Final to upload .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc'></a>\n",
    "<center><h1>The Battle of Neighborhood</font></h1>\n",
    "Segmenting and Clustering Neighborhoods of Toronto and Waterloo Ontario\n",
    "by Jahangir Nasimi</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "- [Introduction](#introduction)\n",
    "- [Data](#data)\n",
    "- [Methodology](#methodology)\n",
    "- [Results](#results)\n",
    "- [Discussion](#discussion)\n",
    "- [Conclusion](#conclusion)\n",
    "- [References](#References)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='introduction'></a>\n",
    "# Introduction\n",
    "This project aims at creating a tool that can be used to explore facilities in and around neighbourhoods in a way that allows residents to make informed decisions and select the most optimal location that best caters to their needs. \n",
    "\n",
    "An example of such need could be smaller cities and towns in various provinces of Canada. Canada has had a long and successful immigration policy that allows regulating the country’s population. From its onset in 1870, the influx of immigrants has been tailored to grow the population, settle the land, and provide labour and financial capital for the economy. However, in a democratic country, such as Canada, both the newcomers and the existing residents have a right to move around and select a location with the housing prices, good schools, medical facilities and job opportunities that suit their needs and financial means. \n",
    "\n",
    "This project would be highly beneficial for those who seek a systematic, algorithm-based approach for comparison and selection of the desired parameters, e.g. access to public or catholic schools, supermarkets, shopping malls, movie theaters, hospitals, or religious communities. This selection can be made using analytical tools rather than basic internet searches, reliance on real estate agencies or word of mouth that could be biased or misleading, whether intentionally or not. Studies such as [1] show that people need to think critically about claims and compare their options using a systematic concept because simply trusting the sources of information may not be a sufficient basis for making the best choice:\n",
    "•\tCompeting interests can result in misleading claims.\n",
    "•\tPersonal experiences or anecdotes alone are an unreliable basis for most claims.\n",
    "•\tOpinions of acquittances, real-estate agents or newspaper/social media publications are not solely a reliable basis for making informed-decisions.\n",
    "•\tEndorsements by community leaders or other respectable individuals do not guarantee that comparisons have been fair.\n",
    "\n",
    "\n",
    "Objective:\n",
    "The main objective of this project is to propose an approach for selection of an optimal community in a new city for someone looking to relocate. The individual using this proposed tool would be able to compile, sort and filter parameters of interest, for example:\n",
    "\n",
    "1.\tA list of houses/residencies that can be sorted in terms of housing prices in an ascending or descending order, or\n",
    "2.\tA list of restaurants that cater particular type of cuisine that can be sorted in terms of location, prices, rating and reviews.\n",
    "\n",
    "The problem for the Canadian province of Ontario is that most immigrants to the province make their home in the GTA, specifically 77 per cent, according to an August report from the Conference Board of Canada [2]. However, as their populations age and young adults move away, small cities and towns across Canada are increasingly looking to immigration as a way to rejuvenate their workforce and expand their tax base. But many struggle to find reliable data and make informed decision to move to smaller rural areas due to lack of data and unavailability of unbiased selection tools. The city of Waterloo, Ontario will be used in this project to demonstrate the proposed approach as opposed to Toronto.\n",
    "Specific Objectives:\n",
    "Specifically, this project looks at a community of Russian-speaking expatriates who are looking to re-settle in the Greater Toronto Area (GTA) or in other smaller neighbouring communities in the province of Ontario. This project will propose a tool that can be used to perform searches and comparisons between various neighbourhoods in order to determine a community with specific desired parameters, e.g., access to grocery stores and marketplaces where Russian-produced goods and food supplies are available. \n",
    "\n",
    "Additional Parameters:\n",
    "Additional parameters could include ease of access to children’s daycares and after-school activities where Russian language is taught. Another important factor could be availability of various medical facilities (e.g., Russian-speaking family doctors, dentists, phycologists and other medical specialists) for Russian-speaking patients and access to nursing homes where services in Russian language are available for seniors. Restaurants offering traditional Russian cuisine is another important factor that may sway a customer’s decision to relocate to a particular community.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='data'></a>\n",
    "# Data\n",
    "\n",
    "The data used in this project was acquired mostly from Wikipedia.com and Toronto.ca websites. In order to be used in the proposed approach it was formatted and restructured into a csv. file.\n",
    "The files used in the project can be found on github repository as well as referenced in [3]. The project data was transferred to a local drive and compiled to dataframes as can be seen from the project code.\n",
    "\n",
    "Based on the 2011 Canadian census data, most of Russian-native speakers in Toronto (GTA) reside in the geographical area of York. Per [4], there are more than 35,000 people in this area who list Russian as their mother tongue.  \n",
    "\n",
    "Next, Foursquare approach was used for data segmentation and clustering as follows:\n",
    "\n",
    "Foursquare API:\n",
    "This project uses Four-square API as its prime data gathering source as it has a database of millions of places, especially their places API which provides the ability to perform location search, location sharing and details about a business. Important to point out that 100% data capture accuracy cannot be achieved due to inherent data source omissions and inaccuracies. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we get the data and start exploring it, let's download all the dependencies that we will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wikipedia installed\n",
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # library to handle data in a vectorized manner\n",
    "\n",
    "import pandas as pd # library for data analsysis\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "import json # library to handle JSON files\n",
    "\n",
    "# !conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "# !conda install -c conda-forge wikipedia  --yes \n",
    "print('Wikipedia installed')\n",
    "import wikipedia as wp\n",
    "import requests # library to handle requests\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "import folium # map rendering library\n",
    "\n",
    "print('Libraries imported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='item1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Download and Explore Dataset for GTA "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load and explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Get the html source\n",
    "html = wp.page(\"List_of_postal_codes_of_Canada:_M\").html().encode(\"UTF-8\")\n",
    "df = pd.read_html(html)[0]\n",
    "#save dataset \n",
    "df.to_csv('neighborhoods_postalcodes.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tranform the data into a *pandas* dataframe and eliminate records with not assigned Borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "#read dataset\n",
    "neighborhoods = pd.read_csv('neighborhoods_postalcodes.csv',sep=\",\",skiprows=1)\n",
    "\n",
    "#eliminate records with not assigned Borough\n",
    "neighborhoods=neighborhoods.replace({'Neighbourhood':['Not assigned']},\"Queen's Park\")\n",
    "neighborhoods = neighborhoods[~neighborhoods[\"Borough\"].isin(['Not assigned'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park / Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor / Lawrence Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park / Ontario Provincial Government</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode           Borough                                  Neighborhood\n",
       "2        M3A        North York                                     Parkwoods\n",
       "3        M4A        North York                              Victoria Village\n",
       "4        M5A  Downtown Toronto                    Regent Park / Harbourfront\n",
       "5        M6A        North York             Lawrence Manor / Lawrence Heights\n",
       "6        M7A  Downtown Toronto  Queen's Park / Ontario Provincial Government"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Change Column names \n",
    "neighborhoods=neighborhoods.rename(columns={'Postal code':'PostalCode'})\n",
    "neighborhoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quickly examine the resulting dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park / Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor / Lawrence Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park / Ontario Provincial Government</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode           Borough                                  Neighborhood\n",
       "2        M3A        North York                                     Parkwoods\n",
       "3        M4A        North York                              Victoria Village\n",
       "4        M5A  Downtown Toronto                    Regent Park / Harbourfront\n",
       "5        M6A        North York             Lawrence Manor / Lawrence Heights\n",
       "6        M7A  Downtown Toronto  Queen's Park / Ontario Provincial Government"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And make sure that the dataset has all 10 boroughs and 103 neighborhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataframe has 10 boroughs and 103 neighborhoods.\n"
     ]
    }
   ],
   "source": [
    "print('The dataframe has {} boroughs and {} neighborhoods.'.format(\n",
    "        len(neighborhoods['Borough'].unique()),\n",
    "        neighborhoods.shape[0]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use geopy library to get the latitude and longitude values of Toronto."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to define an instance of the geocoder, we need to define a user_agent. We will name our agent <em>toronto_explorer</em>, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Toronto city are 43.6534817, -79.3839347.\n"
     ]
    }
   ],
   "source": [
    "address = \"Toronto, ON\"\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 city are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get Latitude and the longitude coordinates of each neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_geo_coor = pd.read_csv(\"./Geospatial_Coordinates.csv\")\n",
    "neighborhoods_geo_coor.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Left Join  2 dataframes \"neighborhoods\" and \"neighborhoods_geo_coor\" into one dataframe to get Latitude,\tLongitude for each neighborhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.753259</td>\n",
       "      <td>-79.329656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park / Harbourfront</td>\n",
       "      <td>43.654260</td>\n",
       "      <td>-79.360636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor / Lawrence Heights</td>\n",
       "      <td>43.718518</td>\n",
       "      <td>-79.464763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park / Ontario Provincial Government</td>\n",
       "      <td>43.662301</td>\n",
       "      <td>-79.389494</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                    Regent Park / Harbourfront   \n",
       "3        M6A        North York             Lawrence Manor / Lawrence Heights   \n",
       "4        M7A  Downtown Toronto  Queen's Park / Ontario Provincial Government   \n",
       "\n",
       "    Latitude  Longitude  \n",
       "0  43.753259 -79.329656  \n",
       "1  43.725882 -79.315572  \n",
       "2  43.654260 -79.360636  \n",
       "3  43.718518 -79.464763  \n",
       "4  43.662301 -79.389494  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neighborhoods = pd.merge(neighborhoods, neighborhoods_geo_coor, how='left', left_on = 'PostalCode', right_on = 'Postal Code')\n",
    "# remove the \"Postal Code\" column\n",
    "df_neighborhoods.drop(\"Postal Code\", axis=1, inplace=True)\n",
    "df_neighborhoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='methodology'></a>\n",
    "# Methodology\n",
    "\n",
    "The approach in this project is based on the methodology discussed in Week 3 lab and is as follows:\n",
    "\n",
    "- First, neighbourhood postal codes were converted into the equivalent latitude and longitude values.\n",
    "- Next, Foursquare API was used to explore neighborhoods in both cities, Toronto and Waterloo. The data for Toronto was limited only to those corresponding to the York region. \n",
    "- Next, a function to determine common venue categories in each neighbourhood was created and executed. \n",
    "- Next, the neighbourhoods were clustered according to the venue features. \n",
    "- Subsequently, K-means clustering algorithm was used to complete the analysis. Folium library was utilized to visualize the emerging clusters for neighborhoods in Toronto and Waterloo.  \n",
    "\n",
    "These steps are described in more detail below:\n",
    "\n",
    "Work Flow:\n",
    "Using credentials of Foursquare API, features of near-by places of the neighborhoods would be mined. Due to http request limitations, the number of places per neighborhood parameter would be reasonably set to 50, the radius parameter would be set to 500 for Toronto, and to 2000 for the city of Waterloo.\n",
    "\n",
    "Clustering Approach:\n",
    "To compare the similarities of the two cities, it was decided to explore neighborhoods, segment them, and group them into clusters to find similar neighborhoods in a big city like Waterloo and Toronto. To be able to do that, it is necessary to cluster data which is a form of unsupervised machine learning: k-means clustering algorithm.\n",
    "\n",
    "Libraries Used to Develop the Project:\n",
    "- Pandas: For creating and manipulating dataframes.\n",
    "- Folium: Python visualization library would be used to visualize the neighborhoods cluster distribution of using interactive leaflet map.\n",
    "- Scikit Learn: For importing k-means clustering.\n",
    "- JSON: Library to handle JSON files.\n",
    "- XML: To separate data from presentation and XML stores data in plain text format.\n",
    "- Geocoder: To retrieve Location Data.\n",
    "- Beautiful Soup and Requests: To scrap and library to handle http requests.\n",
    "- Matplotlib: Python Plotting Module."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='results'></a>\n",
    "# Results                                                                         \n",
    "<div style=\"text-align: right\">[Top](#toc)</div>\n",
    "Using the proposed methodology, it was determined that North York area in Toronto and Waterloo-Central area in the city of Waterloo both showed the highest number of neighbourhoods in a cluster. Shown below are the code and the geo-spacial graphs for the project data. Tables for each cluster with the venue categories are also shown here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Create a map of the whole  GTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "# create map of Toronto using latitude and longitude values\n",
    "map_neighborhoods = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "map_neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add markers to GTA map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
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   },
   "outputs": [
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       "<folium.folium.Map at 0x22c0f087e48>"
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     "metadata": {},
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   "source": [
    "for lat, lng, borough, neighborhood in zip(\n",
    "        df_neighborhoods['Latitude'], \n",
    "        df_neighborhoods['Longitude'], \n",
    "        df_neighborhoods['Borough'], \n",
    "        df_neighborhoods['Neighborhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_neighborhoods)\n",
    "    \n",
    "\n",
    "map_neighborhoods\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_neighborhoods.save(outfile= \"Toront_map_neighborhoods.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a data frame with boroughs that contain the word \"York\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    .dataframe tbody tr th {\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",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.753259</td>\n",
       "      <td>-79.329656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor / Lawrence Heights</td>\n",
       "      <td>43.718518</td>\n",
       "      <td>-79.464763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M3B</td>\n",
       "      <td>North York</td>\n",
       "      <td>Don Mills</td>\n",
       "      <td>43.745906</td>\n",
       "      <td>-79.352188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M4B</td>\n",
       "      <td>East York</td>\n",
       "      <td>Parkview Hill / Woodbine Gardens</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M6B</td>\n",
       "      <td>North York</td>\n",
       "      <td>Glencairn</td>\n",
       "      <td>43.709577</td>\n",
       "      <td>-79.445073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M3C</td>\n",
       "      <td>North York</td>\n",
       "      <td>Don Mills</td>\n",
       "      <td>43.725900</td>\n",
       "      <td>-79.340923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M4C</td>\n",
       "      <td>East York</td>\n",
       "      <td>Woodbine Heights</td>\n",
       "      <td>43.695344</td>\n",
       "      <td>-79.318389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M6C</td>\n",
       "      <td>York</td>\n",
       "      <td>Humewood-Cedarvale</td>\n",
       "      <td>43.693781</td>\n",
       "      <td>-79.428191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M6E</td>\n",
       "      <td>York</td>\n",
       "      <td>Caledonia-Fairbanks</td>\n",
       "      <td>43.689026</td>\n",
       "      <td>-79.453512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M4G</td>\n",
       "      <td>East York</td>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode     Borough                       Neighborhood   Latitude  \\\n",
       "0         M3A  North York                          Parkwoods  43.753259   \n",
       "1         M4A  North York                   Victoria Village  43.725882   \n",
       "2         M6A  North York  Lawrence Manor / Lawrence Heights  43.718518   \n",
       "3         M3B  North York                          Don Mills  43.745906   \n",
       "4         M4B   East York   Parkview Hill / Woodbine Gardens  43.706397   \n",
       "5         M6B  North York                          Glencairn  43.709577   \n",
       "6         M3C  North York                          Don Mills  43.725900   \n",
       "7         M4C   East York                   Woodbine Heights  43.695344   \n",
       "8         M6C        York                 Humewood-Cedarvale  43.693781   \n",
       "9         M6E        York                Caledonia-Fairbanks  43.689026   \n",
       "10        M4G   East York                            Leaside  43.709060   \n",
       "\n",
       "    Longitude  \n",
       "0  -79.329656  \n",
       "1  -79.315572  \n",
       "2  -79.464763  \n",
       "3  -79.352188  \n",
       "4  -79.309937  \n",
       "5  -79.445073  \n",
       "6  -79.340923  \n",
       "7  -79.318389  \n",
       "8  -79.428191  \n",
       "9  -79.453512  \n",
       "10 -79.363452  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neighborhoods_to = df_neighborhoods[df_neighborhoods['Borough'].str.contains(\"York\")].reset_index(drop=True)\n",
    "#df_neighborhoods_to = df_neighborhoods\n",
    "df_neighborhoods_to.head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x22c0f405cc0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_neighborhoods_to = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "for lat, lng, borough, neighborhood in zip(\n",
    "        df_neighborhoods_to['Latitude'], \n",
    "        df_neighborhoods_to['Longitude'], \n",
    "        df_neighborhoods_to['Borough'], \n",
    "        df_neighborhoods_to['Neighborhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_neighborhoods_to)  \n",
    "\n",
    "map_neighborhoods_to"
   ]
  },
  {
   "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": [
    "#### Define Foursquare Credentials and Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your credentails:\n",
      "CLIENT_ID: 41BUH4RPNJI2JDHGAUFKIMORK55CUOQHBVQWMLS2RKMARK13\n",
      "CLIENT_SECRET:CQUM4PUTMBRRRNUPUXW2SJ25MAMGCN0TGWRIWU334Z43NTLE\n"
     ]
    }
   ],
   "source": [
    "CLIENT_ID = 'ChangeMe' # your Foursquare ID\n",
    "CLIENT_SECRET = 'ChangeMe' # your Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version\n",
    "\n",
    "print('Your credentails:')\n",
    "print('CLIENT_ID: ' + CLIENT_ID)\n",
    "print('CLIENT_SECRET:' + CLIENT_SECRET)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's explore the first neighborhood in our dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the neighborhood's name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first neighborhood's name is 'Parkwoods'.\n",
      "Latitude and longitude values of Parkwoods are 43.7532586, -79.3296565.\n"
     ]
    }
   ],
   "source": [
    "neighborhood_name = df_neighborhoods_to.loc[0, 'Neighborhood']\n",
    "print(f\"The first neighborhood's name is '{neighborhood_name}'.\")\n",
    "\n",
    "neighborhood_latitude = df_neighborhoods_to.loc[0, 'Latitude'] \n",
    "neighborhood_longitude = df_neighborhoods_to.loc[0, 'Longitude'] \n",
    "\n",
    "print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n",
    "                                                               neighborhood_latitude, \n",
    "                                                               neighborhood_longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the neighborhood's latitude and longitude values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of Parkwoods are 43.7532586, -79.3296565.\n"
     ]
    }
   ],
   "source": [
    "neighborhood_latitude = df_neighborhoods_to.loc[0, 'Latitude'] # neighborhood latitude value\n",
    "neighborhood_longitude = df_neighborhoods_to.loc[0, 'Longitude'] # neighborhood longitude value\n",
    "radius = 500\n",
    "neighborhood_name = df_neighborhoods_to.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 Parkwoods  within a radius of 500 meters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's create the GET request URL. Name your URL **url**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# limit of number of venues returned by Foursquare API\n",
    "LIMIT = 50\n",
    "# define radius\n",
    "radius = 500 \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 the result to a json file\n",
    "results = requests.get(url).json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract the category of the venue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Foursquare lab in the previous module, we know that all the information is in the *items* key. Before we proceed, let's borrow the **get_category_type** function from the Foursquare lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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 we are ready to clean the json and structure it into a *pandas* dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>Brookbanks Park</td>\n",
       "      <td>Park</td>\n",
       "      <td>43.751976</td>\n",
       "      <td>-79.332140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Variety Store</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>43.751974</td>\n",
       "      <td>-79.333114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name         categories        lat        lng\n",
       "0  Brookbanks Park               Park  43.751976 -79.332140\n",
       "1    Variety Store  Food & Drink Shop  43.751974 -79.333114"
      ]
     },
     "execution_count": 20,
     "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": [
    "And how many venues were returned by Foursquare?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 venues were returned by Foursquare.\n"
     ]
    }
   ],
   "source": [
    "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='item2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Explore Neighborhoods in North York"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's create a function to repeat the same process to all the neighborhoods in North York"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    venues_list=[]\n",
