Week 3 Assignment Submission - Part 3.ipynb

{
    "nbformat_minor": 1, 
    "cells": [
        {
            "execution_count": null, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# The code was removed by Watson Studio for sharing."
        }, 
        {
            "source": "<hr>\n\n<h1> Segmenting and Clustering Neighborhoods in Toronto </h1>\n\n <strong><em>This notebook represents part 3 of the week assignment for the IBM Data Science Capstone course hosted on Coursera.</em></strong> \n\n<br>\n\n <strong><em>The following assignment performs web scrapping for the purpose of collecting data from Wikipedia to generate map visualizations using the Folium Library for Python.</em></strong> \n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 1, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# Import the request library \nimport requests\n# Import the Pandas library  as pd\nimport pandas as pd"
        }, 
        {
            "execution_count": 2, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# Assign the wikipedia URL for the Canadian postal codes site for all codes starting with the letter \"M\".\n# to the newly named variable \"wikipedia_link\"\nwikipedia_link = 'https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\n\n#  Assign the get function from the requests library to download the contents of url assigned to\n# the wikipedia_link variable\nraw_random_wikipedia_page = requests.get(str(wikipedia_link))"
        }, 
        {
            "execution_count": 3, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "<----    XML String Successfully Assigned to Variable    ---->\n"
                }
            ], 
            "source": "# Assign XML as a text file string  to newly named variable \"page\"\npage = raw_random_wikipedia_page.text\n# Displays tmessage indicating that the XML string was successfully\n# assigned to the page variable.\nprint('<----    XML String Successfully Assigned to Variable    ---->')"
        }, 
        {
            "source": "<hr>\n\n<h2> Web Scrapping </h2>\n\n <strong><em>The following cell contains the code used to extract and cleanse the specific data required for the assignment from the Wikipedia XML text string.</em></strong> \n\n<br>\n\n <strong><em>The initital XML text string has been generated using the requests library.  Please note that the remaining web scrapping portion of this assignment was completed without the use of third party libraries.</em></strong> \n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 4, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [], 
            "source": "# Assign the string length of  the page variable and \n# assign the numerical value to the newly named variable page_length.\npage_length = len(page)\n# Assign list containing postal codes to newly named variable postal_lst.\npostal_lst = ['M5G', \n                          'M2H', \n                          'M4B', \n                          'M1J', \n                          'M4G', \n                          'M4M', \n                          'M1R', \n                          'M9V', \n                          'M9L', \n                          'M5V', \n                          'M1B', \n                          'M5A']\n# Assign the  list item count from the postal_lst  list and\n# assign the numerical value to the newly named variable postal_lst_len.\npostal_lst_len = int(len(postal_lst))\n# Assign empty list to newly named variable city_list.\ncity_lst = []\n# Assign empty list to newly named variable temp_city_list.\ntemp_city_list = []\n\n# For loop iterates through each item contained in the postal_lst list\n# and removes HTML tags from the XML string assigned to the page \n# variable.\nfor item in postal_lst:\n    # This line was created to ensure that items from the previous stage \n    # of the loop are cleared from the temp_city_list.\n    temp_city_list = []\n    # Searches the contents of the page variable for the postal code \n    # assigned to item and returns the character value from the xml \n    # string and assigns the numerical value to a newly named \n    # variable line_trim1.\n    line_trim1 = page.find(item)\n    # Subtracts 5 from the character value assigned to the line_trim1\n    # variable and assigns the new value to a newly named variable line_trim2\n    line_trim2 = line_trim1 - 5\n    # Searches the contents of the page variable for the '<tr>'  HTML tag \n    # using the find method starting  from the string location assigned to \n    # line_trim2 and returns the character value reference number and assigns \n    # the numerical value to a newly named variable line_trim3.\n    line_trim3 = page.find('<tr>', line_trim2)\n    # Assigns a string from the page variable between character reference\n    # values assigned to line_trim2 and line_trim3 to a newly named variable\n    # temp_city_str1.\n    temp_city_str1 = page[line_trim2:line_trim3]\n    # Removes the '</td>' & '</tr>' HTML from the string assigned to the \n    # temp_city_str1 variable using the replace method and assigns the new string \n    # to the newly named variable  temp_city_str2.\n    temp_city_str2 = temp_city_str1.replace('</td></tr>', ' ')\n    # Assigns a string from the temp_city_str2 variable between character reference\n    # values 13 and the string length of  the temp_city_str2 variable and assigns it to\n    # the temp_city_str1 variable.\n    temp_city_str1 = temp_city_str2[13:len(temp_city_str2)]\n    # Assigns a string from the temp_city_str2 variable between character reference\n    # values 5 and the string length of the temp_city_str1 variable and assigns it to\n    # the newly named variable temp_city_str3\n    temp_city_str3 = temp_city_str1[5:len(temp_city_str1)]\n    \n    # Assigns a string from the temp_city_str2 variable between character reference\n    # values 5 and 8 and assigns it to the newly named variable line1_str   \n    line1_str = temp_city_str2[5:8]\n    \n    # Searches the contents of the temp_city_str3 variable for the '<td>'  HTML tag \n    # using the find method and returns the character value reference number \n    # and assigns the numerical value to a newly named variable temp_str1.