Web scraping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scrapping a Wikipedia page\n",
    "\n",
    "### <a href=https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M>View page here</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "page = urllib.request.urlopen('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "http.client.HTTPResponse"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(page)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import the BeautifulSoup library so we can parse HTML and XML documents**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(page, \"lxml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(soup.prettify())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'List of postal codes of Canada: M - Wikipedia'"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.title.string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To see all the tables present in the page**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_tables=soup.find_all(\"table\")\n",
    "#all_tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can see that there is a class named wikitable sortable from where the table begins**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "right_table=soup.find('table', class_='wikitable sortable')\n",
    "#right_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "A=[]\n",
    "B=[]\n",
    "C=[]\n",
    "\n",
    "\n",
    "for row in right_table.findAll('tr'):\n",
    "    cells=row.findAll('td')\n",
    "    if len(cells)==3:\n",
    "        b = cells[1].find(text=True)\n",
    "        if b!='Not assigned\\n': #To ignore rows which have Borough as Not assigned\n",
    "            A.append(cells[0].find(text=True))\n",
    "            B.append(cells[1].find(text=True))\n",
    "            C.append(cells[2].find(text=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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\n",
       "0        M3A\n",
       "        North York\n",
       "                                    Parkwoods\n",
       "\n",
       "1        M4A\n",
       "        North York\n",
       "                             Victoria Village\n",
       "\n",
       "2        M5A\n",
       "  Downtown Toronto\n",
       "                    Regent Park, Harbourfront\n",
       "\n",
       "3        M6A\n",
       "        North York\n",
       "             Lawrence Manor, Lawrence Heights\n",
       "\n",
       "4        M7A\n",
       "  Downtown Toronto\n",
       "  Queen's Park, Ontario Provincial Government"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df=pd.DataFrame(A,columns=['Postal Code'])\n",
    "\n",
    "df['Borough']=B\n",
    "df['Neighborhood']=C\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['North York\\n', 'Downtown Toronto\\n', 'Etobicoke\\n',\n",
       "       'Scarborough\\n', 'East York\\n', 'York\\n', 'East Toronto\\n',\n",
       "       'West Toronto\\n', 'Central Toronto\\n', 'Mississauga\\n'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Borough'].unique()"
   ]
  }
 ],
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