A dashboard powered by Django, showing a notepad, scraping news articles and finance data with dash.py django django-rest-framework pandas dash Python MIT 43 49 4 3 Updated Dec 3, 2020. Web Scraping courses offered through Coursera equip learners with knowledge in treating the internet as a source of data; working with HTML, XML, and JSON data formats in Python; using Python to access web data; getting and cleaning data; and more.
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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
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:
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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:
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:
Basic Web Scraping In Python
Then you can write it to Excel or do other things:
Web Scraping Python Example
Related course:Data Analysis with Python Pandas