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.
Metadata
Release files for numpy 1.16.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| numpy-1.16.6.zip | 5.1 MB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| numpy-1.16.6-cp37-cp37m-win_amd64.whl | CPython 3.7 | CPython 3.7 pymalloc | Windows x86-64 | Details |
| numpy-1.16.6-cp37-cp37m-win32.whl | CPython 3.7 | CPython 3.7 pymalloc | Windows x86-32 | Details |
| numpy-1.16.6-cp37-cp37m-manylinux1_x86_64.whl | CPython 3.7 | CPython 3.7 pymalloc | Linux glibc 2.5+ x86-64 | Details |
| numpy-1.16.6-cp37-cp37m-manylinux1_i686.whl | CPython 3.7 | CPython 3.7 pymalloc | Linux glibc 2.5+ x86-32 | Details |
| numpy-1.16.6-cp37-cp37m-macosx_10_9_x86_64.whl | CPython 3.7 | CPython 3.7 pymalloc | macOS 10.9+ x86-64 | Details |
| numpy-1.16.6-cp36-cp36m-win_amd64.whl | CPython 3.6 | CPython 3.6 pymalloc | Windows x86-64 | Details |
| numpy-1.16.6-cp36-cp36m-win32.whl | CPython 3.6 | CPython 3.6 pymalloc | Windows x86-32 | Details |
| numpy-1.16.6-cp36-cp36m-manylinux1_x86_64.whl | CPython 3.6 | CPython 3.6 pymalloc | Linux glibc 2.5+ x86-64 | Details |
| numpy-1.16.6-cp36-cp36m-manylinux1_i686.whl | CPython 3.6 | CPython 3.6 pymalloc | Linux glibc 2.5+ x86-32 | Details |
| numpy-1.16.6-cp36-cp36m-macosx_10_9_x86_64.whl | CPython 3.6 | CPython 3.6 pymalloc | macOS 10.9+ x86-64 | Details |
| numpy-1.16.6-cp35-cp35m-win_amd64.whl | CPython 3.5 | CPython 3.5 pymalloc | Windows x86-64 | Details |
| numpy-1.16.6-cp35-cp35m-win32.whl | CPython 3.5 | CPython 3.5 pymalloc | Windows x86-32 | Details |
| numpy-1.16.6-cp35-cp35m-manylinux1_x86_64.whl | CPython 3.5 | CPython 3.5 pymalloc | Linux glibc 2.5+ x86-64 | Details |
| numpy-1.16.6-cp35-cp35m-manylinux1_i686.whl | CPython 3.5 | CPython 3.5 pymalloc | Linux glibc 2.5+ x86-32 | Details |
| numpy-1.16.6-cp35-cp35m-macosx_10_9_intel.whl | CPython 3.5 | CPython 3.5 pymalloc | macOS 10.9+ Intel (x86-64, i386) | Details |
| numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl | CPython 2.7 | CPython 2.7 pymalloc wide-unicode | Linux glibc 2.5+ x86-64 | Details |
| numpy-1.16.6-cp27-cp27mu-manylinux1_i686.whl | CPython 2.7 | CPython 2.7 pymalloc wide-unicode | Linux glibc 2.5+ x86-32 | Details |
| numpy-1.16.6-cp27-cp27m-win_amd64.whl | CPython 2.7 | CPython 2.7 pymalloc | Windows x86-64 | Details |
| numpy-1.16.6-cp27-cp27m-win32.whl | CPython 2.7 | CPython 2.7 pymalloc | Windows x86-32 | Details |
| numpy-1.16.6-cp27-cp27m-manylinux1_x86_64.whl | CPython 2.7 | CPython 2.7 pymalloc | Linux glibc 2.5+ x86-64 | Details |
| numpy-1.16.6-cp27-cp27m-manylinux1_i686.whl | CPython 2.7 | CPython 2.7 pymalloc | Linux glibc 2.5+ x86-32 | Details |
| numpy-1.16.6-cp27-cp27m-macosx_10_9_x86_64.whl | CPython 2.7 | CPython 2.7 pymalloc | macOS 10.9+ x86-64 | Details |
Total release size: 307.8 MB
Release files / numpy-1.16.6.zip
| Download URL | numpy-1.16.6.zip |
|---|---|
| Size | 5.1 MB |
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Release files / numpy-1.16.6-cp37-cp37m-win_amd64.whl
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Release files / numpy-1.16.6-cp37-cp37m-win32.whl
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Release files / numpy-1.16.6-cp37-cp37m-manylinux1_x86_64.whl
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Release files / numpy-1.16.6-cp37-cp37m-manylinux1_i686.whl
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Release files / numpy-1.16.6-cp37-cp37m-macosx_10_9_x86_64.whl
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Release files / numpy-1.16.6-cp36-cp36m-win_amd64.whl
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Release files / numpy-1.16.6-cp36-cp36m-win32.whl
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Release files / numpy-1.16.6-cp36-cp36m-manylinux1_x86_64.whl
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Release files / numpy-1.16.6-cp36-cp36m-manylinux1_i686.whl
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Release files / numpy-1.16.6-cp36-cp36m-macosx_10_9_x86_64.whl
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Release files / numpy-1.16.6-cp35-cp35m-win_amd64.whl
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Release files / numpy-1.16.6-cp27-cp27m-win_amd64.whl
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Release files / numpy-1.16.6-cp27-cp27m-win32.whl
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Release files / numpy-1.16.6-cp27-cp27m-manylinux1_x86_64.whl
| Download URL | numpy-1.16.6-cp27-cp27m-manylinux1_x86_64.whl |
|---|---|
| Size | 17.0 MB |
| Tags | CPython 2.7 CPython 2.7 pymalloc Linux glibc 2.5+ x86-64 |
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twine/3.1.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.7.2 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5
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Release files / numpy-1.16.6-cp27-cp27m-manylinux1_i686.whl
| Download URL | numpy-1.16.6-cp27-cp27m-manylinux1_i686.whl |
|---|---|
| Size | 14.5 MB |
| Tags | CPython 2.7 CPython 2.7 pymalloc Linux glibc 2.5+ x86-32 |
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SHA-256 checksum How to use checksums |
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twine/3.1.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.7.2 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5
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Release files / numpy-1.16.6-cp27-cp27m-macosx_10_9_x86_64.whl
| Download URL | numpy-1.16.6-cp27-cp27m-macosx_10_9_x86_64.whl |
|---|---|
| Size | 13.9 MB |
| Tags | CPython 2.7 CPython 2.7 pymalloc macOS 10.9+ x86-64 |
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SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.7.2 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5
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