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connectwithprakash/clustering.ipynb

Created June 26, 2019 12:50
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clustering.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting package metadata: done\n",
"Solving environment: | \n",
"The environment is inconsistent, please check the package plan carefully\n",
"The following packages are causing the inconsistency:\n",
"\n",
" - defaults/linux-64::anaconda==5.3.1=py37_0\n",
" - defaults/linux-64::astropy==3.0.4=py37h14c3975_0\n",
" - defaults/linux-64::bkcharts==0.2=py37_0\n",
" - defaults/linux-64::blaze==0.11.3=py37_0\n",
" - defaults/linux-64::bokeh==0.13.0=py37_0\n",
" - defaults/linux-64::bottleneck==1.2.1=py37h035aef0_1\n",
" - defaults/linux-64::dask==0.19.1=py37_0\n",
" - defaults/linux-64::datashape==0.5.4=py37_1\n",
" - defaults/linux-64::mkl-service==1.1.2=py37h90e4bf4_5\n",
" - defaults/linux-64::numba==0.39.0=py37h04863e7_0\n",
" - defaults/linux-64::numexpr==2.6.8=py37hd89afb7_0\n",
" - defaults/linux-64::odo==0.5.1=py37_0\n",
" - defaults/linux-64::pytables==3.4.4=py37ha205bf6_0\n",
" - defaults/linux-64::pytest-arraydiff==0.2=py37h39e3cac_0\n",
" - defaults/linux-64::pytest-astropy==0.4.0=py37_0\n",
" - defaults/linux-64::pytest-doctestplus==0.1.3=py37_0\n",
" - defaults/linux-64::pywavelets==1.0.0=py37hdd07704_0\n",
" - defaults/linux-64::scikit-image==0.14.0=py37hf484d3e_1\n",
"done\n",
"\n",
"# All requested packages already installed.\n",
"\n",
"Collecting package metadata: done\n",
"Solving environment: / \n",
"The environment is inconsistent, please check the package plan carefully\n",
"The following packages are causing the inconsistency:\n",
"\n",
" - defaults/linux-64::anaconda==5.3.1=py37_0\n",
" - defaults/linux-64::astropy==3.0.4=py37h14c3975_0\n",
" - defaults/linux-64::bkcharts==0.2=py37_0\n",
" - defaults/linux-64::blaze==0.11.3=py37_0\n",
" - defaults/linux-64::bokeh==0.13.0=py37_0\n",
" - defaults/linux-64::bottleneck==1.2.1=py37h035aef0_1\n",
" - defaults/linux-64::dask==0.19.1=py37_0\n",
" - defaults/linux-64::datashape==0.5.4=py37_1\n",
" - defaults/linux-64::mkl-service==1.1.2=py37h90e4bf4_5\n",
" - defaults/linux-64::numba==0.39.0=py37h04863e7_0\n",
" - defaults/linux-64::numexpr==2.6.8=py37hd89afb7_0\n",
" - defaults/linux-64::odo==0.5.1=py37_0\n",
" - defaults/linux-64::pytables==3.4.4=py37ha205bf6_0\n",
" - defaults/linux-64::pytest-arraydiff==0.2=py37h39e3cac_0\n",
" - defaults/linux-64::pytest-astropy==0.4.0=py37_0\n",
" - defaults/linux-64::pytest-doctestplus==0.1.3=py37_0\n",
" - defaults/linux-64::pywavelets==1.0.0=py37hdd07704_0\n",
" - defaults/linux-64::scikit-image==0.14.0=py37hf484d3e_1\n",
"done\n",
"\n",
"## Package Plan ##\n",
"\n",
" environment location: /home/jupyterlab/conda\n",
"\n",
" added / updated specs:\n",
" - geopy\n",
"\n",
"\n",
"The following packages will be downloaded:\n",
"\n",
" package | build\n",
" ---------------------------|-----------------\n",
" ca-certificates-2019.6.16 | hecc5488_0 145 KB conda-forge\n",
" certifi-2019.6.16 | py36_0 148 KB conda-forge\n",
" conda-4.7.5 | py36_0 3.0 MB conda-forge\n",
" conda-package-handling-1.3.10| py36_0 257 KB conda-forge\n",
" geographiclib-1.49 | py_0 32 KB conda-forge\n",
" geopy-1.20.0 | py_0 57 KB conda-forge\n",
" ------------------------------------------------------------\n",
" Total: 3.6 MB\n",
"\n",
"The following NEW packages will be INSTALLED:\n",
"\n",
" conda-package-han~ conda-forge/linux-64::conda-package-handling-1.3.10-py36_0\n",
" geographiclib conda-forge/noarch::geographiclib-1.49-py_0\n",
"\n",
"The following packages will be UPDATED:\n",
"\n",
" ca-certificates anaconda::ca-certificates-2019.5.15-0 --> conda-forge::ca-certificates-2019.6.16-hecc5488_0\n",
" conda anaconda::conda-4.6.14-py36_0 --> conda-forge::conda-4.7.5-py36_0\n",
" geopy conda-forge/linux-64::geopy-1.11.0-py~ --> conda-forge/noarch::geopy-1.20.0-py_0\n",
"\n",
"The following packages will be SUPERSEDED by a higher-priority channel:\n",
"\n",
" certifi anaconda --> conda-forge\n",
" openssl anaconda::openssl-1.1.1-h7b6447c_0 --> conda-forge::openssl-1.1.1b-h14c3975_1\n",
"\n",
"\n",
"\n",
"Downloading and Extracting Packages\n",
"conda-package-handli | 257 KB | ##################################### | 100% \n",
"conda-4.7.5 | 3.0 MB | ##################################### | 100% \n",
"ca-certificates-2019 | 145 KB | ##################################### | 100% \n",
"geopy-1.20.0 | 57 KB | ##################################### | 100% \n",
"certifi-2019.6.16 | 148 KB | ##################################### | 100% \n",
"geographiclib-1.49 | 32 KB | ##################################### | 100% \n",
"Preparing transaction: done\n",
"Verifying transaction: done\n",
"Executing transaction: done\n",
"WARNING conda.base.context:use_only_tar_bz2(632): Conda is constrained to only using the old .tar.bz2 file format because you have conda-build installed, and it is <3.18.3. Update or remove conda-build to get smaller downloads and faster extractions.\n",
"Collecting package metadata (repodata.json): done\n",
"Solving environment: / \n",
"The environment is inconsistent, please check the package plan carefully\n",
"The following packages are causing the inconsistency:\n",
"\n",
" - defaults/linux-64::anaconda==5.3.1=py37_0\n",
" - defaults/linux-64::astropy==3.0.4=py37h14c3975_0\n",
" - defaults/linux-64::bkcharts==0.2=py37_0\n",
" - defaults/linux-64::blaze==0.11.3=py37_0\n",
" - defaults/linux-64::bokeh==0.13.0=py37_0\n",
" - defaults/linux-64::bottleneck==1.2.1=py37h035aef0_1\n",
" - defaults/linux-64::dask==0.19.1=py37_0\n",
" - defaults/linux-64::datashape==0.5.4=py37_1\n",
" - defaults/linux-64::mkl-service==1.1.2=py37h90e4bf4_5\n",
" - defaults/linux-64::numba==0.39.0=py37h04863e7_0\n",
" - defaults/linux-64::numexpr==2.6.8=py37hd89afb7_0\n",
" - defaults/linux-64::odo==0.5.1=py37_0\n",
" - defaults/linux-64::pytables==3.4.4=py37ha205bf6_0\n",
" - defaults/linux-64::pytest-arraydiff==0.2=py37h39e3cac_0\n",
" - defaults/linux-64::pytest-astropy==0.4.0=py37_0\n",
" - defaults/linux-64::pytest-doctestplus==0.1.3=py37_0\n",
" - defaults/linux-64::pywavelets==1.0.0=py37hdd07704_0\n",
" - defaults/linux-64::scikit-image==0.14.0=py37hf484d3e_1\n",
"failed\n",
"\n",
"UnsatisfiableError: The following specifications were found to be incompatible with each other:\n",
"\n",
" - anaconda/linux-64::anaconda-navigator==1.9.7=py36_0\n",
" - anaconda/linux-64::graphviz==2.40.1=h21bd128_2 -> pango[version='>=1.42.1,<2.0a0']\n",
" - anaconda/linux-64::importlib_metadata==0.8=py36_0\n",
" - anaconda/linux-64::lxml==4.3.0=py36hefd8a0e_0\n",
" - anaconda/linux-64::mkl_fft==1.0.6=py36h7dd41cf_0 -> mkl[version='>=2018.0.3']\n",
" - anaconda/linux-64::mkl_random==1.0.1=py36h4414c95_1 -> mkl[version='>=2018.0.3']\n",
" - anaconda/linux-64::navigator-updater==0.2.1=py36_0\n",
" - anaconda/linux-64::numpy-base==1.15.4=py36h81de0dd_0 -> mkl[version='>=2018.0.3']\n",
" - anaconda/linux-64::numpy==1.15.4=py36h1d66e8a_0 -> mkl[version='>=2018.0.3']\n",
" - anaconda/linux-64::pytorch==0.4.1=py36ha74772b_0 -> mkl[version='>=2018.0.3']\n",
" - anaconda/linux-64::scikit-learn==0.20.1=py36h4989274_0 -> mkl[version='>=2018.0.3']\n",
" - anaconda/linux-64::scipy==1.1.0=py36hfa4b5c9_1 -> mkl[version='>=2018.0.3']\n",
" - anaconda/linux-64::spyder==3.3.4=py36_0\n",
" - anaconda/linux-64::sympy==1.4=py36_0\n",
" - anaconda/linux-64::torchvision==0.2.1=py36_0 -> pytorch[version='>=0.4'] -> mkl[version='>=2019.1,<2020.0a0']\n",
" - anaconda/noarch::openpyxl==2.6.2=py_0\n",
" - anaconda/noarch::path.py==12.0.1=py_0 -> importlib_metadata[version='>=0.5']\n",
" - anaconda/noarch::xlsxwriter==1.1.6=py_0\n",
" - mkl-service -> mkl[version='>=2019.4,<2020.0a0']\n",
" - pkgs/main/linux-64::mkl==2019.0=118\n",
" - pkgs/main/linux-64::pango==1.42.4=h049681c_0\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"!conda install -c anaconda beautifulsoup4 --yes\n",
"!conda install -c conda-forge geopy --yes\n",
"!conda install -c conda-forge folium=0.5.0 --yes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installing required Libraries"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import requests\n",
"\n",
"from bs4 import BeautifulSoup\n",
"\n",
"from geopy.geocoders import Nominatim\n",
"\n",
"import folium\n",
"import matplotlib.cm as cm\n",
"import matplotlib.colors as colors\n",
"\n",
"from sklearn.cluster import KMeans\n",
"\n",
"from tqdm import tqdm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Libraries Import"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"url = 'https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\n",
"html_data = requests.get(url).text\n",
"soup = BeautifulSoup(html_data, 'html.parser')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scrapping web for HTML data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 289/289 [00:00<00:00, 22171.38it/s]\n"
]
}
],
"source": [
"post_code = []\n",
"borough = []\n",
"neighborhood = []\n",
"for row in tqdm(soup.find('table', {'class' : 'wikitable sortable'}).find_all('tr')):\n",
" columns = row.find_all('td')\n",
" if(len(columns) > 0):\n",
" post_code.append(columns[0].text)\n",
" borough.append(columns[1].text)\n",
" neighborhood.append(columns[2].text.rstrip('\\n'))\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PostalCode</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M1A</td>\n",
" <td>Not assigned</td>\n",
" <td>Not assigned</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M2A</td>\n",
" <td>Not assigned</td>\n",
" <td>Not assigned</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M3A</td>\n",
" <td>North York</td>\n",
" <td>Parkwoods</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M4A</td>\n",
" <td>North York</td>\n",
" <td>Victoria Village</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M5A</td>\n",
" <td>Downtown Toronto</td>\n",
" <td>Harbourfront</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PostalCode Borough Neighborhood\n",
"0 M1A Not assigned Not assigned\n",
"1 M2A Not assigned Not assigned\n",
"2 M3A North York Parkwoods\n",
"3 M4A North York Victoria Village\n",
"4 M5A Downtown Toronto Harbourfront"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(data=[post_code, borough, neighborhood])\n",
