Neighborhood Clustering.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1 of assignment - scraping data from wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "import numpy as np\n",
    "import json\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import geocoder\n",
    "from geopy.geocoders import Nominatim\n",
    "from pandas.io.json import json_normalize\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "from sklearn.cluster import KMeans\n",
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "C:\\Users\\Shivam\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4263: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  method=method,\n",
      "C:\\Users\\Shivam\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\Shivam\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n"
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       "<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",
       "      <td>0</td>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Port Union</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>East Birchmount Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Golden Mile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffcrest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>M1N</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffside West</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode      Borough         Neighbourhood\n",
       "0      M1B  Scarborough                 Rouge\n",
       "1      M1C  Scarborough            Port Union\n",
       "2      M1E  Scarborough             Guildwood\n",
       "3      M1G  Scarborough                Woburn\n",
       "4      M1H  Scarborough             Cedarbrae\n",
       "5      M1J  Scarborough   Scarborough Village\n",
       "6      M1K  Scarborough  East Birchmount Park\n",
       "7      M1L  Scarborough           Golden Mile\n",
       "8      M1M  Scarborough            Cliffcrest\n",
       "9      M1N  Scarborough        Cliffside West"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = requests.get(\"https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\")\n",
    "soup = BeautifulSoup(res.content, 'lxml')\n",
    "table = soup.find_all('table')[0]\n",
    "df = pd.read_html(str(table))[0]\n",
    "df.head()\n",
    "df_clean = df[df.Borough!='Not assigned']\n",
    "df_clean.replace(to_replace=\"Not assigned\", value=\"Queen's Park\", inplace=True)\n",
    "df_clean.sort_values(\"Postcode\", inplace = True)\n",
    "df_clean.drop_duplicates(subset=\"Postcode\", keep=\"first\", inplace = True)\n",
    "df_clean.reset_index(drop = True, inplace = True)\n",
    "df_clean.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 of the Assignment - generating coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_lng_coords = None\n",
    "post_code='M3A'\n",
    "\n",
    "while(lat_lng_coords is None):\n",
    "    g=geocoder.google('{}, Toronto, Ontario'.format(post_code))\n",
    "    lat_lng_coords = g.latlng\n",
    "    \n",
    "lattitude = lat_lng_coords[0]\n",
    "longitude = lat_lng_coords[1]\n",
    "print(lattitude, longitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
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       "      <td>0</td>\n",
       "      <td>M1B</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M1C</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M1E</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
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       "      <td>3</td>\n",
       "      <td>M1G</td>\n",
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       "  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"
      ]
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df_cord = pd.read_csv('http://cocl.us/Geospatial_data')\n",
    "df_cord.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shivam\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\Shivam\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df_clean['Latitude'] = df_cord['Latitude'].astype(float)\n",
    "df_clean['Longitude'] = df_cord['Longitude'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
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       "      <td>0</td>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
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       "      <td>3</td>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>East Birchmount Park</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Golden Mile</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffcrest</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>M1N</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffside West</td>\n",
       "      <td>43.692657</td>\n",
       "      <td>-79.264848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode      Borough         Neighbourhood   Latitude  Longitude\n",
       "0      M1B  Scarborough                 Rouge  43.806686 -79.194353\n",
       "1      M1C  Scarborough            Port Union  43.784535 -79.160497\n",
       "2      M1E  Scarborough             Guildwood  43.763573 -79.188711\n",
       "3      M1G  Scarborough                Woburn  43.770992 -79.216917\n",
       "4      M1H  Scarborough             Cedarbrae  43.773136 -79.239476\n",
       "5      M1J  Scarborough   Scarborough Village  43.744734 -79.239476\n",
       "6      M1K  Scarborough  East Birchmount Park  43.727929 -79.262029\n",
       "7      M1L  Scarborough           Golden Mile  43.711112 -79.284577\n",
       "8      M1M  Scarborough            Cliffcrest  43.716316 -79.239476\n",
       "9      M1N  Scarborough        Cliffside West  43.692657 -79.264848"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3 of assignment - explore and cluster neighbourhoods in Toronto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Toronto are 43.653963, -79.387207.\n"
     ]
    }
   ],
   "source": [
    "address = 'Toronto, Ontario'\n",
    "geolocator = Nominatim(user_agent=\"ny_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": [
    "### Create a map of Toronto with neighborhoods superimposed on top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x2338eb780c8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "\n",
    "for lat, lng, borough, neighborhood in zip(df_clean['Latitude'], df_clean['Longitude'], df_clean['Borough'], df_clean['Neighbourhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\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": [
    "### I am going to consider the Scarborough borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "        text-align: right;\n",
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       "      <th></th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>East Birchmount Park</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Golden Mile</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffcrest</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>M1N</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffside West</td>\n",
       "      <td>43.692657</td>\n",
       "      <td>-79.264848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode      Borough         Neighbourhood   Latitude  Longitude\n",
       "0      M1B  Scarborough                 Rouge  43.806686 -79.194353\n",
       "1      M1C  Scarborough            Port Union  43.784535 -79.160497\n",
       "2      M1E  Scarborough             Guildwood  43.763573 -79.188711\n",
       "3      M1G  Scarborough                Woburn  43.770992 -79.216917\n",
       "4      M1H  Scarborough             Cedarbrae  43.773136 -79.239476\n",
       "5      M1J  Scarborough   Scarborough Village  43.744734 -79.239476\n",
       "6      M1K  Scarborough  East Birchmount Park  43.727929 -79.262029\n",
       "7      M1L  Scarborough           Golden Mile  43.711112 -79.284577\n",
       "8      M1M  Scarborough            Cliffcrest  43.716316 -79.239476\n",
       "9      M1N  Scarborough        Cliffside West  43.692657 -79.264848"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_data = df_clean[df_clean['Borough'] == 'Scarborough'].reset_index(drop=True)\n",
    "scarborough_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Scarborough are 43.773077, -79.257774.\n"
     ]
    }
   ],
   "source": [
    "address = 'Scarborough, Toronto'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"ny_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Scarborough are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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                popup_f050ae7972fa42938af9e02355de4b8b.setContent(html_0c3f4cbbb2ea443ab766cea95b02697f);
            

            circle_marker_37b0a6af299f4d99a1e8b057a79b8502.bindPopup(popup_f050ae7972fa42938af9e02355de4b8b);

            
        
    
            var circle_marker_fcd42641bff14232a03c4a07e16d8ada = L.circleMarker(
                [43.7816375,-79.3043021],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_f5c9bcb2ea6940e1b434794f362e1a74);
            
    
            var popup_1f3144ce1e2340e0ae88e3c570b6d2c9 = L.popup({maxWidth: '300'});

            
                var html_0578a077cea444ffaf8365dd6f0f58b5 = $('<div id="html_0578a077cea444ffaf8365dd6f0f58b5" style="width: 100.0%; height: 100.0%;">Sullivan</div>')[0];
                popup_1f3144ce1e2340e0ae88e3c570b6d2c9.setContent(html_0578a077cea444ffaf8365dd6f0f58b5);
            

            circle_marker_fcd42641bff14232a03c4a07e16d8ada.bindPopup(popup_1f3144ce1e2340e0ae88e3c570b6d2c9);

            
        
    
            var circle_marker_91533441bd674e9fae786070cb838f62 = L.circleMarker(
                [43.8152522,-79.2845772],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_f5c9bcb2ea6940e1b434794f362e1a74);
            
    
            var popup_0dfb5ad503174d40bee676abd69bc474 = L.popup({maxWidth: '300'});

            
                var html_52cb79cbfb3c4f2c8a691e2eadd4ba55 = $('<div id="html_52cb79cbfb3c4f2c8a691e2eadd4ba55" style="width: 100.0%; height: 100.0%;">Steeles East</div>')[0];
                popup_0dfb5ad503174d40bee676abd69bc474.setContent(html_52cb79cbfb3c4f2c8a691e2eadd4ba55);
            

            circle_marker_91533441bd674e9fae786070cb838f62.bindPopup(popup_0dfb5ad503174d40bee676abd69bc474);

