We are a group of people passionate about data structures and algorithms. We eye for implementing all the data structures given here.

There are many pre-exisiting packages available in the open source world based on the above idea. However, they lack the implementation of complex data structures and this makes us different. If you have worked with C++ and Python then you know how hard it is to code bug free AVL trees :-).Well, after this project you will not have to worry about it. In fact, we will keep each data structure independent from each other for easy code reusability.

Follow the steps given below,

1. Fork, https://github.com/codezonediitj/pydatastructs/
2. Execute, git clone https://github.com/<your-github-username>/pydatastructs/
3. Change your working directory to ../pydatastructs.
4. Execute, git remote add origin_user https://github.com/<your-github-username>/pydatastructs/
5. Execute, git checkout -b <your-new-branch-for-working>.
6. Make changes to the code.
7. Add your name and email to the AUTHORS, if you wish to.
8. Execute, git add ..
9. Execute, git commit -m "your-commit-message".
10. Execute, git push origin_user <your-current-branch>.
11. Make a PR.

That's it, 10 easy steps for your first contribution. For future contributions just follow steps 5 to 10. Make sure that before starting work, always checkout to master and pull the recent changes using the remote origin and then start following steps 5 to 10.

See you soon with your first PR.

We recommend you to introduce yourself on our gitter channel. You can include the courses you have taken relevant to data strucutres and algorithms, some projects, prior experience, in your introduction. This will help us to allocate you issues of suitable difficulty.

Please follow the rules and guidelines given below,

1. Follow the numpydoc docstring guide.
2. If you are planning to contribute a new data structure then first raise an issue for discussing the API, rather than directly making a PR.
3. For the first-time contributors we recommend not to take a complex data strucutre, rather start with linear data structures or abstract data types. You can also pick issues labelled as good_first_issues.

Keep contributing!!