                 Venue Category_Park   \n",
       "3         M4M -79.340923               0                 Venue Category_Café   \n",
       "5         M4P -79.390197               0         Venue Category_Burger Joint   \n",
       "6         M4R -79.405678               0       Venue Category_Clothing Store   \n",
       "7         M4S -79.388790               0         Venue Category_Dessert Shop   \n",
       "9         M4V -79.400049               0          Venue Category_Coffee Shop   \n",
       "11        M4X -79.367675               0          Venue Category_Coffee Shop   \n",
       "12        M4Y -79.383160               0  Venue Category_Japanese Restaurant   \n",
       "13        M5A -79.360636               0          Venue Category_Coffee Shop   \n",
       "14        M5B -79.378937               0          Venue Category_Coffee Shop   \n",
       "15        M5C -79.375418               0          Venue Category_Coffee Shop   \n",
       "16        M5E -79.373306               0          Venue Category_Coffee Shop   \n",
       "17        M5G -79.387383               0          Venue Category_Coffee Shop   \n",
       "18        M5H -79.384568               0          Venue Category_Coffee Shop   \n",
       "19        M5J -79.381752               0          Venue Category_Coffee Shop   \n",
       "20        M5K -79.381576               0          Venue Category_Coffee Shop   \n",
       "21        M5L -79.379817               0          Venue Category_Coffee Shop   \n",
       "24        M5R -79.405678               0          Venue Category_Coffee Shop   \n",
       "25        M5S -79.400049               0                 Venue Category_Café   \n",
       "26        M5T -79.400049               0                 Venue Category_Café   \n",
       "27        M5V -79.394420               0     Venue Category_Airport Terminal   \n",
       "28        M5W -79.374846               0          Venue Category_Coffee Shop   \n",
       "29        M5X -79.382280               0          Venue Category_Coffee Shop   \n",
       "30        M6G -79.422564               0                 Venue Category_Café   \n",
       "31        M6H -79.442259               0               Venue Category_Bakery   \n",
       "32        M6J -79.419750               0                  Venue Category_Bar   \n",
       "33        M6K -79.428191               0       Venue Category_Breakfast Spot   \n",
       "34        M6P -79.464763               0                 Venue Category_Café   \n",
       "35        M6R -79.456325               0       Venue Category_Breakfast Spot   \n",
       "36        M6S -79.484450               0          Venue Category_Coffee Shop   \n",
       "37        M7Y -79.321558               0   Venue Category_Light Rail Station   \n",
       "\n",
       "                  2nd Most Common Venue                 3rd Most Common Venue  \\\n",
       "0            Venue Category_Coffee Shop                    Venue Category_Pub   \n",
       "1            Venue Category_Coffee Shop         Venue Category_Ice Cream Shop   \n",
       "2   Venue Category_Fast Food Restaurant         Venue Category_Ice Cream Shop   \n",
       "3            Venue Category_Coffee Shop              Venue Category_Gastropub   \n",
       "5         Venue Category_Sandwich Place         Venue Category_Breakfast Spot   \n",
       "6    Venue Category_Sporting Goods Shop            Venue Category_Coffee Shop   \n",
       "7         Venue Category_Sandwich Place            Venue Category_Pizza Place   \n",
       "9                    Venue Category_Pub            Venue Category_Pizza Place   \n",
       "11            Venue Category_Restaurant                 Venue Category_Bakery   \n",
       "12           Venue Category_Coffee Shop       Venue Category_Sushi Restaurant   \n",
       "13                  Venue Category_Café                 Venue Category_Bakery   \n",
       "14        Venue Category_Clothing Store         Venue Category_Cosmetics Shop   \n",
       "15            Venue Category_Restaurant                  Venue Category_Hotel   \n",
       "16          Venue Category_Cocktail Bar                 Venue Category_Bakery   \n",
       "17    Venue Category_Italian Restaurant         Venue Category_Sandwich Place   \n",
       "18            Venue Category_Steakhouse                   Venue Category_Café   \n",
       "19              Venue Category_Aquarium                  Venue Category_Hotel   \n",
       "20                  Venue Category_Café                  Venue Category_Hotel   \n",
       "21                  Venue Category_Café                  Venue Category_Hotel   \n",
       "24                  Venue Category_Café         Venue Category_Sandwich Place   \n",
