Week3_Toronto_Neighborhoods_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Segmenting and Clustering Neighborhoods in Toronto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # library to handle data in a vectorized manner\n",
    "\n",
    "import pandas as pd # library for data analysis\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 \n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "\n",
    "import requests # library to handle requests\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#!conda install -c conda-forge folium=0.5.0 --yes\n",
    "import folium # map rendering library\n",
    "\n",
    "import requests # library to fetch URLs\n",
    "\n",
    "from bs4 import BeautifulSoup # library to scrape data from a webpage\n",
    "\n",
    "print('Libraries imported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scrape Postal Codes from Wikipedia\n",
    "Scrape the postal codes table from this Wikipedia page:\n",
    "https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\n",
    "\n",
    "<i>\"This is a list of postal codes in Canada where the first letter is M. Postal codes beginning with M are located within the city of Toronto in the province of Ontario. Only the first three characters are listed, corresponding to the Forward Sortation Area.\"</i>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded from: https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\n"
     ]
    }
   ],
   "source": [
    "url = 'https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\n",
    "\n",
    "response = requests.get(url)\n",
    "if response.status_code != 200:\n",
    "    print('Failed to get data:', response.status_code)\n",
    "else:\n",
    "    print(f'Data downloaded from: {url}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\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>Harbourfront</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode           Borough     Neighbourhood\n",
       "0      M1A      Not assigned      Not assigned\n",
       "1      M2A      Not assigned      Not assigned\n",
       "2      M3A        North York         Parkwoods\n",
       "3      M4A        North York  Victoria Village\n",
       "4      M5A  Downtown Toronto      Harbourfront"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup = BeautifulSoup(response.text, 'html.parser')\n",
    "table = soup.find(\"table\", {\"class\":\"wikitable sortable\"})\n",
    "df = pd.read_html(str(table),header=0)[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up the postal code dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 77 rows with unassigned boroughs.\n",
      "As expected, there is only one borough in each postcode. Yay!\n",
      "1 rows with unassigned neighbourhoods (now assigned to borough).\n",
      "Collapsed 108 duplicated postcodes into single rows.\n",
      "\n",
      "\n",
      "  Postcode      Borough                           Neighbourhood\n",
      "0      M1B  Scarborough                          Rouge, Malvern\n",
      "1      M1C  Scarborough  Highland Creek, Rouge Hill, Port Union\n",
      "2      M1E  Scarborough       Guildwood, Morningside, West Hill\n",
      "3      M1G  Scarborough                                  Woburn\n",
      "4      M1H  Scarborough                               Cedarbrae\n"
     ]
    }
   ],
   "source": [
    "# Remove unassigned boroughs\n",
    "n = df.shape[0]\n",
    "df = df.loc[df['Borough']!='Not assigned',:]\n",
    "print(f'Removed {n-df.shape[0]:,} rows with unassigned boroughs.')\n",
    "del n\n",
    "\n",
    "# Check if a postcode can have more than one borough:\n",
    "if np.max(df.groupby('Postcode')['Borough'].nunique())==1:\n",
    "    print('As expected, there is only one borough in each postcode. Yay!')\n",
    "else:\n",
    "    print('Watch out! There is at least one postcode with multiple distinct boroughs...')\n",
    "\n",
    "# Give unassigned neighborhoods the name of their borough\n",
    "n = sum(df['Neighbourhood']=='Not assigned')\n",
    "print(f'{n:,} rows with unassigned neighbourhoods (now assigned to borough).')\n",
    "del n\n",
    "\n",
    "df.loc[df['Neighbourhood']=='Not assigned','Neighbourhood']=df['Borough']\n",
    "\n",
    "# Combine multiple rows for a given postcode into one comma-separated list of neighbourhoods\n",
    "n = df.shape[0]\n",
    "df=df.groupby(['Postcode','Borough'])['Neighbourhood'].apply(', '.join).reset_index()\n",
    "print(f'Collapsed {n-df.shape[0]:,} duplicated postcodes into single rows.')\n",
    "del n\n",
    "print('\\n')\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cleaned dataframe has 103 total rows, making up the following buroughs:\n",
      "North York          24\n",
      "Downtown Toronto    18\n",
      "Scarborough         17\n",
      "Etobicoke           12\n",
      "Central Toronto      9\n",
      "West Toronto         6\n",
      "York                 5\n",
      "East Toronto         5\n",
      "East York            5\n",
      "Queen's Park         1\n",