            
        
    
            var circle_marker_c6c1844afe3d419f8e9b72580102f153 = L.circleMarker(
                [43.799525200000005,-79.3183887],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_f5c9bcb2ea6940e1b434794f362e1a74);
            
    
            var popup_6d03894d6ea441278504d5a8b04e2e4a = L.popup({maxWidth: '300'});

            
                var html_3e0799da6dd946df878420b2d827bb14 = $('<div id="html_3e0799da6dd946df878420b2d827bb14" style="width: 100.0%; height: 100.0%;">L&#39;Amoreaux West</div>')[0];
                popup_6d03894d6ea441278504d5a8b04e2e4a.setContent(html_3e0799da6dd946df878420b2d827bb14);
            

            circle_marker_c6c1844afe3d419f8e9b72580102f153.bindPopup(popup_6d03894d6ea441278504d5a8b04e2e4a);

            
        
    
            var circle_marker_28ad2e173eaa419abe3e7876de700823 = L.circleMarker(
                [43.836124700000006,-79.20563609999999],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_f5c9bcb2ea6940e1b434794f362e1a74);
            
    
            var popup_5e165f1b09af4d35a6de74a80e8b6672 = L.popup({maxWidth: '300'});

            
                var html_d753bc0b5b3842a393b382582664d619 = $('<div id="html_d753bc0b5b3842a393b382582664d619" style="width: 100.0%; height: 100.0%;">Upper Rouge</div>')[0];
                popup_5e165f1b09af4d35a6de74a80e8b6672.setContent(html_d753bc0b5b3842a393b382582664d619);
            

            circle_marker_28ad2e173eaa419abe3e7876de700823.bindPopup(popup_5e165f1b09af4d35a6de74a80e8b6672);

            
        
