Skip to main content

NumPy is a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays. NumPy is built on the Numeric code base and adds features introduced by numarray as well as an extended C-API and the ability to create arrays of arbitrary type which also makes NumPy suitable for interfacing with general-purpose data-base applications.

There are also basic facilities for discrete fourier transform, basic linear algebra and random number generation.

All numpy wheels distributed from pypi are BSD licensed.

Windows wheels are linked against the ATLAS BLAS / LAPACK library, restricted to SSE2 instructions, so may not give optimal linear algebra performance for your machine. See http://docs.scipy.org/doc/numpy/user/install.html for alternatives.

Release files for numpy 1.13.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for numpy 1.13.3
File Size Uploaded
numpy-1.13.3.zip 5.0 MB Details

Built distributions (wheels)

Table of built distributions (wheels) for numpy 1.13.3
File
numpy-1.13.3-cp36-none-win_amd64.whl CPython 3.6 none Windows x86-64 Details
numpy-1.13.3-cp36-cp36m-manylinux1_x86_64.whl CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.5+ x86-64 Details
numpy-1.13.3-cp36-cp36m-manylinux1_i686.whl CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.5+ x86-32 Details
numpy-1.13.3-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl CPython 3.6 CPython 3.6 pymalloc macOS 10.10+ x86-64, macOS 10.10+ Intel (x86-64, i386), macOS 10.9+ x86-64, macOS 10.9+ Intel (x86-64, i386), macOS 10.6+ Intel (x86-64, i386) Details
numpy-1.13.3-cp35-none-win_amd64.whl CPython 3.5 none Windows x86-64 Details
numpy-1.13.3-cp35-cp35m-manylinux1_x86_64.whl CPython 3.5 CPython 3.5 pymalloc Linux glibc 2.5+ x86-64 Details
numpy-1.13.3-cp35-cp35m-manylinux1_i686.whl CPython 3.5 CPython 3.5 pymalloc Linux glibc 2.5+ x86-32 Details
numpy-1.13.3-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl CPython 3.5 CPython 3.5 pymalloc macOS 10.9+ x86-64, macOS 10.10+ x86-64, macOS 10.10+ Intel (x86-64, i386), macOS 10.9+ Intel (x86-64, i386), macOS 10.6+ Intel (x86-64, i386) Details
numpy-1.13.3-cp34-none-win_amd64.whl CPython 3.4 none Windows x86-64 Details
numpy-1.13.3-cp34-cp34m-manylinux1_x86_64.whl CPython 3.4 CPython 3.4 pymalloc Linux glibc 2.5+ x86-64 Details
numpy-1.13.3-cp34-cp34m-manylinux1_i686.whl CPython 3.4 CPython 3.4 pymalloc Linux glibc 2.5+ x86-32 Details
numpy-1.13.3-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl CPython 3.4 CPython 3.4 pymalloc macOS 10.10+ x86-64, macOS 10.9+ Intel (x86-64, i386), macOS 10.6+ Intel (x86-64, i386), macOS 10.10+ Intel (x86-64, i386), macOS 10.9+ x86-64 Details
numpy-1.13.3-cp27-none-win_amd64.whl CPython 2.7 none Windows x86-64 Details
numpy-1.13.3-cp27-cp27mu-manylinux1_x86_64.whl CPython 2.7 CPython 2.7 pymalloc wide-unicode Linux glibc 2.5+ x86-64 Details
numpy-1.13.3-cp27-cp27mu-manylinux1_i686.whl CPython 2.7 CPython 2.7 pymalloc wide-unicode Linux glibc 2.5+ x86-32 Details
numpy-1.13.3-cp27-cp27m-manylinux1_x86_64.whl CPython 2.7 CPython 2.7 pymalloc Linux glibc 2.5+ x86-64 Details
numpy-1.13.3-cp27-cp27m-manylinux1_i686.whl CPython 2.7 CPython 2.7 pymalloc Linux glibc 2.5+ x86-32 Details
numpy-1.13.3-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl CPython 2.7 CPython 2.7 pymalloc macOS 10.10+ Intel (x86-64, i386), macOS 10.10+ x86-64, macOS 10.9+ Intel (x86-64, i386), macOS 10.6+ Intel (x86-64, i386), macOS 10.9+ x86-64 Details
numpy-1.13.3-2-cp36-none-win32.whl CPython 3.6 none Windows x86-32 Details
numpy-1.13.3-2-cp35-none-win32.whl CPython 3.5 none Windows x86-32 Details
numpy-1.13.3-2-cp34-none-win32.whl CPython 3.4 none Windows x86-32 Details
numpy-1.13.3-2-cp27-none-win32.whl CPython 2.7 none Windows x86-32 Details

