Lessons learned open sourcing (and maintaining) my first library

by Nicole Carlson

data science open source maintenance documentation 10 minutes

Last year, I open-sourced my first library, PyMC3 Models. This talk has two parts: things I learned as I was writing my library and some of the issues I faced being the sole maintainer of the library. I hope you’ll be encouraged to open source and maintain your own library after this!

Last year, I open sourced my first library, PyMC3 Models, which features custom PyMC3 models built on top of the scikit-learn API. This talk is about some of the things I learned along the way.

The first half will focus on lessons learned while writing my library\: - The importance of just starting: releasing even a small bit of work is worthwhile because it could help someone. - Using toy examples: implementing a basic linear regression helped me improve the broader structure of my package. - Documentation: Spending time on documentation was tedious in the beginning, but paid off in the long run. - Copying: I borrowed heavily from other open source libraries and that’s totally ok!

The second half will focus on being the sole maintainer of the library including: - how to handle normal contributions - strategies for dealing with aggressive contributors - taking care of yourself when you’re the only one working on a library

I hope this talk will encourage you to start your own library and give you some tools as you maintain it.

About the Author

Nicole is on the data science team at ShopRunner. Previously, she worked at Shiftgig, the University of Chicago, and the Exploratorium.

She has a PhD in Physics from Berkeley; her research focused on computational neuroscience.

You can find Nicole at PyLadies and PyData Chicago meetups or climbing at First Ascent in Chicago.

Author website: http://parsingscience.com/