Contribute To Open Source Projects Python

Python has the tools and libraries to help you with many hard problems, from working with databases to exploring your own genomic data. Since Python and its ecosystem is extensive and vibrant, we wanted to make it easy for anyone to find a project they love and can contribute to, as well as guide contributors through the process of working on that project.

Looking for a way to contribute to open source projects? Learn how to contribute to an open source project hosted on GitHub. Starting with some background information about GitHub, the instructor introduces the fundamental concepts of using version control. With these basics under your belt, you can start collaborating with others. From here, go on to explore the fundamentals of using Git for open source contributions. After taking this course, you’ll be able to use Git to collaborate with other developers on projects hosted on GitHub.

Step by Step Guide on How to Contribute to Open Source

When we say contributing to open-source, it does not necesarilly mean that you need to know how to code. There are different ways in which you can contribute even if you are a non-coder – but having some coding skills will help you (and the projects) out a lot.

Some common contributions can be through:

  • Adding a description to a project’s documentation to elaborate on a certain point, mostly referred to as a README file (check this guide on how to write a Good README file).
  • Giving guidance on a specific project and how to use it.
  • Adding sample output to show how the code works.
  • Writing in-depth tutorials for the project.
  • Adding translation for a project – A good place to start with this might be with the freeCodeCamp’s translation program.
  • Answering questions about a project (like on Stack Overflow or Reddit)
  • You can offer to mentor another contributor.
  • You can fix typos and arrange the project’s work folder correctly.

All these ways, and many more, count towards contributions. Now what exactly should you know before you start contributing to an OS project?

Get to know GitHub

GitHub is the most popular platform for open source collaboration, so you’ll probably use it when exploring the world of OSS. First, you need to create a GitHub account and read the guide that helps you get started. On GitHub, you can contribute to projects by submitting issues and contributing code. Submitting issues means sending messages about errors in applications and suggesting ways to fix them. Contributing code involves sending pull requests with your corrections and improvements.


Computer vision is among the most in-demand applications of artificial intelligence. Computer vision focuses on AI models that can analyze images and videos for specific implementations such as object identification, face recognition, etc. 

If you want to work on computer vision projects, you must get familiar with OpenCV. OpenCV is a Python library to solve computer vision problems. Gary Brodsky had started OpenCV in 1999, and since then, it has become one of the most popular Python projects with source code Github.

OpenCV allows you to perform image analysis and forms the basis for many sophisticated computer vision algorithms. To use OpenCV, you should be familiar with Numpy, a popular library to perform numerical operations through Python code. You’d have to integrate OpenCV with other libraries to work on computer vision projects, that’s why we’ve kept it at the top of our list.

Learn the basics

When working with GitHub, you should know how to use Git – one of the most popular version control tools (also known as revision control tools). Because developers constantly make changes to their code, they need a system that can manage those changes in a central repository. In this way, everyone involved in the development process can download a given piece of software, make changes, and submit updates.


Keras is probably the most popular tool for deep learning in Python. It’s a library that simplifies deep learning for Python developers due to which it has become widely popular among AI professionals. Keras offers you consistent and straightforward APIs that minimize the total actions you have to perform.

It automates many necessary use cases, so you only have to focus on your neural network’s advanced sections. Keras is also famous for its accurate and quick error messages that alert you when you make any mistakes. 

If you want to become a deep learning professional, you should be familiar with Keras and its applications. Keras was among the top-5 winning teams on Kaggle and ranked no. 1 among most used deep learning frameworks.

Because Keras uses TensorFlow, it is accessible from anywhere and provides a wide range of additional benefits. NASA, LHC, CERN, and many other top organizations use Keras for research. So it’s certainly worth looking into.

Join the community

You can easily join an open source project by subscribing to the mailing list for that project. You can find mailing lists on official websites or on GitHub pages. After being accepted to the list, you can communicate with team members and get support if necessary. Thanks to the vibrant communities present in nearly every OSS project, you are likely to get quick replies to your questions.


Flask is a mini framework because it doesn’t have any specific libraries or tools. Additionally, it doesn’t have any form of validation or database abstraction layer so that the developer can choose them according to their requirements. Flask started as a wrapper around Jinja and Wekrzeug but became one of the most popular Python projects with source code Github.

Apart from being famous, Flask also has one of the most active communities of developers. So if you face any issues, you can ask in the community and quickly get helpful responses. Flask’s community has added numerous extensions to it, which enhances its functionality considerably. Due to these reasons, Flask is an essential framework for any Python web developer. 

All skills are welcomed

Even non-programmers can contribute to open source projects! Documentation is needed for all projects, and sometimes this is poorly written and maintained. Thus, you can help by writing, updating or even translating documentation. Also, your design skills might come in handy: every application needs an interface, after all. Finally, you can contribute by managing a community by replying to questions and guiding newcomers.


Python has many applications in artificial intelligence, and that’s why it has many libraries and frameworks for AI-specific needs. If you plan to become an AI professional, then this project is undoubtedly the best for you. spaCy is an open-source library for high-level NLP (Natural Language Processing) in Python. NLP is the fundamental technology behind many advanced AI applications, such as text analysis, sentiment analysis, and others.

The primary use of spaCy is to build production-grade applications that can process large quantities of text. You can use spaCy to create natural language understanding solutions, information extraction systems, or pre-processing an enhanced deep learning model. One of many reasons behind its high popularity is its long list of features. 

You can assign word types to tokens (like segmenting figures of speech or voice), lemmatize the text (converting words into their base forms), find and label individual sentences, and perform many other tasks with ease. spaCy is a must-have Python project Github. You should be familiar with it if you want to become a proficient AI-professional or if you want to work on NLP projects.

Contribute to existing open source projects

You can find many projects you are free to participate in on GitHub – a developer-oriented platform with a simple but essential set of functionality. GitHub attracts developers with public APIs, a sleek and frequently updated UI, gists (Git repositories) that allow you to share pieces of code or even whole applications, and much more. You can contribute to free software in many ways. Developers can fork projects, make changes to code, and send pull requests. And quality assurance is always appreciated. Sometimes developers are too busy or too lazy to check the quality of their code. So go ahead and report a bug or try to fix it – your help is appreciated.

You can reach the hottest GitHub projects by following the “Trending” link. And in order to make your search more relevant, use advanced search: select the language you would like to code in and choose “best match” criteria. Best match ranks projects according to relevance, taking into account the number of forks (which represents how actively the project is updated) and stars (“likes”, in the language of Facebook). Most projects have known issues (however, some don’t) with labels like “bug”, “discussion”, “security”, or “refactor”, or other labels according to the level of difficulty: “easy”, “medium”, “hard.”


This is a fantastic project for any deep learning professional. Nilearn is a Python module that focuses on performing statistical learning on NeuroImaging data. To use Nilearn, you should be familiar with scikit-learn, a prominent Python tool for scientific applications. You can perform numerous tasks on NeuroImaging data by using nilearn. For example, you can perform decoding, classification, connectivity analysis, predictive modeling, and various other jobs.

NeuroImaging is a prominent medical field, and Nilearn’s capabilities solve many problems present in this sector. By using Nilearn, you can make the analysis of Neuroimages relatively easy and effective. AI finds many applications in the healthcare sector, and if you’ve been interested in using your AI-skills to work in that industry, you should get familiar with Nilearn.


Are you working on a Python project and looking for collaborators? Don’t miss out on this opportunity to connect with the open source community. Our contributors help you with your topic selection, give you feedback and suggestions, answer your questions, and get your contribution merged into their project. Come join us on GitHub today!

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