Numpy main repository
C Python C++ Other
Latest commit f4092e8 Jan 18, 2018 @matheusportela matheusportela committed with eric-wieser DOC: Record when axis was added to linalg.norm (#10426)
Fixes #5727

The axis argument was introduced in #3387
Failed to load latest commit information.
benchmarks DOC: update asv url Nov 4, 2017
branding/icons add .gitattributes and fix line endings Oct 31, 2010
doc DOC: added "steals a reference" to PyArray_FromAny Jan 14, 2018
numpy DOC: Record when axis was added to linalg.norm (#10426) Jan 18, 2018
tools MAINT: Update zesty to artful for i386 testing Jan 17, 2018
.appveyor.yml CI: appveyor: test OpenBLAS with editable mode Dec 24, 2017
.gitattributes MAINT: Remove numpy-macosx-installer and win32build directories. Feb 25, 2017
.gitignore MAINT: add .cache to gitignore Dec 24, 2017
.gitmodules Add Numpydoc as a git submodule Jul 25, 2013
.mailmap DOC: Post 1.14.0 release updates. Jan 7, 2018
.travis.yml MAINT: Update zesty to artful for i386 testing Jan 17, 2018 MAINT give pointers to coding style and dev environment Aug 28, 2015
INSTALL.rst.txt BUG: financial.pmt modifies input #8055 Sep 23, 2016
LICENSE.txt DOC: Update license documentation. Nov 27, 2017 MAINT: Fix nose features to work on pytest Dec 22, 2017 MAINT: Add appveyor badge to README Oct 1, 2017
THANKS.txt ENH: core: Start einsum function, add copyright notices to files Jan 23, 2011 MAINT: Prepare master for 1.15 development. Dec 8, 2017
pytest.ini MAINT: Add a pytest.ini Dec 22, 2017 BUG: Fix runtests --benchmark-compare in python 3 Oct 15, 2017 MAINT: Update download URL in Jan 10, 2018
site.cfg.example Update site.cfg.example on the MKL part. Nov 22, 2017
tox.ini TST: Add Python 3.5 and 3.6 to Tox environments Mar 8, 2017


Travis AppVeyor

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

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

If nose is installed, tests can be run after installation with:

python -c 'import numpy; numpy.test()'

Powered by NumFOCUS