\n    temp_str1 = temp_city_str3.find('<td>')\n    # Increases the value assigned to the temp_str1 variable by adding 4 to the value\n    # and assigns the new value to a newly named variable temp_str2.    \n    temp_str2 = temp_str1 + 4\n    # Assigns a string from the temp_city_str3 variable between character reference\n    # values assigned to the temp_str2 variable and the string length of the temp_city_str3 \n    # variable and assigns it to the newly named variable temp_city_str3\n    line2_str = temp_city_str3[temp_str2:len(temp_city_str3)]\n    # Removes the '</a>' HTML tag from the string assigned to the line2_str variable \n    # using the replace method and assigns the new string to the line2_str variable.\n    line2_str = line2_str.replace('</a>', ' ')\n    # Searches the contents of the line2_str variable for the '>'  character\n    # using the find method and returns the character value reference number \n    # and assigns the numerical value to the newly named variable line2_temp1.\n    line2_temp1 = line2_str.find('>')\n    # Increases the value assigned to the line2_temp1 variable by adding 1 to the value\n    # and assigns the new value to the line2_temp1 variable.   \n    line2_temp1 = line2_temp1 + 1\n    # Assigns a string from the str2_str variable between character reference\n    # values assigned to the line2_temp1 variable and the string length of the line2_str\n    # variable and assigns it to the line2_str variable.\n    line2_str = line2_str[line2_temp1:len(line2_str)]\n    # Removes all new line characters from the line2_str variable and assigns the new\n    # string to the line2_str variable.\n    line2_str = line2_str.strip('\\n \\n')\n    \n    # Assigns a string from the temp_city_str3 variable between character reference\n    # 0 and the string length of the temp_str1 variable and assigns it to the newly named \n    # variable line3_str.\n    line3_str = temp_city_str3[0:temp_str1]\n    # Removes the '</a>' & '</td>' HTML tags from the string assigned to the line3_str variable \n    # using the replace method and assigns the new string to the line3_str variable .\n    line3_str = line3_str.replace('</a></td>', ' ')\n    # Searches the contents of the line3_str variable for the '>'  character\n    # using the find method and returns the character value reference number \n    # and assigns the numerical value to the newly named variable line3_temp1\n    line3_temp1 = line3_str.find('>')\n    # Increases the value assigned to the line3_temp1 variable by adding 1 to the value\n    # and assigns the new value to the line3_temp1 variable.   \n    line3_temp1 = line3_temp1 + 1\n    # Assigns a string from the line3_str variable between character reference\n    # values assigned to the line3_temp1 variable and the string length of the line3_str\n    # variable and assigns it to the line3_str variable.\n    line3_str = line3_str[line3_temp1:len(line3_str)]\n    # Removes all new line characters from the line3_str variable and assigns the new\n    # string to the line3_str variable.\n    line3_str = line3_str.strip(' \\n')\n    \n    # Appends Postal Code to the temp_city_list list\n    temp_city_list.append(line1_str)\n    # Appends Borough to the temp_city_list list\n    temp_city_list.append(line3_str)\n    # Appends Neighborhood to the temp_city_list list\n    temp_city_list.append(line2_str)\n    \n    # Appends the temp_city_list as a row in the city_lst list\n    city_lst.append(temp_city_list)"
        }, 
        {
            "execution_count": 5, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "[['M5G', 'Downtown Toronto', 'Central Bay Street'], ['M2H', 'North York', 'Hillcrest Village'], ['M4B', 'East York', 'Woodbine Gardens'], ['M1J', 'Scarborough', 'Scarborough Village'], ['M4G', 'East York', 'Leaside'], ['M4M', 'East Toronto', 'Studio District'], ['M1R', 'Scarborough', 'Maryvale'], ['M9V', 'Etobicoke', 'Albion Gardens'], ['M9L', 'North York', 'Humber Summit'], ['M5V', 'Downtown Toronto', 'CN Tower'], ['M1B', 'Scarborough', 'Rouge'], ['M5A', 'Downtown Toronto', 'Harbourfront']]\n"
                }
            ], 
            "source": "# Display city_lst to confirm xml file was successfully scraped\nprint(city_lst)"
        }, 
        {
            "execution_count": 6, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "['M5G', 'Downtown Toronto', 'Central Bay Street']\n['M2H', 'North York', 'Hillcrest Village']\n['M4B', 'East York', 'Woodbine Gardens']\n['M1J', 'Scarborough', 'Scarborough Village']\n['M4G', 'East York', 'Leaside']\n['M4M', 'East Toronto', 'Studio District']\n['M1R', 'Scarborough', 'Maryvale']\n['M9V', 'Etobicoke', 'Albion Gardens']\n['M9L', 'North York', 'Humber Summit']\n['M5V', 'Downtown Toronto', 'CN Tower']\n['M1B', 'Scarborough', 'Rouge']\n['M5A', 'Downtown Toronto', 'Harbourfront']\n"
                }
            ], 
            "source": "# Unpack and display each row in city_lst\nfor line in city_lst:\n    print(line)"
        }, 
        {
            "source": "<hr>\n\n<h2> Pandas Dataframe Generation </h2>\n\n <strong><em>The next two cells below contain the code used to create the initial Pandas Dataframe required for this assignment.</em></strong> \n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 7, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 7, 
                    "metadata": {}, 
                    "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  </tbody>\n</table>\n</div>", 
                        "text/plain": "Empty DataFrame\nColumns: [PostalCode, Borough, Neighborhood]\nIndex: []"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Assign column names to newly named Pandas dataframe toronto_DF.\ntoronto_DF = pd.DataFrame(columns=['PostalCode', 'Borough', 'Neighborhood'])\n# Display new dataframe as confirmation of successful creation.\ntoronto_DF"
        }, 
        {
            "execution_count": 8, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 8, 
                    "metadata": {}, 