      "Mississauga          1\n",
      "Name: Borough, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f'The cleaned dataframe has {df.shape[0]:,} total rows, making up the following buroughs:')\n",
    "print(df['Borough'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>In the last cell of the notebook, use the .shape method to print the number of rows in the dataframe:</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103\n"
     ]
    }
   ],
   "source": [
    "print(df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Latitude and Longitude Coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded from: https://cocl.us/Geospatial_data\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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download csv from course website\n",
    "url = 'https://cocl.us/Geospatial_data'\n",
    "lat_lng_coords = pd.read_csv('https://cocl.us/Geospatial_data')\n",
    "print(f'Data downloaded from: {url}')\n",
    "\n",
    "lat_lng_coords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merge results:\n",
      "both          103\n",
      "right_only      0\n",
      "left_only       0\n",
      "Name: _merge, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Combine the two dataframes to create the specified dataframe for this assignment\n",
    "toronto_df = pd.merge(\n",
    "    left=df, right=lat_lng_coords,\n",
    "    left_on='Postcode',right_on='Postal Code',\n",
    "    how='left', validate='1:1', indicator=True)\n",
    "\n",
    "# Confirm that all of the postal codes matched to a latitude/longitude coordinate\n",
    "print('Merge results:')\n",
    "print(toronto_df['_merge'].value_counts())\n",
    "\n",
    "# Adjust the column names to match the assignment\n",
    "toronto_df.drop(['Postal Code','_merge'],axis=1,inplace=True)\n",
    "toronto_df.rename(columns={'Postcode':'PostalCode','Neighbourhood':'Neighborhood'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Display the first 15 lines of the dataframe.</b>"
   ]
  },
  {
   "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>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>East Birchmount Park, Ionview, Kennedy Park</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Clairlea, Golden Mile, Oakridge</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffcrest, Cliffside, Scarborough Village West</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M1N</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Birch Cliff, Cliffside West</td>\n",
       "      <td>43.692657</td>\n",
       "      <td>-79.264848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M1P</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Dorset Park, Scarborough Town Centre, Wexford ...</td>\n",
       "      <td>43.757410</td>\n",
       "      <td>-79.273304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M1R</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Maryvale, Wexford</td>\n",
       "      <td>43.750072</td>\n",
       "      <td>-79.295849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M1S</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Agincourt</td>\n",
       "      <td>43.794200</td>\n",
       "      <td>-79.262029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M1T</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Clarks Corners, Sullivan, Tam O'Shanter</td>\n",
       "      <td>43.781638</td>\n",
       "      <td>-79.304302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M1V</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Agincourt North, L'Amoreaux East, Milliken, St...</td>\n",
       "      <td>43.815252</td>\n",
       "      <td>-79.284577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode      Borough                                       Neighborhood  \\\n",
       "0         M1B  Scarborough                                     Rouge, Malvern   \n",
       "1         M1C  Scarborough             Highland Creek, Rouge Hill, Port Union   \n",
       "2         M1E  Scarborough                  Guildwood, Morningside, West Hill   \n",
       "3         M1G  Scarborough                                             Woburn   \n",
       "4         M1H  Scarborough                                          Cedarbrae   \n",
       "5         M1J  Scarborough                                Scarborough Village   \n",
       "6         M1K  Scarborough        East Birchmount Park, Ionview, Kennedy Park   \n",
       "7         M1L  Scarborough                    Clairlea, Golden Mile, Oakridge   \n",
       "8         M1M  Scarborough    Cliffcrest, Cliffside, Scarborough Village West   \n",
       "9         M1N  Scarborough                        Birch Cliff, Cliffside West   \n",
       "10        M1P  Scarborough  Dorset Park, Scarborough Town Centre, Wexford ...   \n",
       "11        M1R  Scarborough                                  Maryvale, Wexford   \n",
       "12        M1S  Scarborough                                          Agincourt   \n",