Total release size: 251.6 MB

Release files / numpy-1.13.3.zip

Download URL numpy-1.13.3.zip
Size 5.0 MB
Tags Source
SHA-256 checksum
How to use checksums
36ee86d5adbabc4fa2643a073f93d5504bdfed37a149a3a49f4dde259f35a750
BLAKE2b-256 checksum
How to use checksums
bf2d005e45738ab07a26e621c9c12dc97381f372e06678adf7dc3356a69b5960
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp36-none-win_amd64.whl

Download URL numpy-1.13.3-cp36-none-win_amd64.whl
Size 13.1 MB
Tags CPython 3.6 Windows x86-64
SHA-256 checksum
How to use checksums
c8dc6aa96882df6323bf9545934e37c6e05959bd789ae4b14d50509b093907aa
BLAKE2b-256 checksum
How to use checksums
e97c5665454a5cea3db586b315fa167d4b8b7963fdcc98c3b6578bc5eb4e6153
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp36-cp36m-manylinux1_x86_64.whl

Download URL numpy-1.13.3-cp36-cp36m-manylinux1_x86_64.whl
Size 17.0 MB
Tags CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum
How to use checksums
e8e0e75db757e41463888939d26c8058b4ecd25e563c597e9119f512dc0ee1da
BLAKE2b-256 checksum
How to use checksums
57a7e3e6bd9d595125e1abbe162e323fd2d06f6f6683185294b79cd2cdb190d5
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp36-cp36m-manylinux1_i686.whl

Download URL numpy-1.13.3-cp36-cp36m-manylinux1_i686.whl
Size 12.9 MB
Tags CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.5+ x86-32
SHA-256 checksum
How to use checksums
7dfa5b49fb2a080bd0d39bfbcff1177bacb14fcb28c857fd65fd0c18938935de
BLAKE2b-256 checksum
How to use checksums
004744a3bd240574fd8369b1c67747da50eb1f5fd09108ce3ab0b49510b5d8da
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl

Download URL numpy-1.13.3-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Size 4.5 MB
Tags CPython 3.6 CPython 3.6 pymalloc macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64
SHA-256 checksum
How to use checksums
fa656dccfa9141774440575a6e7875d08b93f4a332eb5ae40877b26bed291c01
BLAKE2b-256 checksum
How to use checksums
7506faf181739f682da35f1310a904e650fc4706558b5657d8ec2f6b29c45220
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp35-none-win_amd64.whl

Download URL numpy-1.13.3-cp35-none-win_amd64.whl
Size 13.1 MB
Tags CPython 3.5 Windows x86-64
SHA-256 checksum
How to use checksums
b162c6b044960b4ea0f42be049ce2af1d18c60f82748f0a27bd5ad182a731bf3
BLAKE2b-256 checksum
How to use checksums
54b57d539652dafd4ef88e5e7aa4d26a604c321e2dcb3e81069a1ed75c4cbd6e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp35-cp35m-manylinux1_x86_64.whl

Download URL numpy-1.13.3-cp35-cp35m-manylinux1_x86_64.whl
Size 16.9 MB
Tags CPython 3.5 CPython 3.5 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum
How to use checksums
479863de17f66810db00bccf35289555365da45d3b053ccf539b95ab3b9c24f6
BLAKE2b-256 checksum
How to use checksums
0d416c224571decd61c2578baedfdb0eec6283617c6679c35b20973f4e68aeaf
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp35-cp35m-manylinux1_i686.whl

Download URL numpy-1.13.3-cp35-cp35m-manylinux1_i686.whl
Size 12.8 MB
Tags CPython 3.5 CPython 3.5 pymalloc Linux glibc 2.5+ x86-32
SHA-256 checksum
How to use checksums
9cad35b911e150f00bb8080950c7e9f172714bbd0234f5ab74b4e3e2d9288b37
BLAKE2b-256 checksum
How to use checksums
3f809f6f864afe4727c86bdd829a0e31e270f2a2975f5a2972c17ccb51989334
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl

