100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)
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100 pandas puzzles

Puzzles notebook

Solutions notebook

Inspired by 100 Numpy exerises, here are 100* short puzzles for testing your knowledge of pandas' power.

Since pandas is a large library with many different specialist features and functions, these excercises focus mainly on the fundamentals of manipulating data (indexing, grouping, aggregating, cleaning), making use of the core DataFrame and Series objects. Many of the excerises here are straightforward in that the solutions require no more than a few lines of code (in pandas or NumPy - don't go using pure Python!). Choosing the right methods and following best practices is the underlying goal.

The exercises are loosely divided in sections. Each section has a difficulty rating; these ratings are subjective, of course, but should be a seen as a rough guide as to how elaborate the required solution needs to be.

Section Name Description Difficulty
Importing pandas Getting started and checking your pandas setup Easy
DataFrame basics A few of the fundamental routines for selecting, sorting, adding and aggregating data in DataFrames Easy
DataFrames: beyond the basics Slightly trickier: you may need to combine two or more methods to get the right answer Medium
DataFrames: harder problems These might require a bit of thinking outside the box... Hard
Series and DatetimeIndex Exercises for creating and manipulating Series with datetime data Easy/Medium
Cleaning Data Making a DataFrame easier to work with Easy/Medium
Using MultiIndexes Go beyond flat DataFrames with additional index levels Medium
Minesweeper Generate the numbers for safe squares in a Minesweeper grid Hard
Plotting Explore pandas' part of plotting functionality to see trends in data Medium

If you feel like rading up on pandas before starting, the official documentation useful and very extensive. Good places get a broader overview of pandas are:

Good luck solving the puzzles!

* the list of puzzles is not yet complete! Pull requests or suggestions for additional exercises, corrections and improvements are welcomed.