       "13        M1T  Scarborough            Clarks Corners, Sullivan, Tam O'Shanter   \n",
       "14        M1V  Scarborough  Agincourt North, L'Amoreaux East, Milliken, St...   \n",
       "\n",
       "     Latitude  Longitude  \n",
       "0   43.806686 -79.194353  \n",
       "1   43.784535 -79.160497  \n",
       "2   43.763573 -79.188711  \n",
       "3   43.770992 -79.216917  \n",
       "4   43.773136 -79.239476  \n",
       "5   43.744734 -79.239476  \n",
       "6   43.727929 -79.262029  \n",
       "7   43.711112 -79.284577  \n",
       "8   43.716316 -79.239476  \n",
       "9   43.692657 -79.264848  \n",
       "10  43.757410 -79.273304  \n",
       "11  43.750072 -79.295849  \n",
       "12  43.794200 -79.262029  \n",
       "13  43.781638 -79.304302  \n",
       "14  43.815252 -79.284577  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore and Cluster Toronto Neighborhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataframe has 11 boroughs and 103 neighborhoods.\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"The dataframe has {len(toronto_df['Borough'].unique()):,} boroughs and {len(toronto_df['Neighborhood'].unique()):,} neighborhoods.\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the geographical coordinates of Toronto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Toronto are 43.653963, -79.387207.\n"
     ]
    }
   ],
   "source": [
    "address = 'Toronto, Canada'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"toronto_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Toronto are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did in the New York City lab, let's visualize Toronto and mark the neighborhoods on a map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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                ).addTo(map_c3bdea15a28541339ffc0e7106753190);
            
    
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x2c3ab760b70>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Toronto using latitude and longitude values\n",
    "map_toronto = folium.Map(location=[latitude,longitude],zoom_start=10)\n",
    "\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood in zip(toronto_df['Latitude'], toronto_df['Longitude'], toronto_df['Borough'], toronto_df['Neighborhood']):\n",
    "    label = f\"{neighborhood}, {borough}\"\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_toronto)  \n",
    "    \n",
    "map_toronto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North York          24\n",
       "Downtown Toronto    18\n",
       "Scarborough         17\n",
       "Etobicoke           12\n",
       "Central Toronto      9\n",
       "West Toronto         6\n",
       "York                 5\n",
       "East Toronto         5\n",
       "East York            5\n",
       "Queen's Park         1\n",
       "Mississauga          1\n",
       "Name: Borough, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_df['Borough'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>For simplicity, let's subset to downtown Toronto</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now the dataframe has 1 boroughs and 18 neighborhoods.\n"
     ]
    }
   ],
   "source": [
    "toronto_df = toronto_df.loc[toronto_df['Borough'].isin(['Downtown Toronto']),:]\n",
    "toronto_df.reset_index(inplace=True,drop=True)\n",
    "toronto_df.head()\n",
    "print(f\"\"\"Now the dataframe has {len(toronto_df['Borough'].unique()):,} boroughs and {len(toronto_df['Neighborhood'].unique()):,} neighborhoods.\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x2c3ab999b00>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Toronto using latitude and longitude values\n",
    "map_toronto = folium.Map(location=[latitude,longitude],zoom_start=12)\n",
    "\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood in zip(toronto_df['Latitude'], toronto_df['Longitude'], toronto_df['Borough'], toronto_df['Neighborhood']):\n",
    "    label = f\"{neighborhood}, {borough}\"\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_toronto)  \n",
    "    \n",
    "map_toronto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's follow the lab and have fun with the FourSquare API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Define Foursquare Credentials and Version</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @hidden_cell\n",
    "CLIENT_ID = 'CN5NRP3FQ3PUY403DEAMPRRA25IZ5SF5YGWGIBK3FJUB5FLG' # your Foursquare ID\n",
    "CLIENT_SECRET = 'AQ3N5JOFGUCUEY1RXYUSIM2ZFBURQRNOSTH43RDPQPE03GYE' # your Foursquare Secret\n",
    "VERSION = '20180604' # Foursquare API version\n",
    "LIMIT = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Borrow and tweak the getNearbyVenues function from the Exploring NY lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \"\"\"Loop through the input locations and request venue information from FourSquare.\n",
    "    Output a single dataframe with all of the results.\"\"\"\n",
    "    \n",
    "    venues_dict={}\n",