"df = df.T\n",
"df.columns = ['PostalCode', 'Borough', 'Neighborhood']\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Converting scrapped data to DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PostalCode</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M3A</td>\n",
" <td>North York</td>\n",
" <td>Parkwoods</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M4A</td>\n",
" <td>North York</td>\n",
" <td>Victoria Village</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M5A</td>\n",
" <td>Downtown Toronto</td>\n",
" <td>Harbourfront</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M5A</td>\n",
" <td>Downtown Toronto</td>\n",
" <td>Regent Park</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M6A</td>\n",
" <td>North York</td>\n",
" <td>Lawrence Heights</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PostalCode Borough Neighborhood\n",
"0 M3A North York Parkwoods\n",
"1 M4A North York Victoria Village\n",
"2 M5A Downtown Toronto Harbourfront\n",
"3 M5A Downtown Toronto Regent Park\n",
"4 M6A North York Lawrence Heights"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dropna = df[df.Borough != 'Not assigned'].reset_index(drop=True) \n",
"df_dropna.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Removing the Borough with values as 'Not assigned'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PostalCode</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M1B</td>\n",
" <td>Scarborough</td>\n",
" <td>Rouge,Malvern</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M1C</td>\n",
" <td>Scarborough</td>\n",
" <td>Highland Creek,Rouge Hill,Port Union</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M1E</td>\n",
" <td>Scarborough</td>\n",
" <td>Guildwood,Morningside,West Hill</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M1G</td>\n",
" <td>Scarborough</td>\n",
" <td>Woburn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M1H</td>\n",
" <td>Scarborough</td>\n",
" <td>Cedarbrae</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PostalCode Borough Neighborhood\n",
"0 M1B Scarborough Rouge,Malvern\n",
"1 M1C Scarborough Highland Creek,Rouge Hill,Port Union\n",
"2 M1E Scarborough Guildwood,Morningside,West Hill\n",
"3 M1G Scarborough Woburn\n",
"4 M1H Scarborough Cedarbrae"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_grouped =df_dropna.groupby(['PostalCode', 'Borough'], as_index=False).agg(lambda x:','.join(x))\n",
"df_grouped.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Grouping neighborhood by postal and borough"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PostalCode</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>M7A</td>\n",
" <td>Queen's Park</td>\n",
" <td>Not assigned</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PostalCode Borough Neighborhood\n",
"85 M7A Queen's Park Not assigned"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_grouped.loc[df_grouped.Neighborhood == 'Not assigned']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Dealing with 'Not assigned' neighborhood"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PostalCode M7A\n",
"Borough Queen's Park\n",
"Neighborhood Queen's Park\n",
"Name: 85, dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_grouped.loc[df_grouped.Neighborhood == 'Not assigned', 'Neighborhood'] = df_grouped.loc[df_grouped.Neighborhood == 'Not assigned', 'Borough']\n",
"df_grouped.iloc[85]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Clean DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(103, 3)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_clean = df_grouped\n",
"df_clean.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get location data fro borough"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Read CSV"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"!wget -q -O \"toronto_coordinates.csv\" http://cocl.us/Geospatial_data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Postal Code</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M1B</td>\n",
" <td>43.806686</td>\n",
" <td>-79.194353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M1C</td>\n",
" <td>43.784535</td>\n",
" <td>-79.160497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M1E</td>\n",
" <td>43.763573</td>\n",
" <td>-79.188711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M1G</td>\n",
" <td>43.770992</td>\n",
" <td>-79.216917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M1H</td>\n",
" <td>43.773136</td>\n",
" <td>-79.239476</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postal Code Latitude Longitude\n",
"0 M1B 43.806686 -79.194353\n",
"1 M1C 43.784535 -79.160497\n",
"2 M1E 43.763573 -79.188711\n",
"3 M1G 43.770992 -79.216917\n",
"4 M1H 43.773136 -79.239476"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coordinates = pd.read_csv('toronto_coordinates.csv')\n",
"coordinates.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Postal Code</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M1B</td>\n",
" <td>Scarborough</td>\n",
" <td>Rouge,Malvern</td>\n",
" <td>43.806686</td>\n",
" <td>-79.194353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M1C</td>\n",
" <td>Scarborough</td>\n",
" <td>Highland Creek,Rouge Hill,Port Union</td>\n",
" <td>43.784535</td>\n",
" <td>-79.160497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M1E</td>\n",
" <td>Scarborough</td>\n",
" <td>Guildwood,Morningside,West Hill</td>\n",
" <td>43.763573</td>\n",
" <td>-79.188711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M1G</td>\n",
" <td>Scarborough</td>\n",
" <td>Woburn</td>\n",
" <td>43.770992</td>\n",
" <td>-79.216917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M1H</td>\n",
" <td>Scarborough</td>\n",
" <td>Cedarbrae</td>\n",
" <td>43.773136</td>\n",
" <td>-79.239476</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postal Code Borough Neighborhood Latitude \\\n",
"0 M1B Scarborough Rouge,Malvern 43.806686 \n",
"1 M1C Scarborough Highland Creek,Rouge Hill,Port Union 43.784535 \n",
"2 M1E Scarborough Guildwood,Morningside,West Hill 43.763573 \n",
"3 M1G Scarborough Woburn 43.770992 \n",
"4 M1H Scarborough Cedarbrae 43.773136 \n",
"\n",
" Longitude \n",
"0 -79.194353 \n",
"1 -79.160497 \n",
"2 -79.188711 \n",
"3 -79.216917 \n",
"4 -79.239476 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_clean_temp = df_clean.set_index('PostalCode')\n",
"coordinates_temp = coordinates.set_index('Postal Code')\n",
"coordinates_df = pd.concat([df_clean_temp, coordinates_temp], axis=1, join='inner')\n",
"coordinates_df.index.name = 'Postal Code'\n",
"coordinates_df.reset_index(inplace=True)\n",
"coordinates_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Explore only those Borough that have Toronto in their name"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Postal Code</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>M4E</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>43.676357</td>\n",
" <td>-79.293031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>M4K</td>\n",
" <td>East Toronto</td>\n",
" <td>The Danforth West,Riverdale</td>\n",
" <td>43.679557</td>\n",
" <td>-79.352188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>M4L</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches West,India Bazaar</td>\n",
" <td>43.668999</td>\n",
" <td>-79.315572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>M4M</td>\n",
" <td>East Toronto</td>\n",
" <td>Studio District</td>\n",
" <td>43.659526</td>\n",
" <td>-79.340923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>M4N</td>\n",
" <td>Central Toronto</td>\n",
" <td>Lawrence Park</td>\n",
" <td>43.728020</td>\n",
" <td>-79.388790</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postal Code Borough Neighborhood Latitude \\\n",
"37 M4E East Toronto The Beaches 43.676357 \n",
"41 M4K East Toronto The Danforth West,Riverdale 43.679557 \n",
"42 M4L East Toronto The Beaches West,India Bazaar 43.668999 \n",
"43 M4M East Toronto Studio District 43.659526 \n",
"44 M4N Central Toronto Lawrence Park 43.728020 \n",
"\n",
" Longitude \n",
"37 -79.293031 \n",
"41 -79.352188 \n",
"42 -79.315572 \n",
"43 -79.340923 \n",
"44 -79.388790 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_df = coordinates_df[coordinates_df.Borough.str.contains('Toronto')]\n",
"toronto_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use geopy library to get the latitude and longitude values of Toronto."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The geograpical coordinates of Toronto are 43.653963, -79.387207.\n"
]
}
],
"source": [
"address = 'Toronto, Ontario'\n",
"\n",
"geolocator = Nominatim(user_agent=\"toronto_explorer\")\n",
"location = geolocator.geocode(address)\n",
"latitude = location.latitude\n",
"longitude = location.longitude\n",
"print('The geograpical coordinates 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": 17,
"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 0x7f50cb33f0f0>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create map of New York using latitude and longitude values\n",
"map_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
"\n",
"# add markers to map\n",
"for lat, lng, borough, neighborhood in zip(toronto_df['Latitude'], toronto_df['Longitude'], toronto_df['Borough'], toronto_df['Neighborhood']):\n",
" label = '{}, {}'.format(neighborhood, borough)\n",
" label = folium.Popup(label, parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lng],\n",
" radius=5,\n",
" popup=label,\n",
" color='blue',\n",
" fill=True,\n",
" fill_color='#3186cc',\n",
" fill_opacity=0.7,\n",
" parse_html=False).add_to(map_toronto) \n",
" \n",
"map_toronto"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Define Foursquare Credentials and Version"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your credentails:\n",
"CLIENT_ID: UGHFMNO1HCOWOZTT0W5MXN0CKUIFZZU2OXV1KIM1CUL1KX31\n",
"CLIENT_SECRET:O0WTNIHI0W2Q1GUUJ34U0KCB3GBRD4OXCESMXMTVBB1SRCJV\n"
]
}
],
"source": [
"CLIENT_ID = 'UGHFMNO1HCOWOZTT0W5MXN0CKUIFZZU2OXV1KIM1CUL1KX31' # your Foursquare ID\n",
"CLIENT_SECRET = 'O0WTNIHI0W2Q1GUUJ34U0KCB3GBRD4OXCESMXMTVBB1SRCJV' # your Foursquare Secret\n",
"VERSION = '201906020' #Foursquare API version\n",
"\n",
"print('Your credentails:')\n",
"print('CLIENT_ID: ' + CLIENT_ID)\n",
