Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset)

I'm prepping a dataset for an upcoming tutorial and I figured walking through the process of cleaning it would work well for a livestream! We use various Python Pandas functions to accomplish our data cleaning goals.

We'll be working off of this repo:
https://github.com/KeithGalli/Olympic...

Some topics that we cover:
- How you can use web scraping to collect data like this (Python beautifulsoup).
- Splitting strings into separate columns
- Using regular expressions (regexes) to extract specific details from columns
- Converting columns to datetime & numeric types
- Grabbing only a subset of our columns

Sorry that this was a bit last minute scheduling-wise, will try to give more advance notice in the future!

Video timeline!
0:00 - Livestream Overview
4:00 - About the Olympics dataset (source website and how it was scraped)
9:50 - Cleaning the dataset (getting started with code & data)
19:26 - What aspects of our data should be cleaned?
29:08 - Get rid of bullet points in Used name column
34:08 - How to split Measurements into two separate height/weight numeric columns.
1:05:00 - Parse out dates from Born & Died columns
1:25:43 - Parse out city, region, and country from Born column (working with regular expressions)
1:41:15 - Get rid of the extra columns
1:46:08 - Next steps (how would we clean the results.csv)
1:49:41 - Questions & Answers


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