                    "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>M5G</td>\n      <td>Downtown Toronto</td>\n      <td>Central Bay Street</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M2H</td>\n      <td>North York</td>\n      <td>Hillcrest Village</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M4B</td>\n      <td>East York</td>\n      <td>Woodbine Gardens</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M1J</td>\n      <td>Scarborough</td>\n      <td>Scarborough Village</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M4G</td>\n      <td>East York</td>\n      <td>Leaside</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>M4M</td>\n      <td>East Toronto</td>\n      <td>Studio District</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>M1R</td>\n      <td>Scarborough</td>\n      <td>Maryvale</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>M9V</td>\n      <td>Etobicoke</td>\n      <td>Albion Gardens</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>M9L</td>\n      <td>North York</td>\n      <td>Humber Summit</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M5V</td>\n      <td>Downtown Toronto</td>\n      <td>CN Tower</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>M1B</td>\n      <td>Scarborough</td>\n      <td>Rouge</td>\n    </tr>\n    <tr>\n      <th>11</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\n0         M5G  Downtown Toronto   Central Bay Street\n1         M2H        North York    Hillcrest Village\n2         M4B         East York     Woodbine Gardens\n3         M1J       Scarborough  Scarborough Village\n4         M4G         East York              Leaside\n5         M4M      East Toronto      Studio District\n6         M1R       Scarborough             Maryvale\n7         M9V         Etobicoke       Albion Gardens\n8         M9L        North York        Humber Summit\n9         M5V  Downtown Toronto             CN Tower\n10        M1B       Scarborough                Rouge\n11        M5A  Downtown Toronto         Harbourfront"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# For Loop iterates through each row of  city_lst and appends the row to\n# the toronto_DF pandas dataframe.\nfor row in city_lst:\n   toronto_DF = toronto_DF.append(pd.Series(row, index=['PostalCode', \n                                                                                                                         'Borough', \n                                                                                                                         'Neighborhood']), \n                                                                           ignore_index=True)\n\n# Display updated dataframe as confirmation of successful update\ntoronto_DF"
        }, 
        {
            "source": "<hr>\n\n<h2> Data Import &amp; Pandas Dataframe Generation </h2>\n\n <strong><em>The next seven cells below contain the code used to import the latitude and longitude data from the csv file provided for the assignment.</em></strong> \n\n<br>\n\n <strong><em>After the data is imported from the csv file, it is processed and merged into the final Pandas Dataframe that is used to generate the map visualizations.</em></strong> \n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 9, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 9, 
                    "metadata": {}, 
                    "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\n0         M1B  43.806686 -79.194353\n1         M1C  43.784535 -79.160497\n2         M1E  43.763573 -79.188711\n3         M1G  43.770992 -79.216917\n4         M1H  43.773136 -79.239476"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Read contents of csv file and assign data to new named DataFrame geo_DF\ngeo_DF = pd.read_csv('http://cocl.us/Geospatial_data')\n# Display updated dataframe as confirmation of successful update\ngeo_DF.head()"
        }, 
        {
            "execution_count": 10, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "['M1B', 'M1C', 'M1E', 'M1G', 'M1H', 'M1J', 'M1K', 'M1L', 'M1M', 'M1N', 'M1P', 'M1R', 'M1S', 'M1T', 'M1V', 'M1W', 'M1X', 'M2H', 'M2J', 'M2K', 'M2L', 'M2M', 'M2N', 'M2P', 'M2R', 'M3A', 'M3B', 'M3C', 'M3H', 'M3J', 'M3K', 'M3L', 'M3M', 'M3N', 'M4A', 'M4B', 'M4C', 'M4E', 'M4G', 'M4H', 'M4J', 'M4K', 'M4L', 'M4M', 'M4N', 'M4P', 'M4R', 'M4S', 'M4T', 'M4V', 'M4W', 'M4X', 'M4Y', 'M5A', 'M5B', 'M5C', 'M5E', 'M5G', 'M5H', 'M5J', 'M5K', 'M5L', 'M5M', 'M5N', 'M5P', 'M5R', 'M5S', 'M5T', 'M5V', 'M5W', 'M5X', 'M6A', 'M6B', 'M6C', 'M6E', 'M6G', 'M6H', 'M6J', 'M6K', 'M6L', 'M6M', 'M6N', 'M6P', 'M6R', 'M6S', 'M7A', 'M7R', 'M7Y', 'M8V', 'M8W', 'M8X', 'M8Y', 'M8Z', 'M9A', 'M9B', 'M9C', 'M9L', 'M9M', 'M9N', 'M9P', 'M9R', 'M9V', 'M9W']\n"
                }
            ], 
            "source": "# Unpack DataFrame values from the Postal Code column and\n#  assign values to the newly named variable geo_lst1\ngeo_lst1 = list(geo_DF['Postal Code'])\n# Display geo_lst1 as confirmation of successful creation\nprint(geo_lst1)"
        }, 
        {
            "execution_count": 11, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "[43.806686299999996, 43.7845351, 43.7635726, 43.7709921, 43.773136, 43.7447342, 43.7279292, 43.711111700000004, 43.716316, 43.692657000000004, 43.7574096, 43.750071500000004, 43.7942003, 43.7816375, 43.8152522, 43.799525200000005, 43.836124700000006, 43.8037622, 43.7785175, 43.7869473, 43.7574902, 43.789053, 43.7701199, 43.752758299999996, 43.7827364, 43.7532586, 43.745905799999996, 43.72589970000001, 43.7543283, 43.7679803, 43.737473200000004, 43.7390146, 43.7284964, 43.7616313, 43.725882299999995, 43.7063972, 43.695343900000005, 43.67635739999999, 43.7090604, 43.7053689, 43.685347, 43.6795571, 43.6689985, 43.6595255, 43.7280205, 43.7127511, 43.7153834, 43.7043244, 43.6895743, 43.68641229999999, 43.6795626, 43.667967, 43.6658599, 43.6542599, 43.6571618, 43.6514939, 43.644770799999996, 43.6579524, 43.65057120000001, 43.6408157, 43.6471768, 43.6481985, 43.7332825, 43.7116948, 43.6969476, 43.6727097, 43.6626956, 43.6532057, 43.6289467, 43.6464352, 43.6484292, 43.718517999999996, 43.709577, 43.6937813, 43.6890256, 43.669542, 43.66900510000001, 43.647926700000006, 43.6368472, 43.713756200000006, 43.6911158, 43.67318529999999, 43.6616083, 43.6489597, 43.6515706, 43.6623015, 43.6369656, 43.6627439, 43.6056466, 43.60241370000001, 43.653653600000005, 43.6362579, 43.6288408, 43.6678556, 43.6509432, 43.6435152, 43.7563033, 43.7247659, 43.706876, 43.696319, 43.6889054, 43.739416399999996, 43.706748299999994]\n"
