Scrape Tables From any website using Python

Scraping is a very essential skill for everyone to get data from any website. Scraping and parsing a table can be very tedious work if we use standard Beautiful soup parser to do so. Therefore, here we will be describing a library with the help of which any table can be scraped from any website easily. With this method you don’t even have to inspect element of a website, you only have to provide the URL of the website. That’s it and the work will be done within seconds.

Installation

You can use pip to install this library:

pip install html-table-parser-python3

Getting Started

Step 1: Import the necessary libraries required for the task

# Library for opening url and creating 
# requests
import urllib.request

# pretty-print python data structures
from pprint import pprint

# for parsing all the tables present 
# on the website
from html_table_parser.parser import HTMLTableParser

# for converting the parsed data in a
# pandas dataframe
import pandas as pd

Step 2 : Defining a function to get contents of the website

# Opens a website and read its
# binary contents (HTTP Response Body)
def url_get_contents(url):

    # Opens a website and read its
    # binary contents (HTTP Response Body)

    #making request to the website
    req = urllib.request.Request(url=url)
    f = urllib.request.urlopen(req)

    #reading contents of the website
    return f.read()

Now, our function is ready so we have to specify the url of the website from which we need to parse tables.

Note: Here we will be taking the example of moneycontrol.com website since it has many tables and will give you a better understanding. You can view the website here . 

Step 3 : Parsing tables

# defining the html contents of a URL.
xhtml = url_get_contents('Link').decode('utf-8')

# Defining the HTMLTableParser object
p = HTMLTableParser()

# feeding the html contents in the
# HTMLTableParser object
p.feed(xhtml)

# Now finally obtaining the data of
# the table required
pprint(p.tables[1])

Each row of the table is stored in an array. This can be converted into a pandas dataframe easily and can be used to perform any analysis. 

Complete Code:

Output: