Aug. 8, 2023, 1 p.m. | Chris Greening

DEV Community dev.to

In today's data-driven world, the ability to extract and synthesize information from various online sources is not just a powerful skill - it's often a necessity!


And often, that data comes in the form of HTML <table>'s scattered across the web. The challenge then becomes: How do we extract and transform this data into a form that's easily accessible in Python?


With the pandas.read_html function, we're offered a convenient solution to extract our data into the highly versatile pd.DataFrame …

challenge data data-driven datascience extract html information python table tables tutorial web webscraping world

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Principal Applied Scientist

@ Microsoft | Redmond, Washington, United States

Data Analyst / Action Officer

@ OASYS, INC. | OASYS, INC., Pratt Avenue Northwest, Huntsville, AL, United States