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
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.
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.
np.random.random_integersbiased generation of 8 and 16 bit integers.
np.einsumregression on Power9 and z/Linux.
- Fixed histogram problem with signed integer arrays.