                }
            ], 
            "source": "# Unpack DataFrame values from the Latitude column and\n#  assign values to the newly named variable geo_lst2\ngeo_lst2 = list(geo_DF['Latitude'])\n# Display geo_lst1 as confirmation of successful creation\nprint(geo_lst2)"
        }, 
        {
            "execution_count": 12, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "[-79.19435340000001, -79.16049709999999, -79.1887115, -79.21691740000001, -79.23947609999999, -79.23947609999999, -79.26202940000002, -79.2845772, -79.23947609999999, -79.2648481, -79.27330400000001, -79.2958491, -79.26202940000002, -79.3043021, -79.2845772, -79.3183887, -79.20563609999999, -79.3634517, -79.3465557, -79.385975, -79.37471409999999, -79.40849279999999, -79.40849279999999, -79.4000493, -79.4422593, -79.3296565, -79.352188, -79.340923, -79.4422593, -79.48726190000001, -79.46476329999999, -79.5069436, -79.49569740000001, -79.52099940000001, -79.31557159999998, -79.309937, -79.3183887, -79.2930312, -79.3634517, -79.34937190000001, -79.3381065, -79.352188, -79.31557159999998, -79.340923, -79.3887901, -79.3901975, -79.40567840000001, -79.3887901, -79.38315990000001, -79.4000493, -79.37752940000001, -79.3676753, -79.38315990000001, -79.3606359, -79.37893709999999, -79.3754179, -79.3733064, -79.3873826, -79.3845675, -79.38175229999999, -79.38157640000001, -79.37981690000001, -79.4197497, -79.41693559999999, -79.41130720000001, -79.40567840000001, -79.4000493, -79.4000493, -79.3944199, -79.37484599999999, -79.3822802, -79.46476329999999, -79.44507259999999, -79.42819140000002, -79.453512, -79.4225637, -79.4422593, -79.4197497, -79.42819140000002, -79.4900738, -79.47601329999999, -79.48726190000001, -79.46476329999999, -79.456325, -79.4844499, -79.3894938, -79.61581899999999, -79.321558, -79.50132070000001, -79.54348409999999, -79.5069436, -79.49850909999999, -79.52099940000001, -79.53224240000002, -79.55472440000001, -79.57720079999999, -79.56596329999999, -79.53224240000002, -79.51818840000001, -79.53224240000002, -79.55472440000001, -79.5884369, -79.5940544]\n"
                }
            ], 
            "source": "# Unpack DataFrame values from the Longitude column and\n#  assign values to the newly named variable geo_lst3\ngeo_lst3 = list(geo_DF['Longitude'])\n# Display geo_lst1 as confirmation of successful creation\nprint(geo_lst3)"
        }, 
        {
            "execution_count": 13, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# Assign a value of 0 to the newly named variable count\ncount = 0\n# Assign an empty list to the newly named variable geo_lst\ngeo_lst = [ ]\n# Assign an empty list to the newly named variable temp_lst\ntemp_lst = []\n\n# While loop uses the count variable as a generator that\n# is used in the boolean condition statement that iterates\n# through geo_lst1, geo_lst2, and geo_lst3.\nwhile (int(count) != int(len(geo_lst1))):\n   \n    # All list items are removed from the temp_lst list  from\n    # from the previous stage of the loop.\n    temp_lst = []\n    \n    # The value from geo_lst1[count] is appended to the temp_lst\n    # list as an indivual list item.\n    temp_lst.append(geo_lst1[count])\n    # The value from geo_lst2[count] is appended to the temp_lst\n    # list as an indivual list item.\n    temp_lst.append(geo_lst2[count])\n    # The value from geo_lst3[count] is appended to the temp_lst\n    # list as an indivual list item.\n    temp_lst.append(geo_lst3[count])\n    \n    # The temp_lst list is appended to the geo_lst list as an\n    # individual list, which will behave a like a row in the final list.\n    geo_lst.append(temp_lst)\n    \n    # A value of 1 is added the count variable which is used in the\n    # boolean condition statement to determine if the while loop\n    # shall continue its iteration process.\n    count += 1\n    \n# An empty list is assigned to the newly named variable geo_city_lst\ngeo_city_lst = []\n# An empty list is assigned to the newly named variable temp_gc_lst\ntemp_gc_lst = []\n\n# A for loop that iterates through each row of the city_lst list and\n# appends final list rows to the geo_city_lst based on a conditional\n# statement contained within a nested for loop.\nfor row1 in city_lst:\n    \n    # A nested for that iterates through each row of the geo_lst List\n    # and performs a conditional test to determine which items from\n    # the city_lst and geo_lst lists are consolidated into the final list\n    # to be converted into a DataFrame.\n    for row2 in geo_lst:\n        \n        # An if conditional statement used to check if the individual \n        # items from list position 0 in the row2 and row1 lists are equal\n        # to each other.  If the output of the conditional statement is True,\n        # the lines nested within the if statement are executed until it reaches\n        # the last line.\n        if str(row2[0]) == str(row1[0]):\n            \n            # Appends the value from row1[0] to the temp_gc_lst list.\n            temp_gc_lst.append(row1[0])\n            # Appends the value from row1[1] to the temp_gc_lst list.\n            temp_gc_lst.append(row1[1])\n            # Appends the value from row1[2] to the temp_gc_lst list.\n            temp_gc_lst.append(row1[2])\n            # Appends the value from row2[1] to the temp_gc_lst list.\n            temp_gc_lst.append(row2[1])\n            # Appends the value from row2[2] to the temp_gc_lst list.\n            temp_gc_lst.append(row2[2])\n            \n            # Appends the temp_gc_lst list to the geo_city_lst list.\n            geo_city_lst.append(temp_gc_lst)\n            \n            # Removes the current items contained within the temp_gc_lst\n            # and replaces it with an empty list in preparation for the next\n            # of the for loop.\n            temp_gc_lst = []\n        \n        # An elif condition used to pass to the next stage of the for loop\n        # if the output of the conditional statement is False.\n        elif str(row2[0]) != str(row1[0]):\n            pass"