       "25            Venue Category_Restaurant    Venue Category_Japanese Restaurant   \n",
       "26                   Venue Category_Bar  Venue Category_Vietnamese Restaurant   \n",
       "27       Venue Category_Airport Service         Venue Category_Airport Lounge   \n",
       "28            Venue Category_Restaurant                   Venue Category_Café   \n",
       "29                  Venue Category_Café                  Venue Category_Hotel   \n",
       "30         Venue Category_Grocery Store                   Venue Category_Park   \n",
       "31        Venue Category_Discount Store               Venue Category_Pharmacy   \n",
       "32           Venue Category_Coffee Shop       Venue Category_Asian Restaurant   \n",
       "33             Venue Category_Nightclub                   Venue Category_Café   \n",
       "34    Venue Category_Mexican Restaurant                    Venue Category_Bar   \n",
       "35             Venue Category_Gift Shop              Venue Category_Bookstore   \n",
       "36                  Venue Category_Café     Venue Category_Italian Restaurant   \n",
       "37           Venue Category_Yoga Studio                 Venue Category_Garden   \n",
       "\n",
       "                           4th Most Common Venue  \\\n",
       "0                    Venue Category_Neighborhood   \n",
       "1              Venue Category_Italian Restaurant   \n",
       "2                    Venue Category_Burger Joint   \n",
       "3              Venue Category_Italian Restaurant   \n",
       "5                             Venue Category_Gym   \n",
       "6                     Venue Category_Yoga Studio   \n",
       "7              Venue Category_Italian Restaurant   \n",
       "9             Venue Category_American Restaurant   \n",
       "11                           Venue Category_Café   \n",
       "12                        Venue Category_Gay Bar   \n",
       "13                            Venue Category_Pub   \n",
       "14                           Venue Category_Café   \n",
       "15                           Venue Category_Café   \n",
       "16                     Venue Category_Steakhouse   \n",
       "17                   Venue Category_Burger Joint   \n",
       "18                Venue Category_Thai Restaurant   \n",
       "19             Venue Category_Italian Restaurant   \n",
       "20            Venue Category_American Restaurant   \n",
       "21                     Venue Category_Restaurant   \n",
       "24                    Venue Category_Pizza Place   \n",
       "25                         Venue Category_Bakery   \n",
       "26  Venue Category_Vegetarian / Vegan Restaurant   \n",
       "27                  Venue Category_Boat or Ferry   \n",
       "28           Venue Category_Fast Food Restaurant   \n",
       "29                     Venue Category_Steakhouse   \n",
       "30              Venue Category_Convenience Store   \n",
       "31                    Venue Category_Supermarket   \n",
       "32                     Venue Category_Restaurant   \n",
       "33                    Venue Category_Coffee Shop   \n",
       "34                    Venue Category_Flea Market   \n",
       "35                   Venue Category_Dessert Shop   \n",
       "36                    Venue Category_Pizza Place   \n",
       "37                    Venue Category_Pizza Place   \n",
       "\n",
       "                       5th Most Common Venue  \\\n",
       "0             Venue Category_Coworking Space   \n",
       "1                 Venue Category_Yoga Studio   \n",
       "2                Venue Category_Liquor Store   \n",
       "3                      Venue Category_Bakery   \n",
       "5                        Venue Category_Park   \n",
       "6        Venue Category_Fast Food Restaurant   \n",
       "7                        Venue Category_Café   \n",
       "9           Venue Category_Convenience Store   \n",
       "11                        Venue Category_Pub   \n",
       "12                 Venue Category_Restaurant   \n",
       "13                       Venue Category_Park   \n",
       "14  Venue Category_Middle Eastern Restaurant   \n",
       "15             Venue Category_Breakfast Spot   \n",
       "16                        Venue Category_Pub   \n",
       "17                        Venue Category_Bar   \n",
       "18                     Venue Category_Bakery   \n",
       "19                       Venue Category_Café   \n",
       "20                 Venue Category_Restaurant   \n",
       "21        Venue Category_American Restaurant   \n",
       "24                  Venue Category_BBQ Joint   \n",
       "25                        Venue Category_Bar   \n",
       "26                     Venue Category_Bakery   \n",
