I love Python and I love the fact that Python is being used in science and engineering more than ever! Python has a mature set of general purposes libraries that aid scientists extract the best out of their data.
numpy (numerical computation), scipy (scientific programming), matplotlib (visualization), pandas (data processing), and scikit-learn (data science/machine learning), are only a few of the awesome myriad of open source libraries developed in Python.
Most of the success around Python and its libraries is due to their fast-growing, energetic, and welcoming communities. Those libraries I mentioned are developed in a daily basis by experts from academia and industry and also by students.
In the past years, I’ve been involved with open source software development primarily thanks to Google, Github, and NASA ❤️.
Here is a list of projects I’m more involved with:
- astropy: A community Python library for Astronomy
- photutils: Image photometry in Python
- pyke: A suite of Python tools to analyze Kepler/K2 data
- oktopus: A soft-bodied, eight-armed package for beautiful inference
- macaw: A multicolor, long-tailed package for beautiful majorization-minimization applied to machine learning