ADS_Week3_Task2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Machine Learning Assignment Week 3 Task 2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### importing required libraries and files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge,Malvern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek,Rouge Hill,Port Union</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood,Morningside,West Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostCode      Borough                          Neighborhood\n",
       "0      M1B  Scarborough                         Rouge,Malvern\n",
       "1      M1C  Scarborough  Highland Creek,Rouge Hill,Port Union\n",
       "2      M1E  Scarborough       Guildwood,Morningside,West Hill\n",
       "3      M1G  Scarborough                                Woburn\n",
       "4      M1H  Scarborough                             Cedarbrae"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('task1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Installing the geocoder library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting geocoder\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4f/6b/13166c909ad2f2d76b929a4227c952630ebaf0d729f6317eb09cbceccbab/geocoder-1.38.1-py2.py3-none-any.whl (98kB)\n",
      "\u001b[K     |████████████████████████████████| 102kB 3.2MB/s ta 0:00:011\n",
      "\u001b[?25hCollecting future (from geocoder)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3f/bf/57733d44afd0cf67580658507bd11d3ec629612d5e0e432beb4b8f6fbb04/future-0.18.1.tar.gz (828kB)\n",
      "\u001b[K     |████████████████████████████████| 829kB 12.2MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: six in /srv/conda/envs/notebook/lib/python3.7/site-packages (from geocoder) (1.12.0)\n",
      "Collecting ratelim (from geocoder)\n",
      "  Downloading https://files.pythonhosted.org/packages/f2/98/7e6d147fd16a10a5f821db6e25f192265d6ecca3d82957a4fdd592cad49c/ratelim-0.1.6-py2.py3-none-any.whl\n",
      "Requirement already satisfied: click in /srv/conda/envs/notebook/lib/python3.7/site-packages (from geocoder) (7.0)\n",
      "Requirement already satisfied: requests in /srv/conda/envs/notebook/lib/python3.7/site-packages (from geocoder) (2.22.0)\n",
      "Requirement already satisfied: decorator in /srv/conda/envs/notebook/lib/python3.7/site-packages (from ratelim->geocoder) (4.4.0)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from requests->geocoder) (1.25.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from requests->geocoder) (2019.6.16)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from requests->geocoder) (2.8)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from requests->geocoder) (3.0.4)\n",
      "Building wheels for collected packages: future\n",
      "  Building wheel for future (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jovyan/.cache/pip/wheels/74/bc/50/ae030267a192919b289f84661cfeb5573cf383841c543e8696\n",
      "Successfully built future\n",
      "Installing collected packages: future, ratelim, geocoder\n",
      "Successfully installed future-0.18.1 geocoder-1.38.1 ratelim-0.1.6\n"
     ]
    }
   ],
   "source": [
    "!pip install geocoder\n",
    "import geocoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[43.70949500000006, -79.36398897099997]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_latlng(postal_code):\n",
    "    # initialize your variable to None\n",
    "    lat_lng_coords = None\n",
    "    # loop until you get the coordinates\n",
    "    while(lat_lng_coords is None):\n",
    "        g = geocoder.arcgis('{}, Toronto, Ontario'.format(postal_code))\n",
    "        lat_lng_coords = g.latlng\n",
    "    return lat_lng_coords\n",
    "    \n",
    "get_latlng('M4G')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_codes = df['PostCode']    \n",
    "coords = [ get_latlng(post_code) for post_code in post_codes.tolist() ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_coords = pd.DataFrame(coords, columns=['Latitude', 'Longitude'])\n",
    "df['Latitude'] = df_coords['Latitude']\n",
    "df['Longitude'] = df_coords['Longitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge,Malvern</td>\n",
       "      <td>43.811525</td>\n",
       "      <td>-79.195517</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>43.785665</td>\n",
       "      <td>-79.158725</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>43.765815</td>\n",
       "      <td>-79.175193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.768369</td>\n",
       "      <td>-79.217590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.769688</td>\n",
       "      <td>-79.239440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>43.743125</td>\n",
       "      <td>-79.231750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>East Birchmount Park,Ionview,Kennedy Park</td>\n",
       "      <td>43.726276</td>\n",
       "      <td>-79.263625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Clairlea,Golden Mile,Oakridge</td>\n",
       "      <td>43.713054</td>\n",
       "      <td>-79.285055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffcrest,Cliffside,Scarborough Village West</td>\n",
       "      <td>43.724235</td>\n",
       "      <td>-79.227925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M1N</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Birch Cliff,Cliffside West</td>\n",
       "      <td>43.696770</td>\n",
       "      <td>-79.259967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M1P</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Dorset Park,Scarborough Town Centre,Wexford He...</td>\n",
       "      <td>43.759975</td>\n",
       "      <td>-79.268974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M1R</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Maryvale,Wexford</td>\n",
       "      <td>43.750710</td>\n",
       "      <td>-79.300560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostCode      Borough                                       Neighborhood  \\\n",
       "0       M1B  Scarborough                                      Rouge,Malvern   \n",
       "1       M1C  Scarborough               Highland Creek,Rouge Hill,Port Union   \n",
       "2       M1E  Scarborough                    Guildwood,Morningside,West Hill   \n",
       "3       M1G  Scarborough                                             Woburn   \n",
       "4       M1H  Scarborough                                          Cedarbrae   \n",
       "5       M1J  Scarborough                                Scarborough Village   \n",
       "6       M1K  Scarborough          East Birchmount Park,Ionview,Kennedy Park   \n",
       "7       M1L  Scarborough                      Clairlea,Golden Mile,Oakridge   \n",
       "8       M1M  Scarborough      Cliffcrest,Cliffside,Scarborough Village West   \n",
       "9       M1N  Scarborough                         Birch Cliff,Cliffside West   \n",
       "10      M1P  Scarborough  Dorset Park,Scarborough Town Centre,Wexford He...   \n",
       "11      M1R  Scarborough                                   Maryvale,Wexford   \n",
       "\n",
       "     Latitude  Longitude  \n",
       "0   43.811525 -79.195517  \n",
       "1   43.785665 -79.158725  \n",
       "2   43.765815 -79.175193  \n",
       "3   43.768369 -79.217590  \n",
       "4   43.769688 -79.239440  \n",
       "5   43.743125 -79.231750  \n",
       "6   43.726276 -79.263625  \n",
       "7   43.713054 -79.285055  \n",
       "8   43.724235 -79.227925  \n",
       "9   43.696770 -79.259967  \n",
       "10  43.759975 -79.268974  \n",
       "11  43.750710 -79.300560  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### This suffices the assignment requirement of producing a dataframe containing 103 rows and 5 columns including logitude and lattitude coordinates. The first 12 rows have been displayed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Saving for future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('task2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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