       "27               Venue Category_Airport Gate   \n",
       "28         Venue Category_Seafood Restaurant   \n",
       "29                  Venue Category_Gastropub   \n",
       "30                  Venue Category_Nightclub   \n",
       "31                Venue Category_Pizza Place   \n",
       "32               Venue Category_Cocktail Bar   \n",
       "33       Venue Category_Caribbean Restaurant   \n",
       "34        Venue Category_Arts & Crafts Store   \n",
       "35              Venue Category_Movie Theater   \n",
       "36           Venue Category_Sushi Restaurant   \n",
       "37                 Venue Category_Restaurant   \n",
       "\n",
       "                 6th Most Common Venue  \\\n",
       "0        Venue Category_Cosmetics Shop   \n",
       "1           Venue Category_Pizza Place   \n",
       "2         Venue Category_Burrito Place   \n",
       "3   Venue Category_American Restaurant   \n",
       "5                 Venue Category_Hotel   \n",
       "6    Venue Category_Mexican Restaurant   \n",
       "7           Venue Category_Coffee Shop   \n",
       "9        Venue Category_Medical Center   \n",
       "11          Venue Category_Pizza Place   \n",
       "12         Venue Category_Burger Joint   \n",
       "13   Venue Category_Mexican Restaurant   \n",
       "14           Venue Category_Restaurant   \n",
       "15               Venue Category_Bakery   \n",
       "16          Venue Category_Cheese Shop   \n",
       "17                 Venue Category_Café   \n",
       "18           Venue Category_Restaurant   \n",
       "19  Venue Category_Fried Chicken Joint   \n",
       "20   Venue Category_Seafood Restaurant   \n",
       "21            Venue Category_Gastropub   \n",
       "24    Venue Category_Indian Restaurant   \n",
       "25            Venue Category_Bookstore   \n",
       "26          Venue Category_Coffee Shop   \n",
       "27     Venue Category_Sculpture Garden   \n",
       "28                Venue Category_Hotel   \n",
       "29                  Venue Category_Bar   \n",
       "30           Venue Category_Baby Store   \n",
       "31         Venue Category_Liquor Store   \n",
       "32          Venue Category_Pizza Place   \n",
       "33              Venue Category_Stadium   \n",
       "34          Venue Category_Music Venue   \n",
       "35           Venue Category_Restaurant   \n",
       "36    Venue Category_Food & Drink Shop   \n",
       "37        Venue Category_Burrito Place   \n",
       "\n",
       "                     7th Most Common Venue  \\\n",
       "0      Venue Category_Fast Food Restaurant   \n",
       "1                 Venue Category_Bookstore   \n",
       "2         Venue Category_Fish & Chips Shop   \n",
       "3               Venue Category_Yoga Studio   \n",
       "5         Venue Category_Food & Drink Shop   \n",
       "6                     Venue Category_Diner   \n",
       "7           Venue Category_Thai Restaurant   \n",
       "9                Venue Category_Sports Bar   \n",
       "11       Venue Category_Italian Restaurant   \n",
       "12                      Venue Category_Pub   \n",
       "13           Venue Category_Breakfast Spot   \n",
       "14                 Venue Category_Tea Room   \n",
       "15       Venue Category_Italian Restaurant   \n",
       "16           Venue Category_Farmers Market   \n",
       "17          Venue Category_Bubble Tea Shop   \n",
       "18                      Venue Category_Gym   \n",
       "19                  Venue Category_Brewery   \n",
       "20                   Venue Category_Bakery   \n",
       "21                      Venue Category_Gym   \n",
       "24        Venue Category_Jewish Restaurant   \n",
       "25                 Venue Category_Beer Bar   \n",
       "26       Venue Category_Mexican Restaurant   \n",
       "27                    Venue Category_Plane   \n",
       "28                      Venue Category_Pub   \n",
       "29                   Venue Category_Bakery   \n",
       "30                    Venue Category_Diner   \n",
       "31     Venue Category_Fast Food Restaurant   \n",
       "32  Venue Category_New American Restaurant   \n",
       "33   Venue Category_Furniture / Home Store   \n",
       "34                    Venue Category_Diner   \n",
       "35                     Venue Category_Bank   \n",
       "36                      Venue Category_Pub   \n",
       "37                  Venue Category_Brewery   \n",
       "\n",
       "                       8th Most Common Venue  \\\n",
       "0              Venue Category_Farmers Market   \n",
       "1                     Venue Category_Brewery   \n",
       "2              Venue Category_Sandwich Place   \n",