</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 0x23390353688>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_scarborough = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "\n",
    "for lat, lng, label in zip(scarborough_data['Latitude'], scarborough_data['Longitude'], scarborough_data['Neighbourhood']):\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_scarborough)\n",
    "map_scarborough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define FourSquare Credentials and Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_ID = 'your client id'\n",
    "CLIENT_SECRET = 'your client secret'\n",
    "VERSION = '20180605'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the first neighborhood in our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Rouge'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_data.loc[0, 'Neighbourhood']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitide and Longitude values of Rouge are 43.806686299999996, -79.19435340000001\n"
     ]
    }
   ],
   "source": [
    "neighborhood_latitude = scarborough_data.loc[0, 'Latitude']\n",
    "neighborhood_longitude = scarborough_data.loc[0,'Longitude']\n",
    "\n",
    "neighborhood_name = scarborough_data.loc[0, 'Neighbourhood']\n",
    "print('Latitide and Longitude values of {} are {}, {}'.format(neighborhood_name, neighborhood_latitude, neighborhood_longitude))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating the top 100 venues in Rouge within a radius of 500 meters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'meta': {'code': 200, 'requestId': '5e428c7eedbcad001b84236b'},\n",
       " 'response': {'warning': {'text': \"There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area.\"},\n",
       "  'headerLocation': 'Malvern',\n",
       "  'headerFullLocation': 'Malvern, Toronto',\n",
       "  'headerLocationGranularity': 'neighborhood',\n",
       "  'totalResults': 2,\n",
       "  'suggestedBounds': {'ne': {'lat': 43.8111863045, 'lng': -79.18812958073042},\n",
       "   'sw': {'lat': 43.80218629549999, 'lng': -79.2005772192696}},\n",
       "  'groups': [{'type': 'Recommended Places',\n",
       "    'name': 'recommended',\n",
       "    'items': [{'reasons': {'count': 0,\n",
       "       'items': [{'summary': 'This spot is popular',\n",
       "         'type': 'general',\n",
       "         'reasonName': 'globalInteractionReason'}]},\n",
       "      'venue': {'id': '4bb6b9446edc76b0d771311c',\n",
       "       'name': \"Wendy's\",\n",
       "       'location': {'crossStreet': 'Morningside & Sheppard',\n",
       "        'lat': 43.80744841934756,\n",
       "        'lng': -79.19905558052072,\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.80744841934756,\n",
       "          'lng': -79.19905558052072}],\n",
       "        'distance': 387,\n",
       "        'cc': 'CA',\n",
       "        'city': 'Toronto',\n",
       "        'state': 'ON',\n",
       "        'country': 'Canada',\n",
       "        'formattedAddress': ['Toronto ON', 'Canada']},\n",
       "       'categories': [{'id': '4bf58dd8d48988d16e941735',\n",
       "         'name': 'Fast Food Restaurant',\n",
       "         'pluralName': 'Fast Food Restaurants',\n",
       "         'shortName': 'Fast Food',\n",
       "         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/fastfood_',\n",
       "          'suffix': '.png'},\n",
       "         'primary': True}],\n",
       "       'photos': {'count': 0, 'groups': []}},\n",
       "      'referralId': 'e-0-4bb6b9446edc76b0d771311c-0'},\n",
       "     {'reasons': {'count': 0,\n",
       "       'items': [{'summary': 'This spot is popular',\n",
       "         'type': 'general',\n",
       "         'reasonName': 'globalInteractionReason'}]},\n",
       "      'venue': {'id': '5539e7d2498edaf4b02673ca',\n",
       "       'name': 'Interprovincial Group',\n",
       "       'location': {'address': '1315 Morningside Avenue',\n",
       "        'lat': 43.8056297,\n",
       "        'lng': -79.2003784,\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.8056297,\n",
       "          'lng': -79.2003784}],\n",
       "        'distance': 498,\n",
       "        'postalCode': 'M1B 3C5',\n",
       "        'cc': 'CA',\n",
       "        'city': 'Scarborough',\n",
       "        'state': 'ON',\n",
       "        'country': 'Canada',\n",
       "        'formattedAddress': ['1315 Morningside Avenue',\n",
       "         'Scarborough ON M1B 3C5',\n",
       "         'Canada']},\n",
       "       'categories': [{'id': '52f2ab2ebcbc57f1066b8b28',\n",
       "         'name': 'Print Shop',\n",
       "         'pluralName': 'Print Shops',\n",
       "         'shortName': 'Print Shop',\n",
       "         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/default_',\n",
       "          'suffix': '.png'},\n",
       "         'primary': True}],\n",
       "       'photos': {'count': 0, 'groups': []}},\n",
       "      'referralId': 'e-0-5539e7d2498edaf4b02673ca-1'}]}]}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LIMIT = 100\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",
    "results = requests.get(url).json()\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function that extracts the category of the venue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Wendy's</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>43.807448</td>\n",
       "      <td>-79.199056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Interprovincial Group</td>\n",
       "      <td>Print Shop</td>\n",
       "      <td>43.805630</td>\n",
       "      <td>-79.200378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name            categories        lat        lng\n",
       "0                Wendy's  Fast Food Restaurant  43.807448 -79.199056\n",
       "1  Interprovincial Group            Print Shop  43.805630 -79.200378"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean the json and structure it into a pandas dataframe\n",
    "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": [
    "## Exploring Neighborhoods in Scarborough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create a functionto repeat the same process to all the neighborhoods in Rouge\n",
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "        \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",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "    \n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rouge\n",
      "Port Union\n",
      "Guildwood\n",
      "Woburn\n",
      "Cedarbrae\n",
      "Scarborough Village\n",
      "East Birchmount Park\n",
      "Golden Mile\n",
      "Cliffcrest\n",
      "Cliffside West\n",
      "Wexford Heights\n",
      "Maryvale\n",
      "Agincourt\n",
      "Sullivan\n",
      "Steeles East\n",
      "L'Amoreaux West\n",
      "Upper Rouge\n"
     ]
    }
   ],
   "source": [
    "#Code to run the above function on eaach neighborhood and create a new dataframe called scarborough_venues\n",
    "\n",
    "scarborough_venues = getNearbyVenues(names=scarborough_data['Neighbourhood'],\n",
    "                                   latitudes=scarborough_data['Latitude'],\n",
    "                                   longitudes=scarborough_data['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84, 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",
       "      <td>0</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "      <td>Wendy's</td>\n",
       "      <td>43.807448</td>\n",
       "      <td>-79.199056</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "      <td>Interprovincial Group</td>\n",
       "      <td>43.805630</td>\n",
       "      <td>-79.200378</td>\n",
       "      <td>Print Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>Chris Effects Painting</td>\n",
       "      <td>43.784343</td>\n",
       "      <td>-79.163742</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>Royal Canadian Legion</td>\n",
       "      <td>43.782533</td>\n",
       "      <td>-79.163085</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>Affordable Toronto Movers</td>\n",
       "      <td>43.787919</td>\n",
       "      <td>-79.162977</td>\n",
       "      <td>Moving Target</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  Neighborhood Latitude  Neighborhood Longitude  \\\n",
       "0        Rouge              43.806686              -79.194353   \n",
       "1        Rouge              43.806686              -79.194353   \n",
       "2   Port Union              43.784535              -79.160497   \n",
       "3   Port Union              43.784535              -79.160497   \n",
       "4   Port Union              43.784535              -79.160497   \n",
       "\n",
       "                       Venue  Venue Latitude  Venue Longitude  \\\n",
       "0                    Wendy's       43.807448       -79.199056   \n",
       "1      Interprovincial Group       43.805630       -79.200378   \n",
       "2     Chris Effects Painting       43.784343       -79.163742   \n",
       "3      Royal Canadian Legion       43.782533       -79.163085   \n",
       "4  Affordable Toronto Movers       43.787919       -79.162977   \n",
       "\n",
       "               Venue Category  \n",
       "0        Fast Food Restaurant  \n",
       "1                  Print Shop  \n",
       "2  Construction & Landscaping  \n",
       "3                         Bar  \n",
       "4               Moving Target  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check the size of the dataframe \n",
    "print(scarborough_venues.shape)\n",
    "scarborough_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Agincourt</td>\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",
       "      <td>Cedarbrae</td>\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",
       "      <td>Cliffcrest</td>\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",
       "      <td>Cliffside West</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>East Birchmount Park</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Golden Mile</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guildwood</td>\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",
       "      <td>L'Amoreaux West</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Maryvale</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Port Union</td>\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",
       "      <td>Rouge</td>\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",
       "      <td>Scarborough Village</td>\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",
       "      <td>Steeles East</td>\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",
       "      <td>Sullivan</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wexford Heights</td>\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",
       "      <td>Woburn</td>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Neighborhood Latitude  Neighborhood Longitude  Venue  \\\n",
       "Neighborhood                                                                 \n",
       "Agincourt                                 5                       5      5   \n",
       "Cedarbrae                                 8                       8      8   \n",
       "Cliffcrest                                2                       2      2   \n",
       "Cliffside West                            4                       4      4   \n",
       "East Birchmount Park                      4                       4      4   \n",
       "Golden Mile                               9                       9      9   \n",
       "Guildwood                                 7                       7      7   \n",
       "L'Amoreaux West                          12                      12     12   \n",
       "Maryvale                                  4                       4      4   \n",
       "Port Union                                3                       3      3   \n",
       "Rouge                                     2                       2      2   \n",
       "Scarborough Village                       1                       1      1   \n",
       "Steeles East                              2                       2      2   \n",
       "Sullivan                                 13                      13     13   \n",
       "Wexford Heights                           5                       5      5   \n",
       "Woburn                                    3                       3      3   \n",
       "\n",
       "                      Venue Latitude  Venue Longitude  Venue Category  \n",
       "Neighborhood                                                           \n",
       "Agincourt                          5                5               5  \n",
       "Cedarbrae                          8                8               8  \n",
       "Cliffcrest                         2                2               2  \n",
       "Cliffside West                     4                4               4  \n",
       "East Birchmount Park               4                4               4  \n",
       "Golden Mile                        9                9               9  \n",
       "Guildwood                          7                7               7  \n",
       "L'Amoreaux West                   12               12              12  \n",
       "Maryvale                           4                4               4  \n",
       "Port Union                         3                3               3  \n",
       "Rouge                              2                2               2  \n",
       "Scarborough Village                1                1               1  \n",