    "    idx = 0\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(f'Getting venues near {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",
    "        for venue in results:            \n",
    "            venues_dict.update({idx :\n",
    "                {'Neighborhood':name, \n",
    "                 'Neighborhood Latitude':lat, \n",
    "                 'Neighborhood Longitude':lng, \n",
    "                 'Venue':venue['venue']['name'], \n",
    "                 'Venue ID':venue['venue']['id'],\n",
    "                 'Distance from Neighborhood':venue['venue']['location']['distance'],\n",
    "                 'Venue Latitude':venue['venue']['location']['lat'], \n",
    "                 'Venue Longitude':venue['venue']['location']['lng'],  \n",
    "                 'Venue Category':venue['venue']['categories'][0]['icon']['prefix'].split('/')[-2] # get broader category bin from icon prefix\n",
    "                }})\n",
    "            idx = idx + 1\n",
    "\n",
    "        print(f'    found {len(results):,} results')\n",
    "    # Use helper method for creating a dataframe by rows\n",
    "    nearby_venues = pd.DataFrame.from_dict(venues_dict, orient='index')\n",
    "    \n",
    "    print('---------------------------------------------------------')\n",
    "    print(f'{nearby_venues.shape[0]:,} total venues collected.')\n",
    "    \n",
    "    return(nearby_venues)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Get a dataframe with all of the venues in our chosen neighborhoods</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting venues near Rosedale...\n",
      "    found 4 results\n",
      "Getting venues near Cabbagetown, St. James Town...\n",
      "    found 47 results\n",
      "Getting venues near Church and Wellesley...\n",
      "    found 87 results\n",
      "Getting venues near Harbourfront, Regent Park...\n",
      "    found 47 results\n",
      "Getting venues near Ryerson, Garden District...\n",
      "    found 100 results\n",
      "Getting venues near St. James Town...\n",
      "    found 100 results\n",
      "Getting venues near Berczy Park...\n",
      "    found 55 results\n",
      "Getting venues near Central Bay Street...\n",
      "    found 88 results\n",
      "Getting venues near Adelaide, King, Richmond...\n",
      "    found 100 results\n",
      "Getting venues near Harbourfront East, Toronto Islands, Union Station...\n",
      "    found 100 results\n",
      "Getting venues near Design Exchange, Toronto Dominion Centre...\n",
      "    found 100 results\n",
      "Getting venues near Commerce Court, Victoria Hotel...\n",
      "    found 100 results\n",
      "Getting venues near Harbord, University of Toronto...\n",
      "    found 34 results\n",
      "Getting venues near Chinatown, Grange Park, Kensington Market...\n",
      "    found 100 results\n",
      "Getting venues near CN Tower, Bathurst Quay, Island airport, Harbourfront West, King and Spadina, Railway Lands, South Niagara...\n",
      "    found 15 results\n",
      "Getting venues near Stn A PO Boxes 25 The Esplanade...\n",
      "    found 94 results\n",
      "Getting venues near First Canadian Place, Underground city...\n",
      "    found 100 results\n",
      "Getting venues near Christie...\n",
      "    found 16 results\n",
      "---------------------------------------------------------\n",
      "1,287 total venues collected.\n"
     ]
    }
   ],
   "source": [
    "toronto_venues = getNearbyVenues(names=toronto_df['Neighborhood'],\n",
    "                                latitudes=toronto_df['Latitude'],\n",
    "                                longitudes=toronto_df['Longitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>De-duplicate, make sure each venue is only assigned to the closest neighborhood</b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "toronto_venues = toronto_venues.loc[toronto_venues.groupby('Venue ID')['Distance from Neighborhood'].idxmin()]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Group the results and do some one-hot coding to prepare a neighborhood-level dataframe for k-means clustering</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot encoding\n",
    "toronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "toronto_onehot['Neighborhood'] = toronto_venues['Neighborhood'] \n",
    "\n",
    "toronto_grouped = toronto_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "toronto_grouped['n'] = toronto_venues.groupby('Neighborhood').size().reset_index()[0]\n",
    "toronto_grouped['n_scaled'] = toronto_grouped['n']/toronto_grouped['n'].max()\n",
    "toronto_grouped.set_index('Neighborhood',inplace=True)\n",
    "toronto_grouped.drop('n',axis=1,inplace=True)\n",
    "toronto_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster Neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run k-means to cluster Toronto neighborhoods into 5 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 4 0 3 3 2 3 0 0 0 0 3 0 1 3 0 0]\n"
     ]
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "print(kmeans.labels_)\n",
