Hw-week3-Neighborhoods-Toronto-part2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import requests\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Page download successful\n"
     ]
    }
   ],
   "source": [
    "#downloading page\n",
    "url  = \"https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\"\n",
    "page = requests.get(url)\n",
    "if page.status_code == 200:\n",
    "    print('Page download successful')\n",
    "else:\n",
    "    print('Page download error. Error code: {}'.format(page.status_code))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Please note, I am using the pandas function \"read_html\" we can easily process the HTML string instead of beautifulsoup\n",
    "#We set header = 0 to use the first row as column names\n",
    "#Since we will discard the \"Not Assigned\" columns, we set them to NaN so we can later use the dropna method.\n",
    "df_html = pd.read_html(url)[0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Postcode_u      = []\n",
    "Borough_u       = []\n",
    "Neighbourhood_u = []\n",
    "\n",
    "for key, value in df_html.iteritems(): \n",
    "    #print('value', value)\n",
    "    #print ('key', key)\n",
    "    \n",
    "    for i in range(len(value)):\n",
    "        temp=value[i]\n",
    "        if 'assigned' in temp:\n",
    "            continue\n",
    "        #print (temp)\n",
    "        pc=temp[0:3]\n",
    "        #print (pc)\n",
    "        neiStartId=temp.find('(')\n",
    "        neiEndId=temp.find(')')\n",
    "        br=temp[3:neiStartId]\n",
    "        #print (br)\n",
    "        stNei=temp[neiStartId+1:neiEndId]\n",
    "        #print (stNei)\n",
    "        #lsNei=stNei.split('/')\n",
    "        #print (lsNei)\n",
    "\n",
    "        Postcode_u.append(pc)\n",
    "        Borough_u.append(br)\n",
    "        nei=stNei.replace(' ','')\n",
    "        nei=nei.replace('/',',')\n",
    "        Neighbourhood_u.append(nei)\n",
    "            #print (pc, br, nei)\n",
    "toronto_dict = {'Postcode':Postcode_u, 'Borough':Borough_u, 'Neighbourhood':Neighbourhood_u}\n",
    "df_toronto = pd.DataFrame.from_dict(toronto_dict)\n",
    "df_toronto.to_csv('toronto_part1.csv')\n",
    "df_toronto.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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>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>Malvern,Rouge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>RougeHill,PortUnion,HighlandCreek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood,Morningside,WestHill</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\n",
       "0      M1B  Scarborough                      Malvern,Rouge\n",
       "1      M1C  Scarborough  RougeHill,PortUnion,HighlandCreek\n",
       "2      M1E  Scarborough     Guildwood,Morningside,WestHill\n",
       "3      M1G  Scarborough                             Woburn\n",
       "4      M1H  Scarborough                          Cedarbrae"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_toronto.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>Neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>LawrenceManor,LawrenceHeights</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Postcode     Borough                  Neighbourhood\n",
       "71      M6A  North York  LawrenceManor,LawrenceHeights"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_toronto.loc[df_toronto['Postcode']=='M6A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</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\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"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Read CSV file from link and load into dataframe\n",
    "url_csv = 'http://cocl.us/Geospatial_data'\n",
    "df_coordinates = pd.read_csv(url_csv)\n",
    "df_coordinates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure both dataframes have the same \n",
    "df_coordinates.rename(columns={'Postal Code': 'PostalCode'}, inplace=True)\n",
    "df_toronto.rename(columns={'Postcode': 'PostalCode'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>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>RougeHill,PortUnion,HighlandCreek</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,WestHill</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  \\\n",
       "0        M1B  Scarborough                      Malvern,Rouge  43.806686   \n",
       "1        M1C  Scarborough  RougeHill,PortUnion,HighlandCreek  43.784535   \n",
       "2        M1E  Scarborough     Guildwood,Morningside,WestHill  43.763573   \n",
       "3        M1G  Scarborough                             Woburn  43.770992   \n",
       "4        M1H  Scarborough                          Cedarbrae  43.773136   \n",
       "\n",
       "   Longitude  \n",
       "0 -79.194353  \n",
       "1 -79.160497  \n",
       "2 -79.188711  \n",
       "3 -79.216917  \n",
       "4 -79.239476  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge both datasets\n",
    "df_neighborhoods_coordinates = pd.merge(df_toronto, df_coordinates, on='PostalCode')\n",
    "df_neighborhoods_coordinates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>HillcrestVillage</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>CentralBayStreet</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\n",
       "17        M2H        North York  HillcrestVillage  43.803762 -79.363452\n",
       "57        M5G  Downtown Toronto  CentralBayStreet  43.657952 -79.387383"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coordinates for a couple of neighborhoods\n",
    "df_neighborhoods_coordinates[(df_neighborhoods_coordinates['PostalCode']=='M5G') |\n",
    "                             (df_neighborhoods_coordinates['PostalCode']=='M2H') ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Export to .CSV\n",
    "df_neighborhoods_coordinates.to_csv('Toronto_Postcodes_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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