"print('CLIENT_SECRET:' + CLIENT_SECRET)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get recommend places inside or near each Borough in Toronto Central"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def getNearbyVenues(names, latitudes, longitudes, postal_code, borough, radius=500, LIMIT=100):\n",
" \n",
" venues_list=[]\n",
" for name, lat, lng, post, borough in zip(names, latitudes, longitudes, postal_code, borough): \n",
" # create the API request URL\n",
" url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
" CLIENT_ID, \n",
" CLIENT_SECRET, \n",
" VERSION, \n",
" lat, \n",
" lng, \n",
" radius, \n",
" LIMIT)\n",
" \n",
" # make the GET request\n",
" results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
" \n",
" # return only relevant information for each nearby venue\n",
" venues_list.append([(\n",
" post,\n",
" borough,\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 = ['Postal Code',\n",
" 'Borough',\n",
" '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": 20,
"metadata": {},
"outputs": [],
"source": [
"venues_df = getNearbyVenues(names=toronto_df['Neighborhood'],\n",
" latitudes=toronto_df['Latitude'],\n",
" longitudes=toronto_df['Longitude'],\n",
" postal_code=toronto_df['Postal Code'],\n",
" borough=toronto_df['Borough']\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Postal Code</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" <th>Neighborhood Latitude</th>\n",
" <th>Neighborhood Longitude</th>\n",
" <th>Venue</th>\n",
" <th>Venue Latitude</th>\n",
" <th>Venue Longitude</th>\n",
" <th>Venue Category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M4E</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>43.676357</td>\n",
" <td>-79.293031</td>\n",
" <td>Glen Manor Ravine</td>\n",
" <td>43.676821</td>\n",
" <td>-79.293942</td>\n",
" <td>Trail</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M4E</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>43.676357</td>\n",
" <td>-79.293031</td>\n",
" <td>The Big Carrot Natural Food Market</td>\n",
" <td>43.678879</td>\n",
" <td>-79.297734</td>\n",
" <td>Health Food Store</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M4E</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>43.676357</td>\n",
" <td>-79.293031</td>\n",
" <td>Grover Pub and Grub</td>\n",
" <td>43.679181</td>\n",
" <td>-79.297215</td>\n",
" <td>Pub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M4E</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>43.676357</td>\n",
" <td>-79.293031</td>\n",
" <td>Glen Stewart Ravine</td>\n",
" <td>43.676300</td>\n",
" <td>-79.294784</td>\n",
" <td>Other Great Outdoors</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M4E</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>43.676357</td>\n",
" <td>-79.293031</td>\n",
" <td>Upper Beaches</td>\n",
" <td>43.680563</td>\n",
" <td>-79.292869</td>\n",
" <td>Neighborhood</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postal Code Borough Neighborhood Neighborhood Latitude \\\n",
"0 M4E East Toronto The Beaches 43.676357 \n",
"1 M4E East Toronto The Beaches 43.676357 \n",
"2 M4E East Toronto The Beaches 43.676357 \n",
"3 M4E East Toronto The Beaches 43.676357 \n",
"4 M4E East Toronto The Beaches 43.676357 \n",
"\n",
" Neighborhood Longitude Venue Venue Latitude \\\n",
"0 -79.293031 Glen Manor Ravine 43.676821 \n",
"1 -79.293031 The Big Carrot Natural Food Market 43.678879 \n",
"2 -79.293031 Grover Pub and Grub 43.679181 \n",
"3 -79.293031 Glen Stewart Ravine 43.676300 \n",
"4 -79.293031 Upper Beaches 43.680563 \n",
"\n",
" Venue Longitude Venue Category \n",
"0 -79.293942 Trail \n",
"1 -79.297734 Health Food Store \n",
"2 -79.297215 Pub \n",
"3 -79.294784 Other Great Outdoors \n",
"4 -79.292869 Neighborhood "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"venues_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Let's find out how many unique categories can be curated from all the returned venues"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 238 uniques categories.\n"
]
}
],
"source": [
"print('There are {} uniques categories.'.format(len(venues_df['Venue Category'].unique())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Analyze Each Neighborhood"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1700, 241)\n"
]
},
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Postal Code</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhoods</th>\n",
" <th>Adult Boutique</th>\n",
" <th>Afghan Restaurant</th>\n",
" <th>Airport</th>\n",
" <th>Airport Food Court</th>\n",
" <th>Airport Gate</th>\n",
" <th>Airport Lounge</th>\n",
" <th>Airport Service</th>\n",
" <th>...</th>\n",
" <th>Trail</th>\n",
" <th>Train Station</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Video Game Store</th>\n",
" <th>Video Store</th>\n",
" <th>Vietnamese Restaurant</th>\n",
" <th>Wine Bar</th>\n",
" <th>Wings Joint</th>\n",
" <th>Women's Store</th>\n",
" <th>Yoga Studio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M4E</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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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",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M4E</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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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",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M4E</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M4E</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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"</table>\n",
"<p>5 rows × 241 columns</p>\n",
"</div>"
],
"text/plain": [
" Postal Code Borough Neighborhoods Adult Boutique Afghan Restaurant \\\n",
"0 M4E East Toronto The Beaches 0 0 \n",
"1 M4E East Toronto The Beaches 0 0 \n",
"2 M4E East Toronto The Beaches 0 0 \n",
"3 M4E East Toronto The Beaches 0 0 \n",
"4 M4E East Toronto The Beaches 0 0 \n",
"\n",
" Airport Airport Food Court Airport Gate Airport Lounge Airport Service \\\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",
" ... Trail Train Station Vegetarian / Vegan Restaurant Video Game Store \\\n",
"0 ... 1 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",
" Video Store Vietnamese Restaurant Wine Bar Wings Joint Women's Store \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Yoga Studio \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
"[5 rows x 241 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# one hot encoding\n",
"toronto_central_onehot = pd.get_dummies(venues_df[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
"\n",
"# add postal, borough and neighborhood column back to dataframe\n",
"toronto_central_onehot['Postal Code'] = venues_df['Postal Code'] \n",
"toronto_central_onehot['Borough'] = venues_df['Borough'] \n",
"toronto_central_onehot['Neighborhoods'] = venues_df['Neighborhood'] \n",
"\n",
"# move postal, borough and neighborhood column to the first column\n",
"fixed_columns = list(toronto_central_onehot.columns[-3:]) + list(toronto_central_onehot.columns[:-3])\n",
"toronto_central_onehot = toronto_central_onehot[fixed_columns]\n",
"\n",
"print(toronto_central_onehot.shape)\n",
"toronto_central_onehot.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get the frequency of occurance of each category in an area"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(38, 241)\n"
]
},
{
"data": {
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" <th>Yoga Studio</th>\n",
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" <tbody>\n",
" <tr>\n",
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" <td>M4E</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M4K</td>\n",
" <td>East Toronto</td>\n",
" <td>The Danforth West,Riverdale</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.02381</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.023810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M4L</td>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches West,India Bazaar</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M4M</td>\n",
" <td>East Toronto</td>\n",
" <td>Studio District</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.026316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M4N</td>\n",
" <td>Central Toronto</td>\n",
" <td>Lawrence Park</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 241 columns</p>\n",
"</div>"
],
"text/plain": [
" Postal Code Borough Neighborhoods Adult Boutique \\\n",
"0 M4E East Toronto The Beaches 0.0 \n",
"1 M4K East Toronto The Danforth West,Riverdale 0.0 \n",
"2 M4L East Toronto The Beaches West,India Bazaar 0.0 \n",
"3 M4M East Toronto Studio District 0.0 \n",
"4 M4N Central Toronto Lawrence Park 0.0 \n",
"\n",
" Afghan Restaurant Airport Airport Food Court Airport Gate \\\n",
"0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 \n",
"\n",
" Airport Lounge Airport Service ... Trail Train Station \\\n",
"0 0.0 0.0 ... 0.20000 0.0 \n",
"1 0.0 0.0 ... 0.02381 0.0 \n",
"2 0.0 0.0 ... 0.00000 0.0 \n",
"3 0.0 0.0 ... 0.00000 0.0 \n",
"4 0.0 0.0 ... 0.00000 0.0 \n",
"\n",
" Vegetarian / Vegan Restaurant Video Game Store Video Store \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"\n",
" Vietnamese Restaurant Wine Bar Wings Joint Women's Store Yoga Studio \n",
"0 0.0 0.0 0.0 0.0 0.000000 \n",
"1 0.0 0.0 0.0 0.0 0.023810 \n",
"2 0.0 0.0 0.0 0.0 0.000000 \n",
"3 0.0 0.0 0.0 0.0 0.026316 \n",
"4 0.0 0.0 0.0 0.0 0.000000 \n",
"\n",
"[5 rows x 241 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_central_venues_freq = toronto_central_onehot.groupby(['Postal Code', 'Borough', 'Neighborhoods']).mean().reset_index()\n",
"print(toronto_central_venues_freq.shape)\n",
"toronto_central_venues_freq.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get 10 most occurance venue types in each area"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Postal Code</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhoods</th>\n",
" <th>1st Most Common Venue</th>\n",
" <th>2nd Most Common Venue</th>\n",
" <th>3rd Most Common Venue</th>\n",
" <th>4th Most Common Venue</th>\n",
" <th>5th Most Common Venue</th>\n",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>M5V</td>\n",
" <td>Downtown Toronto</td>\n",
" <td>CN Tower,Bathurst Quay,Island airport,Harbourf...</td>\n",