       "Steeles East                       2                2               2  \n",
       "Sullivan                          13               13              13  \n",
       "Wexford Heights                    5                5               5  \n",
       "Woburn                             3                3               3  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_venues.groupby('Neighborhood').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 54 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "# To find out how many unique categories can be curated from all the returned venues\n",
    "print('There are {} uniques categories.'.format(len(scarborough_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Each Neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Athletics &amp; Sports</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Bus Line</th>\n",
       "      <th>Bus Station</th>\n",
       "      <th>Café</th>\n",
       "      <th>Caribbean Restaurant</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Clothing Store</th>\n",
       "      <th>Coffee Shop</th>\n",
       "      <th>College Stadium</th>\n",
       "      <th>Construction &amp; Landscaping</th>\n",
       "      <th>Convenience Store</th>\n",
       "      <th>Department Store</th>\n",
       "      <th>Electronics Store</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Fried Chicken Joint</th>\n",
       "      <th>Gas Station</th>\n",
       "      <th>General Entertainment</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Gym Pool</th>\n",
       "      <th>Hakka Restaurant</th>\n",
       "      <th>Hobby Shop</th>\n",
       "      <th>Ice Cream Shop</th>\n",
       "      <th>Indian Restaurant</th>\n",
       "      <th>Intersection</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Korean Restaurant</th>\n",
       "      <th>Latin American Restaurant</th>\n",
       "      <th>Lounge</th>\n",
       "      <th>Medical Center</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Middle Eastern Restaurant</th>\n",
       "      <th>Motel</th>\n",
       "      <th>Moving Target</th>\n",
       "      <th>Noodle House</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Playground</th>\n",
       "      <th>Print Shop</th>\n",
       "      <th>Rental Car Location</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Skating Rink</th>\n",
       "      <th>Smoke Shop</th>\n",
       "      <th>Soccer Field</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Port Union</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Port Union</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Port Union</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  American Restaurant  Athletics & Sports  Bakery  Bank  Bar  \\\n",
       "0        Rouge                    0                   0       0     0    0   \n",
       "1        Rouge                    0                   0       0     0    0   \n",
       "2   Port Union                    0                   0       0     0    0   \n",
       "3   Port Union                    0                   0       0     0    1   \n",
       "4   Port Union                    0                   0       0     0    0   \n",
       "\n",
       "   Breakfast Spot  Burger Joint  Bus Line  Bus Station  Café  \\\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",
       "   Caribbean Restaurant  Chinese Restaurant  Clothing Store  Coffee 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",
       "   College Stadium  Construction & Landscaping  Convenience Store  \\\n",
       "0                0                           0                  0   \n",
       "1                0                           0                  0   \n",
       "2                0                           1                  0   \n",
       "3                0                           0                  0   \n",
       "4                0                           0                  0   \n",
       "\n",
       "   Department Store  Electronics Store  Fast Food Restaurant  \\\n",
       "0                 0                  0                     1   \n",
       "1                 0                  0                     0   \n",
       "2                 0                  0                     0   \n",
       "3                 0                  0                     0   \n",
       "4                 0                  0                     0   \n",
       "\n",
       "   Fried Chicken Joint  Gas Station  General Entertainment  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",
       "   Gym Pool  Hakka Restaurant  Hobby Shop  Ice Cream Shop  Indian 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",
       "   Intersection  Italian Restaurant  Korean 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",
       "   Latin American Restaurant  Lounge  Medical Center  Mexican 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",
       "   Middle Eastern Restaurant  Motel  Moving Target  Noodle House  Park  \\\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              1             0     0   \n",
       "\n",
       "   Pet Store  Pharmacy  Pizza Place  Playground  Print Shop  \\\n",
       "0          0         0            0           0           0   \n",
       "1          0         0            0           0           1   \n",
       "2          0         0            0           0           0   \n",
       "3          0         0            0           0           0   \n",
       "4          0         0            0           0           0   \n",
       "\n",
       "   Rental Car Location  Sandwich Place  Shopping Mall  Skating Rink  \\\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",
       "   Smoke Shop  Soccer Field  Thai Restaurant  Vietnamese 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  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_onehot = pd.get_dummies(scarborough_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "scarborough_onehot['Neighborhood'] = scarborough_venues['Neighborhood'] \n",
    "\n",
    "fixed_columns = [scarborough_onehot.columns[-1]] + list(scarborough_onehot.columns[:-1])\n",
    "scarborough_onehot = scarborough_onehot[fixed_columns]\n",
    "\n",
    "scarborough_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84, 55)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_onehot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Athletics &amp; Sports</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Bus Line</th>\n",
       "      <th>Bus Station</th>\n",
       "      <th>Café</th>\n",
       "      <th>Caribbean Restaurant</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Clothing Store</th>\n",
       "      <th>Coffee Shop</th>\n",
       "      <th>College Stadium</th>\n",
       "      <th>Construction &amp; Landscaping</th>\n",
       "      <th>Convenience Store</th>\n",
       "      <th>Department Store</th>\n",
       "      <th>Electronics Store</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Fried Chicken Joint</th>\n",
       "      <th>Gas Station</th>\n",
       "      <th>General Entertainment</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Gym Pool</th>\n",
       "      <th>Hakka Restaurant</th>\n",
       "      <th>Hobby Shop</th>\n",
       "      <th>Ice Cream Shop</th>\n",
       "      <th>Indian Restaurant</th>\n",
       "      <th>Intersection</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Korean Restaurant</th>\n",
       "      <th>Latin American Restaurant</th>\n",
       "      <th>Lounge</th>\n",
       "      <th>Medical Center</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Middle Eastern Restaurant</th>\n",
       "      <th>Motel</th>\n",
       "      <th>Moving Target</th>\n",
       "      <th>Noodle House</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Playground</th>\n",
       "      <th>Print Shop</th>\n",
       "      <th>Rental Car Location</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Skating Rink</th>\n",
       "      <th>Smoke Shop</th>\n",
       "      <th>Soccer Field</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Agincourt</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Cliffcrest</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Cliffside West</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>East Birchmount Park</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Golden Mile</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</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>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>L'Amoreaux West</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Maryvale</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Port Union</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Steeles East</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Sullivan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</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>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Wexford Heights</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Neighborhood  American Restaurant  Athletics & Sports    Bakery  \\\n",
       "0              Agincourt                  0.0               0.000  0.000000   \n",
       "1              Cedarbrae                  0.0               0.125  0.125000   \n",
       "2             Cliffcrest                  0.5               0.000  0.000000   \n",
       "3         Cliffside West                  0.0               0.000  0.000000   \n",
       "4   East Birchmount Park                  0.0               0.000  0.000000   \n",
       "5            Golden Mile                  0.0               0.000  0.222222   \n",
       "6              Guildwood                  0.0               0.000  0.000000   \n",
       "7        L'Amoreaux West                  0.0               0.000  0.000000   \n",
       "8               Maryvale                  0.0               0.000  0.250000   \n",
       "9             Port Union                  0.0               0.000  0.000000   \n",
       "10                 Rouge                  0.0               0.000  0.000000   \n",
       "11   Scarborough Village                  0.0               0.000  0.000000   \n",
       "12          Steeles East                  0.0               0.000  0.000000   \n",
       "13              Sullivan                  0.0               0.000  0.000000   \n",
       "14       Wexford Heights                  0.0               0.000  0.000000   \n",
       "15                Woburn                  0.0               0.000  0.000000   \n",
       "\n",
       "        Bank       Bar  Breakfast Spot  Burger Joint  Bus Line  Bus Station  \\\n",
       "0   0.000000  0.000000        0.200000      0.000000  0.000000     0.000000   \n",
       "1   0.125000  0.000000        0.000000      0.000000  0.000000     0.000000   \n",
       "2   0.000000  0.000000        0.000000      0.000000  0.000000     0.000000   \n",
       "3   0.000000  0.000000        0.000000      0.000000  0.000000     0.000000   \n",
       "4   0.000000  0.000000        0.000000      0.000000  0.000000     0.000000   \n",
       "5   0.000000  0.000000        0.000000      0.000000  0.222222     0.111111   \n",
       "6   0.000000  0.000000        0.142857      0.000000  0.000000     0.000000   \n",
       "7   0.000000  0.000000        0.083333      0.083333  0.000000     0.000000   \n",
       "8   0.000000  0.000000        0.250000      0.000000  0.000000     0.000000   \n",
       "9   0.000000  0.333333        0.000000      0.000000  0.000000     0.000000   \n",
       "10  0.000000  0.000000        0.000000      0.000000  0.000000     0.000000   \n",
       "11  0.000000  0.000000        0.000000      0.000000  0.000000     0.000000   \n",
       "12  0.000000  0.000000        0.000000      0.000000  0.000000     0.000000   \n",
       "13  0.076923  0.000000        0.000000      0.000000  0.000000     0.000000   \n",
       "14  0.000000  0.000000        0.000000      0.000000  0.000000     0.000000   \n",
       "15  0.000000  0.000000        0.000000      0.000000  0.000000     0.000000   \n",
       "\n",
       "    Café  Caribbean Restaurant  Chinese Restaurant  Clothing Store  \\\n",
       "0   0.00                 0.000            0.000000             0.2   \n",
       "1   0.00                 0.125            0.000000             0.0   \n",
       "2   0.00                 0.000            0.000000             0.0   \n",
       "3   0.25                 0.000            0.000000             0.0   \n",
       "4   0.00                 0.000            0.250000             0.0   \n",
       "5   0.00                 0.000            0.000000             0.0   \n",
       "6   0.00                 0.000            0.000000             0.0   \n",
       "7   0.00                 0.000            0.166667             0.0   \n",
       "8   0.00                 0.000            0.000000             0.0   \n",
       "9   0.00                 0.000            0.000000             0.0   \n",
       "10  0.00                 0.000            0.000000             0.0   \n",
       "11  0.00                 0.000            0.000000             0.0   \n",
       "12  0.00                 0.000            0.000000             0.0   \n",
       "13  0.00                 0.000            0.076923             0.0   \n",
       "14  0.00                 0.000            0.200000             0.0   \n",
       "15  0.00                 0.000            0.000000             0.0   \n",
       "\n",
       "    Coffee Shop  College Stadium  Construction & Landscaping  \\\n",
