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Some nice open-source python repositories to work

I have been looking to contribute in some nice data science repositories in python. I have searched some nice repos which are in their begining phases or rather they are in a position that they can be touched to improve and amateurs like me in machine learning with almost no commit in open sources, can do a pretty good job committing to those. Keeping that in mind, I am starting to list down some of the repos like that:

(1) dython:This is a python data tools repo. Although this is published now and has somewhat no issues; it looks like being somewhat open-ended creative people can add a lots of other tools and functionality to this tool, make it more main streamed and therefore increase their individual and the package's overall progress.

(2)simpletransformers : This is simple transformer written by Thilina Rajapakse. This is an amazing package which uses the huggingface's transformer library and then combines its high level knowledge requirements into its intrinsic program and then tones it down as much as to a small level as sklearn packages. This, being a great package, is ridden with numbers of issues and therefore an interested contributor should be able to solve some of them and make some code correction commit as well as small functionality development to this repository.
If you are already working with nlp and transformers; know the transformers library really well, then you are already in advantage to work on this repo. Head to the link and start solving some of the issues today!

(3)pyGAM : This is pygam written by daniel serven martin (spelling maybe wrong). This is a package for implementing GAM model in python; being a alternative for the mgcv package in R written by simon wood. This package is really good, elaborate and contains complex coding exercises. But pygam, being the interesting package it can be, has yet a lot of road to progress to become at par with mgcv. So people who are interested in statistical programming, can definitely try and contribute to such a complex repository.

(4)scikit-tda: This is scikit_tda, built for topological data analysis. This is intended for non-topologist people. Being a bit math heavy and topology "infested", topological data analysis is a bit hard to start with. This library sort of tries to solve that issue. With the 4 membered team, this is a growing and interesting repository. But I still don't know what are the requirements for contributing to core code, although some examples and documentation as well as question issue resolving can be done by interested beginners. Go to the link and try to explore the code more.

This is a small list yet I know, and will develop as I find growing and amazing repos like the ones I noted here.
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