Pandas makes it easy to scrape a table (<table> tag) on a web page. After obtaining it as a DataFrame, it is of course possible to do various processing and save it as an Excel file or csv file.

In this article you’ll learn how to extract a table from any webpage. Sometimes there are multiple tables on a webpage, so you can select the table you need.

Related course:Data Analysis with Python Pandas

Pandas web scraping

Install modules

Feb 23, 2021 For web scraping in Python, there are many tools available. We'll go through a few popular (and self-tested) options and when to use which. For scraping simple websites quickly, I've found the combination of Python Requests (to handle sessions and make HTTP requests) and Beautiful Soup (for parsing the response and navigating through it to. In this course, you will learn to navigate and parse html code, and build tools to crawl websites automatically. Although our scraping will be conducted using the versatile Python library scrapy, many of the techniques you learn in this course can be applied to other popular Python libraries as well, including BeautifulSoup and Selenium. Pandas Web Scraping. It is of course possible to do various processing and save it as an Excel file or csv file. Data Analysis with Python Pandas. Feb 09, 2018 Prerequisite: Downloading files in Python, Web Scraping with BeautifulSoup. We all know that Python is a very easy programming language but what makes it cool are the great number of open source library written for it. Requests is one of the most widely used library. ScrapingBee is a Web Scraping API that handles proxies and Headless browser for you, so you can focus on extracting the data you want, and nothing else.

It needs the modules lxml, html5lib, beautifulsoup4. You can install it with pip.

pands.read_html()

Python Web Scraping Sample

You can use the function read_html(url) to get webpage contents.

The table we’ll get is from Wikipedia. We get version history table from Wikipedia Python page:

This outputs:

Because there is one table on the page. If you change the url, the output will differ.
To output the table:

Python Web Scraping Tools

You can access columns like this:

Web Scraping Using Python Course

Pandas Web Scraping

Once you get it with DataFrame, it’s easy to post-process. If the table has many columns, you can select the columns you want. See code below:

Then you can write it to Excel or do other things:

Related course:Data Analysis with Python Pandas