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| author | Jarrod Millman <jarrod.millman@gmail.com> | 2017-09-30 18:27:27 -0700 |
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| committer | Jarrod Millman <jarrod.millman@gmail.com> | 2017-09-30 21:17:36 -0700 |
| commit | 5386f8d5c7804425339a661f4a413081d4847a8c (patch) | |
| tree | 70ec48c6ab9df3df384dcbcaeb7bcf548eefe671 /README.md | |
| parent | d05fd3032cccabf5ed18840b43513a73499af5d6 (diff) | |
| download | numpy-5386f8d5c7804425339a661f4a413081d4847a8c.tar.gz | |
DOC: Clean up README
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 37 |
1 files changed, 13 insertions, 24 deletions
@@ -1,34 +1,23 @@ -<div align="center"> - <img src="http://www.numpy.org/_static/numpy_logo.png"><br> -</div> - -[](https://numfocus.org) - ------------------ -| **`Travis CI Status`** | -|-------------------| -[](https://travis-ci.org/numpy/numpy)| +# <img alt="NumPy" src="branding/icons/numpylogo.svg" height="60"> +[](https://travis-ci.org/numpy/numpy) NumPy is the fundamental package needed for scientific computing with Python. -This package contains: - * 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. +- **Website (including documentation):** http://www.numpy.org +- **Mailing list:** https://mail.python.org/mailman/listinfo/numpy-discussion +- **Source:** https://github.com/numpy/numpy +- **Bug reports:** https://github.com/numpy/numpy/issues -It derives from the old Numeric code base and can be used as a replacement for Numeric. It also adds the features introduced by numarray and can be used to replace numarray. +It provides: -More information can be found at the website: +- 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 -* http://www.numpy.org - -After installation, tests can be run (if ``nose`` is installed) with: +If ``nose`` is installed, tests can be run after installation with: python -c 'import numpy; numpy.test()' -The most current development version is always available from our -git repository: - -* http://github.com/numpy/numpy +[](https://numfocus.org) |