<commit_before># <img alt="NumPy" src="https://cdn.rawgit.com/numpy/numpy/master/branding/icons/numpylogo.svg" height="60">

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    https://travis-ci.org/numpy/numpy)
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    https://dev.azure.com/numpy/numpy/_build/latest?definitionId=5)
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    https://codecov.io/gh/numpy/numpy)

NumPy is the fundamental package needed for scientific computing with Python.

- **Website:** https://www.numpy.org
- **Documentation:** https://docs.scipy.org/
- **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 &ge; 1.15 require `pytest`
- NumPy versions &lt; 1.15 require `nose`

Tests can then be run after installation with:
<commit_msg>DOC: Fix link to numpy docs in README.<commit_after># <img alt="NumPy" src="https://cdn.rawgit.com/numpy/numpy/master/branding/icons/numpylogo.svg" height="60">

[![Travis](https://img.shields.io/travis/numpy/numpy/master.svg?label=Travis%20CI)](
    https://travis-ci.org/numpy/numpy)
[![Azure](https://dev.azure.com/numpy/numpy/_apis/build/status/azure-pipeline%20numpy.numpy)](
    https://dev.azure.com/numpy/numpy/_build/latest?definitionId=5)
[![codecov](https://codecov.io/gh/numpy/numpy/branch/master/graph/badge.svg)](
    https://codecov.io/gh/numpy/numpy)

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 &ge; 1.15 require `pytest`
- NumPy versions &lt; 1.15 require `nose`

Tests can then be run after installation with:

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