" <td>Airport Terminal</td>\n",
" <td>Airport Lounge</td>\n",
" <td>Airport Service</td>\n",
" <td>Boutique</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Boat or Ferry</td>\n",
" <td>Sculpture Garden</td>\n",
" <td>Harbor / Marina</td>\n",
" <td>Plane</td>\n",
" <td>Airport Gate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>M6J</td>\n",
" <td>West Toronto</td>\n",
" <td>Little Portugal,Trinity</td>\n",
" <td>Bar</td>\n",
" <td>Asian Restaurant</td>\n",
" <td>Men's Store</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Pizza Place</td>\n",
" <td>New American Restaurant</td>\n",
" <td>Restaurant</td>\n",
" <td>Vietnamese Restaurant</td>\n",
" <td>Café</td>\n",
" <td>Cocktail Bar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>M6P</td>\n",
" <td>West Toronto</td>\n",
" <td>High Park,The Junction South</td>\n",
" <td>Bar</td>\n",
" <td>Mexican Restaurant</td>\n",
" <td>Café</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>Fried Chicken Joint</td>\n",
" <td>Bakery</td>\n",
" <td>Italian Restaurant</td>\n",
" <td>Gastropub</td>\n",
" <td>Music Venue</td>\n",
" <td>Arts &amp; Crafts Store</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>M6K</td>\n",
" <td>West Toronto</td>\n",
" <td>Brockton,Exhibition Place,Parkdale Village</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Café</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Intersection</td>\n",
" <td>Performing Arts Venue</td>\n",
" <td>Caribbean Restaurant</td>\n",
" <td>Stadium</td>\n",
" <td>Restaurant</td>\n",
" <td>Bar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>M6R</td>\n",
" <td>West Toronto</td>\n",
" <td>Parkdale,Roncesvalles</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Gift Shop</td>\n",
" <td>Bookstore</td>\n",
" <td>Bank</td>\n",
" <td>Bar</td>\n",
" <td>Movie Theater</td>\n",
" <td>Restaurant</td>\n",
" <td>Dog Run</td>\n",
" <td>Italian Restaurant</td>\n",
" <td>Dessert Shop</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postal Code Borough \\\n",
"27 M5V Downtown Toronto \n",
"32 M6J West Toronto \n",
"34 M6P West Toronto \n",
"33 M6K West Toronto \n",
"35 M6R West Toronto \n",
"\n",
" Neighborhoods 1st Most Common Venue \\\n",
"27 CN Tower,Bathurst Quay,Island airport,Harbourf... Airport Terminal \n",
"32 Little Portugal,Trinity Bar \n",
"34 High Park,The Junction South Bar \n",
"33 Brockton,Exhibition Place,Parkdale Village Breakfast Spot \n",
"35 Parkdale,Roncesvalles Breakfast Spot \n",
"\n",
" 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue \\\n",
"27 Airport Lounge Airport Service Boutique \n",
"32 Asian Restaurant Men's Store Coffee Shop \n",
"34 Mexican Restaurant Café Fast Food Restaurant \n",
"33 Café Coffee Shop Yoga Studio \n",
"35 Gift Shop Bookstore Bank \n",
"\n",
" 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue \\\n",
"27 Coffee Shop Boat or Ferry Sculpture Garden \n",
"32 Pizza Place New American Restaurant Restaurant \n",
"34 Fried Chicken Joint Bakery Italian Restaurant \n",
"33 Intersection Performing Arts Venue Caribbean Restaurant \n",
"35 Bar Movie Theater Restaurant \n",
"\n",
" 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue \n",
"27 Harbor / Marina Plane Airport Gate \n",
"32 Vietnamese Restaurant Café Cocktail Bar \n",
"34 Gastropub Music Venue Arts & Crafts Store \n",
"33 Stadium Restaurant Bar \n",
"35 Dog Run Italian Restaurant Dessert Shop "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"num_top_venues = 10\n",
"\n",
"indicators = ['st', 'nd', 'rd']\n",
"\n",
"# create columns according to number of top venues\n",
"area_columns = ['Postal Code', 'Borough', 'Neighborhoods']\n",
"freq_columns = []\n",
"for ind in np.arange(num_top_venues):\n",
" try:\n",
" freq_columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
" except:\n",
" freq_columns.append('{}th Most Common Venue'.format(ind+1))\n",
"columns = area_columns+freq_columns\n",
"# create a new dataframe\n",
"neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
"neighborhoods_venues_sorted['Postal Code'] = toronto_central_venues_freq['Postal Code']\n",
"neighborhoods_venues_sorted['Borough'] = toronto_central_venues_freq['Borough']\n",
"neighborhoods_venues_sorted['Neighborhoods'] = toronto_central_venues_freq['Neighborhoods']\n",
"\n",
"for ind in np.arange(toronto_central_venues_freq.shape[0]):\n",
" row_categories = toronto_central_venues_freq.iloc[ind, :].iloc[3:]\n",
" row_categories_sorted = row_categories.sort_values(ascending=False)\n",
" neighborhoods_venues_sorted.iloc[ind, 3:] = row_categories_sorted.index.values[0:num_top_venues]\n",
"\n",
"neighborhoods_venues_sorted.sort_values(freq_columns, inplace=True)\n",
"neighborhoods_venues_sorted.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Clustering Neighborhoods"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/lib/python3.6/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",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
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" <th></th>\n",
" <th>Postal Code</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
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" <th>Longitude</th>\n",
" <th>Cluster</th>\n",
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" <th>3rd Most Common Venue</th>\n",
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" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>M5P</td>\n",
" <td>Central Toronto</td>\n",
" <td>Forest Hill North,Forest Hill West</td>\n",
" <td>43.696948</td>\n",
" <td>-79.411307</td>\n",
" <td>0</td>\n",
" <td>Jewelry Store</td>\n",
" <td>Trail</td>\n",
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" <th>50</th>\n",
" <td>M4W</td>\n",
" <td>Downtown Toronto</td>\n",
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" <td>43.679563</td>\n",
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" <td>Park</td>\n",
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" <td>Falafel Restaurant</td>\n",
" <td>Event Space</td>\n",
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" <tr>\n",
" <th>44</th>\n",
" <td>M4N</td>\n",
" <td>Central Toronto</td>\n",
" <td>Lawrence Park</td>\n",
" <td>43.728020</td>\n",
" <td>-79.388790</td>\n",
" <td>0</td>\n",
" <td>Park</td>\n",
" <td>Swim School</td>\n",
" <td>Bus Line</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Fish &amp; Chips Shop</td>\n",
" <td>Filipino Restaurant</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>Farmers Market</td>\n",
" <td>Falafel Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>M5N</td>\n",
" <td>Central Toronto</td>\n",
" <td>Roselawn</td>\n",
" <td>43.711695</td>\n",
" <td>-79.416936</td>\n",
" <td>1</td>\n",
" <td>Ice Cream Shop</td>\n",
" <td>Garden</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Fish &amp; Chips Shop</td>\n",
" <td>Filipino Restaurant</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>Farmers Market</td>\n",
" <td>Falafel Restaurant</td>\n",
" <td>Event Space</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>M5V</td>\n",
" <td>Downtown Toronto</td>\n",
" <td>CN Tower,Bathurst Quay,Island airport,Harbourf...</td>\n",
" <td>43.628947</td>\n",
" <td>-79.394420</td>\n",
" <td>2</td>\n",
" <td>Airport Terminal</td>\n",
" <td>Airport Lounge</td>\n",
" <td>Airport Service</td>\n",
" <td>Boutique</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Boat or Ferry</td>\n",
" <td>Sculpture Garden</td>\n",
" <td>Harbor / Marina</td>\n",
" <td>Plane</td>\n",
" <td>Airport Gate</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"text/plain": [
" Postal Code Borough \\\n",
"64 M5P Central Toronto \n",
"50 M4W Downtown Toronto \n",
"44 M4N Central Toronto \n",
"63 M5N Central Toronto \n",
"68 M5V Downtown Toronto \n",
"\n",
" Neighborhood Latitude Longitude \\\n",
"64 Forest Hill North,Forest Hill West 43.696948 -79.411307 \n",
"50 Rosedale 43.679563 -79.377529 \n",
"44 Lawrence Park 43.728020 -79.388790 \n",
"63 Roselawn 43.711695 -79.416936 \n",
"68 CN Tower,Bathurst Quay,Island airport,Harbourf... 43.628947 -79.394420 \n",
"\n",
" Cluster 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue \\\n",
"64 0 Jewelry Store Trail Park \n",
"50 0 Park Playground Trail \n",
"44 0 Park Swim School Bus Line \n",
"63 1 Ice Cream Shop Garden Yoga Studio \n",
"68 2 Airport Terminal Airport Lounge Airport Service \n",
"\n",
" 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue \\\n",
"64 Sushi Restaurant Donut Shop Dumpling Restaurant \n",
"50 Dog Run Fish & Chips Shop Filipino Restaurant \n",
"44 Yoga Studio Doner Restaurant Fish & Chips Shop \n",
"63 Doner Restaurant Fish & Chips Shop Filipino Restaurant \n",
"68 Boutique Coffee Shop Boat or Ferry \n",
"\n",
" 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue \\\n",
"64 Eastern European Restaurant Electronics Store Ethiopian Restaurant \n",
"50 Fast Food Restaurant Farmers Market Falafel Restaurant \n",
"44 Filipino Restaurant Fast Food Restaurant Farmers Market \n",
"63 Fast Food Restaurant Farmers Market Falafel Restaurant \n",
"68 Sculpture Garden Harbor / Marina Plane \n",
"\n",
" 10th Most Common Venue \n",
"64 Yoga Studio \n",
"50 Event Space \n",
"44 Falafel Restaurant \n",
"63 Event Space \n",
"68 Airport Gate "
]
},
"execution_count": 30,
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"source": [
"k_clusters = 3\n",
"\n",
"toronto_central_venues_freq_clustering = toronto_central_venues_freq.drop(['Postal Code', 'Borough', 'Neighborhoods'], 1)\n",
"\n",
"kmeans = KMeans(n_clusters=k_clusters, random_state=0, n_jobs=-1).fit(toronto_central_venues_freq_clustering)\n",
"\n",
"toronto_central_clustered_df = toronto_df\n",
"toronto_central_clustered_df['Cluster'] = kmeans.labels_\n",
"\n",
"toronto_central_clustered_df = toronto_central_clustered_df.join(neighborhoods_venues_sorted.drop(['Borough', 'Neighborhoods'], 1).set_index('Postal Code'), on='Postal Code')\n",
"toronto_central_clustered_df.sort_values(['Cluster'] + freq_columns, inplace=True)\n",
"toronto_central_clustered_df.head()"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"#### Cluster Map"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
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            var popup_ee8300a22fb3402985bad0d789a3a149 = L.popup({maxWidth: '300'});

