Manipulate JSON-like data with NumPy-like idioms.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for agoose77 from gravatar.com agoose77 Avatar for pivarski from gravatar.com pivarski Avatar for scikit-hep from gravatar.com scikit-hep

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Project description

PyPI version Conda-Forge Python 3.7‒3.11 BSD-3 Clause License Continuous integration tests

Scikit-HEP NSF-1836650 DOI Documentation Gitter

Awkward Array is a library for nested, variable-sized data, including arbitrary-length lists, records, mixed types, and missing data, using NumPy-like idioms.

Arrays are dynamically typed, but operations on them are compiled and fast. Their behavior coincides with NumPy when array dimensions are regular and generalizes when they're not.

Motivating example

Given an array of objects with x, y fields and variable-length nested lists like

array = ak.Array([
    [{"x": 1.1, "y": [1]}, {"x": 2.2, "y": [1, 2]}, {"x": 3.3, "y": [1, 2, 3]}],
    [],
    [{"x": 4.4, "y": {1, 2, 3, 4]}, {"x": 5.5, "y": [1, 2, 3, 4, 5]}]
])

the following slices out the y values, drops the first element from each inner list, and runs NumPy's np.square function on everything that is left:

output = np.square(array["y", ..., 1:])

The result is

[
    [[], [4], [4, 9]],
    [],
    [[4, 9, 16], [4, 9, 16, 25]]
]

The equivalent using only Python is

output = []
for sublist in array:
    tmp1 = []
    for record in sublist:
        tmp2 = []
        for number in record["y"][1:]:
            tmp2.append(np.square(number))
        tmp1.append(tmp2)
    output.append(tmp1)

Not only is the expression using Awkward Arrays more concise, using idioms familiar from NumPy, but it's much faster and uses less memory.

For a similar problem 10 million times larger than the one above (on a single-threaded 2.2 GHz processor),

  • the Awkward Array one-liner takes 4.6 seconds to run and uses 2.1 GB of memory,
  • the equivalent using Python lists and dicts takes 138 seconds to run and uses 22 GB of memory.

Speed and memory factors in the double digits are common because we're replacing Python's dynamically typed, pointer-chasing virtual machine with type-specialized, precompiled routines on contiguous data. (In other words, for the same reasons as NumPy.) Even higher speedups are possible when Awkward Array is paired with Numba.

Our presentation at SciPy 2020 provides a good introduction, showing how to use these arrays in a real analysis.

Installation

Awkward Array can be installed from PyPI using pip:

pip install awkward

You will likely get a precompiled binary (wheel), depending on your operating system and Python version. If not, pip attempts to compile from source (which requires a C++ compiler, make, and CMake).

Awkward Array is also available using conda, which always installs a binary:

conda install -c conda-forge awkward

If you have already added conda-forge as a channel, the -c conda-forge is unnecessary. Adding the channel is recommended because it ensures that all of your packages use compatible versions:

conda config --add channels conda-forge
conda update --all

Getting help

How-to tutorials

Python API reference

C++ API reference

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for agoose77 from gravatar.com agoose77 Avatar for pivarski from gravatar.com pivarski Avatar for scikit-hep from gravatar.com scikit-hep

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

2.7.0

2.6.10

2.6.9

2.6.8

2.6.7

2.6.6

2.6.5

2.6.4

2.6.3

2.6.3rc2 pre-release

2.6.2

2.6.1

2.6.0

2.5.2

2.5.1

2.5.1rc1 pre-release

2.5.0

2.5.0rc0 pre-release yanked

Reason this release was yanked:

UFunc handling is broken

2.4.10

2.4.9

2.4.8 yanked

Reason this release was yanked:

UFunc handling is broken

2.4.7 yanked

Reason this release was yanked:

This version introduced an implicit dependency upon NumPy >= 1.24

2.4.6

2.4.5

2.4.4

2.4.3

2.4.2

2.4.1

2.4.0

2.3.3

2.3.2

2.3.1

2.3.0

2.2.4

2.2.3

2.2.2

2.2.1

2.2.0

2.1.4

2.1.3

2.1.2

2.1.1

2.1.0

2.0.10

2.0.9

2.0.8

2.0.7

2.0.6

2.0.5

2.0.4

2.0.3 yanked

Reason this release was yanked:

The `flatten_records` argument shouldn't have been removed, just the no-longer-implemented value for that argument (`True`). Fixed in 2.0.4.