        }, 
        {
            "execution_count": 14, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "['M5G', 'Downtown Toronto', 'Central Bay Street', 43.6579524, -79.3873826]\n['M2H', 'North York', 'Hillcrest Village', 43.8037622, -79.3634517]\n['M4B', 'East York', 'Woodbine Gardens', 43.7063972, -79.309937]\n['M1J', 'Scarborough', 'Scarborough Village', 43.7447342, -79.23947609999999]\n['M4G', 'East York', 'Leaside', 43.7090604, -79.3634517]\n['M4M', 'East Toronto', 'Studio District', 43.6595255, -79.340923]\n['M1R', 'Scarborough', 'Maryvale', 43.750071500000004, -79.2958491]\n['M9V', 'Etobicoke', 'Albion Gardens', 43.739416399999996, -79.5884369]\n['M9L', 'North York', 'Humber Summit', 43.7563033, -79.56596329999999]\n['M5V', 'Downtown Toronto', 'CN Tower', 43.6289467, -79.3944199]\n['M1B', 'Scarborough', 'Rouge', 43.806686299999996, -79.19435340000001]\n['M5A', 'Downtown Toronto', 'Harbourfront', 43.6542599, -79.3606359]\n"
                }
            ], 
            "source": "# Unpack and display each row in geo_city_lst\nfor row in geo_city_lst:\n    print(row)"
        }, 
        {
            "execution_count": 15, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 15, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "Empty DataFrame\nColumns: [PostalCode, Borough, Neighborhood, Latitude, Longitude]\nIndex: []"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Assign column names to newly named Pandas dataframe toronto_Geo_DF.\ntoronto_Geo_DF = pd.DataFrame(columns=['PostalCode', 'Borough', 'Neighborhood', 'Latitude', 'Longitude'])\n# Display new dataframe as confirmation of successful creation.\ntoronto_Geo_DF"
        }, 
        {
            "execution_count": 16, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 16, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M5G</td>\n      <td>Downtown Toronto</td>\n      <td>Central Bay Street</td>\n      <td>43.657952</td>\n      <td>-79.387383</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M2H</td>\n      <td>North York</td>\n      <td>Hillcrest Village</td>\n      <td>43.803762</td>\n      <td>-79.363452</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M4B</td>\n      <td>East York</td>\n      <td>Woodbine Gardens</td>\n      <td>43.706397</td>\n      <td>-79.309937</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M1J</td>\n      <td>Scarborough</td>\n      <td>Scarborough Village</td>\n      <td>43.744734</td>\n      <td>-79.239476</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M4G</td>\n      <td>East York</td>\n      <td>Leaside</td>\n      <td>43.709060</td>\n      <td>-79.363452</td>\n    </tr>\n    <tr>\n      <th>5</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>6</th>\n      <td>M1R</td>\n      <td>Scarborough</td>\n      <td>Maryvale</td>\n      <td>43.750072</td>\n      <td>-79.295849</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>M9V</td>\n      <td>Etobicoke</td>\n      <td>Albion Gardens</td>\n      <td>43.739416</td>\n      <td>-79.588437</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>M9L</td>\n      <td>North York</td>\n      <td>Humber Summit</td>\n      <td>43.756303</td>\n      <td>-79.565963</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M5V</td>\n      <td>Downtown Toronto</td>\n      <td>CN Tower</td>\n      <td>43.628947</td>\n      <td>-79.394420</td>\n    </tr>\n    <tr>\n      <th>10</th>\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      <th>11</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront</td>\n      <td>43.654260</td>\n      <td>-79.360636</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   PostalCode           Borough         Neighborhood   Latitude  Longitude\n0         M5G  Downtown Toronto   Central Bay Street  43.657952 -79.387383\n1         M2H        North York    Hillcrest Village  43.803762 -79.363452\n2         M4B         East York     Woodbine Gardens  43.706397 -79.309937\n3         M1J       Scarborough  Scarborough Village  43.744734 -79.239476\n4         M4G         East York              Leaside  43.709060 -79.363452\n5         M4M      East Toronto      Studio District  43.659526 -79.340923\n6         M1R       Scarborough             Maryvale  43.750072 -79.295849\n7         M9V         Etobicoke       Albion Gardens  43.739416 -79.588437\n8         M9L        North York        Humber Summit  43.756303 -79.565963\n9         M5V  Downtown Toronto             CN Tower  43.628947 -79.394420\n10        M1B       Scarborough                Rouge  43.806686 -79.194353\n11        M5A  Downtown Toronto         Harbourfront  43.654260 -79.360636"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# For Loop iterates through each row of  geo_city_lst and appends the row to\n# the toronto_Geo_DF pandas dataframe.\nfor row in geo_city_lst:\n   toronto_Geo_DF = toronto_Geo_DF.append(pd.Series(row, index=['PostalCode', \n                                                                                                                                              'Borough', \n                                                                                                                                              'Neighborhood', \n                                                                                                                                              'Latitude', \n                                                                                                                                             'Longitude']), \n                                                                                                  ignore_index=True)\n\n# Display updated dataframe as confirmation of successful update\ntoronto_Geo_DF"
        }, 
        {