    "    \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": [
    "#### Now write the code to run the above function on each neighborhood and create a new dataframe called *neighborhoods_to_venues*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "neighborhoods_to_venues = getNearbyVenues(names=df_neighborhoods_to['Neighborhood'],\n",
    "                                   latitudes=df_neighborhoods_to['Latitude'],\n",
    "                                   longitudes=df_neighborhoods_to['Longitude']\n",
    "                                  )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's check the size of the resulting dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(322, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.753259</td>\n",
       "      <td>-79.329656</td>\n",
       "      <td>Brookbanks Park</td>\n",
       "      <td>43.751976</td>\n",
       "      <td>-79.332140</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.753259</td>\n",
       "      <td>-79.329656</td>\n",
       "      <td>Variety Store</td>\n",
       "      <td>43.751974</td>\n",
       "      <td>-79.333114</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "      <td>Victoria Village Arena</td>\n",
       "      <td>43.723481</td>\n",
       "      <td>-79.315635</td>\n",
       "      <td>Hockey Arena</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "      <td>Tim Hortons</td>\n",
       "      <td>43.725517</td>\n",
       "      <td>-79.313103</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "      <td>Portugril</td>\n",
       "      <td>43.725819</td>\n",
       "      <td>-79.312785</td>\n",
       "      <td>Portuguese Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighborhood  Neighborhood Latitude  Neighborhood Longitude  \\\n",
       "0         Parkwoods              43.753259              -79.329656   \n",
       "1         Parkwoods              43.753259              -79.329656   \n",
       "2  Victoria Village              43.725882              -79.315572   \n",
       "3  Victoria Village              43.725882              -79.315572   \n",
       "4  Victoria Village              43.725882              -79.315572   \n",
       "\n",
       "                    Venue  Venue Latitude  Venue Longitude  \\\n",
       "0         Brookbanks Park       43.751976       -79.332140   \n",
       "1           Variety Store       43.751974       -79.333114   \n",
       "2  Victoria Village Arena       43.723481       -79.315635   \n",
       "3             Tim Hortons       43.725517       -79.313103   \n",
       "4               Portugril       43.725819       -79.312785   \n",
       "\n",
       "          Venue Category  \n",
       "0                   Park  \n",
       "1      Food & Drink Shop  \n",
       "2           Hockey Arena  \n",
       "3            Coffee Shop  \n",
       "4  Portuguese Restaurant  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(neighborhoods_to_venues.shape)\n",
    "neighborhoods_to_venues.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check how many venues were returned for each neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bathurst Manor / Wilson Heights / Downsview North</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bayview Village</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedford Park / Lawrence Manor East</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caledonia-Fairbanks</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Del Ray / Mount Dennis / Keelsdale and Silverthorn</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don Mills</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Downsview</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Toronto</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairview / Henry Farm / Oriole</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glencairn</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hillcrest Village</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humber Summit</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humberlea / Emery</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humewood-Cedarvale</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lawrence Manor / Lawrence Heights</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leaside</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Park / Maple Leaf Park / Upwood Park</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwood Park / York University</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parkview Hill / Woodbine Gardens</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parkwoods</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runnymede / The Junction North</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thorncliffe Park</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Victoria Village</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weston</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Willowdale</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woodbine Heights</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>York Mills West</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Neighborhood Latitude  \\\n",
       "Neighborhood                                                                \n",
       "Bathurst Manor / Wilson Heights / Downsview North                      19   \n",
       "Bayview Village                                                         4   \n",
       "Bedford Park / Lawrence Manor East                                     24   \n",
       "Caledonia-Fairbanks                                                     4   \n",
       "Del Ray / Mount Dennis / Keelsdale and Silverthorn                      4   \n",
       "Don Mills                                                              27   \n",
       "Downsview                                                              15   \n",
       "East Toronto                                                            3   \n",
       "Fairview / Henry Farm / Oriole                                         50   \n",
       "Glencairn                                                               5   \n",
       "Hillcrest Village                                                       5   \n",
       "Humber Summit                                                           1   \n",
       "Humberlea / Emery                                                       2   \n",
       "Humewood-Cedarvale                                                      4   \n",
       "Lawrence Manor / Lawrence Heights                                      17   \n",
       "Leaside                                                                32   \n",
       "North Park / Maple Leaf Park / Upwood Park                              4   \n",
       "Northwood Park / York University                                        5   \n",
       "Parkview Hill / Woodbine Gardens                                       11   \n",
       "Parkwoods                                                               2   \n",
       "Runnymede / The Junction North                                          3   \n",
       "Thorncliffe Park                                                       20   \n",
       "Victoria Village                                                        6   \n",
       "Weston                                                                  1   \n",
       "Willowdale                                                             39   \n",
       "Woodbine Heights                                                       10   \n",
       "York Mills West                                                         5   \n",
       "\n",
       "                                                    Neighborhood Longitude  \\\n",
       "Neighborhood                                                                 \n",
       "Bathurst Manor / Wilson Heights / Downsview North                       19   \n",
       "Bayview Village                                                          4   \n",
       "Bedford Park / Lawrence Manor East                                      24   \n",
       "Caledonia-Fairbanks                                                      4   \n",
       "Del Ray / Mount Dennis / Keelsdale and Silverthorn                       4   \n",
       "Don Mills                                                               27   \n",
       "Downsview                                                               15   \n",
       "East Toronto                                                             3   \n",
       "Fairview / Henry Farm / Oriole                                          50   \n",
       "Glencairn                                                                5   \n",
       "Hillcrest Village                                                        5   \n",
       "Humber Summit                                                            1   \n",
       "Humberlea / Emery                                                        2   \n",
       "Humewood-Cedarvale                                                       4   \n",
       "Lawrence Manor / Lawrence Heights                                       17   \n",
       "Leaside                                                                 32   \n",
       "North Park / Maple Leaf Park / Upwood Park                               4   \n",
       "Northwood Park / York University                                         5   \n",
       "Parkview Hill / Woodbine Gardens                                        11   \n",
       "Parkwoods                                                                2   \n",
       "Runnymede / The Junction North                                           3   \n",
       "Thorncliffe Park                                                        20   \n",
       "Victoria Village                                                         6   \n",
       "Weston                                                                   1   \n",
       "Willowdale                                                              39   \n",
       "Woodbine Heights                                                        10   \n",
       "York Mills West                                                          5   \n",
       "\n",
       "                                                    Venue  Venue Latitude  \\\n",
       "Neighborhood                                                                \n",
       "Bathurst Manor / Wilson Heights / Downsview North      19              19   \n",
       "Bayview Village                                         4               4   \n",
       "Bedford Park / Lawrence Manor East                     24              24   \n",
       "Caledonia-Fairbanks                                     4               4   \n",
       "Del Ray / Mount Dennis / Keelsdale and Silverthorn      4               4   \n",
       "Don Mills                                              27              27   \n",
       "Downsview                                              15              15   \n",
       "East Toronto                                            3               3   \n",
       "Fairview / Henry Farm / Oriole                         50              50   \n",
       "Glencairn                                               5               5   \n",
       "Hillcrest Village                                       5               5   \n",
       "Humber Summit                                           1               1   \n",
       "Humberlea / Emery                                       2               2   \n",
       "Humewood-Cedarvale                                      4               4   \n",
       "Lawrence Manor / Lawrence Heights                      17              17   \n",
       "Leaside                                                32              32   \n",
       "North Park / Maple Leaf Park / Upwood Park              4               4   \n",
       "Northwood Park / York University                        5               5   \n",
       "Parkview Hill / Woodbine Gardens                       11              11   \n",
       "Parkwoods                                               2               2   \n",
       "Runnymede / The Junction North                          3               3   \n",
       "Thorncliffe Park                                       20              20   \n",
       "Victoria Village                                        6               6   \n",
       "Weston                                                  1               1   \n",
       "Willowdale                                             39              39   \n",
       "Woodbine Heights                                       10              10   \n",
       "York Mills West                                         5               5   \n",
       "\n",
       "                                                    Venue Longitude  \\\n",
       "Neighborhood                                                          \n",
       "Bathurst Manor / Wilson Heights / Downsview North                19   \n",
       "Bayview Village                                                   4   \n",
       "Bedford Park / Lawrence Manor East                               24   \n",
       "Caledonia-Fairbanks                                               4   \n",
       "Del Ray / Mount Dennis / Keelsdale and Silverthorn                4   \n",
       "Don Mills                                                        27   \n",
       "Downsview                                                        15   \n",
       "East Toronto                                                      3   \n",
       "Fairview / Henry Farm / Oriole                                   50   \n",
       "Glencairn                                                         5   \n",
       "Hillcrest Village                                                 5   \n",
       "Humber Summit                                                     1   \n",
       "Humberlea / Emery                                                 2   \n",
       "Humewood-Cedarvale                                                4   \n",
       "Lawrence Manor / Lawrence Heights                                17   \n",
       "Leaside                                                          32   \n",
       "North Park / Maple Leaf Park / Upwood Park                        4   \n",
       "Northwood Park / York University                                  5   \n",
       "Parkview Hill / Woodbine Gardens                                 11   \n",
       "Parkwoods                                                         2   \n",
       "Runnymede / The Junction North                                    3   \n",
       "Thorncliffe Park                                                 20   \n",
       "Victoria Village                                                  6   \n",
       "Weston                                                            1   \n",
       "Willowdale                                                       39   \n",
       "Woodbine Heights                                                 10   \n",
       "York Mills West                                                   5   \n",
       "\n",
       "                                                    Venue Category  \n",
       "Neighborhood                                                        \n",
       "Bathurst Manor / Wilson Heights / Downsview North               19  \n",
       "Bayview Village                                                  4  \n",
       "Bedford Park / Lawrence Manor East                              24  \n",
       "Caledonia-Fairbanks                                              4  \n",
       "Del Ray / Mount Dennis / Keelsdale and Silverthorn               4  \n",
       "Don Mills                                                       27  \n",
       "Downsview                                                       15  \n",
       "East Toronto                                                     3  \n",
       "Fairview / Henry Farm / Oriole                                  50  \n",
       "Glencairn                                                        5  \n",
       "Hillcrest Village                                                5  \n",
       "Humber Summit                                                    1  \n",
       "Humberlea / Emery                                                2  \n",
       "Humewood-Cedarvale                                               4  \n",
       "Lawrence Manor / Lawrence Heights                               17  \n",
       "Leaside                                                         32  \n",
       "North Park / Maple Leaf Park / Upwood Park                       4  \n",
       "Northwood Park / York University                                 5  \n",
       "Parkview Hill / Woodbine Gardens                                11  \n",
       "Parkwoods                                                        2  \n",
       "Runnymede / The Junction North                                   3  \n",
       "Thorncliffe Park                                                20  \n",
       "Victoria Village                                                 6  \n",
       "Weston                                                           1  \n",
       "Willowdale                                                      39  \n",
       "Woodbine Heights                                                10  \n",
       "York Mills West                                                  5  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_venues.groupby('Neighborhood').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's find out how many unique categories can be curated from all the returned venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 118 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} uniques categories.'.format(len(neighborhoods_to_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='item3'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Analyze Each Neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>Accessories Store</th>\n",
       "      <th>Airport</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Arts &amp; Crafts Store</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Athletics &amp; Sports</th>\n",
       "      <th>Bagel Shop</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Baseball Field</th>\n",
       "      <th>Basketball Court</th>\n",
       "      <th>Beer Store</th>\n",
       "      <th>Bike Shop</th>\n",
       "      <th>Boutique</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Brewery</th>\n",
       "      <th>Bridal Shop</th>\n",
       "      <th>Bubble Tea Shop</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Burrito Place</th>\n",
       "      <th>Bus Line</th>\n",
       "      <th>Business Service</th>\n",
       "      <th>Butcher</th>\n",
       "      <th>Café</th>\n",
       "      <th>Caribbean Restaurant</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Chocolate Shop</th>\n",
       "      <th>Clothing Store</th>\n",
       "      <th>Coffee Shop</th>\n",
       "      <th>Comfort Food Restaurant</th>\n",
       "      <th>Concert Hall</th>\n",
       "      <th>Construction &amp; Landscaping</th>\n",
       "      <th>Convenience Store</th>\n",
       "      <th>Cosmetics Shop</th>\n",
       "      <th>Curling Ice</th>\n",
       "      <th>Dance Studio</th>\n",
       "      <th>Deli / Bodega</th>\n",
       "      <th>Department Store</th>\n",
       "      <th>Dessert Shop</th>\n",
       "      <th>Dim Sum Restaurant</th>\n",
       "      <th>Diner</th>\n",
       "      <th>Discount Store</th>\n",
       "      <th>Dog Run</th>\n",
       "      <th>Electronics Store</th>\n",
       "      <th>Event Space</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Field</th>\n",
       "      <th>Fish &amp; Chips Shop</th>\n",
       "      <th>Food &amp; Drink Shop</th>\n",
       "      <th>Food Court</th>\n",
       "      <th>Food Truck</th>\n",
       "      <th>French Restaurant</th>\n",
       "      <th>Fried Chicken Joint</th>\n",
       "      <th>Frozen Yogurt Shop</th>\n",
       "      <th>Furniture / Home Store</th>\n",
       "      <th>Gas Station</th>\n",
       "      <th>Gastropub</th>\n",
       "      <th>Golf Course</th>\n",
       "      <th>Greek Restaurant</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Gym / Fitness Center</th>\n",
       "      <th>Hockey Arena</th>\n",
       "      <th>Home Service</th>\n",
       "      <th>Hotel</th>\n",
       "      <th>Housing Development</th>\n",
       "      <th>Ice Cream Shop</th>\n",
       "      <th>Indian Restaurant</th>\n",
       "      <th>Indonesian Restaurant</th>\n",
       "      <th>Intersection</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Japanese Restaurant</th>\n",
       "      <th>Juice Bar</th>\n",
       "      <th>Lingerie Store</th>\n",
       "      <th>Liquor Store</th>\n",
       "      <th>Lounge</th>\n",
       "      <th>Massage Studio</th>\n",
       "      <th>Mediterranean Restaurant</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Middle Eastern Restaurant</th>\n",
       "      <th>Miscellaneous Shop</th>\n",
       "      <th>Movie Theater</th>\n",
       "      <th>Paper / Office Supplies Store</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Plaza</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Portuguese Restaurant</th>\n",
       "      <th>Pub</th>\n",
       "      <th>Ramen Restaurant</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Salon / Barbershop</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Shoe Store</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Skating Rink</th>\n",
       "      <th>Spa</th>\n",
       "      <th>Sporting Goods Shop</th>\n",
       "      <th>Sports Bar</th>\n",
       "      <th>Steakhouse</th>\n",
       "      <th>Supermarket</th>\n",
       "      <th>Supplement Shop</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Tea Room</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Theater</th>\n",
       "      <th>Toy / Game Store</th>\n",
       "      <th>Trail</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Video Store</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Warehouse Store</th>\n",
       "      <th>Women's Store</th>\n",
       "      <th>Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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>Parkwoods</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Victoria Village</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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>Victoria Village</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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Victoria Village</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "</div>"
      ],
      "text/plain": [
       "       Neighborhood  Accessories Store  Airport  American Restaurant  \\\n",
       "0         Parkwoods                  0        0                    0   \n",
       "1         Parkwoods                  0        0                    0   \n",
       "2  Victoria Village                  0        0                    0   \n",
       "3  Victoria Village                  0        0                    0   \n",
       "4  Victoria Village                  0        0                    0   \n",
       "\n",
       "   Arts & Crafts Store  Asian Restaurant  Athletics & Sports  Bagel Shop  \\\n",
       "0                    0                 0                   0           0   \n",
       "1                    0                 0                   0           0   \n",
       "2                    0                 0                   0           0   \n",
       "3                    0                 0                   0           0   \n",
       "4                    0                 0                   0           0   \n",
       "\n",
       "   Bakery  Bank  Bar  Baseball Field  Basketball Court  Beer Store  Bike Shop  \\\n",
       "0       0     0    0               0                 0           0          0   \n",
       "1       0     0    0               0                 0           0          0   \n",
       "2       0     0    0               0                 0           0          0   \n",
       "3       0     0    0               0                 0           0          0   \n",
       "4       0     0    0               0                 0           0          0   \n",
       "\n",
       "   Boutique  Breakfast Spot  Brewery  Bridal Shop  Bubble Tea Shop  \\\n",
       "0         0               0        0            0                0   \n",
       "1         0               0        0            0                0   \n",
       "2         0               0        0            0                0   \n",
       "3         0               0        0            0                0   \n",
       "4         0               0        0            0                0   \n",
       "\n",
       "   Burger Joint  Burrito Place  Bus Line  Business Service  Butcher  Café  \\\n",
       "0             0              0         0                 0        0     0   \n",
       "1             0              0         0                 0        0     0   \n",
