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.
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- Nov 06, 2020 In this article, you’ll see how to perform a quick, efficient scraping of these elements with two main different approaches: using only the Pandas library and using the traditional scraping library BeautifulSoup. As an example, I scraped the Premier L e ague classification table. This is good because it’s a common table that can be found on.
In this article, you’ll see how to perform a quick, efficient scraping of these elements with two main different approaches: using only the Pandas library and using the traditional scraping library BeautifulSoup. As an example, I scraped the Premier L e ague classification table. This is good because it’s a common table that can be found on. 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.
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
It needs the modules lxml, html5lib, beautifulsoup4. You can install it with pip.
pands.read_html()
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:
You can access columns like this:
Web Scraping With Pandas Wikipedia
Pandas Web Scraping
Web Scraping With Pandas Answers
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:
Web Scraping Through Pandas
Related course:Data Analysis with Python Pandas