week3_assigment_2_carol.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assigment of week 3.\n",
    "## Segmenting and Clustering Neighborhoods in Toronto\n",
    "### Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that I built a dataframe of the postal code of each neighborhood along with the borough name and neighborhood name, in order to utilize the Foursquare location data, \n",
    "I need to get the latitude and the longitude coordinates of each neighborhood.\n",
    "I got a csv file that has the geographical coordinates of each postal code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We read the file and after that concatenate with muy first dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I get my first dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>M9N</td>\n",
       "      <td>York</td>\n",
       "      <td>Weston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M9P</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Westmount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>M9R</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Kingsview Village, St. Phillips, Martin Grove ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>South Steeles, Silverstone, Humbergate, Jamest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>M9W</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Northwest, West Humber - Clairville</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Postal Code      Borough  \\\n",
       "0           M1B  Scarborough   \n",
       "1           M1C  Scarborough   \n",
       "2           M1E  Scarborough   \n",
       "3           M1G  Scarborough   \n",
       "4           M1H  Scarborough   \n",
       "..          ...          ...   \n",
       "98          M9N         York   \n",
       "99          M9P    Etobicoke   \n",
       "100         M9R    Etobicoke   \n",
       "101         M9V    Etobicoke   \n",
       "102         M9W    Etobicoke   \n",
       "\n",
       "                                          Neighborhood  \n",
       "0                                       Malvern, Rouge  \n",
       "1               Rouge Hill, Port Union, Highland Creek  \n",
       "2                    Guildwood, Morningside, West Hill  \n",
       "3                                               Woburn  \n",
       "4                                            Cedarbrae  \n",
       "..                                                 ...  \n",
       "98                                              Weston  \n",
       "99                                           Westmount  \n",
       "100  Kingsview Village, St. Phillips, Martin Grove ...  \n",
       "101  South Steeles, Silverstone, Humbergate, Jamest...  \n",
       "102                Northwest, West Humber - Clairville  \n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('torontozipcode.csv', usecols=['Postal Code','Borough','Neighborhood'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I get the latitude and longitude values from Geospatial_Coordinates.csv that I found in this link http://cocl.us/Geospatial_data :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>M9N</td>\n",
       "      <td>43.706876</td>\n",
       "      <td>-79.518188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M9P</td>\n",
       "      <td>43.696319</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>M9R</td>\n",
       "      <td>43.688905</td>\n",
       "      <td>-79.554724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>M9V</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>M9W</td>\n",
       "      <td>43.706748</td>\n",
       "      <td>-79.594054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Postal Code   Latitude  Longitude\n",
       "0           M1B  43.806686 -79.194353\n",
       "1           M1C  43.784535 -79.160497\n",
       "2           M1E  43.763573 -79.188711\n",
       "3           M1G  43.770992 -79.216917\n",
       "4           M1H  43.773136 -79.239476\n",
       "..          ...        ...        ...\n",
       "98          M9N  43.706876 -79.518188\n",
       "99          M9P  43.696319 -79.532242\n",
       "100         M9R  43.688905 -79.554724\n",
       "101         M9V  43.739416 -79.588437\n",
       "102         M9W  43.706748 -79.594054\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.read_csv('Geospatial_Coordinates.csv')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "After to check that both dataframe have the same number of rows and are order by Postal Code, I concatenated both dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Malvern, Rouge</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>Rouge Hill, Port Union, Highland Creek</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",
       "    <tr>\n",
       "      <th>5</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>6</th>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Kennedy Park, Ionview, East Birchmount Park</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Golden Mile, Clairlea, Oakridge</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffside, Cliffcrest, Scarborough Village West</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</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>43.692657</td>\n",
       "      <td>-79.264848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M1P</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Dorset Park, Wexford Heights, Scarborough Town...</td>\n",
       "      <td>43.757410</td>\n",
       "      <td>-79.273304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M1R</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Wexford, Maryvale</td>\n",
       "      <td>43.750072</td>\n",
       "      <td>-79.295849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Postal Code      Borough  \\\n",
       "0          M1B  Scarborough   \n",
       "1          M1C  Scarborough   \n",
       "2          M1E  Scarborough   \n",
       "3          M1G  Scarborough   \n",
       "4          M1H  Scarborough   \n",
       "5          M1J  Scarborough   \n",
       "6          M1K  Scarborough   \n",
       "7          M1L  Scarborough   \n",
       "8          M1M  Scarborough   \n",
       "9          M1N  Scarborough   \n",
       "10         M1P  Scarborough   \n",
       "11         M1R  Scarborough   \n",
       "\n",
       "                                         Neighborhood   Latitude  Longitude  \n",
       "0                                      Malvern, Rouge  43.806686 -79.194353  \n",
       "1              Rouge Hill, Port Union, Highland Creek  43.784535 -79.160497  \n",
       "2                   Guildwood, Morningside, West Hill  43.763573 -79.188711  \n",
       "3                                              Woburn  43.770992 -79.216917  \n",
       "4                                           Cedarbrae  43.773136 -79.239476  \n",
       "5                                 Scarborough Village  43.744734 -79.239476  \n",
       "6         Kennedy Park, Ionview, East Birchmount Park  43.727929 -79.262029  \n",
       "7                     Golden Mile, Clairlea, Oakridge  43.711112 -79.284577  \n",
       "8     Cliffside, Cliffcrest, Scarborough Village West  43.716316 -79.239476  \n",
       "9                         Birch Cliff, Cliffside West  43.692657 -79.264848  \n",
       "10  Dorset Park, Wexford Heights, Scarborough Town...  43.757410 -79.273304  \n",
       "11                                  Wexford, Maryvale  43.750072 -79.295849  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1[['Latitude','Longitude']]\n",
    "result = pd.concat([df, df2], axis=1)\n",
    "result.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, I saved changes for next part of assigment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('toronto_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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