       "3                        Venue Category_Park   \n",
       "5                 Venue Category_Yoga Studio   \n",
       "6                Venue Category_Dessert Shop   \n",
       "7            Venue Category_Sushi Restaurant   \n",
       "9                  Venue Category_Bagel Shop   \n",
       "11                 Venue Category_Beer Store   \n",
       "12            Venue Category_Bubble Tea Shop   \n",
       "13                 Venue Category_Restaurant   \n",
       "14                        Venue Category_Bar   \n",
       "15             Venue Category_Cosmetics Shop   \n",
       "16                 Venue Category_Restaurant   \n",
       "17             Venue Category_Ice Cream Shop   \n",
       "18               Venue Category_Burger Joint   \n",
       "19                     Venue Category_Bakery   \n",
       "20         Venue Category_Italian Restaurant   \n",
       "21              Venue Category_Deli / Bodega   \n",
       "24                Venue Category_Flower Shop   \n",
       "25                 Venue Category_Beer Store   \n",
       "26         Venue Category_Chinese Restaurant   \n",
       "27         Venue Category_Airport Food Court   \n",
       "28               Venue Category_Cocktail Bar   \n",
       "29                 Venue Category_Restaurant   \n",
       "30                 Venue Category_Restaurant   \n",
       "31  Venue Category_Middle Eastern Restaurant   \n",
       "32                     Venue Category_Bakery   \n",
       "33         Venue Category_Italian Restaurant   \n",
       "34  Venue Category_Cajun / Creole Restaurant   \n",
       "35                    Venue Category_Dog Run   \n",
       "36        Venue Category_Indie Movie Theater   \n",
       "37                 Venue Category_Skate Park   \n",
       "\n",
       "                       9th Most Common Venue  \\\n",
       "0          Venue Category_Falafel Restaurant   \n",
       "1             Venue Category_Bubble Tea Shop   \n",
       "2                  Venue Category_Steakhouse   \n",
       "3                     Venue Category_Brewery   \n",
       "5                  Venue Category_Donut Shop   \n",
       "6              Venue Category_Cosmetics Shop   \n",
       "7                 Venue Category_Flower Shop   \n",
       "9                 Venue Category_Supermarket   \n",
       "11                       Venue Category_Bank   \n",
       "12                       Venue Category_Café   \n",
       "13                    Venue Category_Theater   \n",
       "14           Venue Category_Ramen Restaurant   \n",
       "15             Venue Category_Clothing Store   \n",
       "16         Venue Category_Seafood Restaurant   \n",
       "17          Venue Category_Indian Restaurant   \n",
       "18                      Venue Category_Hotel   \n",
       "19                Venue Category_Pizza Place   \n",
       "20                  Venue Category_Gastropub   \n",
       "21         Venue Category_Italian Restaurant   \n",
       "24               Venue Category_Liquor Store   \n",
       "25               Venue Category_Noodle House   \n",
       "26               Venue Category_Cocktail Bar   \n",
       "27                    Venue Category_Airport   \n",
       "28                Venue Category_Art Gallery   \n",
       "29               Venue Category_Burger Joint   \n",
       "30         Venue Category_Italian Restaurant   \n",
       "31                Venue Category_Music Venue   \n",
       "32                Venue Category_Men's Store   \n",
       "33               Venue Category_Climbing Gym   \n",
       "34             Venue Category_Sandwich Place   \n",
       "35         Venue Category_Italian Restaurant   \n",
       "36  Venue Category_Latin American Restaurant   \n",
       "37                 Venue Category_Smoke Shop   \n",
       "\n",
       "                 10th Most Common Venue  \n",
       "0            Venue Category_Event Space  \n",
       "1                   Venue Category_Café  \n",
       "2       Venue Category_Sushi Restaurant  \n",
       "3     Venue Category_Seafood Restaurant  \n",
       "5    Venue Category_Dumpling Restaurant  \n",
       "6                   Venue Category_Park  \n",
       "7         Venue Category_Farmers Market  \n",
       "9       Venue Category_Sushi Restaurant  \n",
       "11                 Venue Category_Diner  \n",
       "12           Venue Category_Men's Store  \n",
       "13                  Venue Category_Bank  \n",
       "14       Venue Category_Bubble Tea Shop  \n",
       "15             Venue Category_Gastropub  \n",
       "16                  Venue Category_Café  \n",
       "17   Venue Category_Japanese Restaurant  \n",
       "18   Venue Category_American Restaurant  \n",