2.0.2

2.0.1

2.0.0

2.0.0rc8 pre-release

2.0.0rc7 pre-release

2.0.0rc6 pre-release

2.0.0rc5 pre-release

2.0.0rc4 pre-release

1.10.5

1.10.4

1.10.3

1.10.2

This version

1.10.1

1.10.0

1.9.0

1.8.0

1.7.0

1.5.1

1.5.0

1.4.0

1.3.0

1.2.3

1.2.2

1.2.1

1.2.0

1.1.2

1.1.1

1.1.0

1.0.2

1.0.1

1.0.0

0.14.0

0.13.0

0.12.22

0.12.21

0.12.20

0.12.19

0.12.18

0.12.17

0.12.16

0.12.15

0.12.14

0.12.13

0.12.12

0.12.11

0.12.10

0.12.9

0.12.8

0.12.7

0.12.6

0.12.5

0.12.4

0.12.3

0.12.2

0.12.1

0.12.0

0.12.0rc2 pre-release

0.12.0rc1 pre-release

0.11.1

0.11.0

0.11.0rc8 pre-release

0.11.0rc7 pre-release

0.11.0rc4 pre-release

0.11.0rc3 pre-release

0.10.3

0.10.2

0.10.1

0.10.0

0.9.0

0.9.0rc3 pre-release

0.9.0rc2 pre-release

0.9.0rc1 pre-release

0.8.16

0.8.15

0.8.14

0.8.13

0.8.12

0.8.11

0.8.10

0.8.9

0.8.8

0.8.7

0.8.6

0.8.5

0.8.4

0.8.3

0.8.2

0.8.1

0.8.0

0.8.0rc13 pre-release

0.8.0rc12 pre-release

0.8.0rc11 pre-release

0.8.0rc10 pre-release

0.8.0rc9 pre-release

0.8.0rc8 pre-release

0.8.0rc7 pre-release

0.8.0rc6 pre-release

0.8.0rc5 pre-release

0.8.0rc4 pre-release

0.8.0rc3 pre-release

0.8.0rc2 pre-release

0.7.3

0.7.2

0.7.1

0.7.0

0.6.2

0.6.1

0.6.0

0.5.6

0.5.5

0.5.4

0.5.3

0.5.2

0.5.1

0.5.0

0.4.5

0.4.4

0.4.3

0.4.2

0.4.1

0.4.0

0.3.0

0.2.1

0.2.0

0.1.0

0.0.10

0.0.9

0.0.8

0.0.7

0.0.6

0.0.5

0.0.4

0.0.3

0.0.2

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

awkward-1.10.1.tar.gz (1.7 MB view details)

Uploaded Source

Built Distributions

awkward-1.10.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

awkward-1.10.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl (11.8 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

awkward-1.10.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

awkward-1.10.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (11.8 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

awkward-1.10.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

awkward-1.10.1-pp37-pypy37_pp73-macosx_10_9_x86_64.whl (11.8 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

awkward-1.10.1-cp311-cp311-win_amd64.whl (15.8 MB view details)

Uploaded CPython 3.11 Windows x86-64

awkward-1.10.1-cp311-cp311-musllinux_1_1_x86_64.whl (12.0 MB view details)

Uploaded CPython 3.11 musllinux: musl 1.1+ x86-64

awkward-1.10.1-cp311-cp311-musllinux_1_1_aarch64.whl (11.1 MB view details)

Uploaded CPython 3.11 musllinux: musl 1.1+ ARM64

awkward-1.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

awkward-1.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (11.1 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ ARM64

awkward-1.10.1-cp311-cp311-macosx_10_9_x86_64.whl (11.8 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

awkward-1.10.1-cp311-cp311-macosx_10_9_universal2.whl (21.6 MB view details)