       "0      0.000000             0.00                    0.000000   \n",
       "1      0.000000             0.00                    0.000000   \n",
       "2      0.000000             0.00                    0.000000   \n",
       "3      0.000000             0.25                    0.000000   \n",
       "4      0.250000             0.00                    0.000000   \n",
       "5      0.000000             0.00                    0.000000   \n",
       "6      0.000000             0.00                    0.000000   \n",
       "7      0.083333             0.00                    0.000000   \n",
       "8      0.000000             0.00                    0.000000   \n",
       "9      0.000000             0.00                    0.333333   \n",
       "10     0.000000             0.00                    0.000000   \n",
       "11     0.000000             0.00                    0.000000   \n",
       "12     0.000000             0.00                    0.000000   \n",
       "13     0.000000             0.00                    0.000000   \n",
       "14     0.000000             0.00                    0.000000   \n",
       "15     0.666667             0.00                    0.000000   \n",
       "\n",
       "    Convenience Store  Department Store  Electronics Store  \\\n",
       "0            0.000000              0.00           0.000000   \n",
       "1            0.000000              0.00           0.000000   \n",
       "2            0.000000              0.00           0.000000   \n",
       "3            0.000000              0.00           0.000000   \n",
       "4            0.000000              0.25           0.000000   \n",
       "5            0.000000              0.00           0.000000   \n",
       "6            0.000000              0.00           0.142857   \n",
       "7            0.000000              0.00           0.000000   \n",
       "8            0.000000              0.00           0.000000   \n",
       "9            0.000000              0.00           0.000000   \n",
       "10           0.000000              0.00           0.000000   \n",
       "11           0.000000              0.00           0.000000   \n",
       "12           0.000000              0.00           0.000000   \n",
       "13           0.076923              0.00           0.000000   \n",
       "14           0.000000              0.00           0.000000   \n",
       "15           0.000000              0.00           0.000000   \n",
       "\n",
       "    Fast Food Restaurant  Fried Chicken Joint  Gas Station  \\\n",
       "0               0.000000             0.000000     0.000000   \n",
       "1               0.000000             0.125000     0.125000   \n",
       "2               0.000000             0.000000     0.000000   \n",
       "3               0.000000             0.000000     0.000000   \n",
       "4               0.000000             0.000000     0.000000   \n",
       "5               0.000000             0.000000     0.000000   \n",
       "6               0.000000             0.000000     0.000000   \n",
       "7               0.166667             0.000000     0.000000   \n",
       "8               0.000000             0.000000     0.000000   \n",
       "9               0.000000             0.000000     0.000000   \n",
       "10              0.500000             0.000000     0.000000   \n",
       "11              0.000000             0.000000     0.000000   \n",
       "12              0.000000             0.000000     0.000000   \n",
       "13              0.076923             0.076923     0.076923   \n",
       "14              0.000000             0.000000     0.000000   \n",
       "15              0.000000             0.000000     0.000000   \n",
       "\n",
       "    General Entertainment  Grocery Store  Gym Pool  Hakka Restaurant  \\\n",
       "0                    0.00       0.000000  0.000000             0.000   \n",
       "1                    0.00       0.000000  0.000000             0.125   \n",
       "2                    0.00       0.000000  0.000000             0.000   \n",
       "3                    0.25       0.000000  0.000000             0.000   \n",
       "4                    0.00       0.000000  0.000000             0.000   \n",
       "5                    0.00       0.000000  0.000000             0.000   \n",
       "6                    0.00       0.000000  0.000000             0.000   \n",
       "7                    0.00       0.083333  0.083333             0.000   \n",
       "8                    0.00       0.000000  0.000000             0.000   \n",
       "9                    0.00       0.000000  0.000000             0.000   \n",
       "10                   0.00       0.000000  0.000000             0.000   \n",
       "11                   0.00       0.000000  0.000000             0.000   \n",
       "12                   0.00       0.000000  0.000000             0.000   \n",
       "13                   0.00       0.000000  0.000000             0.000   \n",
       "14                   0.00       0.000000  0.000000             0.000   \n",
       "15                   0.00       0.000000  0.000000             0.000   \n",
       "\n",
       "    Hobby Shop  Ice Cream Shop  Indian Restaurant  Intersection  \\\n",
       "0         0.00        0.000000                0.0      0.000000   \n",
       "1         0.00        0.000000                0.0      0.000000   \n",
       "2         0.00        0.000000                0.0      0.000000   \n",
       "3         0.00        0.000000                0.0      0.000000   \n",
       "4         0.25        0.000000                0.0      0.000000   \n",
       "5         0.00        0.111111                0.0      0.111111   \n",
       "6         0.00        0.000000                0.0      0.142857   \n",
       "7         0.00        0.000000                0.0      0.000000   \n",
       "8         0.00        0.000000                0.0      0.000000   \n",
       "9         0.00        0.000000                0.0      0.000000   \n",
       "10        0.00        0.000000                0.0      0.000000   \n",
       "11        0.00        0.000000                0.0      0.000000   \n",
       "12        0.00        0.000000                0.0      0.000000   \n",
       "13        0.00        0.000000                0.0      0.000000   \n",
       "14        0.00        0.000000                0.4      0.000000   \n",
       "15        0.00        0.000000                0.0      0.000000   \n",
       "\n",
       "    Italian Restaurant  Korean Restaurant  Latin American Restaurant  Lounge  \\\n",
       "0             0.000000           0.000000                        0.2     0.2   \n",
       "1             0.000000           0.000000                        0.0     0.0   \n",
       "2             0.000000           0.000000                        0.0     0.0   \n",
       "3             0.000000           0.000000                        0.0     0.0   \n",
       "4             0.000000           0.000000                        0.0     0.0   \n",
       "5             0.000000           0.000000                        0.0     0.0   \n",
       "6             0.000000           0.000000                        0.0     0.0   \n",
       "7             0.000000           0.000000                        0.0     0.0   \n",
       "8             0.000000           0.000000                        0.0     0.0   \n",
       "9             0.000000           0.000000                        0.0     0.0   \n",
       "10            0.000000           0.000000                        0.0     0.0   \n",
       "11            0.000000           0.000000                        0.0     0.0   \n",
       "12            0.000000           0.000000                        0.0     0.0   \n",
       "13            0.076923           0.000000                        0.0     0.0   \n",
       "14            0.000000           0.000000                        0.0     0.0   \n",
       "15            0.000000           0.333333                        0.0     0.0   \n",
       "\n",
       "    Medical Center  Mexican Restaurant  Middle Eastern Restaurant  Motel  \\\n",
       "0         0.000000            0.000000                       0.00    0.0   \n",
       "1         0.000000            0.000000                       0.00    0.0   \n",
       "2         0.000000            0.000000                       0.00    0.5   \n",
       "3         0.000000            0.000000                       0.00    0.0   \n",
       "4         0.000000            0.000000                       0.00    0.0   \n",
       "5         0.000000            0.000000                       0.00    0.0   \n",
       "6         0.142857            0.142857                       0.00    0.0   \n",
       "7         0.000000            0.000000                       0.00    0.0   \n",
       "8         0.000000            0.000000                       0.25    0.0   \n",
       "9         0.000000            0.000000                       0.00    0.0   \n",
       "10        0.000000            0.000000                       0.00    0.0   \n",
       "11        0.000000            0.000000                       0.00    0.0   \n",
       "12        0.000000            0.000000                       0.00    0.0   \n",
       "13        0.000000            0.000000                       0.00    0.0   \n",
       "14        0.000000            0.000000                       0.00    0.0   \n",
       "15        0.000000            0.000000                       0.00    0.0   \n",
       "\n",
       "    Moving Target  Noodle House      Park  Pet Store  Pharmacy  Pizza Place  \\\n",
       "0        0.000000      0.000000  0.000000        0.0  0.000000     0.000000   \n",
       "1        0.000000      0.000000  0.000000        0.0  0.000000     0.000000   \n",
       "2        0.000000      0.000000  0.000000        0.0  0.000000     0.000000   \n",
       "3        0.000000      0.000000  0.000000        0.0  0.000000     0.000000   \n",
       "4        0.000000      0.000000  0.000000        0.0  0.000000     0.000000   \n",
       "5        0.000000      0.000000  0.111111        0.0  0.000000     0.000000   \n",
       "6        0.000000      0.000000  0.000000        0.0  0.000000     0.142857   \n",
       "7        0.000000      0.000000  0.000000        0.0  0.083333     0.083333   \n",
       "8        0.000000      0.000000  0.000000        0.0  0.000000     0.000000   \n",
       "9        0.333333      0.000000  0.000000        0.0  0.000000     0.000000   \n",
       "10       0.000000      0.000000  0.000000        0.0  0.000000     0.000000   \n",
       "11       0.000000      0.000000  0.000000        0.0  0.000000     0.000000   \n",
       "12       0.000000      0.000000  0.500000        0.0  0.000000     0.000000   \n",
       "13       0.000000      0.076923  0.000000        0.0  0.076923     0.153846   \n",
       "14       0.000000      0.000000  0.000000        0.2  0.000000     0.000000   \n",
       "15       0.000000      0.000000  0.000000        0.0  0.000000     0.000000   \n",
       "\n",
       "    Playground  Print Shop  Rental Car Location  Sandwich Place  \\\n",
       "0          0.0         0.0             0.000000        0.000000   \n",
       "1          0.0         0.0             0.000000        0.000000   \n",
       "2          0.0         0.0             0.000000        0.000000   \n",
       "3          0.0         0.0             0.000000        0.000000   \n",
       "4          0.0         0.0             0.000000        0.000000   \n",
       "5          0.0         0.0             0.000000        0.000000   \n",
       "6          0.0         0.0             0.142857        0.000000   \n",
       "7          0.0         0.0             0.000000        0.083333   \n",
       "8          0.0         0.0             0.000000        0.000000   \n",
       "9          0.0         0.0             0.000000        0.000000   \n",
       "10         0.0         0.5             0.000000        0.000000   \n",
       "11         1.0         0.0             0.000000        0.000000   \n",
       "12         0.5         0.0             0.000000        0.000000   \n",
       "13         0.0         0.0             0.000000        0.000000   \n",
       "14         0.0         0.0             0.000000        0.000000   \n",
       "15         0.0         0.0             0.000000        0.000000   \n",
       "\n",
       "    Shopping Mall  Skating Rink  Smoke Shop  Soccer Field  Thai Restaurant  \\\n",
       "0        0.000000          0.20        0.00      0.000000         0.000000   \n",
       "1        0.000000          0.00        0.00      0.000000         0.125000   \n",
       "2        0.000000          0.00        0.00      0.000000         0.000000   \n",
       "3        0.000000          0.25        0.00      0.000000         0.000000   \n",
       "4        0.000000          0.00        0.00      0.000000         0.000000   \n",
       "5        0.000000          0.00        0.00      0.111111         0.000000   \n",
       "6        0.000000          0.00        0.00      0.000000         0.000000   \n",
       "7        0.000000          0.00        0.00      0.000000         0.000000   \n",
       "8        0.000000          0.00        0.25      0.000000         0.000000   \n",
       "9        0.000000          0.00        0.00      0.000000         0.000000   \n",
       "10       0.000000          0.00        0.00      0.000000         0.000000   \n",
       "11       0.000000          0.00        0.00      0.000000         0.000000   \n",
       "12       0.000000          0.00        0.00      0.000000         0.000000   \n",
