Neighborhood Toronto - part 1.ipynb

{
    "cells": [
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "<div class=\"alert alert-success alertsucess\" style=\"margin-top: 5px\">\n<h1> Part 1 </h1>\n</div>"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Import Libraries"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "import numpy as np # library to handle data in a vectorized manner\n\nimport pandas as pd # library for data analsysis\npd.set_option('display.max_columns', None)\npd.set_option('display.max_rows', None)\n\nimport json # library to handle JSON files\n\n#!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\nfrom geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n\nimport requests # library to handle requests\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 # uncomment this line if you haven't completed the Foursquare API lab\nimport folium # map rendering library\n\nprint('Libraries imported.')",
            "execution_count": 1,
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Libraries imported.\n"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Import Data"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "url='https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\ndf=pd.read_html(url, header=0)[0]\ndf.head()",
            "execution_count": 2,
            "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1A</td>\n      <td>Not assigned</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M2A</td>\n      <td>Not assigned</td>\n      <td>NaN</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>Regent Park, Harbourfront</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "  Postal Code           Borough               Neighborhood\n0         M1A      Not assigned                        NaN\n1         M2A      Not assigned                        NaN\n2         M3A        North York                  Parkwoods\n3         M4A        North York           Victoria Village\n4         M5A  Downtown Toronto  Regent Park, Harbourfront"
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Drop \"Not assigned\" Boroughs"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df.replace(\"Not assigned\", np.nan, inplace = True)\ndf.dropna(subset=['Borough'], axis=0, inplace=True)\ndf.reset_index(drop=True, inplace=True) \ndf.head()",
            "execution_count": 3,
            "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    </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>Regent Park, Harbourfront</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M6A</td>\n      <td>North York</td>\n      <td>Lawrence Manor, Lawrence Heights</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M7A</td>\n      <td>Downtown Toronto</td>\n      <td>Queen's Park, Ontario Provincial Government</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "  Postal Code           Borough                                 Neighborhood\n0         M3A        North York                                    Parkwoods\n1         M4A        North York                             Victoria Village\n2         M5A  Downtown Toronto                    Regent Park, Harbourfront\n3         M6A        North York             Lawrence Manor, Lawrence Heights\n4         M7A  Downtown Toronto  Queen's Park, Ontario Provincial Government"
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Group neighborhoods in the same borough"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df = df.groupby([\"Postal Code\", \"Borough\"], as_index=False).agg(lambda x: \", \".join(x))\ndf.head()",
            "execution_count": 4,
            "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1B</td>\n      <td>Scarborough</td>\n      <td>Malvern, Rouge</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1C</td>\n      <td>Scarborough</td>\n      <td>Rouge Hill, Port Union, Highland Creek</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": "  Postal Code      Borough                            Neighborhood\n0         M1B  Scarborough                          Malvern, Rouge\n1         M1C  Scarborough  Rouge Hill, Port Union, Highland Creek\n2         M1E  Scarborough       Guildwood, Morningside, West Hill\n3         M1G  Scarborough                                  Woburn\n4         M1H  Scarborough                               Cedarbrae"
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### For Neighborhood that is \"Not assigned\", make it the same as Borough "
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "for index, row in df.iterrows():\n    if row[\"Neighborhood\"] == \"Not assigned\":\n        row[\"Neighborhood\"] = row[\"Borough\"]    \ndf.head()",
            "execution_count": 5,
            "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1B</td>\n      <td>Scarborough</td>\n      <td>Malvern, Rouge</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1C</td>\n      <td>Scarborough</td>\n      <td>Rouge Hill, Port Union, Highland Creek</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": "  Postal Code      Borough                            Neighborhood\n0         M1B  Scarborough                          Malvern, Rouge\n1         M1C  Scarborough  Rouge Hill, Port Union, Highland Creek\n2         M1E  Scarborough       Guildwood, Morningside, West Hill\n3         M1G  Scarborough                                  Woburn\n4         M1H  Scarborough                               Cedarbrae"
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 5px\">\n<h1> Explanation notes </h1>\nPlease note that the instructions given to us are outdated, as after droping \"Not assigned\" Boroughs, the rest is automatically done. There was no need for the code to make changes to neighborhood as those changes are automatically there in wiki website itself. However, to make sure that I get full points I included the pointless codes nonetheless. \n</div>"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Print the number of rows of the cleaned dataframe"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df.shape",
            "execution_count": 6,
            "outputs": [
                {
                    "data": {
                        "text/plain": "(103, 3)"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "",
            "execution_count": null,
            "outputs": []
        }
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