week-3-assignment-part-2.ipynb

{
  "cells": [
    {
      "metadata": {
        "collapsed": true
      },
      "cell_type": "markdown",
      "source": "This is a continued notebook from part 1 which can be found here: https://github.com/zinkohlaing/Coursera_Capstone/blob/master/week-3-assignment-part-1.ipynb"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nprint('Libraries Imported!')",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Libraries Imported!\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Since GeoEncoder library is not working for me, I will use the csv file downloaded"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Read CSV file from link and load into dataframe\nurl_csv = 'http://cocl.us/Geospatial_data'\ndf_coordinates = pd.read_csv(url_csv)\ndf_coordinates.head()",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#previously I have exported data from part 1 into csv file\n\ndf_neighborhoods = pd.read_csv('Toronto_Postcodes.csv',index_col=[0])\ndf_neighborhoods.head()",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</th>\n      <th>Borough</th>\n      <th>Neighbourhood</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>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": "  Postcode      Borough                           Neighbourhood\n0      M1B  Scarborough                          Rouge, Malvern\n1      M1C  Scarborough  Highland Creek, Rouge Hill, Port Union\n2      M1E  Scarborough       Guildwood, Morningside, West Hill\n3      M1G  Scarborough                                  Woburn\n4      M1H  Scarborough                               Cedarbrae"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Make sure both dataframes have the same \ndf_coordinates.rename(columns={'Postal Code': 'PostalCode'}, inplace=True)\ndf_neighborhoods.rename(columns={'Postcode': 'PostalCode'}, inplace=True)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Merge both datasets\ndf_neighborhoods_coordinates = pd.merge(df_neighborhoods, df_coordinates, on='PostalCode')\ndf_neighborhoods_coordinates.head()",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "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>Neighbourhood</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": "  PostalCode      Borough                           Neighbourhood   Latitude  \\\n0        M1B  Scarborough                          Rouge, Malvern  43.806686   \n1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union  43.784535   \n2        M1E  Scarborough       Guildwood, Morningside, West Hill  43.763573   \n3        M1G  Scarborough                                  Woburn  43.770992   \n4        M1H  Scarborough                               Cedarbrae  43.773136   \n\n   Longitude  \n0 -79.194353  \n1 -79.160497  \n2 -79.188711  \n3 -79.216917  \n4 -79.239476  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Check coordinates for a couple of neighborhoods\ndf_neighborhoods_coordinates[(df_neighborhoods_coordinates['PostalCode']=='M5G') |\n                             (df_neighborhoods_coordinates['PostalCode']=='M2H') ]\n",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "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>Neighbourhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>17</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>57</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  </tbody>\n</table>\n</div>",
            "text/plain": "   PostalCode           Borough       Neighbourhood   Latitude  Longitude\n17        M2H        North York   Hillcrest Village  43.803762 -79.363452\n57        M5G  Downtown Toronto  Central Bay Street  43.657952 -79.387383"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Export to .CSV\ndf_neighborhoods_coordinates.to_csv('Toronto_Postcodes_2.csv')",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
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    "kernelspec": {
      "name": "python36",
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      "language": "python"
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      "nbconvert_exporter": "python",
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