NumPy is the fundamental package needed for scientific computing with Python.
- Website: https://www.numpy.org
- Documentation: https://numpy.org/doc
- Mailing list: https://mail.python.org/mailman/listinfo/numpy-discussion
- Source code: https://github.com/numpy/numpy
- Contributing: https://www.numpy.org/devdocs/dev/index.html
- Bug reports: https://github.com/numpy/numpy/issues
- Report a security vulnerability: https://tidelift.com/docs/security
It provides:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities
Testing:
- NumPy versions ≥ 1.15 require
pytest - NumPy versions < 1.15 require
nose
Tests can then be run after installation with:
python -c 'import numpy; numpy.test()'
NumPy appreciates help from a wide range of different backgrounds. Work such as high level documentation or website improvements are valuable and we would like to grow our team with people filling these roles. Small improvements or fixes are always appreciated and issues labeled as easy may be a good starting point. If you are considering larger contributions outside the traditional coding work, please contact us through the mailing list.
