Fri. Nov 15th, 2019

NumPy v1.17.4 releases: fundamental package for scientific computing with Python

1 min read

NumPy is the fundamental package for scientific computing with Python. It contains among other things:

  • 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

NumPy

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Changelog v1.17.4

This release contains fixes for bugs reported against NumPy 1.17.3 along with some build improvements. The Python versions supported in this release are 3.5-3.8.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Highlights

  • Fixed np.random.random_integers biased generation of 8 and 16 bit integers.
  • Fixed np.einsum regression on Power9 and z/Linux.
  • Fixed histogram problem with signed integer arrays.

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