Download URL numpy-1.13.3-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Size 4.5 MB
Tags CPython 3.5 CPython 3.5 pymalloc macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64
SHA-256 checksum
How to use checksums
09b87d652c03508447d0f618e1d3ae57595acd3e0f0c11ac91bf68ed7bdb3a28
BLAKE2b-256 checksum
How to use checksums
f3afe4c538ef267b7eaf8a13655ccc1d88e0364f35d751fd80964f1d914130e3
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp34-none-win_amd64.whl

Download URL numpy-1.13.3-cp34-none-win_amd64.whl
Size 14.4 MB
Tags CPython 3.4 Windows x86-64
SHA-256 checksum
How to use checksums
2875e8055a1ea8d933b1c9d0f8714c0aa11c097bfadfcb8564c4d868fbf09a41
BLAKE2b-256 checksum
How to use checksums
20ac360bab96eb8d0fce64fbbd1bf0263a494fededdd4d6298636aded6e78efa
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp34-cp34m-manylinux1_x86_64.whl

Download URL numpy-1.13.3-cp34-cp34m-manylinux1_x86_64.whl
Size 16.9 MB
Tags CPython 3.4 CPython 3.4 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum
How to use checksums
8969c8f987f8bcc3e30c014532cfc20e4a8f86a50c361596e086310853adacb7
BLAKE2b-256 checksum
How to use checksums
dd3c78edab4a88addb6aae8c0b2b675bb5a0d383d0915451823c02a35a02ac7c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp34-cp34m-manylinux1_i686.whl

Download URL numpy-1.13.3-cp34-cp34m-manylinux1_i686.whl
Size 12.9 MB
Tags CPython 3.4 CPython 3.4 pymalloc Linux glibc 2.5+ x86-32
SHA-256 checksum
How to use checksums
11fcbed36c101a3b9c4636e791efccba82409ebbedaba938c97be8bdddd029cc
BLAKE2b-256 checksum
How to use checksums
1a0b013d5b1d6ca57e9f6cd1684be18e195d4c28a06ba608b3c0ea192d18c6fd
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl

Download URL numpy-1.13.3-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Size 4.5 MB
Tags CPython 3.4 CPython 3.4 pymalloc macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64
SHA-256 checksum
How to use checksums
b2f98838f4bbc3bf23af7e97ffcad18a2dc6bbb0726796781e02b9347af6685f
BLAKE2b-256 checksum
How to use checksums
57ea48d2720b4b63e77f5dc9fda76b43ac40cd11f96f5503dd3d033afba76ac7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp27-none-win_amd64.whl

Download URL numpy-1.13.3-cp27-none-win_amd64.whl
Size 13.0 MB
Tags CPython 2.7 Windows x86-64
SHA-256 checksum
How to use checksums
4c767b6d9c9a071bb36ea34eb240ee5192fe0bc4c13be5e6c51e0350a30f7ac0
BLAKE2b-256 checksum
How to use checksums
9146fd556f3222ccfd1a7bb7f72a194193aab6a086355b89e985a7cb321b9f3f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp27-cp27mu-manylinux1_x86_64.whl

Download URL numpy-1.13.3-cp27-cp27mu-manylinux1_x86_64.whl
Size 16.6 MB
Tags CPython 2.7 CPython 2.7 pymalloc wide-unicode Linux glibc 2.5+ x86-64
SHA-256 checksum
How to use checksums
da2f47e46d7a93b73891d1981378717dc73c6ad5cc4fd23c934bfea7847fa958
BLAKE2b-256 checksum
How to use checksums
ebbe737f3df5806192ac4096e549e48c8c76cfaa2fb880a1c62a7bb085adaa9b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp27-cp27mu-manylinux1_i686.whl

Download URL numpy-1.13.3-cp27-cp27mu-manylinux1_i686.whl
Size 12.6 MB
Tags CPython 2.7 CPython 2.7 pymalloc wide-unicode Linux glibc 2.5+ x86-32
SHA-256 checksum
How to use checksums
6c6feb0647380db6e1d5d49ef9fb59c42240f25fb8df8b6e82ecb436c7e0621a
BLAKE2b-256 checksum
How to use checksums
25861a1453bb2354092779a1de6707bdbf1e47d060d33aee7a22dbabd0691c84
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp27-cp27m-manylinux1_x86_64.whl

Download URL numpy-1.13.3-cp27-cp27m-manylinux1_x86_64.whl
Size 16.6 MB
Tags CPython 2.7 CPython 2.7 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum
How to use checksums
c4b1914d86c43399438518a2ac8bcba2fb64dd5a18efddded3783b9daae70933
BLAKE2b-256 checksum
How to use checksums
b6fc34342ee8ea0c413679ddfb23d73a132fc7ecbf479383de9f0946344ec73c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp27-cp27m-manylinux1_i686.whl