       "19        Venue Category_Scenic Lookout  \n",
       "20         Venue Category_Deli / Bodega  \n",
       "21                Venue Category_Bakery  \n",
       "24        Venue Category_Cosmetics Shop  \n",
       "25        Venue Category_Sandwich Place  \n",
       "26          Venue Category_Burger Joint  \n",
       "27       Venue Category_Harbor / Marina  \n",
       "28              Venue Category_Beer Bar  \n",
       "29   Venue Category_American Restaurant  \n",
       "30           Venue Category_Coffee Shop  \n",
       "31                  Venue Category_Park  \n",
       "32                  Venue Category_Café  \n",
       "33            Venue Category_Restaurant  \n",
       "34  Venue Category_Fast Food Restaurant  \n",
       "35                   Venue Category_Bar  \n",
       "36          Venue Category_Dessert Shop  \n",
       "37        Venue Category_Farmers Market  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Labels'] == 0, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>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",
       "      <th>8</th>\n",
       "      <td>M4T</td>\n",
       "      <td>-79.38316</td>\n",
       "      <td>1</td>\n",
       "      <td>Venue Category_Playground</td>\n",
       "      <td>Venue Category_Historic Site</td>\n",
       "      <td>Venue Category_Filipino Restaurant</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "      <td>Venue Category_Falafel Restaurant</td>\n",
       "      <td>Venue Category_Event Space</td>\n",
       "      <td>Venue Category_Ethiopian Restaurant</td>\n",
       "      <td>Venue Category_Electronics Store</td>\n",
       "      <td>Venue Category_Eastern European Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode  Longitude  Cluster Labels      1st Most Common Venue  \\\n",
       "8        M4T  -79.38316               1  Venue Category_Playground   \n",
       "\n",
       "          2nd Most Common Venue               3rd Most Common Venue  \\\n",
       "8  Venue Category_Historic Site  Venue Category_Filipino Restaurant   \n",
       "\n",
       "                 4th Most Common Venue          5th Most Common Venue  \\\n",
       "8  Venue Category_Fast Food Restaurant  Venue Category_Farmers Market   \n",
       "\n",
       "               6th Most Common Venue       7th Most Common Venue  \\\n",
       "8  Venue Category_Falafel Restaurant  Venue Category_Event Space   \n",
       "\n",
       "                 8th Most Common Venue             9th Most Common Venue  \\\n",
       "8  Venue Category_Ethiopian Restaurant  Venue Category_Electronics Store   \n",
       "\n",
       "                       10th Most Common Venue  \n",
       "8  Venue Category_Eastern European Restaurant  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Labels'] == 1, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>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",
       "      <th>22</th>\n",
       "      <td>M5N</td>\n",
       "      <td>-79.416936</td>\n",
       "      <td>2</td>\n",
       "      <td>Venue Category_Garden</td>\n",
       "      <td>Venue Category_Yoga Studio</td>\n",
       "      <td>Venue Category_Fish Market</td>\n",
       "      <td>Venue Category_Filipino Restaurant</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "      <td>Venue Category_Falafel Restaurant</td>\n",
       "      <td>Venue Category_Event Space</td>\n",
       "      <td>Venue Category_Ethiopian Restaurant</td>\n",
       "      <td>Venue Category_Electronics Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode  Longitude  Cluster Labels  1st Most Common Venue  \\\n",
       "22        M5N -79.416936               2  Venue Category_Garden   \n",
       "\n",
       "         2nd Most Common Venue       3rd Most Common Venue  \\\n",
       "22  Venue Category_Yoga Studio  Venue Category_Fish Market   \n",
       "\n",
       "                 4th Most Common Venue                5th Most Common Venue  \\\n",
       "22  Venue Category_Filipino Restaurant  Venue Category_Fast Food Restaurant   \n",
       "\n",
       "            6th Most Common Venue              7th Most Common Venue  \\\n",
       "22  Venue Category_Farmers Market  Venue Category_Falafel Restaurant   \n",
       "\n",
       "         8th Most Common Venue                9th Most Common Venue  \\\n",
       "22  Venue Category_Event Space  Venue Category_Ethiopian Restaurant   \n",
       "\n",
       "              10th Most Common Venue  \n",
       "22  Venue Category_Electronics Store  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Labels'] == 2, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>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",
       "      <th>10</th>\n",
       "      <td>M4W</td>\n",