       "2             0              0         0                 0        0     0   \n",
       "3             0              0         0                 0        0     0   \n",
       "4             0              0         0                 0        0     0   \n",
       "\n",
       "   Caribbean Restaurant  Chinese Restaurant  Chocolate Shop  Clothing Store  \\\n",
       "0                     0                   0               0               0   \n",
       "1                     0                   0               0               0   \n",
       "2                     0                   0               0               0   \n",
       "3                     0                   0               0               0   \n",
       "4                     0                   0               0               0   \n",
       "\n",
       "   Coffee Shop  Comfort Food Restaurant  Concert Hall  \\\n",
       "0            0                        0             0   \n",
       "1            0                        0             0   \n",
       "2            0                        0             0   \n",
       "3            1                        0             0   \n",
       "4            0                        0             0   \n",
       "\n",
       "   Construction & Landscaping  Convenience Store  Cosmetics Shop  Curling Ice  \\\n",
       "0                           0                  0               0            0   \n",
       "1                           0                  0               0            0   \n",
       "2                           0                  0               0            0   \n",
       "3                           0                  0               0            0   \n",
       "4                           0                  0               0            0   \n",
       "\n",
       "   Dance Studio  Deli / Bodega  Department Store  Dessert Shop  \\\n",
       "0             0              0                 0             0   \n",
       "1             0              0                 0             0   \n",
       "2             0              0                 0             0   \n",
       "3             0              0                 0             0   \n",
       "4             0              0                 0             0   \n",
       "\n",
       "   Dim Sum Restaurant  Diner  Discount Store  Dog Run  Electronics Store  \\\n",
       "0                   0      0               0        0                  0   \n",
       "1                   0      0               0        0                  0   \n",
       "2                   0      0               0        0                  0   \n",
       "3                   0      0               0        0                  0   \n",
       "4                   0      0               0        0                  0   \n",
       "\n",
       "   Event Space  Fast Food Restaurant  Field  Fish & Chips Shop  \\\n",
       "0            0                     0      0                  0   \n",
       "1            0                     0      0                  0   \n",
       "2            0                     0      0                  0   \n",
       "3            0                     0      0                  0   \n",
       "4            0                     0      0                  0   \n",
       "\n",
       "   Food & Drink Shop  Food Court  Food Truck  French Restaurant  \\\n",
       "0                  0           0           0                  0   \n",
       "1                  1           0           0                  0   \n",
       "2                  0           0           0                  0   \n",
       "3                  0           0           0                  0   \n",
       "4                  0           0           0                  0   \n",
       "\n",
       "   Fried Chicken Joint  Frozen Yogurt Shop  Furniture / Home Store  \\\n",
       "0                    0                   0                       0   \n",
       "1                    0                   0                       0   \n",
       "2                    0                   0                       0   \n",
       "3                    0                   0                       0   \n",
       "4                    0                   0                       0   \n",
       "\n",
       "   Gas Station  Gastropub  Golf Course  Greek Restaurant  Grocery Store  Gym  \\\n",
       "0            0          0            0                 0              0    0   \n",
       "1            0          0            0                 0              0    0   \n",
       "2            0          0            0                 0              0    0   \n",
       "3            0          0            0                 0              0    0   \n",
       "4            0          0            0                 0              0    0   \n",
       "\n",
       "   Gym / Fitness Center  Hockey Arena  Home Service  Hotel  \\\n",
       "0                     0             0             0      0   \n",
       "1                     0             0             0      0   \n",
       "2                     0             1             0      0   \n",
       "3                     0             0             0      0   \n",
       "4                     0             0             0      0   \n",
       "\n",
       "   Housing Development  Ice Cream Shop  Indian Restaurant  \\\n",
       "0                    0               0                  0   \n",
       "1                    0               0                  0   \n",
       "2                    0               0                  0   \n",
       "3                    0               0                  0   \n",
       "4                    0               0                  0   \n",
       "\n",
       "   Indonesian Restaurant  Intersection  Italian Restaurant  \\\n",
       "0                      0             0                   0   \n",
       "1                      0             0                   0   \n",
       "2                      0             0                   0   \n",
       "3                      0             0                   0   \n",
       "4                      0             0                   0   \n",
       "\n",
       "   Japanese Restaurant  Juice Bar  Lingerie Store  Liquor Store  Lounge  \\\n",
       "0                    0          0               0             0       0   \n",
       "1                    0          0               0             0       0   \n",
       "2                    0          0               0             0       0   \n",
       "3                    0          0               0             0       0   \n",
       "4                    0          0               0             0       0   \n",
       "\n",
       "   Massage Studio  Mediterranean Restaurant  Metro Station  \\\n",
       "0               0                         0              0   \n",
       "1               0                         0              0   \n",
       "2               0                         0              0   \n",
       "3               0                         0              0   \n",
       "4               0                         0              0   \n",
       "\n",
       "   Mexican Restaurant  Middle Eastern Restaurant  Miscellaneous Shop  \\\n",
       "0                   0                          0                   0   \n",
       "1                   0                          0                   0   \n",
       "2                   0                          0                   0   \n",
       "3                   0                          0                   0   \n",
       "4                   0                          0                   0   \n",
       "\n",
       "   Movie Theater  Paper / Office Supplies Store  Park  Pet Store  Pharmacy  \\\n",
       "0              0                              0     1          0         0   \n",
       "1              0                              0     0          0         0   \n",
       "2              0                              0     0          0         0   \n",
       "3              0                              0     0          0         0   \n",
       "4              0                              0     0          0         0   \n",
       "\n",
       "   Pizza Place  Plaza  Pool  Portuguese Restaurant  Pub  Ramen Restaurant  \\\n",
       "0            0      0     0                      0    0                 0   \n",
       "1            0      0     0                      0    0                 0   \n",
       "2            0      0     0                      0    0                 0   \n",
       "3            0      0     0                      0    0                 0   \n",
       "4            0      0     0                      1    0                 0   \n",
       "\n",
       "   Restaurant  Salon / Barbershop  Sandwich Place  Shoe Store  Shopping Mall  \\\n",
       "0           0                   0               0           0              0   \n",
       "1           0                   0               0           0              0   \n",
       "2           0                   0               0           0              0   \n",
       "3           0                   0               0           0              0   \n",
       "4           0                   0               0           0              0   \n",
       "\n",
       "   Skating Rink  Spa  Sporting Goods Shop  Sports Bar  Steakhouse  \\\n",
       "0             0    0                    0           0           0   \n",
       "1             0    0                    0           0           0   \n",
       "2             0    0                    0           0           0   \n",
       "3             0    0                    0           0           0   \n",
       "4             0    0                    0           0           0   \n",
       "\n",
       "   Supermarket  Supplement Shop  Sushi Restaurant  Tea Room  Thai Restaurant  \\\n",
       "0            0                0                 0         0                0   \n",
       "1            0                0                 0         0                0   \n",
       "2            0                0                 0         0                0   \n",
       "3            0                0                 0         0                0   \n",
       "4            0                0                 0         0                0   \n",
       "\n",
       "   Theater  Toy / Game Store  Trail  Video Game Store  Video Store  \\\n",
       "0        0                 0      0                 0            0   \n",
       "1        0                 0      0                 0            0   \n",
       "2        0                 0      0                 0            0   \n",
       "3        0                 0      0                 0            0   \n",
       "4        0                 0      0                 0            0   \n",
       "\n",
       "   Vietnamese Restaurant  Warehouse Store  Women's Store  Yoga Studio  \n",
       "0                      0                0              0            0  \n",
       "1                      0                0              0            0  \n",
       "2                      0                0              0            0  \n",
       "3                      0                0              0            0  \n",
       "4                      0                0              0            0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "neighborhoods_to_onehot = pd.get_dummies(neighborhoods_to_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "neighborhoods_to_onehot['Neighborhood'] = neighborhoods_to_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [neighborhoods_to_onehot.columns[-1]] + list(neighborhoods_to_onehot.columns[:-1])\n",
    "neighborhoods_to_onehot = neighborhoods_to_onehot[fixed_columns]\n",
    "\n",
    "neighborhoods_to_onehot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's examine the new dataframe size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(322, 119)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_onehot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>Accessories Store</th>\n",
       "      <th>Airport</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Arts &amp; Crafts Store</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Athletics &amp; Sports</th>\n",
       "      <th>Bagel Shop</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Baseball Field</th>\n",
       "      <th>Basketball Court</th>\n",
       "      <th>Beer Store</th>\n",
       "      <th>Bike Shop</th>\n",
       "      <th>Boutique</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Brewery</th>\n",
       "      <th>Bridal Shop</th>\n",
       "      <th>Bubble Tea Shop</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Burrito Place</th>\n",
       "      <th>Bus Line</th>\n",
       "      <th>Business Service</th>\n",
       "      <th>Butcher</th>\n",
       "      <th>Café</th>\n",
       "      <th>Caribbean Restaurant</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Chocolate Shop</th>\n",
       "      <th>Clothing Store</th>\n",
       "      <th>Coffee Shop</th>\n",
       "      <th>Comfort Food Restaurant</th>\n",
       "      <th>Concert Hall</th>\n",
       "      <th>Construction &amp; Landscaping</th>\n",
       "      <th>Convenience Store</th>\n",
       "      <th>Cosmetics Shop</th>\n",
       "      <th>Curling Ice</th>\n",
       "      <th>Dance Studio</th>\n",
       "      <th>Deli / Bodega</th>\n",
       "      <th>Department Store</th>\n",
       "      <th>Dessert Shop</th>\n",
       "      <th>Dim Sum Restaurant</th>\n",
       "      <th>Diner</th>\n",
       "      <th>Discount Store</th>\n",
       "      <th>Dog Run</th>\n",
       "      <th>Electronics Store</th>\n",
       "      <th>Event Space</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Field</th>\n",
       "      <th>Fish &amp; Chips Shop</th>\n",
       "      <th>Food &amp; Drink Shop</th>\n",
       "      <th>Food Court</th>\n",
       "      <th>Food Truck</th>\n",
       "      <th>French Restaurant</th>\n",
       "      <th>Fried Chicken Joint</th>\n",
       "      <th>Frozen Yogurt Shop</th>\n",
       "      <th>Furniture / Home Store</th>\n",
       "      <th>Gas Station</th>\n",
       "      <th>Gastropub</th>\n",
       "      <th>Golf Course</th>\n",
       "      <th>Greek Restaurant</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Gym / Fitness Center</th>\n",
       "      <th>Hockey Arena</th>\n",
       "      <th>Home Service</th>\n",
       "      <th>Hotel</th>\n",
       "      <th>Housing Development</th>\n",
       "      <th>Ice Cream Shop</th>\n",
       "      <th>Indian Restaurant</th>\n",
       "      <th>Indonesian Restaurant</th>\n",
       "      <th>Intersection</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Japanese Restaurant</th>\n",
       "      <th>Juice Bar</th>\n",
       "      <th>Lingerie Store</th>\n",
       "      <th>Liquor Store</th>\n",
       "      <th>Lounge</th>\n",
       "      <th>Massage Studio</th>\n",
       "      <th>Mediterranean Restaurant</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Middle Eastern Restaurant</th>\n",
       "      <th>Miscellaneous Shop</th>\n",
       "      <th>Movie Theater</th>\n",
       "      <th>Paper / Office Supplies Store</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Plaza</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Portuguese Restaurant</th>\n",
       "      <th>Pub</th>\n",
       "      <th>Ramen Restaurant</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Salon / Barbershop</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Shoe Store</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Skating Rink</th>\n",
       "      <th>Spa</th>\n",
       "      <th>Sporting Goods Shop</th>\n",
       "      <th>Sports Bar</th>\n",
       "      <th>Steakhouse</th>\n",
       "      <th>Supermarket</th>\n",
       "      <th>Supplement Shop</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Tea Room</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Theater</th>\n",
       "      <th>Toy / Game Store</th>\n",
       "      <th>Trail</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Video Store</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Warehouse Store</th>\n",
       "      <th>Women's Store</th>\n",
       "      <th>Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bedford Park / Lawrence Manor East</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Caledonia-Fairbanks</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Del Ray / Mount Dennis / Keelsdale and Silvert...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Neighborhood  Accessories Store  \\\n",
       "0  Bathurst Manor / Wilson Heights / Downsview North                0.0   \n",
       "1                                    Bayview Village                0.0   \n",
       "2                 Bedford Park / Lawrence Manor East                0.0   \n",
       "3                                Caledonia-Fairbanks                0.0   \n",
       "4  Del Ray / Mount Dennis / Keelsdale and Silvert...                0.0   \n",
       "\n",
       "   Airport  American Restaurant  Arts & Crafts Store  Asian Restaurant  \\\n",
       "0      0.0             0.000000                  0.0               0.0   \n",
       "1      0.0             0.000000                  0.0               0.0   \n",
       "2      0.0             0.041667                  0.0               0.0   \n",
       "3      0.0             0.000000                  0.0               0.0   \n",
       "4      0.0             0.000000                  0.0               0.0   \n",
       "\n",
       "   Athletics & Sports  Bagel Shop  Bakery      Bank  Bar  Baseball Field  \\\n",
       "0                 0.0         0.0     0.0  0.105263  0.0             0.0   \n",
       "1                 0.0         0.0     0.0  0.250000  0.0             0.0   \n",
       "2                 0.0         0.0     0.0  0.000000  0.0             0.0   \n",
       "3                 0.0         0.0     0.0  0.000000  0.0             0.0   \n",
       "4                 0.0         0.0     0.0  0.000000  0.0             0.0   \n",
       "\n",
       "   Basketball Court  Beer Store  Bike Shop  Boutique  Breakfast Spot  Brewery  \\\n",
       "0               0.0         0.0        0.0       0.0             0.0      0.0   \n",
       "1               0.0         0.0        0.0       0.0             0.0      0.0   \n",
       "2               0.0         0.0        0.0       0.0             0.0      0.0   \n",
       "3               0.0         0.0        0.0       0.0             0.0      0.0   \n",
       "4               0.0         0.0        0.0       0.0             0.0      0.0   \n",
       "\n",
       "   Bridal Shop  Bubble Tea Shop  Burger Joint  Burrito Place  Bus Line  \\\n",
       "0     0.052632              0.0           0.0            0.0       0.0   \n",
       "1     0.000000              0.0           0.0            0.0       0.0   \n",
       "2     0.000000              0.0           0.0            0.0       0.0   \n",
       "3     0.000000              0.0           0.0            0.0       0.0   \n",
       "4     0.000000              0.0           0.0            0.0       0.0   \n",
       "\n",
       "   Business Service   Butcher      Café  Caribbean Restaurant  \\\n",
       "0               0.0  0.000000  0.000000                   0.0   \n",
       "1               0.0  0.000000  0.250000                   0.0   \n",
       "2               0.0  0.041667  0.041667                   0.0   \n",
       "3               0.0  0.000000  0.000000                   0.0   \n",
       "4               0.0  0.000000  0.000000                   0.0   \n",
       "\n",
       "   Chinese Restaurant  Chocolate Shop  Clothing Store  Coffee Shop  \\\n",
       "0                0.00             0.0             0.0     0.105263   \n",
       "1                0.25             0.0             0.0     0.000000   \n",
       "2                0.00             0.0             0.0     0.083333   \n",
       "3                0.00             0.0             0.0     0.000000   \n",
       "4                0.00             0.0             0.0     0.250000   \n",
       "\n",
       "   Comfort Food Restaurant  Concert Hall  Construction & Landscaping  \\\n",
       "0                 0.000000           0.0                         0.0   \n",
       "1                 0.000000           0.0                         0.0   \n",
       "2                 0.041667           0.0                         0.0   \n",
       "3                 0.000000           0.0                         0.0   \n",
       "4                 0.000000           0.0                         0.0   \n",
       "\n",
       "   Convenience Store  Cosmetics Shop  Curling Ice  Dance Studio  \\\n",
       "0                0.0        0.000000          0.0           0.0   \n",
       "1                0.0        0.000000          0.0           0.0   \n",
       "2                0.0        0.041667          0.0           0.0   \n",
       "3                0.0        0.000000          0.0           0.0   \n",
       "4                0.0        0.000000          0.0           0.0   \n",
       "\n",
       "   Deli / Bodega  Department Store  Dessert Shop  Dim Sum Restaurant  \\\n",
       "0       0.052632               0.0           0.0                 0.0   \n",
       "1       0.000000               0.0           0.0                 0.0   \n",
       "2       0.000000               0.0           0.0                 0.0   \n",
       "3       0.000000               0.0           0.0                 0.0   \n",
       "4       0.000000               0.0           0.0                 0.0   \n",
       "\n",
       "      Diner  Discount Store  Dog Run  Electronics Store  Event Space  \\\n",
       "0  0.052632             0.0      0.0                0.0          0.0   \n",
       "1  0.000000             0.0      0.0                0.0          0.0   \n",
       "2  0.000000             0.0      0.0                0.0          0.0   \n",
       "3  0.000000             0.0      0.0                0.0          0.0   \n",
       "4  0.000000             0.0      0.0                0.0          0.0   \n",
       "\n",
       "   Fast Food Restaurant  Field  Fish & Chips Shop  Food & Drink Shop  \\\n",
       "0                   0.0    0.0                0.0                0.0   \n",
       "1                   0.0    0.0                0.0                0.0   \n",
       "2                   0.0    0.0                0.0                0.0   \n",
       "3                   0.0    0.0                0.0                0.0   \n",
       "4                   0.5    0.0                0.0                0.0   \n",
       "\n",
       "   Food Court  Food Truck  French Restaurant  Fried Chicken Joint  \\\n",
       "0         0.0         0.0                0.0             0.052632   \n",
       "1         0.0         0.0                0.0             0.000000   \n",
       "2         0.0         0.0                0.0             0.000000   \n",
       "3         0.0         0.0                0.0             0.000000   \n",
       "4         0.0         0.0                0.0             0.000000   \n",
       "\n",
       "   Frozen Yogurt Shop  Furniture / Home Store  Gas Station  Gastropub  \\\n",
       "0                 0.0                     0.0     0.052632        0.0   \n",
       "1                 0.0                     0.0     0.000000        0.0   \n",
       "2                 0.0                     0.0     0.000000        0.0   \n",
       "3                 0.0                     0.0     0.000000        0.0   \n",
       "4                 0.0                     0.0     0.000000        0.0   \n",
       "\n",
       "   Golf Course  Greek Restaurant  Grocery Store  Gym  Gym / Fitness Center  \\\n",
       "0          0.0          0.000000       0.000000  0.0                   0.0   \n",
       "1          0.0          0.000000       0.000000  0.0                   0.0   \n",
       "2          0.0          0.041667       0.041667  0.0                   0.0   \n",
       "3          0.0          0.000000       0.000000  0.0                   0.0   \n",
       "4          0.0          0.000000       0.000000  0.0                   0.0   \n",
       "\n",
       "   Hockey Arena  Home Service  Hotel  Housing Development  Ice Cream Shop  \\\n",
       "0           0.0           0.0    0.0                  0.0        0.052632   \n",
       "1           0.0           0.0    0.0                  0.0        0.000000   \n",
       "2           0.0           0.0    0.0                  0.0        0.000000   \n",
       "3           0.0           0.0    0.0                  0.0        0.000000   \n",
       "4           0.0           0.0    0.0                  0.0        0.000000   \n",
       "\n",
       "   Indian Restaurant  Indonesian Restaurant  Intersection  Italian Restaurant  \\\n",
       "0           0.000000                    0.0           0.0            0.000000   \n",
       "1           0.000000                    0.0           0.0            0.000000   \n",
       "2           0.041667                    0.0           0.0            0.083333   \n",