            
                var html_a30ea61b5a854033bc9ab09a6220daa9 = $('<div id="html_a30ea61b5a854033bc9ab09a6220daa9" style="width: 100.0%; height: 100.0%;">Central Toronto (M5N): Roselawn - Cluster 1</div>')[0];
                popup_ee8300a22fb3402985bad0d789a3a149.setContent(html_a30ea61b5a854033bc9ab09a6220daa9);
            

            circle_marker_7dc7dfe15ed94cafa474c04554ab7a8e.bindPopup(popup_ee8300a22fb3402985bad0d789a3a149);

            
        
    
            var circle_marker_1b36e34617d04a78a3f6f4d1d7c891ad = L.circleMarker(
                [43.6289467,-79.3944199],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_4259ade5bfb245c991948e8197f43853 = L.popup({maxWidth: '300'});

            
                var html_41db353a341644b6adb6c1a4977a4550 = $('<div id="html_41db353a341644b6adb6c1a4977a4550" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5V): CN Tower,Bathurst Quay,Island airport,Harbourfront West,King and Spadina,Railway Lands,South Niagara - Cluster 2</div>')[0];
                popup_4259ade5bfb245c991948e8197f43853.setContent(html_41db353a341644b6adb6c1a4977a4550);
            

            circle_marker_1b36e34617d04a78a3f6f4d1d7c891ad.bindPopup(popup_4259ade5bfb245c991948e8197f43853);

            
        
    
            var circle_marker_bcab02d752dd4639bb212178791165d3 = L.circleMarker(
                [43.647926700000006,-79.4197497],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_22a746ef55234f7989513785ff957446 = L.popup({maxWidth: '300'});

            
                var html_c1053a98ed574e479beee882f310b816 = $('<div id="html_c1053a98ed574e479beee882f310b816" style="width: 100.0%; height: 100.0%;">West Toronto (M6J): Little Portugal,Trinity - Cluster 2</div>')[0];
                popup_22a746ef55234f7989513785ff957446.setContent(html_c1053a98ed574e479beee882f310b816);
            

            circle_marker_bcab02d752dd4639bb212178791165d3.bindPopup(popup_22a746ef55234f7989513785ff957446);