    "\n",
    "# add cluster label to dataframe\n",
    "toronto_grouped['Cluster Label']=kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  PostalCode           Borough                 Neighborhood   Latitude  \\\n",
      "0        M4W  Downtown Toronto                     Rosedale  43.679563   \n",
      "1        M4X  Downtown Toronto  Cabbagetown, St. James Town  43.667967   \n",
      "2        M4Y  Downtown Toronto         Church and Wellesley  43.665860   \n",
      "3        M5A  Downtown Toronto    Harbourfront, Regent Park  43.654260   \n",
      "4        M5B  Downtown Toronto     Ryerson, Garden District  43.657162   \n",
      "\n",
      "   Longitude  arts_entertainment  building  education      food  nightlife  \\\n",
      "0 -79.377529            0.000000  0.000000   0.000000  0.000000   0.000000   \n",
      "1 -79.367675            0.021277  0.000000   0.000000  0.574468   0.063830   \n",
      "2 -79.383160            0.034483  0.022989   0.000000  0.620690   0.114943   \n",
      "3 -79.360636            0.106383  0.042553   0.000000  0.489362   0.085106   \n",
      "4 -79.378937            0.045977  0.022989   0.011494  0.528736   0.045977   \n",
      "\n",
      "   parks_outdoors     shops    travel  n_scaled  Cluster Label  \n",
      "0        1.000000  0.000000  0.000000      0.04              1  \n",
      "1        0.127660  0.212766  0.000000      0.47              0  \n",
      "2        0.034483  0.149425  0.022989      0.87              3  \n",
      "3        0.063830  0.191489  0.021277      0.47              0  \n",
      "4        0.034483  0.298851  0.011494      0.87              3  \n"
     ]
    }
   ],
   "source": [
    "toronto_merged = pd.merge(left=toronto_df,right=toronto_grouped,left_on='Neighborhood',right_index=True,how='left')\n",
    "print(toronto_merged.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's look at the clusters and see if we can describe them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>Cabbagetown, St. James Town</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.021277</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.574468</td>\n",
       "      <td>0.063830</td>\n",
       "      <td>0.127660</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Harbourfront, Regent Park</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.106383</td>\n",
       "      <td>0.042553</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>0.085106</td>\n",
       "      <td>0.063830</td>\n",
       "      <td>0.191489</td>\n",
       "      <td>0.021277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>St. James Town</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Adelaide, King, Richmond</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.035088</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Design Exchange, Toronto Dominion Centre</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Commerce Court, Victoria Hotel</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.085714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Harbord, University of Toronto</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.617647</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Stn A PO Boxes 25 The Esplanade</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>First Canadian Place, Underground city</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Neighborhood  n_scaled  arts_entertainment  \\\n",
       "1                Cabbagetown, St. James Town      0.47            0.021277   \n",
       "3                  Harbourfront, Regent Park      0.47            0.106383   \n",
       "5                             St. James Town      0.50            0.080000   \n",
       "8                   Adelaide, King, Richmond      0.57            0.070175   \n",
       "10  Design Exchange, Toronto Dominion Centre      0.30            0.033333   \n",
       "11            Commerce Court, Victoria Hotel      0.35            0.028571   \n",
       "12            Harbord, University of Toronto      0.34            0.058824   \n",
       "15           Stn A PO Boxes 25 The Esplanade      0.48            0.062500   \n",
       "16    First Canadian Place, Underground city      0.24            0.041667   \n",
       "\n",
       "    building  education      food  nightlife  parks_outdoors     shops  \\\n",
       "1   0.000000        0.0  0.574468   0.063830        0.127660  0.212766   \n",
       "3   0.042553        0.0  0.489362   0.085106        0.063830  0.191489   \n",
       "5   0.060000        0.0  0.620000   0.020000        0.020000  0.140000   \n",
       "8   0.017544        0.0  0.578947   0.105263        0.035088  0.140351   \n",
       "10  0.000000        0.0  0.700000   0.100000        0.000000  0.033333   \n",
       "11  0.028571        0.0  0.685714   0.085714        0.000000  0.085714   \n",
       "12  0.058824        0.0  0.617647   0.176471        0.000000  0.088235   \n",
       "15  0.020833        0.0  0.562500   0.125000        0.041667  0.187500   \n",