       "3           0.000000                    0.0           0.0            0.000000   \n",
       "4           0.000000                    0.0           0.0            0.000000   \n",
       "\n",
       "   Japanese Restaurant  Juice Bar  Lingerie Store  Liquor Store  Lounge  \\\n",
       "0                 0.00   0.000000             0.0      0.000000     0.0   \n",
       "1                 0.25   0.000000             0.0      0.000000     0.0   \n",
       "2                 0.00   0.041667             0.0      0.041667     0.0   \n",
       "3                 0.00   0.000000             0.0      0.000000     0.0   \n",
       "4                 0.00   0.000000             0.0      0.000000     0.0   \n",
       "\n",
       "   Massage Studio  Mediterranean Restaurant  Metro Station  \\\n",
       "0             0.0                       0.0            0.0   \n",
       "1             0.0                       0.0            0.0   \n",
       "2             0.0                       0.0            0.0   \n",
       "3             0.0                       0.0            0.0   \n",
       "4             0.0                       0.0            0.0   \n",
       "\n",
       "   Mexican Restaurant  Middle Eastern Restaurant  Miscellaneous Shop  \\\n",
       "0                 0.0                   0.052632                 0.0   \n",
       "1                 0.0                   0.000000                 0.0   \n",
       "2                 0.0                   0.000000                 0.0   \n",
       "3                 0.0                   0.000000                 0.0   \n",
       "4                 0.0                   0.000000                 0.0   \n",
       "\n",
       "   Movie Theater  Paper / Office Supplies Store  Park  Pet Store  Pharmacy  \\\n",
       "0            0.0                            0.0   0.0        0.0  0.052632   \n",
       "1            0.0                            0.0   0.0        0.0  0.000000   \n",
       "2            0.0                            0.0   0.0        0.0  0.041667   \n",
       "3            0.0                            0.0   0.5        0.0  0.000000   \n",
       "4            0.0                            0.0   0.0        0.0  0.000000   \n",
       "\n",
       "   Pizza Place  Plaza  Pool  Portuguese Restaurant       Pub  \\\n",
       "0     0.052632    0.0   0.0                    0.0  0.000000   \n",
       "1     0.000000    0.0   0.0                    0.0  0.000000   \n",
       "2     0.041667    0.0   0.0                    0.0  0.041667   \n",
       "3     0.000000    0.0   0.0                    0.0  0.000000   \n",
       "4     0.000000    0.0   0.0                    0.0  0.000000   \n",
       "\n",
       "   Ramen Restaurant  Restaurant  Salon / Barbershop  Sandwich Place  \\\n",
       "0               0.0    0.052632                 0.0        0.052632   \n",
       "1               0.0    0.000000                 0.0        0.000000   \n",
       "2               0.0    0.083333                 0.0        0.083333   \n",
       "3               0.0    0.000000                 0.0        0.000000   \n",
       "4               0.0    0.000000                 0.0        0.250000   \n",
       "\n",
       "   Shoe Store  Shopping Mall  Skating Rink   Spa  Sporting Goods Shop  \\\n",
       "0         0.0       0.052632           0.0  0.00                  0.0   \n",
       "1         0.0       0.000000           0.0  0.00                  0.0   \n",
       "2         0.0       0.000000           0.0  0.00                  0.0   \n",
       "3         0.0       0.000000           0.0  0.25                  0.0   \n",
       "4         0.0       0.000000           0.0  0.00                  0.0   \n",
       "\n",
       "   Sports Bar  Steakhouse  Supermarket  Supplement Shop  Sushi Restaurant  \\\n",
       "0         0.0         0.0     0.052632              0.0          0.052632   \n",
       "1         0.0         0.0     0.000000              0.0          0.000000   \n",
       "2         0.0         0.0     0.000000              0.0          0.083333   \n",
       "3         0.0         0.0     0.000000              0.0          0.000000   \n",
       "4         0.0         0.0     0.000000              0.0          0.000000   \n",
       "\n",
       "   Tea Room  Thai Restaurant  Theater  Toy / Game Store  Trail  \\\n",
       "0       0.0         0.000000      0.0               0.0    0.0   \n",
       "1       0.0         0.000000      0.0               0.0    0.0   \n",
       "2       0.0         0.041667      0.0               0.0    0.0   \n",
       "3       0.0         0.000000      0.0               0.0    0.0   \n",
       "4       0.0         0.000000      0.0               0.0    0.0   \n",
       "\n",
       "   Video Game Store  Video Store  Vietnamese Restaurant  Warehouse Store  \\\n",
       "0               0.0     0.052632                    0.0              0.0   \n",
       "1               0.0     0.000000                    0.0              0.0   \n",
       "2               0.0     0.000000                    0.0              0.0   \n",
       "3               0.0     0.000000                    0.0              0.0   \n",
       "4               0.0     0.000000                    0.0              0.0   \n",
       "\n",
       "   Women's Store  Yoga Studio  \n",
       "0           0.00          0.0  \n",
       "1           0.00          0.0  \n",
       "2           0.00          0.0  \n",
       "3           0.25          0.0  \n",
       "4           0.00          0.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_grouped = neighborhoods_to_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "neighborhoods_to_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's confirm the new size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 119)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_grouped.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's print each neighborhood along with the top 10 most common venues and put that into a *pandas* dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bathurst Manor / Wilson Heights / Downsview North</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Bridal Shop</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bayview Village</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bedford Park / Lawrence Manor East</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Café</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Butcher</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Caledonia-Fairbanks</td>\n",
       "      <td>Park</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>Frozen Yogurt Shop</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Del Ray / Mount Dennis / Keelsdale and Silvert...</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Neighborhood 1st Most Common Venue  \\\n",
       "0  Bathurst Manor / Wilson Heights / Downsview North           Coffee Shop   \n",
       "1                                    Bayview Village   Japanese Restaurant   \n",
       "2                 Bedford Park / Lawrence Manor East           Coffee Shop   \n",
       "3                                Caledonia-Fairbanks                  Park   \n",
       "4  Del Ray / Mount Dennis / Keelsdale and Silvert...  Fast Food Restaurant   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue      4th Most Common Venue  \\\n",
       "0                  Bank           Supermarket  Middle Eastern Restaurant   \n",
       "1    Chinese Restaurant                  Café                       Bank   \n",
       "2    Italian Restaurant            Restaurant           Sushi Restaurant   \n",
       "3                   Spa         Women's Store         Frozen Yogurt Shop   \n",
       "4           Coffee Shop        Sandwich Place                      Diner   \n",
       "\n",
       "     5th Most Common Venue       6th Most Common Venue  \\\n",
       "0                 Pharmacy                 Pizza Place   \n",
       "1                  Dog Run  Construction & Landscaping   \n",
       "2           Sandwich Place                        Café   \n",
       "3   Furniture / Home Store     Comfort Food Restaurant   \n",
       "4  Comfort Food Restaurant                Concert Hall   \n",
       "\n",
       "        7th Most Common Venue       8th Most Common Venue  \\\n",
       "0               Deli / Bodega                 Bridal Shop   \n",
       "1           Convenience Store              Cosmetics Shop   \n",
       "2                Liquor Store                     Butcher   \n",
       "3                Concert Hall  Construction & Landscaping   \n",
       "4  Construction & Landscaping           Convenience Store   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0            Restaurant         Sandwich Place  \n",
       "1           Curling Ice           Dance Studio  \n",
       "2              Pharmacy            Pizza Place  \n",
       "3     Convenience Store         Cosmetics Shop  \n",
       "4        Cosmetics Shop            Curling Ice  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "    return row_categories_sorted.index.values[0:num_top_venues]\n",
    "\n",
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = neighborhoods_to_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(neighborhoods_to_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(neighborhoods_to_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Cluster Neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run *k*-means to cluster the neighborhood into 3 clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 2, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 3\n",
    "\n",
    "neighborhoods_to_grouped_clustering = neighborhoods_to_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(neighborhoods_to_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.753259</td>\n",
       "      <td>-79.329656</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Portuguese Restaurant</td>\n",
       "      <td>Hockey Arena</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor / Lawrence Heights</td>\n",
       "      <td>43.718518</td>\n",
       "      <td>-79.464763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Accessories Store</td>\n",
       "      <td>Event Space</td>\n",
       "      <td>Shoe Store</td>\n",
       "      <td>Miscellaneous Shop</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Boutique</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Women's Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M3B</td>\n",
       "      <td>North York</td>\n",
       "      <td>Don Mills</td>\n",
       "      <td>43.745906</td>\n",
       "      <td>-79.352188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M4B</td>\n",
       "      <td>East York</td>\n",
       "      <td>Parkview Hill / Woodbine Gardens</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M6B</td>\n",
       "      <td>North York</td>\n",
       "      <td>Glencairn</td>\n",
       "      <td>43.709577</td>\n",
       "      <td>-79.445073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Metro Station</td>\n",
       "      <td>Park</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M3C</td>\n",
       "      <td>North York</td>\n",
       "      <td>Don Mills</td>\n",
       "      <td>43.725900</td>\n",
       "      <td>-79.340923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M4C</td>\n",
       "      <td>East York</td>\n",
       "      <td>Woodbine Heights</td>\n",
       "      <td>43.695344</td>\n",
       "      <td>-79.318389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Park</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Video Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M6C</td>\n",
       "      <td>York</td>\n",
       "      <td>Humewood-Cedarvale</td>\n",
       "      <td>43.693781</td>\n",
       "      <td>-79.428191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Field</td>\n",
       "      <td>Trail</td>\n",
       "      <td>Hockey Arena</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M6E</td>\n",
       "      <td>York</td>\n",
       "      <td>Caledonia-Fairbanks</td>\n",
       "      <td>43.689026</td>\n",
       "      <td>-79.453512</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>Frozen Yogurt Shop</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M4G</td>\n",
       "      <td>East York</td>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Sports Bar</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M2H</td>\n",
       "      <td>North York</td>\n",
       "      <td>Hillcrest Village</td>\n",
       "      <td>43.803762</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Golf Course</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M3H</td>\n",
       "      <td>North York</td>\n",
       "      <td>Bathurst Manor / Wilson Heights / Downsview North</td>\n",
       "      <td>43.754328</td>\n",
       "      <td>-79.442259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Bridal Shop</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M4H</td>\n",
       "      <td>East York</td>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Housing Development</td>\n",
       "      <td>Liquor Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M2J</td>\n",
       "      <td>North York</td>\n",
       "      <td>Fairview / Henry Farm / Oriole</td>\n",
       "      <td>43.778517</td>\n",
       "      <td>-79.346556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Movie Theater</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Pharmacy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M3J</td>\n",
       "      <td>North York</td>\n",
       "      <td>Northwood Park / York University</td>\n",
       "      <td>43.767980</td>\n",
       "      <td>-79.487262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Miscellaneous Shop</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Massage Studio</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>M4J</td>\n",
       "      <td>East York</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M2K</td>\n",
       "      <td>North York</td>\n",
       "      <td>Bayview Village</td>\n",
       "      <td>43.786947</td>\n",
       "      <td>-79.385975</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M3K</td>\n",
       "      <td>North York</td>\n",
       "      <td>Downsview</td>\n",
       "      <td>43.737473</td>\n",
       "      <td>-79.464763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Business Service</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>M2L</td>\n",
       "      <td>North York</td>\n",
       "      <td>York Mills / Silver Hills</td>\n",
       "      <td>43.757490</td>\n",
       "      <td>-79.374714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode     Borough                                       Neighborhood  \\\n",
       "0         M3A  North York                                          Parkwoods   \n",
       "1         M4A  North York                                   Victoria Village   \n",
       "2         M6A  North York                  Lawrence Manor / Lawrence Heights   \n",
       "3         M3B  North York                                          Don Mills   \n",
       "4         M4B   East York                   Parkview Hill / Woodbine Gardens   \n",
       "5         M6B  North York                                          Glencairn   \n",
       "6         M3C  North York                                          Don Mills   \n",
       "7         M4C   East York                                   Woodbine Heights   \n",
       "8         M6C        York                                 Humewood-Cedarvale   \n",
       "9         M6E        York                                Caledonia-Fairbanks   \n",
       "10        M4G   East York                                            Leaside   \n",
       "11        M2H  North York                                  Hillcrest Village   \n",
       "12        M3H  North York  Bathurst Manor / Wilson Heights / Downsview North   \n",
       "13        M4H   East York                                   Thorncliffe Park   \n",
       "14        M2J  North York                     Fairview / Henry Farm / Oriole   \n",
       "15        M3J  North York                   Northwood Park / York University   \n",
       "16        M4J   East York                                       East Toronto   \n",
       "17        M2K  North York                                    Bayview Village   \n",
       "18        M3K  North York                                          Downsview   \n",
       "19        M2L  North York                          York Mills / Silver Hills   \n",
       "\n",
       "     Latitude  Longitude  Cluster Labels 1st Most Common Venue  \\\n",
       "0   43.753259 -79.329656             2.0                  Park   \n",
       "1   43.725882 -79.315572             0.0           Pizza Place   \n",
       "2   43.718518 -79.464763             0.0        Clothing Store   \n",
       "3   43.745906 -79.352188             0.0            Beer Store   \n",
       "4   43.706397 -79.309937             0.0           Pizza Place   \n",
       "5   43.709577 -79.445073             0.0   Japanese Restaurant   \n",
       "6   43.725900 -79.340923             0.0            Beer Store   \n",
       "7   43.695344 -79.318389             0.0              Pharmacy   \n",
       "8   43.693781 -79.428191             0.0               Dog Run   \n",
       "9   43.689026 -79.453512             2.0                  Park   \n",
       "10  43.709060 -79.363452             0.0   Sporting Goods Shop   \n",
       "11  43.803762 -79.363452             0.0           Golf Course   \n",
       "12  43.754328 -79.442259             0.0           Coffee Shop   \n",
       "13  43.705369 -79.349372             0.0     Indian Restaurant   \n",
       "14  43.778517 -79.346556             0.0        Clothing Store   \n",
       "15  43.767980 -79.487262             0.0           Coffee Shop   \n",
       "16  43.685347 -79.338106             0.0                  Park   \n",
       "17  43.786947 -79.385975             0.0   Japanese Restaurant   \n",
       "18  43.737473 -79.464763             0.0                  Park   \n",
       "19  43.757490 -79.374714             NaN                   NaN   \n",
       "\n",
       "     2nd Most Common Venue   3rd Most Common Venue      4th Most Common Venue  \\\n",
       "0        Food & Drink Shop             Yoga Studio             Discount Store   \n",
       "1              Coffee Shop       French Restaurant               Intersection   \n",
       "2   Furniture / Home Store       Accessories Store                Event Space   \n",
       "3               Restaurant             Coffee Shop           Asian Restaurant   \n",
       "4                Pet Store    Gym / Fitness Center                   Pharmacy   \n",
       "5              Pizza Place                     Pub              Metro Station   \n",
       "6               Restaurant             Coffee Shop           Asian Restaurant   \n",
       "7               Beer Store          Cosmetics Shop                Curling Ice   \n",
       "8                    Field                   Trail               Hockey Arena   \n",
       "9                      Spa           Women's Store         Frozen Yogurt Shop   \n",
       "10             Coffee Shop  Furniture / Home Store               Burger Joint   \n",
       "11      Athletics & Sports                    Pool   Mediterranean Restaurant   \n",
       "12                    Bank             Supermarket  Middle Eastern Restaurant   \n",
       "13             Yoga Studio          Sandwich Place                Gas Station   \n",
       "14             Coffee Shop    Fast Food Restaurant                       Bank   \n",
       "15      Miscellaneous Shop    Caribbean Restaurant                        Bar   \n",
       "16       Convenience Store             Coffee Shop                Yoga Studio   \n",
       "17      Chinese Restaurant                    Café                       Bank   \n",
       "18           Grocery Store          Baseball Field                       Bank   \n",
       "19                     NaN                     NaN                        NaN   \n",
       "\n",
       "     5th Most Common Venue       6th Most Common Venue  \\\n",
       "0             Concert Hall  Construction & Landscaping   \n",
       "1    Portuguese Restaurant                Hockey Arena   \n",
       "2               Shoe Store          Miscellaneous Shop   \n",
       "3      Japanese Restaurant                         Gym   \n",
       "4             Intersection        Fast Food Restaurant   \n",
       "5                     Park                 Yoga Studio   \n",
       "6      Japanese Restaurant                         Gym   \n",
       "7             Dance Studio                       Diner   \n",
       "8            Deli / Bodega                       Diner   \n",
       "9   Furniture / Home Store     Comfort Food Restaurant   \n",
       "10                    Bank                  Sports Bar   \n",
       "11                 Dog Run           French Restaurant   \n",
       "12                Pharmacy                 Pizza Place   \n",
       "13    Fast Food Restaurant               Grocery Store   \n",
       "14                Tea Room                  Restaurant   \n",
       "15          Massage Studio              Discount Store   \n",
       "16          Discount Store                Concert Hall   \n",
       "17                 Dog Run  Construction & Landscaping   \n",
       "18           Shopping Mall            Business Service   \n",
       "19                     NaN                         NaN   \n",
       "\n",
       "         7th Most Common Venue       8th Most Common Venue  \\\n",
       "0            Convenience Store              Cosmetics Shop   \n",
       "1                  Yoga Studio                Dance Studio   \n",
       "2          Sporting Goods Shop                    Boutique   \n",
       "3           Chinese Restaurant              Discount Store   \n",
       "4                         Bank              Breakfast Spot   \n",
       "5      Comfort Food Restaurant                Concert Hall   \n",
       "6           Chinese Restaurant              Discount Store   \n",
       "7                 Skating Rink                        Park   \n",
       "8           Dim Sum Restaurant                Dessert Shop   \n",
       "9                 Concert Hall  Construction & Landscaping   \n",
       "10                Dessert Shop                     Brewery   \n",
       "11         Fried Chicken Joint                Concert Hall   \n",
       "12               Deli / Bodega                 Bridal Shop   \n",
       "13                         Gym              Discount Store   \n",
       "14               Movie Theater                Liquor Store   \n",
       "15  Construction & Landscaping           Convenience Store   \n",
       "16  Construction & Landscaping              Cosmetics Shop   \n",
       "17           Convenience Store              Cosmetics Shop   \n",
       "18                Liquor Store                       Hotel   \n",
       "19                         NaN                         NaN   \n",
       "\n",
       "         9th Most Common Venue  10th Most Common Venue  \n",
       "0                  Curling Ice            Dance Studio  \n",
       "1             Department Store           Deli / Bodega  \n",
       "2                  Coffee Shop           Women's Store  \n",
       "3               Sandwich Place                    Café  \n",
       "4                    Gastropub      Athletics & Sports  \n",
       "5   Construction & Landscaping       Convenience Store  \n",
       "6               Sandwich Place                    Café  \n",
       "7           Athletics & Sports             Video Store  \n",
       "8             Department Store             Yoga Studio  \n",
       "9            Convenience Store          Cosmetics Shop  \n",
       "10                  Restaurant          Breakfast Spot  \n",
       "11  Construction & Landscaping  Furniture / Home Store  \n",
       "12                  Restaurant          Sandwich Place  \n",
       "13         Housing Development            Liquor Store  \n",
       "14                Burger Joint                Pharmacy  \n",
       "15              Cosmetics Shop             Curling Ice  \n",
       "16                 Curling Ice            Dance Studio  \n",
       "17                 Curling Ice            Dance Studio  \n",
       "18        Gym / Fitness Center          Discount Store  \n",
       "19                         NaN                     NaN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "\n",
    "neighborhoods_to_merged = df_neighborhoods_to\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "neighborhoods_to_merged = neighborhoods_to_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "neighborhoods_to_merged.head(20) # check the last columns!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PostalCode                 object\n",
      "Borough                    object\n",
      "Neighborhood               object\n",
      "Latitude                  float64\n",
      "Longitude                 float64\n",
      "Cluster Labels            float64\n",