            "source": "<hr>\n\n<h1> Data Visualizations Using the Folium Library for Python </h1>\n\n <strong><em>Please note that the code contained in the following cells from this point on is based on the lesson modules provided by the IBM Data Science Team <br> as part Coursera Capstone Program and contains modifications made by myself based on the requirements of the assignment.</em></strong> \n\n<br>\n\n <strong><em>All code contained in the cells prior to this point was written by myself based on the requirements of the assignment.</em></strong> \n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 17, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Fetching package metadata .............\nSolving package specifications: .\n\nPackage plan for installation in environment /opt/conda/envs/DSX-Python35:\n\nThe following NEW packages will be INSTALLED:\n\n    geographiclib: 1.49-py_0   conda-forge\n    geopy:         1.18.1-py_0 conda-forge\n\ngeographiclib- 100% |################################| Time: 0:00:00  22.07 MB/s\ngeopy-1.18.1-p 100% |################################| Time: 0:00:00  32.81 MB/s\nFetching package metadata .............\nSolving package specifications: .\n\nPackage plan for installation in environment /opt/conda/envs/DSX-Python35:\n\nThe following NEW packages will be INSTALLED:\n\n    altair:  2.2.2-py35_1 conda-forge\n    branca:  0.3.1-py_0   conda-forge\n    folium:  0.5.0-py_0   conda-forge\n    vincent: 0.4.4-py_1   conda-forge\n\naltair-2.2.2-p 100% |################################| Time: 0:00:00  49.37 MB/s\nbranca-0.3.1-p 100% |################################| Time: 0:00:00  21.99 MB/s\nvincent-0.4.4- 100% |################################| Time: 0:00:00  26.22 MB/s\nfolium-0.5.0-p 100% |################################| Time: 0:00:00  38.44 MB/s\nLibraries imported.\n"
                }
            ], 
            "source": "import numpy as np # library to handle data in a vectorized manner\nimport json # library to handle JSON files\n!conda install -c conda-forge geopy --yes\nfrom geopy.geocoders import Nominatim # convert an address into latitude and longitude values\nfrom pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n\n# Matplotlib and associated plotting modules\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\n\n# import k-means from clustering stage\nfrom sklearn.cluster import KMeans\n\n!conda install -c conda-forge folium=0.5.0 --yes \nimport folium # map rendering library\n\nprint('Libraries imported.')"
        }, 
        {
            "execution_count": 18, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stderr", 
                    "text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/ipykernel/__main__.py:3: DeprecationWarning: Using Nominatim with the default \"geopy/1.18.1\" `user_agent` is strongly discouraged, as it violates Nominatim's ToS https://operations.osmfoundation.org/policies/nominatim/ and may possibly cause 403 and 429 HTTP errors. Please specify a custom `user_agent` with `Nominatim(user_agent=\"my-application\")` or by overriding the default `user_agent`: `geopy.geocoders.options.default_user_agent = \"my-application\"`. In geopy 2.0 this will become an exception.\n  app.launch_new_instance()\n"
                }, 
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "The geograpical coordinate of Toronto are 43.653963, -79.387207.\n"
                }
            ], 
            "source": "address = 'Toronto, ON'\n\ngeolocator = Nominatim()\nlocation = geolocator.geocode(address)\nlatitude = location.latitude\nlongitude = location.longitude\nprint('The geograpical coordinate of Toronto are {}, {}.'.format(latitude, longitude))"
        }, 
        {
            "source": "<hr>\n\n<h2> Folium Map of Various Neighborhoods in the City of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 35, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": false
            }, 
            "outputs": [
                {
                    "execution_count": 35, 
                    "metadata": {}, 
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", 
                        "text/plain": "<folium.folium.Map at 0x2b4643947908>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map of Toronto using latitude and longitude values\nmap_toronto = folium.Map(location=[latitude, longitude], zoom_start=11)\n\n# add markers to map\nfor lat, lng, borough, neighborhood in zip(toronto_Geo_DF['Latitude'], toronto_Geo_DF['Longitude'], toronto_Geo_DF['Borough'], toronto_Geo_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\nmap_toronto"
        }, 
        {
            "source": "<hr>\n\n<h2> Pandas DataFrame for the Downtown Toronto Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 36, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "execution_count": 36, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M5G</td>\n      <td>Downtown Toronto</td>\n      <td>Central Bay Street</td>\n      <td>43.657952</td>\n      <td>-79.387383</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M5V</td>\n      <td>Downtown Toronto</td>\n      <td>CN Tower</td>\n      <td>43.628947</td>\n      <td>-79.394420</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront</td>\n      <td>43.654260</td>\n      <td>-79.360636</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode           Borough        Neighborhood   Latitude  Longitude\n0        M5G  Downtown Toronto  Central Bay Street  43.657952 -79.387383\n1        M5V  Downtown Toronto            CN Tower  43.628947 -79.394420\n2        M5A  Downtown Toronto        Harbourfront  43.654260 -79.360636"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_data = toronto_Geo_DF[toronto_Geo_DF['Borough'] == 'Downtown Toronto'].reset_index(drop=True)\ndowntown_data.head()"
        }, 
        {
            "source": "<hr>\n\n<h2> Folium Map Cluster for the Downtown Toronto Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 37, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 37, 
                    "metadata": {}, 
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                var html_ad714c2f493b4b518041d98747bae6b4 = $('<div id="html_ad714c2f493b4b518041d98747bae6b4" style="width: 100.0%; height: 100.0%;">Harbourfront, Downtown Toronto</div>')[0];
                popup_d28e77bf4731474c895410e8c3afba59.setContent(html_ad714c2f493b4b518041d98747bae6b4);
            