       "13       0.076923          0.00        0.00      0.000000         0.076923   \n",
       "14       0.000000          0.00        0.00      0.000000         0.000000   \n",
       "15       0.000000          0.00        0.00      0.000000         0.000000   \n",
       "\n",
       "    Vietnamese Restaurant  \n",
       "0                     0.0  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     0.0  \n",
       "4                     0.0  \n",
       "5                     0.0  \n",
       "6                     0.0  \n",
       "7                     0.0  \n",
       "8                     0.0  \n",
       "9                     0.0  \n",
       "10                    0.0  \n",
       "11                    0.0  \n",
       "12                    0.0  \n",
       "13                    0.0  \n",
       "14                    0.2  \n",
       "15                    0.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group rows by neighborhood and by taking the mean of the frequency of occurrence of each category\n",
    "scarborough_grouped = scarborough_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "scarborough_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 55)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_grouped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Agincourt----\n",
      "                       venue  freq\n",
      "0  Latin American Restaurant   0.2\n",
      "1             Breakfast Spot   0.2\n",
      "2               Skating Rink   0.2\n",
      "3                     Lounge   0.2\n",
      "4             Clothing Store   0.2\n",
      "\n",
      "\n",
      "----Cedarbrae----\n",
      "                venue  freq\n",
      "0    Hakka Restaurant  0.12\n",
      "1              Bakery  0.12\n",
      "2                Bank  0.12\n",
      "3     Thai Restaurant  0.12\n",
      "4  Athletics & Sports  0.12\n",
      "\n",
      "\n",
      "----Cliffcrest----\n",
      "                 venue  freq\n",
      "0  American Restaurant   0.5\n",
      "1                Motel   0.5\n",
      "2                 Park   0.0\n",
      "3         Intersection   0.0\n",
      "4   Italian Restaurant   0.0\n",
      "\n",
      "\n",
      "----Cliffside West----\n",
      "                   venue  freq\n",
      "0        College Stadium  0.25\n",
      "1           Skating Rink  0.25\n",
      "2  General Entertainment  0.25\n",
      "3                   Café  0.25\n",
      "4              Pet Store  0.00\n",
      "\n",
      "\n",
      "----East Birchmount Park----\n",
      "                 venue  freq\n",
      "0           Hobby Shop  0.25\n",
      "1   Chinese Restaurant  0.25\n",
      "2          Coffee Shop  0.25\n",
      "3     Department Store  0.25\n",
      "4  American Restaurant  0.00\n",
      "\n",
      "\n",
      "----Golden Mile----\n",
      "            venue  freq\n",
      "0          Bakery  0.22\n",
      "1        Bus Line  0.22\n",
      "2  Ice Cream Shop  0.11\n",
      "3            Park  0.11\n",
      "4    Soccer Field  0.11\n",
      "\n",
      "\n",
      "----Guildwood----\n",
      "                 venue  freq\n",
      "0         Intersection  0.14\n",
      "1       Breakfast Spot  0.14\n",
      "2       Medical Center  0.14\n",
      "3   Mexican Restaurant  0.14\n",
      "4  Rental Car Location  0.14\n",
      "\n",
      "\n",
      "----L'Amoreaux West----\n",
      "                  venue  freq\n",
      "0  Fast Food Restaurant  0.17\n",
      "1    Chinese Restaurant  0.17\n",
      "2              Gym Pool  0.08\n",
      "3              Pharmacy  0.08\n",
      "4           Coffee Shop  0.08\n",
      "\n",
      "\n",
      "----Maryvale----\n",
      "                       venue  freq\n",
      "0                     Bakery  0.25\n",
      "1             Breakfast Spot  0.25\n",
      "2                 Smoke Shop  0.25\n",
      "3  Middle Eastern Restaurant  0.25\n",
      "4        American Restaurant  0.00\n",
      "\n",
      "\n",
      "----Port Union----\n",
      "                        venue  freq\n",
      "0  Construction & Landscaping  0.33\n",
      "1                         Bar  0.33\n",
      "2               Moving Target  0.33\n",
      "3         American Restaurant  0.00\n",
      "4          Italian Restaurant  0.00\n",
      "\n",
      "\n",
      "----Rouge----\n",
      "                  venue  freq\n",
      "0  Fast Food Restaurant   0.5\n",
      "1            Print Shop   0.5\n",
      "2   American Restaurant   0.0\n",
      "3                  Park   0.0\n",
      "4    Italian Restaurant   0.0\n",
      "\n",
      "\n",
      "----Scarborough Village----\n",
      "                 venue  freq\n",
      "0           Playground   1.0\n",
      "1  American Restaurant   0.0\n",
      "2                 Park   0.0\n",
      "3         Intersection   0.0\n",
      "4   Italian Restaurant   0.0\n",
      "\n",
      "\n",
      "----Steeles East----\n",
      "                 venue  freq\n",
      "0                 Park   0.5\n",
      "1           Playground   0.5\n",
      "2  American Restaurant   0.0\n",
      "3         Intersection   0.0\n",
      "4   Italian Restaurant   0.0\n",
      "\n",
      "\n",
      "----Sullivan----\n",
      "                  venue  freq\n",
      "0           Pizza Place  0.15\n",
      "1  Fast Food Restaurant  0.08\n",
      "2       Thai Restaurant  0.08\n",
      "3                  Bank  0.08\n",
      "4          Noodle House  0.08\n",
      "\n",
      "\n",
      "----Wexford Heights----\n",
      "                   venue  freq\n",
      "0      Indian Restaurant   0.4\n",
      "1  Vietnamese Restaurant   0.2\n",
      "2     Chinese Restaurant   0.2\n",
      "3              Pet Store   0.2\n",
      "4                   Park   0.0\n",
      "\n",
      "\n",
      "----Woburn----\n",
      "                       venue  freq\n",
      "0                Coffee Shop  0.67\n",
      "1          Korean Restaurant  0.33\n",
      "2                  Pet Store  0.00\n",
      "3         Italian Restaurant  0.00\n",
      "4  Latin American Restaurant  0.00\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Let's print each neighborhood along with the top 5 most common venues\n",
    "num_top_venues = 5\n",
    "\n",
    "for hood in scarborough_grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = scarborough_grouped[scarborough_grouped['Neighborhood'] == hood].T.reset_index()\n",
    "    temp.columns = ['venue','freq']\n",
    "    temp = temp.iloc[1:]\n",
    "    temp['freq'] = temp['freq'].astype(float)\n",
    "    temp = temp.round({'freq': 2})\n",
    "    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to sort the venues in descending order\n",
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Agincourt</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Gym Pool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Cliffcrest</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Motel</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Department Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Cliffside West</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Café</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>East Birchmount Park</td>\n",
       "      <td>Hobby Shop</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Gym Pool</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Neighborhood  1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0             Agincourt         Clothing Store          Skating Rink   \n",
       "1             Cedarbrae       Hakka Restaurant    Athletics & Sports   \n",
       "2            Cliffcrest    American Restaurant                 Motel   \n",
       "3        Cliffside West  General Entertainment          Skating Rink   \n",
       "4  East Birchmount Park             Hobby Shop           Coffee Shop   \n",
       "\n",
       "  3rd Most Common Venue      4th Most Common Venue  5th Most Common Venue  \\\n",
       "0        Breakfast Spot  Latin American Restaurant                 Lounge   \n",
       "1                Bakery                       Bank            Gas Station   \n",
       "2           Coffee Shop              Grocery Store  General Entertainment   \n",
       "3                  Café            College Stadium  Vietnamese Restaurant   \n",
       "4      Department Store         Chinese Restaurant               Gym Pool   \n",
       "\n",
       "  6th Most Common Venue  7th Most Common Venue 8th Most Common Venue  \\\n",
       "0           Coffee Shop  General Entertainment           Gas Station   \n",
       "1   Fried Chicken Joint   Caribbean Restaurant       Thai Restaurant   \n",
       "2           Gas Station    Fried Chicken Joint  Fast Food Restaurant   \n",
       "3           Coffee Shop          Grocery Store           Gas Station   \n",
       "4         Grocery Store  General Entertainment           Gas Station   \n",
       "\n",
       "        9th Most Common Venue 10th Most Common Venue  \n",
       "0         Fried Chicken Joint   Fast Food Restaurant  \n",
       "1  Construction & Landscaping               Gym Pool  \n",
       "2           Electronics Store       Department Store  \n",
       "3         Fried Chicken Joint   Fast Food Restaurant  \n",
       "4         Fried Chicken Joint   Fast Food Restaurant  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#new dataframe and display the top 10 venues for each neighborhood.\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'] = scarborough_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(scarborough_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(scarborough_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster Neighbourhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#k-means to cluster the neighborhood into 5 clusters.\n",
    "kclusters = 5\n",
    "scarborough_grouped_clustering = scarborough_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(scarborough_grouped_clustering)\n",
    "\n",
    "kmeans.labels_[0:10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Print Shop</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Department Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Moving Target</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Medical Center</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Department Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Gym Pool</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode      Borough Neighbourhood   Latitude  Longitude  Cluster Labels  \\\n",
       "0      M1B  Scarborough         Rouge  43.806686 -79.194353             4.0   \n",
       "1      M1C  Scarborough    Port Union  43.784535 -79.160497             0.0   \n",
       "2      M1E  Scarborough     Guildwood  43.763573 -79.188711             0.0   \n",
       "3      M1G  Scarborough        Woburn  43.770992 -79.216917             1.0   \n",
       "4      M1H  Scarborough     Cedarbrae  43.773136 -79.239476             0.0   \n",
       "\n",
       "  1st Most Common Venue       2nd Most Common Venue  3rd Most Common Venue  \\\n",
       "0  Fast Food Restaurant                  Print Shop  Vietnamese Restaurant   \n",
       "1                   Bar  Construction & Landscaping          Moving Target   \n",
       "2          Intersection              Breakfast Spot    Rental Car Location   \n",
       "3           Coffee Shop           Korean Restaurant  Vietnamese Restaurant   \n",
       "4      Hakka Restaurant          Athletics & Sports                 Bakery   \n",
       "\n",
       "   4th Most Common Venue  5th Most Common Venue  6th Most Common Venue  \\\n",
       "0         Clothing Store          Grocery Store  General Entertainment   \n",
       "1  Vietnamese Restaurant            Coffee Shop          Grocery Store   \n",
       "2      Electronics Store            Pizza Place         Medical Center   \n",
       "3          Grocery Store  General Entertainment            Gas Station   \n",
       "4                   Bank            Gas Station    Fried Chicken Joint   \n",
       "\n",
       "   7th Most Common Venue  8th Most Common Venue       9th Most Common Venue  \\\n",
       "0            Gas Station    Fried Chicken Joint           Electronics Store   \n",
       "1  General Entertainment            Gas Station         Fried Chicken Joint   \n",
       "2     Mexican Restaurant  Vietnamese Restaurant                 Coffee Shop   \n",
       "3    Fried Chicken Joint   Fast Food Restaurant           Electronics Store   \n",
       "4   Caribbean Restaurant        Thai Restaurant  Construction & Landscaping   \n",
       "\n",
       "  10th Most Common Venue  \n",
       "0       Department Store  \n",
       "1   Fast Food Restaurant  \n",
       "2            Gas Station  \n",
       "3       Department Store  \n",
       "4               Gym Pool  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood.\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "# neighborhoods_venues_sorted.drop('Cluster Labels', axis=1, inplace=True)\n",
    "\n",
    "scarborough_merged = scarborough_data\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "scarborough_merged = scarborough_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighbourhood')\n",
    "\n",
    "scarborough_merged.head() # check the last columns!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
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  "bubblingMouseEvents": true,
  "color": "#3388ff",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3388ff",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_32197be4a9b6481b8efd052477563bc0);
            