      "1st Most Common Venue      object\n",
      "2nd Most Common Venue      object\n",
      "3rd Most Common Venue      object\n",
      "4th Most Common Venue      object\n",
      "5th Most Common Venue      object\n",
      "6th Most Common Venue      object\n",
      "7th Most Common Venue      object\n",
      "8th Most Common Venue      object\n",
      "9th Most Common Venue      object\n",
      "10th Most Common Venue     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(neighborhoods_to_merged.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PostalCode                 object\n",
      "Borough                    object\n",
      "Neighborhood               object\n",
      "Latitude                  float64\n",
      "Longitude                 float64\n",
      "Cluster Labels              int64\n",
      "1st Most Common Venue      object\n",
      "2nd Most Common Venue      object\n",
      "3rd Most Common Venue      object\n",
      "4th Most Common Venue      object\n",
      "5th Most Common Venue      object\n",
      "6th Most Common Venue      object\n",
      "7th Most Common Venue      object\n",
      "8th Most Common Venue      object\n",
      "9th Most Common Venue      object\n",
      "10th Most Common Venue     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "neighborhoods_to_merged = neighborhoods_to_merged[~neighborhoods_to_merged[\"Cluster Labels\"].isin(['NaN'])]\n",
    "neighborhoods_to_merged['Cluster Labels'] = neighborhoods_to_merged['Cluster Labels'].astype(np.int64)\n",
    "print(neighborhoods_to_merged.dtypes)"
   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
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   "metadata": {},
   "source": [
    "Finally, let's visualize the resulting clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "jupyter": {
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                {
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  "dashOffset": null,
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            var popup_fced242637634d429d0781ea3bd3d3c6 = L.popup({maxWidth: '300'});

            
                var html_58691cee805b483f836e8165ea85a544 = $('<div id="html_58691cee805b483f836e8165ea85a544" style="width: 100.0%; height: 100.0%;">Willowdale Cluster 0</div>')[0];
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x22c0f335cf8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 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(\n",
    "        neighborhoods_to_merged['Latitude'], \n",
    "        neighborhoods_to_merged['Longitude'], \n",
    "        neighborhoods_to_merged['Neighborhood'], \n",
    "        neighborhoods_to_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": [
    "<a id='item5'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Examine Clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets examine each cluster and determine the discriminating venue categories that distinguish each cluster. Based on the defining categories, we can then assign a name to each cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Portuguese Restaurant</td>\n",
       "      <td>Hockey Arena</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Accessories Store</td>\n",
       "      <td>Event Space</td>\n",
       "      <td>Shoe Store</td>\n",
       "      <td>Miscellaneous Shop</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Boutique</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Women's Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>East York</td>\n",
       "      <td>0</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Metro Station</td>\n",
       "      <td>Park</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>East York</td>\n",
       "      <td>0</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Park</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Video Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>York</td>\n",
       "      <td>0</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Field</td>\n",
       "      <td>Trail</td>\n",
       "      <td>Hockey Arena</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Yoga Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>East York</td>\n",
       "      <td>0</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Sports Bar</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Golf Course</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Bridal Shop</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>East York</td>\n",
       "      <td>0</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Housing Development</td>\n",
       "      <td>Liquor Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Movie Theater</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Pharmacy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Miscellaneous Shop</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Massage Studio</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>East York</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Business Service</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Business Service</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Basketball Court</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Business Service</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Café</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Butcher</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>York</td>\n",
       "      <td>0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Paper / Office Supplies Store</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Pet Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Business Service</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>York</td>\n",
       "      <td>0</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Bus Line</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North York</td>\n",
       "      <td>0</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Pet Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels          1st Most Common Venue  \\\n",
       "1   North York               0                    Pizza Place   \n",
       "2   North York               0                 Clothing Store   \n",
       "3   North York               0                     Beer Store   \n",
       "4    East York               0                    Pizza Place   \n",
       "5   North York               0            Japanese Restaurant   \n",
       "6   North York               0                     Beer Store   \n",
       "7    East York               0                       Pharmacy   \n",
       "8         York               0                        Dog Run   \n",
       "10   East York               0            Sporting Goods Shop   \n",
       "11  North York               0                    Golf Course   \n",
       "12  North York               0                    Coffee Shop   \n",
       "13   East York               0              Indian Restaurant   \n",
       "14  North York               0                 Clothing Store   \n",
       "15  North York               0                    Coffee Shop   \n",
       "16   East York               0                           Park   \n",
       "17  North York               0            Japanese Restaurant   \n",
       "18  North York               0                           Park   \n",
       "20  North York               0                           Park   \n",
       "21  North York               0                           Park   \n",
       "24  North York               0                           Park   \n",
       "25  North York               0                    Coffee Shop   \n",
       "26        York               0           Fast Food Restaurant   \n",
       "27  North York               0  Paper / Office Supplies Store   \n",
       "28  North York               0               Ramen Restaurant   \n",
       "29  North York               0                           Park   \n",
       "30        York               0           Caribbean Restaurant   \n",
       "33  North York               0               Ramen Restaurant   \n",
       "\n",
       "         2nd Most Common Venue   3rd Most Common Venue  \\\n",
       "1                  Coffee Shop       French Restaurant   \n",
       "2       Furniture / Home Store       Accessories Store   \n",
       "3                   Restaurant             Coffee Shop   \n",
       "4                    Pet Store    Gym / Fitness Center   \n",
       "5                  Pizza Place                     Pub   \n",
       "6                   Restaurant             Coffee Shop   \n",
       "7                   Beer Store          Cosmetics Shop   \n",
       "8                        Field                   Trail   \n",
       "10                 Coffee Shop  Furniture / Home Store   \n",
       "11          Athletics & Sports                    Pool   \n",
       "12                        Bank             Supermarket   \n",
       "13                 Yoga Studio          Sandwich Place   \n",
       "14                 Coffee Shop    Fast Food Restaurant   \n",
       "15          Miscellaneous Shop    Caribbean Restaurant   \n",
       "16           Convenience Store             Coffee Shop   \n",
       "17          Chinese Restaurant                    Café   \n",
       "18               Grocery Store          Baseball Field   \n",
       "20               Grocery Store          Baseball Field   \n",
       "21  Construction & Landscaping                  Bakery   \n",
       "24               Grocery Store          Baseball Field   \n",
       "25          Italian Restaurant              Restaurant   \n",
       "26                 Coffee Shop          Sandwich Place   \n",
       "27              Baseball Field             Yoga Studio   \n",
       "28                 Pizza Place             Coffee Shop   \n",
       "29               Grocery Store          Baseball Field   \n",
       "30                    Bus Line                 Brewery   \n",
       "33                 Pizza Place             Coffee Shop   \n",
       "\n",
       "        4th Most Common Venue    5th Most Common Venue  \\\n",
       "1                Intersection    Portuguese Restaurant   \n",
       "2                 Event Space               Shoe Store   \n",
       "3            Asian Restaurant      Japanese Restaurant   \n",
       "4                    Pharmacy             Intersection   \n",
       "5               Metro Station                     Park   \n",
       "6            Asian Restaurant      Japanese Restaurant   \n",
       "7                 Curling Ice             Dance Studio   \n",
       "8                Hockey Arena            Deli / Bodega   \n",
       "10               Burger Joint                     Bank   \n",
       "11   Mediterranean Restaurant                  Dog Run   \n",
       "12  Middle Eastern Restaurant                 Pharmacy   \n",
       "13                Gas Station     Fast Food Restaurant   \n",
       "14                       Bank                 Tea Room   \n",
       "15                        Bar           Massage Studio   \n",
       "16                Yoga Studio           Discount Store   \n",
       "17                       Bank                  Dog Run   \n",
       "18                       Bank            Shopping Mall   \n",
       "20                       Bank            Shopping Mall   \n",
       "21           Basketball Court                  Dog Run   \n",
       "24                       Bank            Shopping Mall   \n",
       "25           Sushi Restaurant           Sandwich Place   \n",
       "26                      Diner  Comfort Food Restaurant   \n",
       "27             Discount Store             Concert Hall   \n",
       "28                       Café           Sandwich Place   \n",
       "29                       Bank            Shopping Mall   \n",
       "30                Yoga Studio           Discount Store   \n",
       "33                       Café           Sandwich Place   \n",
       "\n",
       "         6th Most Common Venue       7th Most Common Venue  \\\n",
       "1                 Hockey Arena                 Yoga Studio   \n",
       "2           Miscellaneous Shop         Sporting Goods Shop   \n",
       "3                          Gym          Chinese Restaurant   \n",
       "4         Fast Food Restaurant                        Bank   \n",
       "5                  Yoga Studio     Comfort Food Restaurant   \n",
       "6                          Gym          Chinese Restaurant   \n",
       "7                        Diner                Skating Rink   \n",
       "8                        Diner          Dim Sum Restaurant   \n",
       "10                  Sports Bar                Dessert Shop   \n",
       "11           French Restaurant         Fried Chicken Joint   \n",
       "12                 Pizza Place               Deli / Bodega   \n",
       "13               Grocery Store                         Gym   \n",
       "14                  Restaurant               Movie Theater   \n",
       "15              Discount Store  Construction & Landscaping   \n",
       "16                Concert Hall  Construction & Landscaping   \n",
       "17  Construction & Landscaping           Convenience Store   \n",
       "18            Business Service                Liquor Store   \n",
       "20            Business Service                Liquor Store   \n",
       "21                Concert Hall           Convenience Store   \n",
       "24            Business Service                Liquor Store   \n",
       "25                        Café                Liquor Store   \n",
       "26                Concert Hall  Construction & Landscaping   \n",
       "27  Construction & Landscaping           Convenience Store   \n",
       "28                  Restaurant             Bubble Tea Shop   \n",
       "29            Business Service                Liquor Store   \n",
       "30  Construction & Landscaping           Convenience Store   \n",
       "33                  Restaurant             Bubble Tea Shop   \n",
       "\n",
       "   8th Most Common Venue       9th Most Common Venue  10th Most Common Venue  \n",
       "1           Dance Studio            Department Store           Deli / Bodega  \n",
       "2               Boutique                 Coffee Shop           Women's Store  \n",
       "3         Discount Store              Sandwich Place                    Café  \n",
       "4         Breakfast Spot                   Gastropub      Athletics & Sports  \n",
       "5           Concert Hall  Construction & Landscaping       Convenience Store  \n",
       "6         Discount Store              Sandwich Place                    Café  \n",
       "7                   Park          Athletics & Sports             Video Store  \n",
       "8           Dessert Shop            Department Store             Yoga Studio  \n",
       "10               Brewery                  Restaurant          Breakfast Spot  \n",
       "11          Concert Hall  Construction & Landscaping  Furniture / Home Store  \n",
       "12           Bridal Shop                  Restaurant          Sandwich Place  \n",
       "13        Discount Store         Housing Development            Liquor Store  \n",
       "14          Liquor Store                Burger Joint                Pharmacy  \n",
       "15     Convenience Store              Cosmetics Shop             Curling Ice  \n",
       "16        Cosmetics Shop                 Curling Ice            Dance Studio  \n",
       "17        Cosmetics Shop                 Curling Ice            Dance Studio  \n",
       "18                 Hotel        Gym / Fitness Center          Discount Store  \n",
       "20                 Hotel        Gym / Fitness Center          Discount Store  \n",
       "21        Cosmetics Shop                 Curling Ice            Dance Studio  \n",
       "24                 Hotel        Gym / Fitness Center          Discount Store  \n",
       "25               Butcher                    Pharmacy             Pizza Place  \n",
       "26     Convenience Store              Cosmetics Shop             Curling Ice  \n",
       "27        Cosmetics Shop                 Curling Ice            Dance Studio  \n",
       "28                 Plaza                    Pharmacy               Pet Store  \n",
       "29                 Hotel        Gym / Fitness Center          Discount Store  \n",
       "30        Cosmetics Shop                 Curling Ice            Dance Studio  \n",
       "33                 Plaza                    Pharmacy               Pet Store  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_merged.loc[neighborhoods_to_merged['Cluster Labels'] == 0, neighborhoods_to_merged.columns[[1] + list(range(5, neighborhoods_to_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>North York</td>\n",
       "      <td>1</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "22  North York               1           Pizza Place        Discount Store   \n",
       "\n",
       "      3rd Most Common Venue 4th Most Common Venue       5th Most Common Venue  \\\n",
       "22  Comfort Food Restaurant          Concert Hall  Construction & Landscaping   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "22     Convenience Store        Cosmetics Shop           Curling Ice   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "22          Dance Studio          Deli / Bodega  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_merged.loc[neighborhoods_to_merged['Cluster Labels'] == 1, neighborhoods_to_merged.columns[[1] + list(range(5, neighborhoods_to_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North York</td>\n",
       "      <td>2</td>\n",
       "      <td>Park</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>York</td>\n",
       "      <td>2</td>\n",
       "      <td>Park</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>Frozen Yogurt Shop</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>York</td>\n",
       "      <td>2</td>\n",
       "      <td>Park</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>North York</td>\n",
       "      <td>2</td>\n",
       "      <td>Park</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Curling Ice</td>\n",
       "      <td>Dance Studio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0   North York               2                  Park     Food & Drink Shop   \n",
       "9         York               2                  Park                   Spa   \n",
       "31        York               2                  Park           Yoga Studio   \n",
       "32  North York               2                  Park     Convenience Store   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue       5th Most Common Venue  \\\n",
       "0            Yoga Studio        Discount Store                Concert Hall   \n",
       "9          Women's Store    Frozen Yogurt Shop      Furniture / Home Store   \n",
       "31        Discount Store          Concert Hall  Construction & Landscaping   \n",
       "32                  Bank                   Bar                     Dog Run   \n",
       "\n",
       "         6th Most Common Venue       7th Most Common Venue  \\\n",
       "0   Construction & Landscaping           Convenience Store   \n",
       "9      Comfort Food Restaurant                Concert Hall   \n",
       "31           Convenience Store              Cosmetics Shop   \n",
       "32                Concert Hall  Construction & Landscaping   \n",
       "\n",
       "         8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "0               Cosmetics Shop           Curling Ice           Dance Studio  \n",
       "9   Construction & Landscaping     Convenience Store         Cosmetics Shop  \n",
       "31                 Curling Ice          Dance Studio          Deli / Bodega  \n",
       "32              Cosmetics Shop           Curling Ice           Dance Studio  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_merged.loc[neighborhoods_to_merged['Cluster Labels'] == 2, neighborhoods_to_merged.columns[[1] + list(range(5, neighborhoods_to_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=center><font size = 5>Segmenting and Clustering Neighborhoods in Waterloo</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Download and Explore Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load and explore the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tranform the data into a *pandas* dataframe and eliminate Coulumn with no name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read Waterloo dataset\n",
    "wneighborhoods = pd.read_csv('waterloo_Geospatial_Coordinates.csv',sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
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       "\n",
       "    .dataframe thead th {\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>Postal code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N2L 5Y9</td>\n",
       "      <td>Waterloo South</td>\n",
       "      <td>Beechwood</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N2T 2Y2</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>Clair Hills</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.454848</td>\n",
       "      <td>-80.579832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N2K 3H7</td>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>Colonial Acres</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.507148</td>\n",
       "      <td>-80.520306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N2L 5Y9</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>COLUMBIA FOREST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N2V 2S2</td>\n",
       "      <td>Waterloo North</td>\n",
       "      <td>Conservation Meadows</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.490019</td>\n",
       "      <td>-80.58534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal code         Borough          Neighborhood  Unnamed: 3   Latitude  \\\n",
       "0     N2L 5Y9  Waterloo South             Beechwood         NaN  43.458418   \n",
       "1     N2T 2Y2   Waterloo West           Clair Hills         NaN  43.454848   \n",
       "2     N2K 3H7   Waterloo East        Colonial Acres         NaN  43.507148   \n",
       "3     N2L 5Y9   Waterloo West       COLUMBIA FOREST         NaN  43.458418   \n",
       "4     N2V 2S2  Waterloo North  Conservation Meadows         NaN  43.490019   \n",
       "\n",
       "     Longitude  \n",
       "0   -80.557473  \n",
       "1   -80.579832  \n",
       "2   -80.520306  \n",
       "3   -80.557473  \n",
       "4    -80.58534  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the data\n",
    "wneighborhoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop Column with no name  and  examine the resulting dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal code</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>N2L 5Y9</td>\n",
       "      <td>Waterloo South</td>\n",
       "      <td>Beechwood</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N2T 2Y2</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>Clair Hills</td>\n",
       "      <td>43.454848</td>\n",
       "      <td>-80.579832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N2K 3H7</td>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>Colonial Acres</td>\n",
       "      <td>43.507148</td>\n",
       "      <td>-80.520306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N2L 5Y9</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>COLUMBIA FOREST</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N2V 2S2</td>\n",
       "      <td>Waterloo North</td>\n",
       "      <td>Conservation Meadows</td>\n",
       "      <td>43.490019</td>\n",
       "      <td>-80.58534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>N2K 3N8</td>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>KIWANIS PARK</td>\n",
       "      <td>43.501733</td>\n",
       "      <td>-80.479472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>N2T 2T6</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>LAURELWOOD</td>\n",
       "      <td>43.466481</td>\n",
       "      <td>-80.582579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>N2K 2X1</td>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>43.502022</td>\n",
       "      <td>-80.500654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>N2J 3W2</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>LINCOLN HEIGHTS</td>\n",
       "      <td>43.479862</td>\n",
       "      <td>-80.503212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>N2K 1V9</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>LINCOLN VILLAGE</td>\n",
       "      <td>43.490705</td>\n",
       "      <td>-80.501044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>N2L 2N3</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>MAPLE HILLS</td>\n",
       "      <td>43.450887</td>\n",
       "      <td>-80.546273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>N2V 1Y6</td>\n",
       "      <td>Waterloo North</td>\n",
       "      <td>NORTH LAKESHORE</td>\n",
       "      <td>43.499129</td>\n",
       "      <td>-80.563996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>N2K 3X2</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>UNIVERSITY DOWNS</td>\n",