Download URL numpy-1.13.3-cp27-cp27m-manylinux1_i686.whl
Size 12.6 MB
Tags CPython 2.7 CPython 2.7 pymalloc Linux glibc 2.5+ x86-32
SHA-256 checksum
How to use checksums
62b09f3d1ea01d79c16a6642cb21599f53b9338c59971b2418a573155d2202ec
BLAKE2b-256 checksum
How to use checksums
fa59903aba834f27f9f35aa8d12b0fc7b7bf742ef59e773d99fe008e9bd1e6b7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl

Download URL numpy-1.13.3-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Size 4.6 MB
Tags CPython 2.7 CPython 2.7 pymalloc macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64
SHA-256 checksum
How to use checksums
929928932f91082a168e36984179deddd58f8e98822ad2f33a2955d7c4eec596
BLAKE2b-256 checksum
How to use checksums
eadff0671353e3d2eab4c87df014ad09505268561f6b09d0dc257d1b32653cfe
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-2-cp36-none-win32.whl

Download URL numpy-1.13.3-2-cp36-none-win32.whl
Size 6.8 MB
Tags CPython 3.6 Windows x86-32
SHA-256 checksum
How to use checksums
539345898a4ae17421c159ae2a350901a5e6ce3da8f24168c6c67b3536e13de8
BLAKE2b-256 checksum
How to use checksums
6af0bccdf94ef3026c925ab46ef14aa47bf2f97758affc873ea3ba5db1da731b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-2-cp35-none-win32.whl

Download URL numpy-1.13.3-2-cp35-none-win32.whl
Size 6.8 MB
Tags CPython 3.5 Windows x86-32
SHA-256 checksum
How to use checksums
d29e72413b66df23c75b9b469253c823698ea2e00f58e9e0df64b7a50696e8ac
BLAKE2b-256 checksum
How to use checksums
d90dd54ee0e8601c05532773eec45a5dc497806606c3294b3bdc20cf106b290c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-2-cp34-none-win32.whl

Download URL numpy-1.13.3-2-cp34-none-win32.whl
Size 6.7 MB
Tags CPython 3.4 Windows x86-32
SHA-256 checksum
How to use checksums
f5c9ca457057cd5e12ddab36cded8b1f38bf1f45bf550d4ca2839b11ec57f597
BLAKE2b-256 checksum
How to use checksums
5e9fb192c2d9c4473b74c755a9368dfa4c0a2ba983feb15886dd49ad740beb44
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / numpy-1.13.3-2-cp27-none-win32.whl

Download URL numpy-1.13.3-2-cp27-none-win32.whl
Size 6.7 MB
Tags CPython 2.7 Windows x86-32
SHA-256 checksum
How to use checksums
910e7ae5eeee8d322775187692c5c66719cd58d230fbfd57245ea3cf75716910
BLAKE2b-256 checksum
How to use checksums
fd32196073188f5b8b464e0fabb470f971fa5dcd91b55726a43b40b008212358
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release history Release notifications | RSS feed

2.5.0

44 release files

2.4.6

72 release files

2.4.5

72 release files

2.4.4

72 release files

2.4.2

72 release files

2.4.1

72 release files

2.4.0

72 release files

2.3.5

74 release files

2.3.4

74 release files

2.3.2

74 release files

2.3.1

51 release files

2.2.6

55 release files

2.2.5

55 release files

2.2.4

55 release files

2.2.3

55 release files

2.2.2

55 release files

2.2.1

55 release files

2.1.0

52 release files

2.0.2

45 release files

2.0.1

45 release files

2.0.0

45 release files

This release

1.13.3 This release

23 release files

1.9.3

13 release files

1.8.1

11 release files

1.8.0

8 release files

1.7.2

8 release files

1.7.0

14 release files

1.6.2

12 release files

1.6.1

12 release files

1.6.0

12 release files

1.5.1

6 release files

1.5.0

2 release files

1.4.1

1 release file

1.4.0

1.3.0

2 release files

1.2.1

1.2.0

1.1.1

1.0.4

1.0.3

1.0

1 release file

1.0rc3

1.0rc2

1.0rc1

1.0b5

1.0b4

1.0b1

0.9.8

0.9.6

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page