    
            var popup_e4b8211a31264910914ed1a166b1da7c = L.popup({maxWidth: '300'});

            
                var html_91c0a15d221f4656b2c60e7ddccb02e9 = $('<div id="html_91c0a15d221f4656b2c60e7ddccb02e9" style="width: 100.0%; height: 100.0%;">Sullivan Cluster 0.0</div>')[0];
                popup_e4b8211a31264910914ed1a166b1da7c.setContent(html_91c0a15d221f4656b2c60e7ddccb02e9);
            

            circle_marker_e978449d689541f5ba858ba63d39a75e.bindPopup(popup_e4b8211a31264910914ed1a166b1da7c);

            
        
    
            var circle_marker_0b9582fd225e4ee6aef0bba0a25692f0 = L.circleMarker(
                [43.8152522,-79.2845772],
                {
  "bubblingMouseEvents": true,
  "color": "#3388ff",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3388ff",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_32197be4a9b6481b8efd052477563bc0);
            
    
            var popup_ba8d2fb367cd4b84a36e4964191d5d94 = L.popup({maxWidth: '300'});

            
                var html_c7ffa63a59ad4e8fb8a47e7d7035fd25 = $('<div id="html_c7ffa63a59ad4e8fb8a47e7d7035fd25" style="width: 100.0%; height: 100.0%;">Steeles East Cluster 2.0</div>')[0];
                popup_ba8d2fb367cd4b84a36e4964191d5d94.setContent(html_c7ffa63a59ad4e8fb8a47e7d7035fd25);
            

            circle_marker_0b9582fd225e4ee6aef0bba0a25692f0.bindPopup(popup_ba8d2fb367cd4b84a36e4964191d5d94);

            
        
    
            var circle_marker_18a192eb05154052ae675b3570286464 = L.circleMarker(
                [43.799525200000005,-79.3183887],
                {
  "bubblingMouseEvents": true,
  "color": "#3388ff",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3388ff",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_32197be4a9b6481b8efd052477563bc0);
            
    
            var popup_ec2d9bac230048fbb82cb719e580cddc = L.popup({maxWidth: '300'});