       "      <td>-79.377529</td>\n",
       "      <td>3</td>\n",
       "      <td>Venue Category_Park</td>\n",
       "      <td>Venue Category_Playground</td>\n",
       "      <td>Venue Category_Trail</td>\n",
       "      <td>Venue Category_Diner</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "      <td>Venue Category_Falafel Restaurant</td>\n",
       "      <td>Venue Category_Event Space</td>\n",
       "      <td>Venue Category_Ethiopian Restaurant</td>\n",
       "      <td>Venue Category_Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>M5P</td>\n",
       "      <td>-79.411307</td>\n",
       "      <td>3</td>\n",
       "      <td>Venue Category_Trail</td>\n",
       "      <td>Venue Category_Bus Line</td>\n",
       "      <td>Venue Category_Park</td>\n",
       "      <td>Venue Category_Jewelry Store</td>\n",
       "      <td>Venue Category_Sushi Restaurant</td>\n",
       "      <td>Venue Category_Electronics Store</td>\n",
       "      <td>Venue Category_Donut Shop</td>\n",
       "      <td>Venue Category_Dumpling Restaurant</td>\n",
       "      <td>Venue Category_Eastern European Restaurant</td>\n",
       "      <td>Venue Category_Yoga Studio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode  Longitude  Cluster Labels 1st Most Common Venue  \\\n",
       "10        M4W -79.377529               3   Venue Category_Park   \n",
       "23        M5P -79.411307               3  Venue Category_Trail   \n",
       "\n",
       "        2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "10  Venue Category_Playground  Venue Category_Trail   \n",
       "23    Venue Category_Bus Line   Venue Category_Park   \n",
       "\n",
       "           4th Most Common Venue                5th Most Common Venue  \\\n",
       "10          Venue Category_Diner  Venue Category_Fast Food Restaurant   \n",
       "23  Venue Category_Jewelry Store      Venue Category_Sushi Restaurant   \n",
       "\n",
       "               6th Most Common Venue              7th Most Common Venue  \\\n",
       "10     Venue Category_Farmers Market  Venue Category_Falafel Restaurant   \n",
       "23  Venue Category_Electronics Store          Venue Category_Donut Shop   \n",
       "\n",
       "                 8th Most Common Venue  \\\n",
       "10          Venue Category_Event Space   \n",
       "23  Venue Category_Dumpling Restaurant   \n",
       "\n",
       "                         9th Most Common Venue  \\\n",
       "10         Venue Category_Ethiopian Restaurant   \n",
       "23  Venue Category_Eastern European Restaurant   \n",
       "\n",
       "              10th Most Common Venue  \n",
       "10  Venue Category_Electronics Store  \n",
       "23        Venue Category_Yoga Studio  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Labels'] == 3, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>PostalCode</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",
       "      <th>4</th>\n",
       "      <td>M4N</td>\n",
       "      <td>-79.38879</td>\n",
       "      <td>4</td>\n",
       "      <td>Venue Category_Park</td>\n",
       "      <td>Venue Category_Swim School</td>\n",
       "      <td>Venue Category_Bus Line</td>\n",
       "      <td>Venue Category_Yoga Studio</td>\n",
       "      <td>Venue Category_Dog Run</td>\n",
       "      <td>Venue Category_Filipino Restaurant</td>\n",
       "      <td>Venue Category_Fast Food Restaurant</td>\n",
       "      <td>Venue Category_Farmers Market</td>\n",
       "      <td>Venue Category_Falafel Restaurant</td>\n",
       "      <td>Venue Category_Event Space</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode  Longitude  Cluster Labels 1st Most Common Venue  \\\n",
       "4        M4N  -79.38879               4   Venue Category_Park   \n",
       "\n",
       "        2nd Most Common Venue    3rd Most Common Venue  \\\n",
       "4  Venue Category_Swim School  Venue Category_Bus Line   \n",
       "\n",
       "        4th Most Common Venue   5th Most Common Venue  \\\n",
       "4  Venue Category_Yoga Studio  Venue Category_Dog Run   \n",
       "\n",
       "                6th Most Common Venue                7th Most Common Venue  \\\n",
       "4  Venue Category_Filipino Restaurant  Venue Category_Fast Food Restaurant   \n",
       "\n",
       "           8th Most Common Venue              9th Most Common Venue  \\\n",
       "4  Venue Category_Farmers Market  Venue Category_Falafel Restaurant   \n",
       "\n",
       "       10th Most Common Venue  \n",
       "4  Venue Category_Event Space  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Labels'] == 4, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]"
   ]
  }
 ],
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