            
        
    
            var circle_marker_55646fa0cc4e4215aee13d84a4c9392d = L.circleMarker(
                [43.6616083,-79.46476329999999],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_32ca7cdba10d4fedbe0c97e1b95a1091 = L.popup({maxWidth: '300'});

            
                var html_8fd9d63348ef4558986a6a789d78bbdd = $('<div id="html_8fd9d63348ef4558986a6a789d78bbdd" style="width: 100.0%; height: 100.0%;">West Toronto (M6P): High Park,The Junction South - Cluster 2</div>')[0];
                popup_32ca7cdba10d4fedbe0c97e1b95a1091.setContent(html_8fd9d63348ef4558986a6a789d78bbdd);
            

            circle_marker_55646fa0cc4e4215aee13d84a4c9392d.bindPopup(popup_32ca7cdba10d4fedbe0c97e1b95a1091);

            
        
    
            var circle_marker_94d8fbfc10254f5a941f67c58ab465b4 = L.circleMarker(
                [43.6368472,-79.42819140000002],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_dfc6e26ab7fc4625b00f24b8b5ad1c9a = L.popup({maxWidth: '300'});

            
                var html_0d1ab0b8f7a646da949fa21c91494dc0 = $('<div id="html_0d1ab0b8f7a646da949fa21c91494dc0" style="width: 100.0%; height: 100.0%;">West Toronto (M6K): Brockton,Exhibition Place,Parkdale Village - Cluster 2</div>')[0];
                popup_dfc6e26ab7fc4625b00f24b8b5ad1c9a.setContent(html_0d1ab0b8f7a646da949fa21c91494dc0);
            

            circle_marker_94d8fbfc10254f5a941f67c58ab465b4.bindPopup(popup_dfc6e26ab7fc4625b00f24b8b5ad1c9a);

            
        
    
            var circle_marker_1888ace725bd49aea52b7aaf6e5cf76e = L.circleMarker(
                [43.6489597,-79.456325],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_9cc8886241ac42b988d8cdb0b6c9093e = L.popup({maxWidth: '300'});

            
                var html_e057a6d31056480694e5da666beead0a = $('<div id="html_e057a6d31056480694e5da666beead0a" style="width: 100.0%; height: 100.0%;">West Toronto (M6R): Parkdale,Roncesvalles - Cluster 2</div>')[0];
                popup_9cc8886241ac42b988d8cdb0b6c9093e.setContent(html_e057a6d31056480694e5da666beead0a);
            

            circle_marker_1888ace725bd49aea52b7aaf6e5cf76e.bindPopup(popup_9cc8886241ac42b988d8cdb0b6c9093e);

            
        
    
            var circle_marker_1fe8b93764734aef812f61ad9dd54903 = L.circleMarker(
                [43.6595255,-79.340923],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_405d943c777a4e41a761a9273d98026d = L.popup({maxWidth: '300'});

            
                var html_ea4d751502d5499f9c56c9cf0aebf071 = $('<div id="html_ea4d751502d5499f9c56c9cf0aebf071" style="width: 100.0%; height: 100.0%;">East Toronto (M4M): Studio District - Cluster 2</div>')[0];
                popup_405d943c777a4e41a761a9273d98026d.setContent(html_ea4d751502d5499f9c56c9cf0aebf071);
            

            circle_marker_1fe8b93764734aef812f61ad9dd54903.bindPopup(popup_405d943c777a4e41a761a9273d98026d);

            
        
    
            var circle_marker_4c67c6b4e7c64c5185a7dcf5a67b968a = L.circleMarker(
                [43.669542,-79.4225637],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_eea46d8c73684c77843f5af1cf381f33 = L.popup({maxWidth: '300'});

            
                var html_8b75b43889ef470c8f98440589c3e8f1 = $('<div id="html_8b75b43889ef470c8f98440589c3e8f1" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M6G): Christie - Cluster 2</div>')[0];
                popup_eea46d8c73684c77843f5af1cf381f33.setContent(html_8b75b43889ef470c8f98440589c3e8f1);
            

            circle_marker_4c67c6b4e7c64c5185a7dcf5a67b968a.bindPopup(popup_eea46d8c73684c77843f5af1cf381f33);

            
        
    
            var circle_marker_176c70b953e247c781a4d791e089b342 = L.circleMarker(
                [43.6626956,-79.4000493],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_0d8b76456e35457e89b5004fd5b2297a = L.popup({maxWidth: '300'});

            
                var html_ebb35c1b84424085a3ac1bd7720f09df = $('<div id="html_ebb35c1b84424085a3ac1bd7720f09df" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5S): Harbord,University of Toronto - Cluster 2</div>')[0];
                popup_0d8b76456e35457e89b5004fd5b2297a.setContent(html_ebb35c1b84424085a3ac1bd7720f09df);
            

            circle_marker_176c70b953e247c781a4d791e089b342.bindPopup(popup_0d8b76456e35457e89b5004fd5b2297a);

            
        
    
            var circle_marker_4d59799848914f5c94dffb4ba6d7c822 = L.circleMarker(
                [43.6532057,-79.4000493],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_39caa2c2bd7f4a60a839f4a9d659d153 = L.popup({maxWidth: '300'});

            
                var html_d0e26736fcfc4e0ebfbe57148e40a059 = $('<div id="html_d0e26736fcfc4e0ebfbe57148e40a059" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5T): Chinatown,Grange Park,Kensington Market - Cluster 2</div>')[0];
                popup_39caa2c2bd7f4a60a839f4a9d659d153.setContent(html_d0e26736fcfc4e0ebfbe57148e40a059);
            

            circle_marker_4d59799848914f5c94dffb4ba6d7c822.bindPopup(popup_39caa2c2bd7f4a60a839f4a9d659d153);

            
        
    
            var circle_marker_2157d1942f0e40f2baab1073c3d5cb72 = L.circleMarker(
                [43.6408157,-79.38175229999999],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_dca4ce64235b4b6b94f56740210f8a8f = L.popup({maxWidth: '300'});

            
                var html_26b2b435c82c4070bf178803f16454b2 = $('<div id="html_26b2b435c82c4070bf178803f16454b2" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5J): Harbourfront East,Toronto Islands,Union Station - Cluster 2</div>')[0];
                popup_dca4ce64235b4b6b94f56740210f8a8f.setContent(html_26b2b435c82c4070bf178803f16454b2);
            

            circle_marker_2157d1942f0e40f2baab1073c3d5cb72.bindPopup(popup_dca4ce64235b4b6b94f56740210f8a8f);

            
        
    
            var circle_marker_0c2afe70ebba4c34b064aed1a99a6b54 = L.circleMarker(
                [43.6542599,-79.3606359],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_a438641eec87439a9e35438da8dc5f11 = L.popup({maxWidth: '300'});

            
                var html_48f29b72ddfd40beb657cf41ddba75e1 = $('<div id="html_48f29b72ddfd40beb657cf41ddba75e1" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5A): Harbourfront,Regent Park - Cluster 2</div>')[0];
                popup_a438641eec87439a9e35438da8dc5f11.setContent(html_48f29b72ddfd40beb657cf41ddba75e1);
            

            circle_marker_0c2afe70ebba4c34b064aed1a99a6b54.bindPopup(popup_a438641eec87439a9e35438da8dc5f11);

            
        
    
            var circle_marker_dcc0d90395074216a7714e0969d0e9ce = L.circleMarker(
                [43.6514939,-79.3754179],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_fd5e905667d3448b92384cf1bd15d200 = L.popup({maxWidth: '300'});

            
                var html_cf78b18b2f004f8da83680248be87ff0 = $('<div id="html_cf78b18b2f004f8da83680248be87ff0" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5C): St. James Town - Cluster 2</div>')[0];
                popup_fd5e905667d3448b92384cf1bd15d200.setContent(html_cf78b18b2f004f8da83680248be87ff0);
            

            circle_marker_dcc0d90395074216a7714e0969d0e9ce.bindPopup(popup_fd5e905667d3448b92384cf1bd15d200);

            
        
    
            var circle_marker_1c27beb3332b48278b138bba467f2b63 = L.circleMarker(
                [43.6471768,-79.38157640000001],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_2a1be4ed609e4015a2c79ece6fcb79fb = L.popup({maxWidth: '300'});

            
                var html_bbb3d45ed6014aeeac9d277e477e46ac = $('<div id="html_bbb3d45ed6014aeeac9d277e477e46ac" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5K): Design Exchange,Toronto Dominion Centre - Cluster 2</div>')[0];
                popup_2a1be4ed609e4015a2c79ece6fcb79fb.setContent(html_bbb3d45ed6014aeeac9d277e477e46ac);
            

            circle_marker_1c27beb3332b48278b138bba467f2b63.bindPopup(popup_2a1be4ed609e4015a2c79ece6fcb79fb);