            
                var html_db343d0c03d64a9bbbcba2a1127b3a6f = $('<div id="html_db343d0c03d64a9bbbcba2a1127b3a6f" style="width: 100.0%; height: 100.0%;">L&#39;Amoreaux West Cluster 0.0</div>')[0];
                popup_ec2d9bac230048fbb82cb719e580cddc.setContent(html_db343d0c03d64a9bbbcba2a1127b3a6f);
            

            circle_marker_18a192eb05154052ae675b3570286464.bindPopup(popup_ec2d9bac230048fbb82cb719e580cddc);

            
        
    
            var circle_marker_a70e9751c7d54e39b0273b3590bf69c8 = L.circleMarker(
                [43.836124700000006,-79.20563609999999],
                {
  "bubblingMouseEvents": true,
  "color": "#3388ff",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3388ff",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_32197be4a9b6481b8efd052477563bc0);
            
    
            var popup_dece8ad85f944f2e8e94f8c5bd5ffedb = L.popup({maxWidth: '300'});

            
                var html_d02d241122874cf09050bb83c6df4868 = $('<div id="html_d02d241122874cf09050bb83c6df4868" style="width: 100.0%; height: 100.0%;">Upper Rouge Cluster nan</div>')[0];
                popup_dece8ad85f944f2e8e94f8c5bd5ffedb.setContent(html_d02d241122874cf09050bb83c6df4868);
            

            circle_marker_a70e9751c7d54e39b0273b3590bf69c8.bindPopup(popup_dece8ad85f944f2e8e94f8c5bd5ffedb);

            
        
</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 0x2338ffa3f88>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visualizing the results\n",
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "\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(scarborough_merged['Latitude'], scarborough_merged['Longitude'], scarborough_merged['Neighbourhood'], scarborough_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": [
    "## Examine Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Moving Target</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Medical Center</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Gas Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Gym Pool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Hobby Shop</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Gym Pool</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Bus Line</td>\n",
       "      <td>Soccer Field</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Park</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Motel</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Department Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Café</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Smoke Shop</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Gym Pool</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough  Cluster Labels  1st Most Common Venue  \\\n",
       "1   Scarborough             0.0                    Bar   \n",
       "2   Scarborough             0.0           Intersection   \n",
       "4   Scarborough             0.0       Hakka Restaurant   \n",
       "6   Scarborough             0.0             Hobby Shop   \n",
       "7   Scarborough             0.0                 Bakery   \n",
       "8   Scarborough             0.0    American Restaurant   \n",
       "9   Scarborough             0.0  General Entertainment   \n",
       "10  Scarborough             0.0      Indian Restaurant   \n",
       "11  Scarborough             0.0             Smoke Shop   \n",
       "12  Scarborough             0.0         Clothing Store   \n",
       "13  Scarborough             0.0            Pizza Place   \n",
       "15  Scarborough             0.0     Chinese Restaurant   \n",
       "\n",
       "         2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "1   Construction & Landscaping         Moving Target   \n",
       "2               Breakfast Spot   Rental Car Location   \n",
       "4           Athletics & Sports                Bakery   \n",
       "6                  Coffee Shop      Department Store   \n",
       "7                     Bus Line          Soccer Field   \n",
       "8                        Motel           Coffee Shop   \n",
       "9                 Skating Rink                  Café   \n",
       "10       Vietnamese Restaurant             Pet Store   \n",
       "11                      Bakery        Breakfast Spot   \n",
       "12                Skating Rink        Breakfast Spot   \n",
       "13          Italian Restaurant     Convenience Store   \n",
       "15        Fast Food Restaurant         Grocery Store   \n",
       "\n",
       "        4th Most Common Venue  5th Most Common Venue 6th Most Common Venue  \\\n",
       "1       Vietnamese Restaurant            Coffee Shop         Grocery Store   \n",
       "2           Electronics Store            Pizza Place        Medical Center   \n",
       "4                        Bank            Gas Station   Fried Chicken Joint   \n",
       "6          Chinese Restaurant               Gym Pool         Grocery Store   \n",
       "7              Ice Cream Shop           Intersection                  Park   \n",
       "8               Grocery Store  General Entertainment           Gas Station   \n",
       "9             College Stadium  Vietnamese Restaurant           Coffee Shop   \n",
       "10         Chinese Restaurant            Coffee Shop         Grocery Store   \n",
       "11  Middle Eastern Restaurant  Vietnamese Restaurant       College Stadium   \n",
       "12  Latin American Restaurant                 Lounge           Coffee Shop   \n",
       "13                Gas Station        Thai Restaurant                  Bank   \n",
       "15               Burger Joint               Gym Pool              Pharmacy   \n",
       "\n",
       "    7th Most Common Venue  8th Most Common Venue       9th Most Common Venue  \\\n",
       "1   General Entertainment            Gas Station         Fried Chicken Joint   \n",
       "2      Mexican Restaurant  Vietnamese Restaurant                 Coffee Shop   \n",
       "4    Caribbean Restaurant        Thai Restaurant  Construction & Landscaping   \n",
       "6   General Entertainment            Gas Station         Fried Chicken Joint   \n",
       "7             Bus Station  Vietnamese Restaurant           Electronics Store   \n",
       "8     Fried Chicken Joint   Fast Food Restaurant           Electronics Store   \n",
       "9           Grocery Store            Gas Station         Fried Chicken Joint   \n",
       "10  General Entertainment            Gas Station         Fried Chicken Joint   \n",
       "11          Grocery Store  General Entertainment                 Gas Station   \n",
       "12  General Entertainment            Gas Station         Fried Chicken Joint   \n",
       "13          Shopping Mall    Fried Chicken Joint        Fast Food Restaurant   \n",
       "15            Pizza Place            Coffee Shop              Breakfast Spot   \n",
       "\n",
       "   10th Most Common Venue  \n",
       "1    Fast Food Restaurant  \n",
       "2             Gas Station  \n",
       "4                Gym Pool  \n",
       "6    Fast Food Restaurant  \n",
       "7       Convenience Store  \n",
       "8        Department Store  \n",
       "9    Fast Food Restaurant  \n",
       "10   Fast Food Restaurant  \n",
       "11    Fried Chicken Joint  \n",
       "12   Fast Food Restaurant  \n",
       "13     Chinese Restaurant  \n",
       "15         Sandwich Place  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster 1\n",
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 0, scarborough_merged.columns[[1] + list(range(5, scarborough_merged.shape[1]))]]"
   ]
  },
  {
   "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>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Department Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "3  Scarborough             1.0           Coffee Shop     Korean Restaurant   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue  5th Most Common Venue  \\\n",
       "3  Vietnamese Restaurant         Grocery Store  General Entertainment   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "3           Gas Station   Fried Chicken Joint  Fast Food Restaurant   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "3     Electronics Store       Department Store  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster 2\n",
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 1, scarborough_merged.columns[[1] + list(range(5, scarborough_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
       "      <td>14</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Park</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "14  Scarborough             2.0            Playground                  Park   \n",
       "\n",
       "    3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "14  Vietnamese Restaurant        Clothing Store         Grocery Store   \n",
       "\n",
       "    6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "14  General Entertainment           Gas Station   Fried Chicken Joint   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "14  Fast Food Restaurant      Electronics Store  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster 3\n",
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 2, scarborough_merged.columns[[1] + list(range(5, scarborough_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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",
       "      <td>5</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Department Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels 1st Most Common Venue  2nd Most Common Venue  \\\n",
       "5  Scarborough             3.0            Playground  Vietnamese Restaurant   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue  5th Most Common Venue  \\\n",
       "5        Clothing Store         Grocery Store  General Entertainment   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "5           Gas Station   Fried Chicken Joint  Fast Food Restaurant   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "5     Electronics Store       Department Store  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster 4\n",
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 3, scarborough_merged.columns[[1] + list(range(5, scarborough_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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",
       "      <td>0</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Print Shop</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Department Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0  Scarborough             4.0  Fast Food Restaurant            Print Shop   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0  Vietnamese Restaurant        Clothing Store         Grocery Store   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0  General Entertainment           Gas Station   Fried Chicken Joint   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0     Electronics Store       Department Store  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster 5\n",
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 4, scarborough_merged.columns[[1] + list(range(5, scarborough_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thank you!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "version": 3
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