Analysis is only as trustworthy as the data underneath it, and most datasets arrive broken in predictable ways: wrong types, out-of-range values, duplicates, inconsistent categories, and gaps. This cheatsheet gathers the whole cleaning toolkit on one page, from type conversion and string fixing through range caps, duplicates, category validation, missing data, and fuzzy matching, each snippet annotated line by line and proved with an assert. Keep it open while you clean, or save the image and pin it nearby.
For the full background, read the guide to data cleaning in Python. To practise, work through the 10 code-along examples.
Download the cheatsheet
Hope this helps, Andrei.
[…] Data Cleaning in Python Cheatsheet […]