            
        
    
            var circle_marker_f8c9033a967342fa8a63130698409992 = L.circleMarker(
                [43.6481985,-79.37981690000001],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_2ec6ff54969a4d6583d472258d4870d5 = L.popup({maxWidth: '300'});

            
                var html_42dae2ca4d284d30aa2e69ef2bd48f2a = $('<div id="html_42dae2ca4d284d30aa2e69ef2bd48f2a" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5L): Commerce Court,Victoria Hotel - Cluster 2</div>')[0];
                popup_2ec6ff54969a4d6583d472258d4870d5.setContent(html_42dae2ca4d284d30aa2e69ef2bd48f2a);
            

            circle_marker_f8c9033a967342fa8a63130698409992.bindPopup(popup_2ec6ff54969a4d6583d472258d4870d5);

            
        
    
            var circle_marker_4847a3fea84c408790c730cd8f224992 = L.circleMarker(
                [43.6484292,-79.3822802],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_0bc68417459041a4a43a9cdde4078376 = L.popup({maxWidth: '300'});

            
                var html_3a1ca98a04444990a4cc041aee49316b = $('<div id="html_3a1ca98a04444990a4cc041aee49316b" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5X): First Canadian Place,Underground city - Cluster 2</div>')[0];
                popup_0bc68417459041a4a43a9cdde4078376.setContent(html_3a1ca98a04444990a4cc041aee49316b);
            

            circle_marker_4847a3fea84c408790c730cd8f224992.bindPopup(popup_0bc68417459041a4a43a9cdde4078376);

            
        
    
            var circle_marker_cd9da78a2744449591ae5ea07efa9fcd = L.circleMarker(
                [43.6579524,-79.3873826],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_d3eea4d8c5f148fdb6a151fb03c6f2fc = L.popup({maxWidth: '300'});

            
                var html_ab6ee0629b73430692f8fcdcea43399f = $('<div id="html_ab6ee0629b73430692f8fcdcea43399f" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5G): Central Bay Street - Cluster 2</div>')[0];
                popup_d3eea4d8c5f148fdb6a151fb03c6f2fc.setContent(html_ab6ee0629b73430692f8fcdcea43399f);
            

            circle_marker_cd9da78a2744449591ae5ea07efa9fcd.bindPopup(popup_d3eea4d8c5f148fdb6a151fb03c6f2fc);

            
        
    
            var circle_marker_20d6143b4b264114926a9672c1cb87f6 = L.circleMarker(
                [43.6464352,-79.37484599999999],
                {
  "bubblingMouseEvents": true,
  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_0ce03c74dfe2453e97d8c29b407d3cbf = L.popup({maxWidth: '300'});

            
                var html_5ca6112300344f4fb9c112a244103c90 = $('<div id="html_5ca6112300344f4fb9c112a244103c90" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5W): Stn A PO Boxes 25 The Esplanade - Cluster 2</div>')[0];
                popup_0ce03c74dfe2453e97d8c29b407d3cbf.setContent(html_5ca6112300344f4fb9c112a244103c90);
            

            circle_marker_20d6143b4b264114926a9672c1cb87f6.bindPopup(popup_0ce03c74dfe2453e97d8c29b407d3cbf);

            
        
    
            var circle_marker_559410837fd9412dba91e752ad3d77f9 = L.circleMarker(
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  "fill": true,
  "fillColor": "#80ffb4",
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  "fillRule": "evenodd",
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                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_9573bfe7da46483db3a83ed0942c71dd = L.popup({maxWidth: '300'});

            
                var html_6b92f3638e4f413dbedadd0e5a46fde0 = $('<div id="html_6b92f3638e4f413dbedadd0e5a46fde0" style="width: 100.0%; height: 100.0%;">Central Toronto (M5R): The Annex,North Midtown,Yorkville - Cluster 2</div>')[0];
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            circle_marker_559410837fd9412dba91e752ad3d77f9.bindPopup(popup_9573bfe7da46483db3a83ed0942c71dd);

            
        
    
            var circle_marker_4f3c5ad5f7ae42a38235a05f866a3412 = L.circleMarker(
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                {
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  "fillColor": "#80ffb4",
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                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_16da4d36d2ef4a78af0c0563e74cf4cc = L.popup({maxWidth: '300'});

            
                var html_28764037494d4927b4e7a9ca6aca9856 = $('<div id="html_28764037494d4927b4e7a9ca6aca9856" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5H): Adelaide,King,Richmond - Cluster 2</div>')[0];
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            circle_marker_4f3c5ad5f7ae42a38235a05f866a3412.bindPopup(popup_16da4d36d2ef4a78af0c0563e74cf4cc);

            
        
    
            var circle_marker_3e3d8c11b7174f8d848290607aa2cc2d = L.circleMarker(
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  "dashArray": null,
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  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
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  "opacity": 1.0,
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                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_812bfc5809ac41869697a2c8859fd659 = L.popup({maxWidth: '300'});

            
                var html_179d544c8f8b4f2394476ed39592cf0a = $('<div id="html_179d544c8f8b4f2394476ed39592cf0a" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5B): Ryerson,Garden District - Cluster 2</div>')[0];
                popup_812bfc5809ac41869697a2c8859fd659.setContent(html_179d544c8f8b4f2394476ed39592cf0a);
            

            circle_marker_3e3d8c11b7174f8d848290607aa2cc2d.bindPopup(popup_812bfc5809ac41869697a2c8859fd659);

            
        
    
            var circle_marker_056385918df8459cbe453dc28439fa9b = L.circleMarker(
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  "dashArray": null,
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  "fill": true,
  "fillColor": "#80ffb4",
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  "fillRule": "evenodd",
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                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_71b417f0b25340258c60b1212c2fd353 = L.popup({maxWidth: '300'});

            
                var html_57594e71edce457fb9cc322e715d18e6 = $('<div id="html_57594e71edce457fb9cc322e715d18e6" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M5E): Berczy Park - Cluster 2</div>')[0];
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            circle_marker_056385918df8459cbe453dc28439fa9b.bindPopup(popup_71b417f0b25340258c60b1212c2fd353);

            
        
    
            var circle_marker_e351ab99b7bc400a9c97356995b428e7 = L.circleMarker(
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  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
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  "fillRule": "evenodd",
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}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_279d39cb18274ce49170ca7225d06501 = L.popup({maxWidth: '300'});

            
                var html_4afb8dfb1ba94d408cc5b02259001c41 = $('<div id="html_4afb8dfb1ba94d408cc5b02259001c41" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M4X): Cabbagetown,St. James Town - Cluster 2</div>')[0];
                popup_279d39cb18274ce49170ca7225d06501.setContent(html_4afb8dfb1ba94d408cc5b02259001c41);
            

            circle_marker_e351ab99b7bc400a9c97356995b428e7.bindPopup(popup_279d39cb18274ce49170ca7225d06501);

            
        
    
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  "dashArray": null,
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  "fill": true,
  "fillColor": "#80ffb4",
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  "fillRule": "evenodd",
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}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_7ecdf882df6140f780cce1abbf261a99 = L.popup({maxWidth: '300'});

            
                var html_2af45cc37c8e41b089c715c3f8c5d74e = $('<div id="html_2af45cc37c8e41b089c715c3f8c5d74e" style="width: 100.0%; height: 100.0%;">Central Toronto (M4R): North Toronto West - Cluster 2</div>')[0];
                popup_7ecdf882df6140f780cce1abbf261a99.setContent(html_2af45cc37c8e41b089c715c3f8c5d74e);
            

            circle_marker_ba352a1d5fbc4ff28e919eeb98663db9.bindPopup(popup_7ecdf882df6140f780cce1abbf261a99);

            
        
    
            var circle_marker_71457afb07854e12a0562993649e93fc = L.circleMarker(
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  "fillColor": "#80ffb4",
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  "fillRule": "evenodd",
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}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_80a00c9e6b9d4edca88a49641e134481 = L.popup({maxWidth: '300'});

            
                var html_8daeb50658814a62b94d0a3cace7a041 = $('<div id="html_8daeb50658814a62b94d0a3cace7a041" style="width: 100.0%; height: 100.0%;">East Toronto (M4K): The Danforth West,Riverdale - Cluster 2</div>')[0];
                popup_80a00c9e6b9d4edca88a49641e134481.setContent(html_8daeb50658814a62b94d0a3cace7a041);
            

            circle_marker_71457afb07854e12a0562993649e93fc.bindPopup(popup_80a00c9e6b9d4edca88a49641e134481);

            
        
    
            var circle_marker_fc9d9b7506e4476e84eecdc4afafa339 = L.circleMarker(
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  "fillColor": "#80ffb4",
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}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_bb80afd9b3d147aeb7ce667adc7244bd = L.popup({maxWidth: '300'});