       "16  0.041667        0.0  0.875000   0.041667        0.000000  0.000000   \n",
       "\n",
       "      travel  \n",
       "1   0.000000  \n",
       "3   0.021277  \n",
       "5   0.060000  \n",
       "8   0.052632  \n",
       "10  0.133333  \n",
       "11  0.085714  \n",
       "12  0.000000  \n",
       "15  0.000000  \n",
       "16  0.000000  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Label']==0,['Neighborhood','n_scaled','arts_entertainment','building','education','food','nightlife','parks_outdoors','shops','travel']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n_scaled              0.413333\n",
       "arts_entertainment    0.055859\n",
       "building              0.029999\n",
       "education             0.000000\n",
       "food                  0.633738\n",
       "nightlife             0.089228\n",
       "parks_outdoors        0.032027\n",
       "shops                 0.119932\n",
       "travel                0.039217\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Label']==0,['n_scaled','arts_entertainment','building','education','food','nightlife','parks_outdoors','shops','travel']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 0 is made up of medium-sized neighborhoods that have a lot of restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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",
       "    .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>n_scaled</th>\n",
       "      <th>arts_entertainment</th>\n",
       "      <th>building</th>\n",
       "      <th>education</th>\n",
       "      <th>food</th>\n",
       "      <th>nightlife</th>\n",
       "      <th>parks_outdoors</th>\n",
       "      <th>shops</th>\n",
       "      <th>travel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rosedale</td>\n",
       "      <td>0.04</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  n_scaled  arts_entertainment  building  education  food  \\\n",
       "0     Rosedale      0.04                 0.0       0.0        0.0   0.0   \n",
       "\n",
       "   nightlife  parks_outdoors  shops  travel  \n",
       "0        0.0             1.0    0.0     0.0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Label']==1,['Neighborhood','n_scaled','arts_entertainment','building','education','food','nightlife','parks_outdoors','shops','travel']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 1 is a single neighborhood made up solely of a few parks/outdoors venues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>n_scaled</th>\n",
       "      <th>arts_entertainment</th>\n",
       "      <th>building</th>\n",
       "      <th>education</th>\n",
       "      <th>food</th>\n",
       "      <th>nightlife</th>\n",
       "      <th>parks_outdoors</th>\n",
       "      <th>shops</th>\n",
       "      <th>travel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Christie</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood  n_scaled  arts_entertainment  building  education      food  \\\n",
       "6   Berczy Park      0.06                 0.0       0.0        0.0  0.333333   \n",
       "17     Christie      0.16                 0.0       0.0        0.0  0.437500   \n",
       "\n",
       "    nightlife  parks_outdoors     shops    travel  \n",
       "6    0.166667        0.166667  0.166667  0.166667  \n",
       "17   0.062500        0.125000  0.375000  0.000000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Label']==2,['Neighborhood','n_scaled','arts_entertainment','building','education','food','nightlife','parks_outdoors','shops','travel']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n_scaled              0.110000\n",
       "arts_entertainment    0.000000\n",
       "building              0.000000\n",
       "education             0.000000\n",
       "food                  0.385417\n",
       "nightlife             0.114583\n",
       "parks_outdoors        0.145833\n",
       "shops                 0.270833\n",
       "travel                0.083333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Label']==2,['n_scaled','arts_entertainment','building','education','food','nightlife','parks_outdoors','shops','travel']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 2 is made up of small neighborhoods with just a few venues, mostly restuarants and shops. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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",
       "    .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>n_scaled</th>\n",
       "      <th>arts_entertainment</th>\n",
       "      <th>building</th>\n",
       "      <th>education</th>\n",
       "      <th>food</th>\n",
       "      <th>nightlife</th>\n",
       "      <th>parks_outdoors</th>\n",
       "      <th>shops</th>\n",
       "      <th>travel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Church and Wellesley</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.022989</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>0.114943</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.149425</td>\n",
