We are a group of people passionate about data structures and algorithms. We eye for implementing all the data structures given here. We aim for distributing the package among enthusiastic programmers so that they don't have to implement complex data structures in their day to day tasks.
There are many pre-exisiting packages available in the open source world for the said task. However, we plan to implement those data structures which are used by both sports programmers as well as the developers. Currently, to the best of our knowledge, no such dedicated attempt has been made. In fact, we will keep each data strucutres independent from other for easy code reusability.
Follow the steps given below,
- Fork, https://github.com/codezonediitj/pydatastructs/
- Execute,
git clone https://github.com/<your-github-username>/pydatastructs/ - Change your working directory to
../pydatastructs. - Execute,
git remote add origin_user https://github.com/<your-github-username>/pydatastructs/ - Execute,
git checkout -b <your-new-branch-for-working>. - Make changes to the code.
- Add your name and email to the AUTHORS.
- Execute,
git add .. - Execute,
git commit -m "your-commit-message". - Execute,
git push origin_user <your-current-branch>. - Make a PR.
That's it, 10 easy steps for your first contribution. For future contributionsm 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,
- Follow the numpydoc docstring guide.
- 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.
- For the first-time contributors we recommend not to take complex data strucutre, rather start with
linear data structuresorabstract data types. You can also with issues labelled asgood_first_issues.
Keep contributing!!