Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

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.

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.

Overview of puzzles

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

Setting up

To tackle the puzzles on your own computer, you'll need a Python 3 environment with the dependencies (namely pandas) installed.

One way to do this is as follows. I'm using a bash shell, the procedure with Mac OS should be essentially the same. Windows, I'm not sure about.

  1. Check you have Python 3 installed by printing the version of Python:
python -V
  1. Clone the puzzle repository using Git:
git clone
  1. Install the dependencies (caution: if you don't want to modify any Python modules in your active environment, consider using a virtual environment instead):
python -m pip install -r requirements.txt
  1. Launch a jupyter notebook server:
jupyter notebook --notebook-dir=100-pandas-puzzles

You should be able to see the notebooks and launch them in your web browser.


This repository has benefitted from numerous contributors, with those who have sent puzzles and fixes listed in CONTRIBUTORS.

Thanks to everyone who has raised an issue too.

Other links

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

There are may other excellent resources and books that are easily searchable and purchaseable.


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








No releases published


No packages published