       "      <td>43.489621</td>\n",
       "      <td>-80.487291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>N2T 1S8</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>UPPER BEECHWOOD</td>\n",
       "      <td>43.462144</td>\n",
       "      <td>-80.569948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>N2J 2Z4</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>UPTOWN WATERLOO</td>\n",
       "      <td>43.483673</td>\n",
       "      <td>-80.526867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>N2V 0B3</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>VISTA HILLS</td>\n",
       "      <td>43.451008</td>\n",
       "      <td>-80.591636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>N2L 2G8</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>WESTMOUNT</td>\n",
       "      <td>43.459421</td>\n",
       "      <td>-80.529979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>N2T 1Y5</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>WESTVALE</td>\n",
       "      <td>43.445358</td>\n",
       "      <td>-80.557746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Postal code         Borough          Neighborhood   Latitude    Longitude\n",
       "0      N2L 5Y9  Waterloo South             Beechwood  43.458418   -80.557473\n",
       "1      N2T 2Y2   Waterloo West           Clair Hills  43.454848   -80.579832\n",
       "2      N2K 3H7   Waterloo East        Colonial Acres  43.507148   -80.520306\n",
       "3      N2L 5Y9   Waterloo West       COLUMBIA FOREST  43.458418   -80.557473\n",
       "4      N2V 2S2  Waterloo North  Conservation Meadows  43.490019    -80.58534\n",
       "5      N2K 3N8   Waterloo East          KIWANIS PARK  43.501733   -80.479472\n",
       "6      N2T 2T6        Waterloo            LAURELWOOD  43.466481   -80.582579\n",
       "7      N2K 2X1   Waterloo East             LEXINGTON  43.502022   -80.500654\n",
       "8      N2J 3W2        Waterloo       LINCOLN HEIGHTS  43.479862   -80.503212\n",
       "9      N2K 1V9        Waterloo       LINCOLN VILLAGE  43.490705   -80.501044\n",
       "10     N2L 2N3        Waterloo           MAPLE HILLS  43.450887   -80.546273\n",
       "11     N2V 1Y6  Waterloo North       NORTH LAKESHORE  43.499129   -80.563996\n",
       "12     N2K 3X2        Waterloo      UNIVERSITY DOWNS  43.489621   -80.487291\n",
       "13     N2T 1S8        Waterloo       UPPER BEECHWOOD  43.462144   -80.569948\n",
       "14     N2J 2Z4        Waterloo       UPTOWN WATERLOO  43.483673   -80.526867\n",
       "15     N2V 0B3   Waterloo West           VISTA HILLS  43.451008   -80.591636\n",
       "16     N2L 2G8        Waterloo             WESTMOUNT  43.459421   -80.529979\n",
       "17     N2T 1Y5   Waterloo West              WESTVALE  43.445358   -80.557746"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wneighborhoods.drop(wneighborhoods.columns[[3]], axis=1, inplace=True)\n",
    "wneighborhoods.head(18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert Longitude from  object to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Postal code      object\n",
      "Borough          object\n",
      "Neighborhood     object\n",
      "Latitude        float64\n",
      "Longitude       float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "wneighborhoods[\"Longitude\"] = wneighborhoods[\"Longitude\"].astype(str).astype(float)\n",
    "print(wneighborhoods.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And make sure that the dataset has all 5 boroughs and 18 neighborhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataframe has 5 boroughs and 18 neighborhoods.\n"
     ]
    }
   ],
   "source": [
    "print('The dataframe has {} boroughs and {} neighborhoods.'.format(\n",
    "        len(wneighborhoods['Borough'].unique()),\n",
    "        wneighborhoods.shape[0]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use geopy library to get the latitude and longitude values of Waterloo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to define an instance of the geocoder, we need to define a user_agent. We will name our agent <em>toronto_explorer</em>, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Waterloo city are 43.466874, -80.524635.\n"
     ]
    }
   ],
   "source": [
    "address = \"Waterloo, ON\"\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"Waterloo_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Waterloo city are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check  Latitude and the longitude coordinates of  neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>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>N2L 5Y9</td>\n",
       "      <td>Waterloo South</td>\n",
       "      <td>Beechwood</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N2T 2Y2</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>Clair Hills</td>\n",
       "      <td>43.454848</td>\n",
       "      <td>-80.579832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N2K 3H7</td>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>Colonial Acres</td>\n",
       "      <td>43.507148</td>\n",
       "      <td>-80.520306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N2L 5Y9</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>COLUMBIA FOREST</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N2V 2S2</td>\n",
       "      <td>Waterloo North</td>\n",
       "      <td>Conservation Meadows</td>\n",
       "      <td>43.490019</td>\n",
       "      <td>-80.585340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal code         Borough          Neighborhood   Latitude  Longitude\n",
       "0     N2L 5Y9  Waterloo South             Beechwood  43.458418 -80.557473\n",
       "1     N2T 2Y2   Waterloo West           Clair Hills  43.454848 -80.579832\n",
       "2     N2K 3H7   Waterloo East        Colonial Acres  43.507148 -80.520306\n",
       "3     N2L 5Y9   Waterloo West       COLUMBIA FOREST  43.458418 -80.557473\n",
       "4     N2V 2S2  Waterloo North  Conservation Meadows  43.490019 -80.585340"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wneighborhoods.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x22c0f357358>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Waterloo using latitude and longitude values\n",
    "map_neighborhoods = folium.Map(location=[latitude, longitude], zoom_start=13)\n",
    "map_neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add markers to Waterloo map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
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  "color": "blue",
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  "lineJoin": "round",
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x22c0f357358>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cretae a new data fraim \n",
    "df_neighborhoods = wneighborhoods\n",
    "\n",
    "for lat, lng, borough, neighborhood in zip(\n",
    "        df_neighborhoods['Latitude'], \n",
    "        df_neighborhoods['Longitude'], \n",
    "        df_neighborhoods['Borough'], \n",
    "        df_neighborhoods['Neighborhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_neighborhoods)  \n",
    "\n",
    "map_neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a data frame with boroughs that contain the word \"Waterloo\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>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>N2L 5Y9</td>\n",
       "      <td>Waterloo South</td>\n",
       "      <td>Beechwood</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N2T 2Y2</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>Clair Hills</td>\n",
       "      <td>43.454848</td>\n",
       "      <td>-80.579832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N2K 3H7</td>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>Colonial Acres</td>\n",
       "      <td>43.507148</td>\n",
       "      <td>-80.520306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N2L 5Y9</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>COLUMBIA FOREST</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N2V 2S2</td>\n",
       "      <td>Waterloo North</td>\n",
       "      <td>Conservation Meadows</td>\n",
       "      <td>43.490019</td>\n",
       "      <td>-80.585340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal code         Borough          Neighborhood   Latitude  Longitude\n",
       "0     N2L 5Y9  Waterloo South             Beechwood  43.458418 -80.557473\n",
       "1     N2T 2Y2   Waterloo West           Clair Hills  43.454848 -80.579832\n",
       "2     N2K 3H7   Waterloo East        Colonial Acres  43.507148 -80.520306\n",
       "3     N2L 5Y9   Waterloo West       COLUMBIA FOREST  43.458418 -80.557473\n",
       "4     N2V 2S2  Waterloo North  Conservation Meadows  43.490019 -80.585340"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neighborhoods_to = df_neighborhoods[df_neighborhoods['Borough'].str.contains(\"Waterloo\")].reset_index(drop=True)\n",
    "df_neighborhoods_to.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### create Neighborhood map "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x22c0fc40f60>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_neighborhoods_to = folium.Map(location=[latitude, longitude], zoom_start=13)\n",
    "for lat, lng, borough, neighborhood in zip(\n",
    "        df_neighborhoods_to['Latitude'], \n",
    "        df_neighborhoods_to['Longitude'], \n",
    "        df_neighborhoods_to['Borough'], \n",
    "        df_neighborhoods_to['Neighborhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_neighborhoods_to)  \n",
    "\n",
    "map_neighborhoods_to"
   ]
  },
  {
   "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": [
    "#### Let's explore the first neighborhood in our dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the neighborhood's name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first neighborhood's name is 'Beechwood'.\n",
      "Latitude and longitude values of Beechwood are 43.458418, -80.557473.\n"
     ]
    }
   ],
   "source": [
    "neighborhood_name = df_neighborhoods_to.loc[0, 'Neighborhood']\n",
    "print(f\"The first neighborhood's name is '{neighborhood_name}'.\")\n",
    "\n",
    "neighborhood_latitude = df_neighborhoods_to.loc[0, 'Latitude'] \n",
    "neighborhood_longitude = df_neighborhoods_to.loc[0, 'Longitude'] \n",
    "\n",
    "print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n",
    "                                                               neighborhood_latitude, \n",
    "                                                               neighborhood_longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of Beechwood are 43.458418, -80.557473.\n"
     ]
    }
   ],
   "source": [
    "neighborhood_latitude = df_neighborhoods_to.loc[0, 'Latitude'] # neighborhood latitude value\n",
    "neighborhood_longitude = df_neighborhoods_to.loc[0, 'Longitude'] # neighborhood longitude value\n",
    "radius = 500\n",
    "neighborhood_name = df_neighborhoods_to.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 50 venues that are in Beechwood  within a radius of 2000 meters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's create the GET request URL. Name your URL **url**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# limit of number of venues returned by Foursquare API\n",
    "LIMIT = 50\n",
    "# define radius\n",
    "radius = 2000 \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 the result to a json file\n",
    "results = requests.get(url).json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract the category of the venue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Foursquare lab in the previous module, we know that all the information is in the *items* key. Before we proceed, let's borrow the **get_category_type** function from the Foursquare lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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 we are ready to clean the json and structure it into a *pandas* dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Zehrs</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>43.453083</td>\n",
       "      <td>-80.555858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GoodLife Fitness</td>\n",
       "      <td>Gym</td>\n",
       "      <td>43.452983</td>\n",
       "      <td>-80.555471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Churchill Arms</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>43.454589</td>\n",
       "      <td>-80.546438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>43.467966</td>\n",
       "      <td>-80.568014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dutchies Fresh Market</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>43.444885</td>\n",
       "      <td>-80.570749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name     categories        lat        lng\n",
       "0                  Zehrs    Supermarket  43.453083 -80.555858\n",
       "1       GoodLife Fitness            Gym  43.452983 -80.555471\n",
       "2         Churchill Arms      Gastropub  43.454589 -80.546438\n",
       "3              Starbucks    Coffee Shop  43.467966 -80.568014\n",
       "4  Dutchies Fresh Market  Grocery Store  43.444885 -80.570749"
      ]
     },
     "execution_count": 56,
     "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": [
    "And how many venues were returned by Foursquare?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 venues were returned by Foursquare.\n"
     ]
    }
   ],
   "source": [
    "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Explore Neighborhoods in Waterloo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's create a function to repeat the same process to all the neighborhoods in Waterloo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    venues_list=[]\n",
    "    \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": [
    "#### Now write the code to run the above function on each neighborhood and create a new dataframe called *neighborhoods_to_venues*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighborhoods_to_venues = getNearbyVenues(names=df_neighborhoods_to['Neighborhood'],\n",
    "                                   latitudes=df_neighborhoods_to['Latitude'],\n",
    "                                   longitudes=df_neighborhoods_to['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's check the size of the resulting dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(76, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beechwood</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "      <td>Regency Park</td>\n",
       "      <td>43.460033</td>\n",
       "      <td>-80.560102</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beechwood</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "      <td>Turtle pond toys</td>\n",
       "      <td>43.460430</td>\n",
       "      <td>-80.561360</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beechwood</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "      <td>Clair Lake Park</td>\n",
       "      <td>43.461903</td>\n",
       "      <td>-80.558080</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beechwood</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "      <td>Sargent Munchell Marketing Headquarters</td>\n",
       "      <td>43.457600</td>\n",
       "      <td>-80.562800</td>\n",
       "      <td>Brewery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Beechwood</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "      <td>Centennial Baseball Field 2</td>\n",
       "      <td>43.461258</td>\n",
       "      <td>-80.552789</td>\n",
       "      <td>Baseball Field</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  Neighborhood Latitude  Neighborhood Longitude  \\\n",
       "0    Beechwood              43.458418              -80.557473   \n",
       "1    Beechwood              43.458418              -80.557473   \n",
       "2    Beechwood              43.458418              -80.557473   \n",
       "3    Beechwood              43.458418              -80.557473   \n",
       "4    Beechwood              43.458418              -80.557473   \n",
       "\n",
       "                                     Venue  Venue Latitude  Venue Longitude  \\\n",
       "0                             Regency Park       43.460033       -80.560102   \n",
       "1                         Turtle pond toys       43.460430       -80.561360   \n",
       "2                          Clair Lake Park       43.461903       -80.558080   \n",
       "3  Sargent Munchell Marketing Headquarters       43.457600       -80.562800   \n",
       "4              Centennial Baseball Field 2       43.461258       -80.552789   \n",
       "\n",
       "     Venue Category  \n",
       "0              Park  \n",
       "1  Toy / Game Store  \n",
       "2              Park  \n",
       "3           Brewery  \n",
       "4    Baseball Field  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(neighborhoods_to_venues.shape)\n",
    "neighborhoods_to_venues.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check how many venues were returned for each neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beechwood</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLUMBIA FOREST</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clair Hills</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colonial Acres</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conservation Meadows</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIWANIS PARK</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAURELWOOD</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEXINGTON</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINCOLN HEIGHTS</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINCOLN VILLAGE</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPLE HILLS</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NORTH LAKESHORE</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNIVERSITY DOWNS</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPPER BEECHWOOD</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPTOWN WATERLOO</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VISTA HILLS</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WESTMOUNT</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WESTVALE</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Neighborhood Latitude  Neighborhood Longitude  Venue  \\\n",
       "Neighborhood                                                                 \n",
       "Beechwood                                 5                       5      5   \n",
       "COLUMBIA FOREST                           5                       5      5   \n",
       "Clair Hills                               2                       2      2   \n",
       "Colonial Acres                            7                       7      7   \n",
       "Conservation Meadows                      2                       2      2   \n",
       "KIWANIS PARK                              5                       5      5   \n",
       "LAURELWOOD                                1                       1      1   \n",
       "LEXINGTON                                 4                       4      4   \n",
       "LINCOLN HEIGHTS                           4                       4      4   \n",
       "LINCOLN VILLAGE                           2                       2      2   \n",
       "MAPLE HILLS                               3                       3      3   \n",
       "NORTH LAKESHORE                           4                       4      4   \n",
       "UNIVERSITY DOWNS                          8                       8      8   \n",
       "UPPER BEECHWOOD                           2                       2      2   \n",
       "UPTOWN WATERLOO                          16                      16     16   \n",
       "VISTA HILLS                               1                       1      1   \n",
       "WESTMOUNT                                 3                       3      3   \n",
       "WESTVALE                                  2                       2      2   \n",
       "\n",
       "                      Venue Latitude  Venue Longitude  Venue Category  \n",
       "Neighborhood                                                           \n",
       "Beechwood                          5                5               5  \n",
       "COLUMBIA FOREST                    5                5               5  \n",
       "Clair Hills                        2                2               2  \n",
       "Colonial Acres                     7                7               7  \n",
       "Conservation Meadows               2                2               2  \n",
       "KIWANIS PARK                       5                5               5  \n",
       "LAURELWOOD                         1                1               1  \n",
       "LEXINGTON                          4                4               4  \n",
       "LINCOLN HEIGHTS                    4                4               4  \n",
       "LINCOLN VILLAGE                    2                2               2  \n",
       "MAPLE HILLS                        3                3               3  \n",
       "NORTH LAKESHORE                    4                4               4  \n",
       "UNIVERSITY DOWNS                   8                8               8  \n",
       "UPPER BEECHWOOD                    2                2               2  \n",
       "UPTOWN WATERLOO                   16               16              16  \n",
       "VISTA HILLS                        1                1               1  \n",
       "WESTMOUNT                          3                3               3  \n",
       "WESTVALE                           2                2               2  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_venues.groupby('Neighborhood').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's find out how many unique categories can be curated from all the returned venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 50 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} uniques categories.'.format(len(neighborhoods_to_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Analyze Each Neighborhood in Waterloo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>American Restaurant</th>\n",
       "      <th>Baseball Field</th>\n",
       "      <th>Bath House</th>\n",
       "      <th>Brewery</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Café</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Clothing Store</th>\n",
       "      <th>Coffee Shop</th>\n",
       "      <th>College Classroom</th>\n",
       "      <th>Construction &amp; Landscaping</th>\n",
       "      <th>Convenience Store</th>\n",
       "      <th>Dog Run</th>\n",
       "      <th>Dry Cleaner</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Food &amp; Drink Shop</th>\n",
       "      <th>Food Truck</th>\n",
       "      <th>Furniture / Home Store</th>\n",
       "      <th>Gas Station</th>\n",
       "      <th>Gastropub</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Home Service</th>\n",
       "      <th>Hotel</th>\n",
       "      <th>Indian Restaurant</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Korean Restaurant</th>\n",
       "      <th>Martial Arts Dojo</th>\n",
       "      <th>Mediterranean Restaurant</th>\n",
       "      <th>Nail Salon</th>\n",
       "      <th>Nature Preserve</th>\n",
       "      <th>Nightclub</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Photography Studio</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Playground</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Pub</th>\n",
       "      <th>Record Shop</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>River</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <th>Skating Rink</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Toy / Game Store</th>\n",
       "      <th>Trailer Park</th>\n",
       "      <th>Warehouse Store</th>\n",
       "      <th>Wings Joint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beechwood</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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beechwood</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",
       "      <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",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beechwood</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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beechwood</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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>Beechwood</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  American Restaurant  Baseball Field  Bath House  Brewery  \\\n",
       "0    Beechwood                    0               0           0        0   \n",
       "1    Beechwood                    0               0           0        0   \n",
       "2    Beechwood                    0               0           0        0   \n",
       "3    Beechwood                    0               0           0        1   \n",
       "4    Beechwood                    0               1           0        0   \n",
       "\n",
       "   Burger Joint  Café  Chinese Restaurant  Clothing Store  Coffee Shop  \\\n",
       "0             0     0                   0               0            0   \n",
