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Programmers like you spend time contributing to documentation, tests, bug fixes, and new features. It's a wonderful opportunity for you to contribute in a guided way to the Python library you use.\n\n## Can I participate if I have never contributed to open source?\nSure. Anyone with some Python knowledge is welcome to participate. PyLadies Amsterdam and Picnic mentors will guide you through.\n\n## Python libraries to contribute to \n[scikit-learn](https://scikit-learn.org/stable/) - machine learning in Python\n\n[transformers](https://huggingface.co/docs/transformers/en/index) - state-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX\n\n[pandas](https://pandas.pydata.org/) - flexible and powerful data analysis/manipulation library for Python\n\n[numpy](https://numpy.org/) - the fundamental package for scientific computing with Python\n\n[dbt-core](https://www.getdbt.com/product/what-is-dbt) - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications\n\n[FastAPI](https://fastapi.tiangolo.com/) - high performance, easy to learn, fast to code, ready for production\n\n*Bonus Python library*\n\n[diepvries](https://diepvries.picnic.tech/) - The Picnic Data Vault framework\n\n## Credits\nThese open source sprints were set up by @pyladiesams \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpyladiesams%2Fpython-oss-sprints-mar2024","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpyladiesams%2Fpython-oss-sprints-mar2024","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpyladiesams%2Fpython-oss-sprints-mar2024/lists"}