            
                var html_24bfbe7a75ce4e318d921daff2257aa7 = $('<div id="html_24bfbe7a75ce4e318d921daff2257aa7" style="width: 100.0%; height: 100.0%;">East Toronto (M4E): The Beaches - Cluster 2</div>')[0];
                popup_bb80afd9b3d147aeb7ce667adc7244bd.setContent(html_24bfbe7a75ce4e318d921daff2257aa7);
            

            circle_marker_fc9d9b7506e4476e84eecdc4afafa339.bindPopup(popup_bb80afd9b3d147aeb7ce667adc7244bd);

            
        
    
            var circle_marker_f26f1b01823c4baaa1caef633f26dbc0 = L.circleMarker(
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                {
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  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
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  "fillRule": "evenodd",
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}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_31e75e71916e49d2940481445c58cf89 = L.popup({maxWidth: '300'});

            
                var html_d856e3c8411643adba04295abfc484b8 = $('<div id="html_d856e3c8411643adba04295abfc484b8" style="width: 100.0%; height: 100.0%;">Downtown Toronto (M4Y): Church and Wellesley - Cluster 2</div>')[0];
                popup_31e75e71916e49d2940481445c58cf89.setContent(html_d856e3c8411643adba04295abfc484b8);
            

            circle_marker_f26f1b01823c4baaa1caef633f26dbc0.bindPopup(popup_31e75e71916e49d2940481445c58cf89);

            
        
    
            var circle_marker_31b182a98cb34a9c922f93ea098910fb = L.circleMarker(
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  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
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  "stroke": true,
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}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_368dec31032b449aa5afbc45a8640ee0 = L.popup({maxWidth: '300'});

            
                var html_aa3d43078f7f4dab88c380a57a1f2342 = $('<div id="html_aa3d43078f7f4dab88c380a57a1f2342" style="width: 100.0%; height: 100.0%;">East Toronto (M4L): The Beaches West,India Bazaar - Cluster 2</div>')[0];
                popup_368dec31032b449aa5afbc45a8640ee0.setContent(html_aa3d43078f7f4dab88c380a57a1f2342);
            

            circle_marker_31b182a98cb34a9c922f93ea098910fb.bindPopup(popup_368dec31032b449aa5afbc45a8640ee0);

            
        
    
            var circle_marker_68d582c67c6c4c3888c971bfe990b5f8 = L.circleMarker(
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  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
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  "fillRule": "evenodd",
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}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_a99af9274bcd44f1bfe610baba0bddb1 = L.popup({maxWidth: '300'});

            
                var html_eeddab26e27c41aea636daaf2eb74fcb = $('<div id="html_eeddab26e27c41aea636daaf2eb74fcb" style="width: 100.0%; height: 100.0%;">West Toronto (M6S): Runnymede,Swansea - Cluster 2</div>')[0];
                popup_a99af9274bcd44f1bfe610baba0bddb1.setContent(html_eeddab26e27c41aea636daaf2eb74fcb);
            

            circle_marker_68d582c67c6c4c3888c971bfe990b5f8.bindPopup(popup_a99af9274bcd44f1bfe610baba0bddb1);

            
        
    
            var circle_marker_763a9ad675b944468fef595e2592cb78 = L.circleMarker(
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  "fill": true,
  "fillColor": "#80ffb4",
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  "fillRule": "evenodd",
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  "stroke": true,
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}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_7337268307f543d4aa116a76070bd03d = L.popup({maxWidth: '300'});

            
                var html_8fd2e77d73014b289c18f73f2545d1b0 = $('<div id="html_8fd2e77d73014b289c18f73f2545d1b0" style="width: 100.0%; height: 100.0%;">Central Toronto (M4S): Davisville - Cluster 2</div>')[0];
                popup_7337268307f543d4aa116a76070bd03d.setContent(html_8fd2e77d73014b289c18f73f2545d1b0);
            

            circle_marker_763a9ad675b944468fef595e2592cb78.bindPopup(popup_7337268307f543d4aa116a76070bd03d);

            
        
    
            var circle_marker_a72fcb9bd2ed47eea597790a3b73ea99 = L.circleMarker(
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  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_ade66403353e45caa648ad4912910497 = L.popup({maxWidth: '300'});

            
                var html_70ecc75b0c36473d95299ba7fc8b3e54 = $('<div id="html_70ecc75b0c36473d95299ba7fc8b3e54" style="width: 100.0%; height: 100.0%;">Central Toronto (M4P): Davisville North - Cluster 2</div>')[0];
                popup_ade66403353e45caa648ad4912910497.setContent(html_70ecc75b0c36473d95299ba7fc8b3e54);
            

            circle_marker_a72fcb9bd2ed47eea597790a3b73ea99.bindPopup(popup_ade66403353e45caa648ad4912910497);

            
        
    
            var circle_marker_faab233ae6d04aa1970eb43e2e325857 = L.circleMarker(
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                {
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  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
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  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_c8039a1b96a749a99e4037e0320932c2 = L.popup({maxWidth: '300'});

            
                var html_93069626729a44c49cbd2408fdcbf53c = $('<div id="html_93069626729a44c49cbd2408fdcbf53c" style="width: 100.0%; height: 100.0%;">Central Toronto (M4V): Deer Park,Forest Hill SE,Rathnelly,South Hill,Summerhill West - Cluster 2</div>')[0];
                popup_c8039a1b96a749a99e4037e0320932c2.setContent(html_93069626729a44c49cbd2408fdcbf53c);
            

            circle_marker_faab233ae6d04aa1970eb43e2e325857.bindPopup(popup_c8039a1b96a749a99e4037e0320932c2);

            
        
    
            var circle_marker_0c000dd5b1224002832c449d66f05223 = L.circleMarker(
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                {
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  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_cad657b949824b98885587a1c6f1bba9 = L.popup({maxWidth: '300'});

            
                var html_19687d088dfc491d8cf5783f8e15898e = $('<div id="html_19687d088dfc491d8cf5783f8e15898e" style="width: 100.0%; height: 100.0%;">West Toronto (M6H): Dovercourt Village,Dufferin - Cluster 2</div>')[0];
                popup_cad657b949824b98885587a1c6f1bba9.setContent(html_19687d088dfc491d8cf5783f8e15898e);
            

            circle_marker_0c000dd5b1224002832c449d66f05223.bindPopup(popup_cad657b949824b98885587a1c6f1bba9);

            
        
    
            var circle_marker_9242030831f0491b8cf5b1c6f2fb3225 = L.circleMarker(
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                {
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  "color": "#80ffb4",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_ea8efcaa308c4843888b2ec2adaa4fb2 = L.popup({maxWidth: '300'});

            
                var html_d1cd03edc7a1427b843a74e3abdd8415 = $('<div id="html_d1cd03edc7a1427b843a74e3abdd8415" style="width: 100.0%; height: 100.0%;">Central Toronto (M4T): Moore Park,Summerhill East - Cluster 2</div>')[0];
                popup_ea8efcaa308c4843888b2ec2adaa4fb2.setContent(html_d1cd03edc7a1427b843a74e3abdd8415);
            

            circle_marker_9242030831f0491b8cf5b1c6f2fb3225.bindPopup(popup_ea8efcaa308c4843888b2ec2adaa4fb2);

            
        
    
            var circle_marker_8a984468d03141c6a48e2bc20df296a7 = L.circleMarker(
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                {
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  "dashOffset": null,
  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_6b91020c564d464fbf2f7d7eff9e32ef);
            
    
            var popup_a1ee89fc666e480783244df7ce0d1680 = L.popup({maxWidth: '300'});

            
                var html_a9f7c75cce0043aaa875a242752f7164 = $('<div id="html_a9f7c75cce0043aaa875a242752f7164" style="width: 100.0%; height: 100.0%;">East Toronto (M7Y): Business Reply Mail Processing Centre 969 Eastern - Cluster 2</div>')[0];
                popup_a1ee89fc666e480783244df7ce0d1680.setContent(html_a9f7c75cce0043aaa875a242752f7164);
            

            circle_marker_8a984468d03141c6a48e2bc20df296a7.bindPopup(popup_a1ee89fc666e480783244df7ce0d1680);

            
        
</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 0x7f50cb9d69e8>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create map\n",
"map_clusters = folium.Map(location=[latitude, longitude], zoom_start=12)\n",
"\n",
"# set color scheme for the clusters\n",
"x = np.arange(k_clusters)\n",
"ys = [i+x+(i*x)**2 for i in range(k_clusters)]\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, post, bor, poi, cluster in zip(toronto_central_clustered_df['Latitude'], toronto_central_clustered_df['Longitude'], toronto_central_clustered_df['Postal Code'], toronto_central_clustered_df['Borough'], toronto_central_clustered_df['Neighborhood'], toronto_central_clustered_df['Cluster']):\n",
" label = folium.Popup('{} ({}): {} - Cluster {}'.format(bor, post, poi, 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": [
"Peeking at the result shows\n",
"- Cluster 0: Park, hill, rosedale\n",
"- Cluster 1: Central Toronto (Roselawn)\n",
"- Cluster 2: Business area (with lots of business venues and some parks)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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