|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|100 Pandas Puzzles||1,977||2 months ago||11||mit||Jupyter Notebook|
|100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)|
|Datasciencegym||6||5 years ago||mit||Jupyter Notebook|
|A series of little puzzles and training exercises aimed at improving python knowledge for data science|
|Chessboard||4||4 years ago||9||gpl-2.0||Python|
|:game_die: CLI to solve combinatoric chess puzzles.|
|Strategic Test Suite||2||3 months ago||mit||Python|
|Strategic Test Suite|
|100 Pandas Puzzles Cn||1||3 years ago||mit||Jupyter Notebook|
|Pandas 循序渐进一百题（60% 已完成）|
|100_pandas_exercises||1||5 years ago||Jupyter Notebook|
|100 mini-exercises to develop fluency in Pandas|
|Pandasprojects||1||5 years ago||gpl-3.0||Jupyter Notebook|
|Solving 100 pandas puzzles to improve proficiency in pandas|
|Pandas100||1||4 years ago||mit||Jupyter Notebook|
|Pandas_puzzles||1||3 years ago||mit||Jupyter Notebook|
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.
|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|
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.
git clone https://github.com/ajcr/100-pandas-puzzles.git
python -m pip install -r requirements.txt
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.
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.