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.
You can use pip to install this library:
pip install html-table-parser-python3
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 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)
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.
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