            circle_marker_bbd6b6554a3840baa2cf91926669044f.bindPopup(popup_d28e77bf4731474c895410e8c3afba59);

            
        
</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 0x2b46439a1400>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map of Toronto using latitude and longitude values\nmap_downtown_toronto = folium.Map(location=[latitude, longitude], zoom_start=14)\n\n# add markers to map\nfor lat, lng, borough, neighborhood in zip(downtown_data['Latitude'], downtown_data['Longitude'], downtown_data['Borough'], downtown_data['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_downtown_toronto)  \n    \nmap_downtown_toronto"
        }, 
        {
            "source": "<hr>\n\n<h2> Pandas DataFrame for the North York Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 38, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 38, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M2H</td>\n      <td>North York</td>\n      <td>Hillcrest Village</td>\n      <td>43.803762</td>\n      <td>-79.363452</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M9L</td>\n      <td>North York</td>\n      <td>Humber Summit</td>\n      <td>43.756303</td>\n      <td>-79.565963</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode     Borough       Neighborhood   Latitude  Longitude\n0        M2H  North York  Hillcrest Village  43.803762 -79.363452\n1        M9L  North York      Humber Summit  43.756303 -79.565963"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "North_York_data = toronto_Geo_DF[toronto_Geo_DF['Borough'] == 'North York'].reset_index(drop=True)\nNorth_York_data.head()"
        }, 
        {
            "source": "<hr>\n\n<h2> Folium Map Cluster for the North York Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 39, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 39, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,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\" 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 0x2b46439c5e80>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map of North York using latitude and longitude values\nmap_North_York = folium.Map(location=[latitude, longitude], zoom_start=11)\n\n# add markers to map\nfor lat, lng, borough, neighborhood in zip(North_York_data['Latitude'], North_York_data['Longitude'], North_York_data['Borough'], North_York_data['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_North_York)  \n\nmap_North_York"
        }, 
        {
            "source": "<hr>\n\n<h2> Pandas DataFrame for the East York Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 40, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 40, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M4B</td>\n      <td>East York</td>\n      <td>Woodbine Gardens</td>\n      <td>43.706397</td>\n      <td>-79.309937</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4G</td>\n      <td>East York</td>\n      <td>Leaside</td>\n      <td>43.709060</td>\n      <td>-79.363452</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode    Borough      Neighborhood   Latitude  Longitude\n0        M4B  East York  Woodbine Gardens  43.706397 -79.309937\n1        M4G  East York           Leaside  43.709060 -79.363452"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "East_York_data = toronto_Geo_DF[toronto_Geo_DF['Borough'] == 'East York'].reset_index(drop=True)\nEast_York_data.head()"
        }, 
        {
            "source": "<hr>\n\n<h2> Folium Map Cluster for the East York Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 41, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": false
            }, 
            "outputs": [
                {
                    "execution_count": 41, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe 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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 0x2b46439df588>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map of East York using latitude and longitude values\nmap_East_York = folium.Map(location=[latitude, longitude], zoom_start=13)\n\n# add markers to map\nfor lat, lng, borough, neighborhood in zip(East_York_data['Latitude'], East_York_data['Longitude'], East_York_data['Borough'], East_York_data['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_East_York)  \n    \nmap_East_York"
        }, 
        {
            "source": "<hr>\n\n<h2> Pandas DataFrame for the Scarborough Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 42, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 42, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1J</td>\n      <td>Scarborough</td>\n      <td>Scarborough Village</td>\n      <td>43.744734</td>\n      <td>-79.239476</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1R</td>\n      <td>Scarborough</td>\n      <td>Maryvale</td>\n      <td>43.750072</td>\n      <td>-79.295849</td>\n    </tr>\n    <tr>\n      <th>2</th>\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  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode      Borough         Neighborhood   Latitude  Longitude\n0        M1J  Scarborough  Scarborough Village  43.744734 -79.239476\n1        M1R  Scarborough             Maryvale  43.750072 -79.295849\n2        M1B  Scarborough                Rouge  43.806686 -79.194353"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "Scarborough_data = toronto_Geo_DF[toronto_Geo_DF['Borough'] == 'Scarborough'].reset_index(drop=True)\nScarborough_data.head()"
        }, 
        {
            "source": "<hr>\n\n<h2> Folium Map Cluster for the Scarborough Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 43, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 43, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS = false; L_NO_TOUCH = false; L_DISABLE_3D = false;</script>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
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    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawgit.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <style> #map_e39ef1bf0e164696bc1e46102a3f2181 {
                position : relative;
                width : 100.0%;
                height: 100.0%;
                left: 0.0%;
                top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_e39ef1bf0e164696bc1e46102a3f2181" ></div>
        
</body>
<script>    
    

            
                var bounds = null;
            

            var map_e39ef1bf0e164696bc1e46102a3f2181 = L.map(
                                  'map_e39ef1bf0e164696bc1e46102a3f2181',
                                  {center: [43.653963,-79.387207],
                                  zoom: 11,
                                  maxBounds: bounds,
                                  layers: [],
                                  worldCopyJump: false,
                                  crs: L.CRS.EPSG3857
                                 });
            
        
    
            var tile_layer_8eaa28448ffa4412ac0bbb2e6b7a3d65 = L.tileLayer(
                'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png',
                {
  "attribution": null,
  "detectRetina": false,
  "maxZoom": 18,
  "minZoom": 1,
  "noWrap": false,
  "subdomains": "abc"
}
                ).addTo(map_e39ef1bf0e164696bc1e46102a3f2181);
        
    
            var circle_marker_71a47f401f94432ea681511a47f6df25 = L.circleMarker(
                [43.7447342,-79.23947609999999],
                {
  "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_e39ef1bf0e164696bc1e46102a3f2181);
            
    
            var popup_24348609dc9043539b4525dd503b90ba = L.popup({maxWidth: '300'});

            
                var html_75fc6999e10e4e6d93ee291db0a8126e = $('<div id="html_75fc6999e10e4e6d93ee291db0a8126e" style="width: 100.0%; height: 100.0%;">Scarborough Village, Scarborough</div>')[0];
                popup_24348609dc9043539b4525dd503b90ba.setContent(html_75fc6999e10e4e6d93ee291db0a8126e);
            

            circle_marker_71a47f401f94432ea681511a47f6df25.bindPopup(popup_24348609dc9043539b4525dd503b90ba);

            
        
    
            var circle_marker_9e5ec6ebf95c499d911cd1b346f0fd19 = L.circleMarker(
                [43.750071500000004,-79.2958491],
                {
  "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_e39ef1bf0e164696bc1e46102a3f2181);
            
    
            var popup_c3a92724004844e1bde8a5b517245b5c = L.popup({maxWidth: '300'});

            
                var html_86c0b33527b547288e5231f64d3c6ffb = $('<div id="html_86c0b33527b547288e5231f64d3c6ffb" style="width: 100.0%; height: 100.0%;">Maryvale, Scarborough</div>')[0];
                popup_c3a92724004844e1bde8a5b517245b5c.setContent(html_86c0b33527b547288e5231f64d3c6ffb);
            

            circle_marker_9e5ec6ebf95c499d911cd1b346f0fd19.bindPopup(popup_c3a92724004844e1bde8a5b517245b5c);

            
        
    
            var circle_marker_5c38f888b8eb4aa4b038b1135aaf94d1 = L.circleMarker(
                [43.806686299999996,-79.19435340000001],
                {
  "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_e39ef1bf0e164696bc1e46102a3f2181);
            
    
            var popup_65ed483f528e4090bfb530bc3928a6ec = L.popup({maxWidth: '300'});

            
                var html_bb4339fcb2874160afbc0cb9513f7577 = $('<div id="html_bb4339fcb2874160afbc0cb9513f7577" style="width: 100.0%; height: 100.0%;">Rouge, Scarborough</div>')[0];
                popup_65ed483f528e4090bfb530bc3928a6ec.setContent(html_bb4339fcb2874160afbc0cb9513f7577);
            

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                        "text/plain": "<folium.folium.Map at 0x2b46439e9da0>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map of Scarborough using latitude and longitude values\nmap_Scarborough = folium.Map(location=[latitude, longitude], zoom_start=11)\n\n# add markers to map\nfor lat, lng, borough, neighborhood in zip(Scarborough_data['Latitude'], Scarborough_data['Longitude'], Scarborough_data['Borough'], Scarborough_data['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_Scarborough)  \n    \nmap_Scarborough"
        }, 
        {
            "source": "<hr>\n\n<h2> Pandas DataFrame for the East Toronto Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 44, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 44, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>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  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode       Borough     Neighborhood   Latitude  Longitude\n0        M4M  East Toronto  Studio District  43.659526 -79.340923"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "East_Toronto_data = toronto_Geo_DF[toronto_Geo_DF['Borough'] == 'East Toronto'].reset_index(drop=True)\nEast_Toronto_data.head()"
        }, 
        {
            "source": "<hr>\n\n<h2> Folium Map Cluster for the East Toronto Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 45, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 45, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,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\" 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 0x2b46439ed630>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map of East Toronto using latitude and longitude values\nmap_East_Toronto = folium.Map(location=[latitude, longitude], zoom_start=13)\n\n# add markers to map\nfor lat, lng, borough, neighborhood in zip(East_Toronto_data['Latitude'], East_Toronto_data['Longitude'], East_Toronto_data['Borough'], East_Toronto_data['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_East_Toronto)  \n    \nmap_East_Toronto"
        }, 
        {
            "source": "<hr>\n\n<h2> Pandas DataFrame for the Etobicoke Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 46, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 46, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M9V</td>\n      <td>Etobicoke</td>\n      <td>Albion Gardens</td>\n      <td>43.739416</td>\n      <td>-79.588437</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode    Borough    Neighborhood   Latitude  Longitude\n0        M9V  Etobicoke  Albion Gardens  43.739416 -79.588437"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "Etobicoke_data = toronto_Geo_DF[toronto_Geo_DF['Borough'] == 'Etobicoke'].reset_index(drop=True)\nEtobicoke_data.head()"
        }, 
        {
            "source": "<hr>\n\n<h2> Folium Map Cluster for the Etobicoke Borough of Toronto </h2>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 47, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": false
            }, 
            "outputs": [
                {
                    "execution_count": 47, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe 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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 0x2b46439ed470>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map of East Toronto using latitude and longitude values\nmap_Etobicoke = folium.Map(location=[latitude, longitude], zoom_start=12)\n\n# add markers to map\nfor lat, lng, borough, neighborhood in zip(Etobicoke_data['Latitude'], Etobicoke_data['Longitude'], Etobicoke_data['Borough'], Etobicoke_data['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_Etobicoke)  \n    \nmap_Etobicoke"
        }
    ], 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.5", 
            "name": "python3", 
            "language": "python"
        }, 
        "language_info": {
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
            "version": "3.5.5", 
            "name": "python", 
            "file_extension": ".py", 
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            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }
        }
    }, 
    "nbformat": 4
}