       "1             0     0                   0               0            0   \n",
       "2             0     0                   0               0            0   \n",
       "3             0     0                   0               0            0   \n",
       "4             0     0                   0               0            0   \n",
       "\n",
       "   College Classroom  Construction & Landscaping  Convenience Store  Dog Run  \\\n",
       "0                  0                           0                  0        0   \n",
       "1                  0                           0                  0        0   \n",
       "2                  0                           0                  0        0   \n",
       "3                  0                           0                  0        0   \n",
       "4                  0                           0                  0        0   \n",
       "\n",
       "   Dry Cleaner  Fast Food Restaurant  Food & Drink Shop  Food Truck  \\\n",
       "0            0                     0                  0           0   \n",
       "1            0                     0                  0           0   \n",
       "2            0                     0                  0           0   \n",
       "3            0                     0                  0           0   \n",
       "4            0                     0                  0           0   \n",
       "\n",
       "   Furniture / Home Store  Gas Station  Gastropub  Grocery Store  \\\n",
       "0                       0            0          0              0   \n",
       "1                       0            0          0              0   \n",
       "2                       0            0          0              0   \n",
       "3                       0            0          0              0   \n",
       "4                       0            0          0              0   \n",
       "\n",
       "   Home Service  Hotel  Indian Restaurant  Italian Restaurant  \\\n",
       "0             0      0                  0                   0   \n",
       "1             0      0                  0                   0   \n",
       "2             0      0                  0                   0   \n",
       "3             0      0                  0                   0   \n",
       "4             0      0                  0                   0   \n",
       "\n",
       "   Korean Restaurant  Martial Arts Dojo  Mediterranean Restaurant  Nail Salon  \\\n",
       "0                  0                  0                         0           0   \n",
       "1                  0                  0                         0           0   \n",
       "2                  0                  0                         0           0   \n",
       "3                  0                  0                         0           0   \n",
       "4                  0                  0                         0           0   \n",
       "\n",
       "   Nature Preserve  Nightclub  Park  Pet Store  Pharmacy  Photography Studio  \\\n",
       "0                0          0     1          0         0                   0   \n",
       "1                0          0     0          0         0                   0   \n",
       "2                0          0     1          0         0                   0   \n",
       "3                0          0     0          0         0                   0   \n",
       "4                0          0     0          0         0                   0   \n",
       "\n",
       "   Pizza Place  Playground  Pool  Pub  Record Shop  Restaurant  River  \\\n",
       "0            0           0     0    0            0           0      0   \n",
       "1            0           0     0    0            0           0      0   \n",
       "2            0           0     0    0            0           0      0   \n",
       "3            0           0     0    0            0           0      0   \n",
       "4            0           0     0    0            0           0      0   \n",
       "\n",
       "   Sandwich Place  Shop & Service  Skating Rink  Sushi Restaurant  \\\n",
       "0               0               0             0                 0   \n",
       "1               0               0             0                 0   \n",
       "2               0               0             0                 0   \n",
       "3               0               0             0                 0   \n",
       "4               0               0             0                 0   \n",
       "\n",
       "   Toy / Game Store  Trailer Park  Warehouse Store  Wings Joint  \n",
       "0                 0             0                0            0  \n",
       "1                 1             0                0            0  \n",
       "2                 0             0                0            0  \n",
       "3                 0             0                0            0  \n",
       "4                 0             0                0            0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "neighborhoods_to_onehot = pd.get_dummies(neighborhoods_to_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "neighborhoods_to_onehot['Neighborhood'] = neighborhoods_to_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [neighborhoods_to_onehot.columns[-1]] + list(neighborhoods_to_onehot.columns[:-1])\n",
    "neighborhoods_to_onehot = neighborhoods_to_onehot[fixed_columns]\n",
    "\n",
    "neighborhoods_to_onehot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's examine the new dataframe size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76, 51)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_onehot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>American Restaurant</th>\n",
       "      <th>Baseball Field</th>\n",
       "      <th>Bath House</th>\n",
       "      <th>Brewery</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Café</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Clothing Store</th>\n",
       "      <th>Coffee Shop</th>\n",
       "      <th>College Classroom</th>\n",
       "      <th>Construction &amp; Landscaping</th>\n",
       "      <th>Convenience Store</th>\n",
       "      <th>Dog Run</th>\n",
       "      <th>Dry Cleaner</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Food &amp; Drink Shop</th>\n",
       "      <th>Food Truck</th>\n",
       "      <th>Furniture / Home Store</th>\n",
       "      <th>Gas Station</th>\n",
       "      <th>Gastropub</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Home Service</th>\n",
       "      <th>Hotel</th>\n",
       "      <th>Indian Restaurant</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Korean Restaurant</th>\n",
       "      <th>Martial Arts Dojo</th>\n",
       "      <th>Mediterranean Restaurant</th>\n",
       "      <th>Nail Salon</th>\n",
       "      <th>Nature Preserve</th>\n",
       "      <th>Nightclub</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Photography Studio</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Playground</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Pub</th>\n",
       "      <th>Record Shop</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>River</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <th>Skating Rink</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Toy / Game Store</th>\n",
       "      <th>Trailer Park</th>\n",
       "      <th>Warehouse Store</th>\n",
       "      <th>Wings Joint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beechwood</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COLUMBIA FOREST</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clair Hills</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Colonial Acres</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Conservation Meadows</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Neighborhood  American Restaurant  Baseball Field  Bath House  \\\n",
       "0             Beechwood                  0.0             0.2         0.0   \n",
       "1       COLUMBIA FOREST                  0.0             0.2         0.0   \n",
       "2           Clair Hills                  0.0             0.0         0.0   \n",
       "3        Colonial Acres                  0.0             0.0         0.0   \n",
       "4  Conservation Meadows                  0.0             0.0         0.0   \n",
       "\n",
       "   Brewery  Burger Joint  Café  Chinese Restaurant  Clothing Store  \\\n",
       "0      0.2           0.0   0.0                 0.0             0.0   \n",
       "1      0.2           0.0   0.0                 0.0             0.0   \n",
       "2      0.0           0.0   0.0                 0.0             0.0   \n",
       "3      0.0           0.0   0.0                 0.0             0.0   \n",
       "4      0.0           0.0   0.0                 0.0             0.0   \n",
       "\n",
       "   Coffee Shop  College Classroom  Construction & Landscaping  \\\n",
       "0          0.0                0.0                         0.0   \n",
       "1          0.0                0.0                         0.0   \n",
       "2          0.0                0.0                         0.0   \n",
       "3          0.0                0.0                         0.0   \n",
       "4          0.0                0.0                         0.0   \n",
       "\n",
       "   Convenience Store  Dog Run  Dry Cleaner  Fast Food Restaurant  \\\n",
       "0                0.0      0.0          0.0                   0.0   \n",
       "1                0.0      0.0          0.0                   0.0   \n",
       "2                0.0      0.0          0.0                   0.0   \n",
       "3                0.0      0.0          0.0                   0.0   \n",
       "4                0.0      0.0          0.0                   0.0   \n",
       "\n",
       "   Food & Drink Shop  Food Truck  Furniture / Home Store  Gas Station  \\\n",
       "0           0.000000         0.0                0.000000          0.0   \n",
       "1           0.000000         0.0                0.000000          0.0   \n",
       "2           0.000000         0.5                0.000000          0.0   \n",
       "3           0.285714         0.0                0.285714          0.0   \n",
       "4           0.000000         0.0                0.000000          0.0   \n",
       "\n",
       "   Gastropub  Grocery Store  Home Service  Hotel  Indian Restaurant  \\\n",
       "0        0.0            0.0           0.0    0.0           0.000000   \n",
       "1        0.0            0.0           0.0    0.0           0.000000   \n",
       "2        0.0            0.0           0.0    0.0           0.000000   \n",
       "3        0.0            0.0           0.0    0.0           0.142857   \n",
       "4        0.0            0.0           0.5    0.0           0.000000   \n",
       "\n",
       "   Italian Restaurant  Korean Restaurant  Martial Arts Dojo  \\\n",
       "0                 0.0                0.0           0.000000   \n",
       "1                 0.0                0.0           0.000000   \n",
       "2                 0.0                0.0           0.000000   \n",
       "3                 0.0                0.0           0.142857   \n",
       "4                 0.0                0.0           0.000000   \n",
       "\n",
       "   Mediterranean Restaurant  Nail Salon  Nature Preserve  Nightclub  Park  \\\n",
       "0                       0.0         0.0              0.0        0.0   0.4   \n",
       "1                       0.0         0.0              0.0        0.0   0.4   \n",
       "2                       0.0         0.0              0.0        0.0   0.0   \n",
       "3                       0.0         0.0              0.0        0.0   0.0   \n",
       "4                       0.0         0.0              0.0        0.0   0.0   \n",
       "\n",
       "   Pet Store  Pharmacy  Photography Studio  Pizza Place  Playground  Pool  \\\n",
       "0        0.0  0.000000                 0.0          0.0         0.0   0.0   \n",
       "1        0.0  0.000000                 0.0          0.0         0.0   0.0   \n",
       "2        0.0  0.000000                 0.0          0.0         0.5   0.0   \n",
       "3        0.0  0.142857                 0.0          0.0         0.0   0.0   \n",
       "4        0.0  0.000000                 0.0          0.0         0.0   0.0   \n",
       "\n",
       "   Pub  Record Shop  Restaurant  River  Sandwich Place  Shop & Service  \\\n",
       "0  0.0          0.0         0.0    0.0             0.0             0.0   \n",
       "1  0.0          0.0         0.0    0.0             0.0             0.0   \n",
       "2  0.0          0.0         0.0    0.0             0.0             0.0   \n",
       "3  0.0          0.0         0.0    0.0             0.0             0.0   \n",
       "4  0.0          0.0         0.0    0.0             0.0             0.0   \n",
       "\n",
       "   Skating Rink  Sushi Restaurant  Toy / Game Store  Trailer Park  \\\n",
       "0           0.0               0.0               0.2           0.0   \n",
       "1           0.0               0.0               0.2           0.0   \n",
       "2           0.0               0.0               0.0           0.0   \n",
       "3           0.0               0.0               0.0           0.0   \n",
       "4           0.0               0.0               0.0           0.5   \n",
       "\n",
       "   Warehouse Store  Wings Joint  \n",
       "0              0.0          0.0  \n",
       "1              0.0          0.0  \n",
       "2              0.0          0.0  \n",
       "3              0.0          0.0  \n",
       "4              0.0          0.0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_grouped = neighborhoods_to_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "neighborhoods_to_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's confirm the new size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 51)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_grouped.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's print each neighborhood along with the top 10 most common venues and put that into a *pandas* dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beechwood</td>\n",
       "      <td>Park</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COLUMBIA FOREST</td>\n",
       "      <td>Park</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clair Hills</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Colonial Acres</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Martial Arts Dojo</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Conservation Meadows</td>\n",
       "      <td>Trailer Park</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KIWANIS PARK</td>\n",
       "      <td>Playground</td>\n",
       "      <td>River</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LAURELWOOD</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Bath House</td>\n",
       "      <td>Nature Preserve</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LINCOLN HEIGHTS</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LINCOLN VILLAGE</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Park</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MAPLE HILLS</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Nail Salon</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NORTH LAKESHORE</td>\n",
       "      <td>Park</td>\n",
       "      <td>College Classroom</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>UNIVERSITY DOWNS</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Gastropub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>UPPER BEECHWOOD</td>\n",
       "      <td>Photography Studio</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UPTOWN WATERLOO</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Record Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Warehouse Store</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>VISTA HILLS</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>WESTMOUNT</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Nightclub</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>WESTVALE</td>\n",
       "      <td>Park</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Neighborhood   1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0              Beechwood                    Park        Baseball Field   \n",
       "1        COLUMBIA FOREST                    Park        Baseball Field   \n",
       "2            Clair Hills              Playground            Food Truck   \n",
       "3         Colonial Acres  Furniture / Home Store     Food & Drink Shop   \n",
       "4   Conservation Meadows            Trailer Park          Home Service   \n",
       "5           KIWANIS PARK              Playground                 River   \n",
       "6             LAURELWOOD                     Pub           Wings Joint   \n",
       "7              LEXINGTON            Home Service            Bath House   \n",
       "8        LINCOLN HEIGHTS             Pizza Place         Grocery Store   \n",
       "9        LINCOLN VILLAGE             Gas Station                  Park   \n",
       "10           MAPLE HILLS        Sushi Restaurant             Gastropub   \n",
       "11       NORTH LAKESHORE                    Park     College Classroom   \n",
       "12      UNIVERSITY DOWNS       Convenience Store         Grocery Store   \n",
       "13       UPPER BEECHWOOD      Photography Studio                  Pool   \n",
       "14       UPTOWN WATERLOO              Restaurant           Wings Joint   \n",
       "15           VISTA HILLS            Burger Joint           Wings Joint   \n",
       "16             WESTMOUNT                   Hotel             Gastropub   \n",
       "17              WESTVALE                    Park           Wings Joint   \n",
       "\n",
       "       3rd Most Common Venue       4th Most Common Venue  \\\n",
       "0           Toy / Game Store                     Brewery   \n",
       "1           Toy / Game Store                     Brewery   \n",
       "2                Wings Joint                Home Service   \n",
       "3          Indian Restaurant           Martial Arts Dojo   \n",
       "4                Wings Joint           Convenience Store   \n",
       "5                    Dog Run                        Pool   \n",
       "6          Convenience Store                Home Service   \n",
       "7            Nature Preserve  Construction & Landscaping   \n",
       "8             Shop & Service                 Dry Cleaner   \n",
       "9                Wings Joint                     Dog Run   \n",
       "10                Nail Salon                 Wings Joint   \n",
       "11      Fast Food Restaurant                 Wings Joint   \n",
       "12  Mediterranean Restaurant              Sandwich Place   \n",
       "13               Wings Joint                     Dog Run   \n",
       "14               Record Shop                        Café   \n",
       "15                   Dog Run                       Hotel   \n",
       "16                 Nightclub                     Dog Run   \n",
       "17                   Dog Run                Home Service   \n",
       "\n",
       "   5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "0            Wings Joint           Dry Cleaner          Home Service   \n",
       "1            Wings Joint           Dry Cleaner          Home Service   \n",
       "2          Grocery Store             Gastropub           Gas Station   \n",
       "3               Pharmacy           Wings Joint          Home Service   \n",
       "4          Grocery Store             Gastropub           Gas Station   \n",
       "5           Home Service          Burger Joint        Baseball Field   \n",
       "6          Grocery Store             Gastropub           Gas Station   \n",
       "7            Wings Joint           Dry Cleaner         Grocery Store   \n",
       "8            Wings Joint          Home Service             Gastropub   \n",
       "9           Home Service         Grocery Store             Gastropub   \n",
       "10               Dog Run          Home Service         Grocery Store   \n",
       "11               Dog Run          Home Service         Grocery Store   \n",
       "12    Chinese Restaurant           Coffee Shop             Pet Store   \n",
       "13          Home Service         Grocery Store             Gastropub   \n",
       "14        Clothing Store           Coffee Shop  Fast Food Restaurant   \n",
       "15          Home Service         Grocery Store             Gastropub   \n",
       "16          Home Service         Grocery Store           Gas Station   \n",
       "17         Grocery Store             Gastropub           Gas Station   \n",
       "\n",
       "     8th Most Common Venue   9th Most Common Venue  10th Most Common Venue  \n",
       "0            Grocery Store               Gastropub             Gas Station  \n",
       "1            Grocery Store               Gastropub             Gas Station  \n",
       "2   Furniture / Home Store       Food & Drink Shop    Fast Food Restaurant  \n",
       "3            Grocery Store               Gastropub             Gas Station  \n",
       "4   Furniture / Home Store              Food Truck       Food & Drink Shop  \n",
       "5            Grocery Store               Gastropub             Gas Station  \n",
       "6   Furniture / Home Store              Food Truck       Food & Drink Shop  \n",
       "7                Gastropub             Gas Station  Furniture / Home Store  \n",
       "8              Gas Station  Furniture / Home Store              Food Truck  \n",
       "9   Furniture / Home Store              Food Truck       Food & Drink Shop  \n",
       "10             Gas Station  Furniture / Home Store              Food Truck  \n",
       "11               Gastropub             Gas Station  Furniture / Home Store  \n",
       "12             Pizza Place             Dry Cleaner               Gastropub  \n",
       "13             Gas Station  Furniture / Home Store              Food Truck  \n",
       "14         Warehouse Store       Korean Restaurant             Pizza Place  \n",
       "15             Gas Station  Furniture / Home Store              Food Truck  \n",
       "16  Furniture / Home Store              Food Truck       Food & Drink Shop  \n",
       "17  Furniture / Home Store              Food Truck       Food & Drink Shop  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "    return row_categories_sorted.index.values[0:num_top_venues]\n",
    "\n",
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = neighborhoods_to_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(neighborhoods_to_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(neighborhoods_to_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head(18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Cluster Neighborhoods in Waterloo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 2, 2, 2, 2, 1, 2, 2, 0])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 3\n",
    "\n",
    "neighborhoods_to_grouped_clustering = neighborhoods_to_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(neighborhoods_to_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>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>0</th>\n",
       "      <td>N2L 5Y9</td>\n",
       "      <td>Waterloo South</td>\n",
       "      <td>Beechwood</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N2T 2Y2</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>Clair Hills</td>\n",
       "      <td>43.454848</td>\n",
       "      <td>-80.579832</td>\n",
       "      <td>2</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N2K 3H7</td>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>Colonial Acres</td>\n",
       "      <td>43.507148</td>\n",
       "      <td>-80.520306</td>\n",
       "      <td>2</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Martial Arts Dojo</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N2L 5Y9</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>COLUMBIA FOREST</td>\n",
       "      <td>43.458418</td>\n",
       "      <td>-80.557473</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N2V 2S2</td>\n",
       "      <td>Waterloo North</td>\n",
       "      <td>Conservation Meadows</td>\n",
       "      <td>43.490019</td>\n",
       "      <td>-80.585340</td>\n",
       "      <td>2</td>\n",
       "      <td>Trailer Park</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>N2K 3N8</td>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>KIWANIS PARK</td>\n",
       "      <td>43.501733</td>\n",
       "      <td>-80.479472</td>\n",
       "      <td>2</td>\n",
       "      <td>Playground</td>\n",
       "      <td>River</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>N2T 2T6</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>LAURELWOOD</td>\n",
       "      <td>43.466481</td>\n",
       "      <td>-80.582579</td>\n",
       "      <td>1</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>N2K 2X1</td>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>43.502022</td>\n",
       "      <td>-80.500654</td>\n",
       "      <td>2</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Bath House</td>\n",
