| Port variant | py39 |
| Summary | Array computing for Python (3.9) |
| Package version | 1.23.5 |
| Homepage | https://www.numpy.org |
| Keywords | python |
| Maintainer | Python Automaton |
| License | Not yet specified |
| Other variants | py310 |
| Ravenports | Buildsheet | History |
| Ravensource | Port Directory | History |
| Last modified | 22 NOV 2022, 03:57:10 UTC |
| Port created | 21 APR 2020, 22:22:44 UTC |
| single | 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 - and much more 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. All NumPy wheels distributed on PyPI are BSD licensed. NumPy requires pytest and hypothesis. Tests can then be run after installation with:: python -c 'import numpy; numpy.test()' |
| Build (only) |
python-Cython:single:py39 python-setuptools:single:py39 autoselect-python:single:standard |
| Build and Runtime |
lapack:blas:standard python39:single:standard |
| main | mirror://PYPI/n/numpy |
| nlopt:standard | Nonlinear optimization library |
| python-pandas:py39 | Data structures for time series, statistics (3.9) |
| python-pythran:py39 | Ahead of Time compiler for numeric kernels (3.9) |
| python-scipy:py39 | Scientific library for Python (3.9) |