       "      <td>0.022989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ryerson, Garden District</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.045977</td>\n",
       "      <td>0.022989</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.528736</td>\n",
       "      <td>0.045977</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.298851</td>\n",
       "      <td>0.011494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Central Bay Street</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Harbourfront East, Toronto Islands, Union Station</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.179775</td>\n",
       "      <td>0.033708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.505618</td>\n",
       "      <td>0.056180</td>\n",
       "      <td>0.101124</td>\n",
       "      <td>0.067416</td>\n",
       "      <td>0.056180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Chinatown, Grange Park, Kensington Market</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Neighborhood  n_scaled  \\\n",
       "2                                Church and Wellesley      0.87   \n",
       "4                            Ryerson, Garden District      0.87   \n",
       "7                                  Central Bay Street      0.70   \n",
       "9   Harbourfront East, Toronto Islands, Union Station      0.89   \n",
       "13          Chinatown, Grange Park, Kensington Market      1.00   \n",
       "\n",
       "    arts_entertainment  building  education      food  nightlife  \\\n",
       "2             0.034483  0.022989   0.000000  0.620690   0.114943   \n",
       "4             0.045977  0.022989   0.011494  0.528736   0.045977   \n",
       "7             0.014286  0.028571   0.000000  0.828571   0.028571   \n",
       "9             0.179775  0.033708   0.000000  0.505618   0.056180   \n",
       "13            0.010000  0.010000   0.000000  0.680000   0.070000   \n",
       "\n",
       "    parks_outdoors     shops    travel  \n",
       "2         0.034483  0.149425  0.022989  \n",
       "4         0.034483  0.298851  0.011494  \n",
       "7         0.014286  0.085714  0.000000  \n",
       "9         0.101124  0.067416  0.056180  \n",
       "13        0.010000  0.200000  0.020000  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Label']==3,['Neighborhood','n_scaled','arts_entertainment','building','education','food','nightlife','parks_outdoors','shops','travel']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n_scaled              0.866000\n",
       "arts_entertainment    0.056904\n",
       "building              0.023651\n",
       "education             0.002299\n",
       "food                  0.632723\n",
       "nightlife             0.063134\n",
       "parks_outdoors        0.038875\n",
       "shops                 0.160281\n",
       "travel                0.022133\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster Label']==3,['Neighborhood','n_scaled','arts_entertainment','building','education','food','nightlife','parks_outdoors','shops','travel']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
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    "Cluster 3 is made up of large neighborhoods with lots of restaurants and shops"
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   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
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       "                                         Neighborhood  n_scaled  \\\n",
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     "execution_count": 84,
     "metadata": {},
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  {
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   "metadata": {},
   "source": [
    "Cluster 4 is just one neighborhood - looks like we successfully identified the airport!"
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  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_descriptions = {\n",
    "    0:'Medium neighborhood',\n",
    "    1:'Parks and outdoors',\n",
    "    2:'Small neighborhood',\n",
    "    3:'Large neighborhood',\n",
    "    4:'Airport'}\n",
    "toronto_merged['Cluster Description'] = toronto_merged['Cluster Label'].map(cluster_descriptions)"
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   "execution_count": 92,
   "metadata": {},
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x2c3ac4faa90>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map\n",
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=12)\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, cluster_desc in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['PostalCode'], toronto_merged['Cluster Label'], toronto_merged['Cluster Description']):\n",
    "    label = folium.Popup(str(poi) + '\\n' + str(cluster_desc), 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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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