       "      <td>Nature Preserve</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>N2J 3W2</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>LINCOLN HEIGHTS</td>\n",
       "      <td>43.479862</td>\n",
       "      <td>-80.503212</td>\n",
       "      <td>2</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>N2K 1V9</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>LINCOLN VILLAGE</td>\n",
       "      <td>43.490705</td>\n",
       "      <td>-80.501044</td>\n",
       "      <td>0</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Park</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>N2L 2N3</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>MAPLE HILLS</td>\n",
       "      <td>43.450887</td>\n",
       "      <td>-80.546273</td>\n",
       "      <td>2</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Nail Salon</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>N2V 1Y6</td>\n",
       "      <td>Waterloo North</td>\n",
       "      <td>NORTH LAKESHORE</td>\n",
       "      <td>43.499129</td>\n",
       "      <td>-80.563996</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>College Classroom</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>N2K 3X2</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>UNIVERSITY DOWNS</td>\n",
       "      <td>43.489621</td>\n",
       "      <td>-80.487291</td>\n",
       "      <td>2</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Gastropub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>N2T 1S8</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>UPPER BEECHWOOD</td>\n",
       "      <td>43.462144</td>\n",
       "      <td>-80.569948</td>\n",
       "      <td>2</td>\n",
       "      <td>Photography Studio</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>N2J 2Z4</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>UPTOWN WATERLOO</td>\n",
       "      <td>43.483673</td>\n",
       "      <td>-80.526867</td>\n",
       "      <td>2</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Record Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Warehouse Store</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>N2V 0B3</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>VISTA HILLS</td>\n",
       "      <td>43.451008</td>\n",
       "      <td>-80.591636</td>\n",
       "      <td>2</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>N2L 2G8</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>WESTMOUNT</td>\n",
       "      <td>43.459421</td>\n",
       "      <td>-80.529979</td>\n",
       "      <td>2</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Nightclub</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>N2T 1Y5</td>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>WESTVALE</td>\n",
       "      <td>43.445358</td>\n",
       "      <td>-80.557746</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Postal code         Borough          Neighborhood   Latitude  Longitude  \\\n",
       "0      N2L 5Y9  Waterloo South             Beechwood  43.458418 -80.557473   \n",
       "1      N2T 2Y2   Waterloo West           Clair Hills  43.454848 -80.579832   \n",
       "2      N2K 3H7   Waterloo East        Colonial Acres  43.507148 -80.520306   \n",
       "3      N2L 5Y9   Waterloo West       COLUMBIA FOREST  43.458418 -80.557473   \n",
       "4      N2V 2S2  Waterloo North  Conservation Meadows  43.490019 -80.585340   \n",
       "5      N2K 3N8   Waterloo East          KIWANIS PARK  43.501733 -80.479472   \n",
       "6      N2T 2T6        Waterloo            LAURELWOOD  43.466481 -80.582579   \n",
       "7      N2K 2X1   Waterloo East             LEXINGTON  43.502022 -80.500654   \n",
       "8      N2J 3W2        Waterloo       LINCOLN HEIGHTS  43.479862 -80.503212   \n",
       "9      N2K 1V9        Waterloo       LINCOLN VILLAGE  43.490705 -80.501044   \n",
       "10     N2L 2N3        Waterloo           MAPLE HILLS  43.450887 -80.546273   \n",
       "11     N2V 1Y6  Waterloo North       NORTH LAKESHORE  43.499129 -80.563996   \n",
       "12     N2K 3X2        Waterloo      UNIVERSITY DOWNS  43.489621 -80.487291   \n",
       "13     N2T 1S8        Waterloo       UPPER BEECHWOOD  43.462144 -80.569948   \n",
       "14     N2J 2Z4        Waterloo       UPTOWN WATERLOO  43.483673 -80.526867   \n",
       "15     N2V 0B3   Waterloo West           VISTA HILLS  43.451008 -80.591636   \n",
       "16     N2L 2G8        Waterloo             WESTMOUNT  43.459421 -80.529979   \n",
       "17     N2T 1Y5   Waterloo West              WESTVALE  43.445358 -80.557746   \n",
       "\n",
       "    Cluster Labels   1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0                0                    Park        Baseball Field   \n",
       "1                2              Playground            Food Truck   \n",
       "2                2  Furniture / Home Store     Food & Drink Shop   \n",
       "3                0                    Park        Baseball Field   \n",
       "4                2            Trailer Park          Home Service   \n",
       "5                2              Playground                 River   \n",
       "6                1                     Pub           Wings Joint   \n",
       "7                2            Home Service            Bath House   \n",
       "8                2             Pizza Place         Grocery Store   \n",
       "9                0             Gas Station                  Park   \n",
       "10               2        Sushi Restaurant             Gastropub   \n",
       "11               0                    Park     College Classroom   \n",
       "12               2       Convenience Store         Grocery Store   \n",
       "13               2      Photography Studio                  Pool   \n",
       "14               2              Restaurant           Wings Joint   \n",
       "15               2            Burger Joint           Wings Joint   \n",
       "16               2                   Hotel             Gastropub   \n",
       "17               0                    Park           Wings Joint   \n",
       "\n",
       "       3rd Most Common Venue       4th Most Common Venue  \\\n",
       "0           Toy / Game Store                     Brewery   \n",
       "1                Wings Joint                Home Service   \n",
       "2          Indian Restaurant           Martial Arts Dojo   \n",
       "3           Toy / Game Store                     Brewery   \n",
       "4                Wings Joint           Convenience Store   \n",
       "5                    Dog Run                        Pool   \n",
       "6          Convenience Store                Home Service   \n",
       "7            Nature Preserve  Construction & Landscaping   \n",
       "8             Shop & Service                 Dry Cleaner   \n",
       "9                Wings Joint                     Dog Run   \n",
       "10                Nail Salon                 Wings Joint   \n",
       "11      Fast Food Restaurant                 Wings Joint   \n",
       "12  Mediterranean Restaurant              Sandwich Place   \n",
       "13               Wings Joint                     Dog Run   \n",
       "14               Record Shop                        Café   \n",
       "15                   Dog Run                       Hotel   \n",
       "16                 Nightclub                     Dog Run   \n",
       "17                   Dog Run                Home Service   \n",
       "\n",
       "   5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "0            Wings Joint           Dry Cleaner          Home Service   \n",
       "1          Grocery Store             Gastropub           Gas Station   \n",
       "2               Pharmacy           Wings Joint          Home Service   \n",
       "3            Wings Joint           Dry Cleaner          Home Service   \n",
       "4          Grocery Store             Gastropub           Gas Station   \n",
       "5           Home Service          Burger Joint        Baseball Field   \n",
       "6          Grocery Store             Gastropub           Gas Station   \n",
       "7            Wings Joint           Dry Cleaner         Grocery Store   \n",
       "8            Wings Joint          Home Service             Gastropub   \n",
       "9           Home Service         Grocery Store             Gastropub   \n",
       "10               Dog Run          Home Service         Grocery Store   \n",
       "11               Dog Run          Home Service         Grocery Store   \n",
       "12    Chinese Restaurant           Coffee Shop             Pet Store   \n",
       "13          Home Service         Grocery Store             Gastropub   \n",
       "14        Clothing Store           Coffee Shop  Fast Food Restaurant   \n",
       "15          Home Service         Grocery Store             Gastropub   \n",
       "16          Home Service         Grocery Store           Gas Station   \n",
       "17         Grocery Store             Gastropub           Gas Station   \n",
       "\n",
       "     8th Most Common Venue   9th Most Common Venue  10th Most Common Venue  \n",
       "0            Grocery Store               Gastropub             Gas Station  \n",
       "1   Furniture / Home Store       Food & Drink Shop    Fast Food Restaurant  \n",
       "2            Grocery Store               Gastropub             Gas Station  \n",
       "3            Grocery Store               Gastropub             Gas Station  \n",
       "4   Furniture / Home Store              Food Truck       Food & Drink Shop  \n",
       "5            Grocery Store               Gastropub             Gas Station  \n",
       "6   Furniture / Home Store              Food Truck       Food & Drink Shop  \n",
       "7                Gastropub             Gas Station  Furniture / Home Store  \n",
       "8              Gas Station  Furniture / Home Store              Food Truck  \n",
       "9   Furniture / Home Store              Food Truck       Food & Drink Shop  \n",
       "10             Gas Station  Furniture / Home Store              Food Truck  \n",
       "11               Gastropub             Gas Station  Furniture / Home Store  \n",
       "12             Pizza Place             Dry Cleaner               Gastropub  \n",
       "13             Gas Station  Furniture / Home Store              Food Truck  \n",
       "14         Warehouse Store       Korean Restaurant             Pizza Place  \n",
       "15             Gas Station  Furniture / Home Store              Food Truck  \n",
       "16  Furniture / Home Store              Food Truck       Food & Drink Shop  \n",
       "17  Furniture / Home Store              Food Truck       Food & Drink Shop  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "\n",
    "neighborhoods_to_merged = df_neighborhoods_to\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "neighborhoods_to_merged = neighborhoods_to_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "neighborhoods_to_merged.head(21) # check the last columns!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's visualize the resulting clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x22c0fd62400>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map\n",
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=13)\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(\n",
    "        neighborhoods_to_merged['Latitude'], \n",
    "        neighborhoods_to_merged['Longitude'], \n",
    "        neighborhoods_to_merged['Neighborhood'], \n",
    "        neighborhoods_to_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": [
    "Lets examine each cluster and determine the discriminating venue categories that distinguish each cluster. Based on the defining categories, we can then assign a name to each cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Waterloo South</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Waterloo</td>\n",
       "      <td>0</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Park</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Waterloo North</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>College Classroom</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Borough  Cluster Labels 1st Most Common Venue  \\\n",
       "0   Waterloo South               0                  Park   \n",
       "3    Waterloo West               0                  Park   \n",
       "9         Waterloo               0           Gas Station   \n",
       "11  Waterloo North               0                  Park   \n",
       "17   Waterloo West               0                  Park   \n",
       "\n",
       "   2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0         Baseball Field      Toy / Game Store               Brewery   \n",
       "3         Baseball Field      Toy / Game Store               Brewery   \n",
       "9                   Park           Wings Joint               Dog Run   \n",
       "11     College Classroom  Fast Food Restaurant           Wings Joint   \n",
       "17           Wings Joint               Dog Run          Home Service   \n",
       "\n",
       "   5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "0            Wings Joint           Dry Cleaner          Home Service   \n",
       "3            Wings Joint           Dry Cleaner          Home Service   \n",
       "9           Home Service         Grocery Store             Gastropub   \n",
       "11               Dog Run          Home Service         Grocery Store   \n",
       "17         Grocery Store             Gastropub           Gas Station   \n",
       "\n",
       "     8th Most Common Venue 9th Most Common Venue  10th Most Common Venue  \n",
       "0            Grocery Store             Gastropub             Gas Station  \n",
       "3            Grocery Store             Gastropub             Gas Station  \n",
       "9   Furniture / Home Store            Food Truck       Food & Drink Shop  \n",
       "11               Gastropub           Gas Station  Furniture / Home Store  \n",
       "17  Furniture / Home Store            Food Truck       Food & Drink Shop  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_merged.loc[neighborhoods_to_merged['Cluster Labels'] == 0, neighborhoods_to_merged.columns[[1] + list(range(5, neighborhoods_to_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>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>6</th>\n",
       "      <td>Waterloo</td>\n",
       "      <td>1</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "6  Waterloo               1                   Pub           Wings Joint   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "6     Convenience Store          Home Service         Grocery Store   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue   8th Most Common Venue  \\\n",
       "6             Gastropub           Gas Station  Furniture / Home Store   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "6            Food Truck      Food & Drink Shop  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_merged.loc[neighborhoods_to_merged['Cluster Labels'] == 1, neighborhoods_to_merged.columns[[1] + list(range(5, neighborhoods_to_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>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>1</th>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>2</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>2</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Martial Arts Dojo</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Waterloo North</td>\n",
       "      <td>2</td>\n",
       "      <td>Trailer Park</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>2</td>\n",
       "      <td>Playground</td>\n",
       "      <td>River</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Waterloo East</td>\n",
       "      <td>2</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Bath House</td>\n",
       "      <td>Nature Preserve</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Waterloo</td>\n",
       "      <td>2</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Waterloo</td>\n",
       "      <td>2</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Nail Salon</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Waterloo</td>\n",
       "      <td>2</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Gastropub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Waterloo</td>\n",
       "      <td>2</td>\n",
       "      <td>Photography Studio</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Waterloo</td>\n",
       "      <td>2</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Record Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Warehouse Store</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Waterloo West</td>\n",
       "      <td>2</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Wings Joint</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Waterloo</td>\n",
       "      <td>2</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Nightclub</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Borough  Cluster Labels   1st Most Common Venue  \\\n",
       "1    Waterloo West               2              Playground   \n",
       "2    Waterloo East               2  Furniture / Home Store   \n",
       "4   Waterloo North               2            Trailer Park   \n",
       "5    Waterloo East               2              Playground   \n",
       "7    Waterloo East               2            Home Service   \n",
       "8         Waterloo               2             Pizza Place   \n",
       "10        Waterloo               2        Sushi Restaurant   \n",
       "12        Waterloo               2       Convenience Store   \n",
       "13        Waterloo               2      Photography Studio   \n",
       "14        Waterloo               2              Restaurant   \n",
       "15   Waterloo West               2            Burger Joint   \n",
       "16        Waterloo               2                   Hotel   \n",
       "\n",
       "   2nd Most Common Venue     3rd Most Common Venue  \\\n",
       "1             Food Truck               Wings Joint   \n",
       "2      Food & Drink Shop         Indian Restaurant   \n",
       "4           Home Service               Wings Joint   \n",
       "5                  River                   Dog Run   \n",
       "7             Bath House           Nature Preserve   \n",
       "8          Grocery Store            Shop & Service   \n",
       "10             Gastropub                Nail Salon   \n",
       "12         Grocery Store  Mediterranean Restaurant   \n",
       "13                  Pool               Wings Joint   \n",
       "14           Wings Joint               Record Shop   \n",
       "15           Wings Joint                   Dog Run   \n",
       "16             Gastropub                 Nightclub   \n",
       "\n",
       "         4th Most Common Venue 5th Most Common Venue 6th Most Common Venue  \\\n",
       "1                 Home Service         Grocery Store             Gastropub   \n",
       "2            Martial Arts Dojo              Pharmacy           Wings Joint   \n",
       "4            Convenience Store         Grocery Store             Gastropub   \n",
       "5                         Pool          Home Service          Burger Joint   \n",
       "7   Construction & Landscaping           Wings Joint           Dry Cleaner   \n",
       "8                  Dry Cleaner           Wings Joint          Home Service   \n",
       "10                 Wings Joint               Dog Run          Home Service   \n",
       "12              Sandwich Place    Chinese Restaurant           Coffee Shop   \n",
       "13                     Dog Run          Home Service         Grocery Store   \n",
       "14                        Café        Clothing Store           Coffee Shop   \n",
       "15                       Hotel          Home Service         Grocery Store   \n",
       "16                     Dog Run          Home Service         Grocery Store   \n",
       "\n",
       "   7th Most Common Venue   8th Most Common Venue   9th Most Common Venue  \\\n",
       "1            Gas Station  Furniture / Home Store       Food & Drink Shop   \n",
       "2           Home Service           Grocery Store               Gastropub   \n",
       "4            Gas Station  Furniture / Home Store              Food Truck   \n",
       "5         Baseball Field           Grocery Store               Gastropub   \n",
       "7          Grocery Store               Gastropub             Gas Station   \n",
       "8              Gastropub             Gas Station  Furniture / Home Store   \n",
       "10         Grocery Store             Gas Station  Furniture / Home Store   \n",
       "12             Pet Store             Pizza Place             Dry Cleaner   \n",
       "13             Gastropub             Gas Station  Furniture / Home Store   \n",
       "14  Fast Food Restaurant         Warehouse Store       Korean Restaurant   \n",
       "15             Gastropub             Gas Station  Furniture / Home Store   \n",
       "16           Gas Station  Furniture / Home Store              Food Truck   \n",
       "\n",
       "    10th Most Common Venue  \n",
       "1     Fast Food Restaurant  \n",
       "2              Gas Station  \n",
       "4        Food & Drink Shop  \n",
       "5              Gas Station  \n",
       "7   Furniture / Home Store  \n",
       "8               Food Truck  \n",
       "10              Food Truck  \n",
       "12               Gastropub  \n",
       "13              Food Truck  \n",
       "14             Pizza Place  \n",
       "15              Food Truck  \n",
       "16       Food & Drink Shop  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods_to_merged.loc[neighborhoods_to_merged['Cluster Labels'] == 2, neighborhoods_to_merged.columns[[1] + list(range(5, neighborhoods_to_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='discussion'></a>\n",
    "# Discussion                                                                         \n",
    "<div style=\"text-align: right\">[Top](#toc)</div>\n",
    "\n",
    "Based on the cluster data for the two cities of interest, the classification for each cluster was done with calculation of venue categories (i.e., the most common category). Analysis of each cluster does not produce conclusive one-to-one results when Foursquare - Most Common Venue data is used. Thus, the following assumption had to be made for each cluster:\n",
    "- Cluster 1: North York: Mix (i.e., more than one (1) category)\n",
    "- Cluster 2: North York: Pizza Place\n",
    "- Cluster 3: North York: Park\n",
    "- Cluster 1: Waterloo: Park\n",
    "- Cluster 2: Waterloo: Pub\n",
    "- Cluster 3: Waterloo: Mix (i.e., more than one (1) category)\n",
    "\n",
    "Next, a gap in determination of specific districts and establishing correlations between common venues was identified.  A systematic, quantitative approach of venues recorded in Foursquare was needed in order to advance onto the next stage. This was further exacerbated by the fact that not all cities have similar common venues despite obvious similarities. Therefore, an additional step of applying a suitable extraction and special integration method was performed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusion'></a>\n",
    "# Conclusion                                                                         \n",
    "<div style=\"text-align: right\">[Top](#toc)</div>\n",
    "\n",
    "The work performed in this Capstone was focused on design and application of a new systematic tool that can be used to perform quantitative analysis of data between different cities. Two large Canadian cities in the province of Ontario, specifically Toronto and Waterloo, were selected to demonstrate the proposed approach. In this project, the specific parameters of interest were the availability of Russian language-based services, such as restaurants offering authentic Russian-style cuisine and service. The results indicate that the chosen methodology was successful in identification of neighbourhoods with similar demographic composition and venue categories. It showed high promise for those who seek a systematic, algorithm-based approach for comparison and selection of the desired parameters and can be applied to any of the desired features, e.g., access to public or catholic schools, supermarkets, shopping malls, movie theaters, hospitals, or religious communities. \n",
    "\n",
    "Future studies can focus on establishing relations withing the acquired data, as well as additional exploration of tools and methods for further optimization of the results.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='References'></a>\n",
    "# References                                                                         \n",
    "<div style=\"text-align: right\">[Top](#toc)</div>\n",
    "[1] Jeffrey K. Aronson, “Key concepts for making informed choices”, A Nature Research Journal, 12 August 2019 \n",
    "\n",
    "[2] https://www.conferenceboard.ca/e-library/abstract.aspx?did=10342&AspxAutoDetectCookieSupport=1\n",
    "\n",
    "[3] https://github.com/jnasimi/Coursera_Capstone\n",
    "\n",
    "[4] https://www.toronto.com/news-story/5588878-russian-in-toronto-10-neighbourhoods-where-you-re-likely-to-hear-it/\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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