Uploaded CPython 3.11 macOS 10.9+ universal2 (ARM64, x86-64)

awkward-1.10.1-cp310-cp310-win_amd64.whl (15.8 MB view details)

Uploaded CPython 3.10 Windows x86-64

awkward-1.10.1-cp310-cp310-musllinux_1_1_x86_64.whl (12.0 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ x86-64

awkward-1.10.1-cp310-cp310-musllinux_1_1_aarch64.whl (11.1 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ ARM64

awkward-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

awkward-1.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (11.1 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

awkward-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl (11.8 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

awkward-1.10.1-cp310-cp310-macosx_10_9_universal2.whl (21.6 MB view details)

Uploaded CPython 3.10 macOS 10.9+ universal2 (ARM64, x86-64)

awkward-1.10.1-cp39-cp39-win_amd64.whl (15.8 MB view details)

Uploaded CPython 3.9 Windows x86-64

awkward-1.10.1-cp39-cp39-win32.whl (12.5 MB view details)

Uploaded CPython 3.9 Windows x86

awkward-1.10.1-cp39-cp39-musllinux_1_1_x86_64.whl (12.0 MB view details)

Uploaded CPython 3.9 musllinux: musl 1.1+ x86-64

awkward-1.10.1-cp39-cp39-musllinux_1_1_aarch64.whl (11.1 MB view details)

Uploaded CPython 3.9 musllinux: musl 1.1+ ARM64

awkward-1.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

awkward-1.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (11.1 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

awkward-1.10.1-cp39-cp39-macosx_10_9_x86_64.whl (11.8 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

awkward-1.10.1-cp39-cp39-macosx_10_9_universal2.whl (21.6 MB view details)

Uploaded CPython 3.9 macOS 10.9+ universal2 (ARM64, x86-64)

awkward-1.10.1-cp38-cp38-win_amd64.whl (15.8 MB view details)

Uploaded CPython 3.8 Windows x86-64

awkward-1.10.1-cp38-cp38-win32.whl (12.5 MB view details)

Uploaded CPython 3.8 Windows x86

awkward-1.10.1-cp38-cp38-musllinux_1_1_x86_64.whl (12.0 MB view details)

Uploaded CPython 3.8 musllinux: musl 1.1+ x86-64

awkward-1.10.1-cp38-cp38-musllinux_1_1_aarch64.whl (11.1 MB view details)

Uploaded CPython 3.8 musllinux: musl 1.1+ ARM64

awkward-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

awkward-1.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (11.1 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

awkward-1.10.1-cp38-cp38-macosx_10_9_x86_64.whl (11.8 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

awkward-1.10.1-cp38-cp38-macosx_10_9_universal2.whl (21.6 MB view details)

Uploaded CPython 3.8 macOS 10.9+ universal2 (ARM64, x86-64)

awkward-1.10.1-cp37-cp37m-win_amd64.whl (15.8 MB view details)

Uploaded CPython 3.7m Windows x86-64

awkward-1.10.1-cp37-cp37m-win32.whl (12.5 MB view details)

Uploaded CPython 3.7m Windows x86

awkward-1.10.1-cp37-cp37m-musllinux_1_1_x86_64.whl (12.2 MB view details)

Uploaded CPython 3.7m musllinux: musl 1.1+ x86-64

awkward-1.10.1-cp37-cp37m-musllinux_1_1_aarch64.whl (11.2 MB view details)

Uploaded CPython 3.7m musllinux: musl 1.1+ ARM64

awkward-1.10.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

awkward-1.10.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (11.3 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

awkward-1.10.1-cp37-cp37m-macosx_10_9_x86_64.whl (11.6 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file awkward-1.10.1.tar.gz.

File metadata

  • Download URL: awkward-1.10.1.tar.gz
  • Upload date:
  • Size: 1.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for awkward-1.10.1.tar.gz
Algorithm Hash digest
SHA256 c6394ed25fb14a086d63621d9d84fdc228f5d42a64586f215731b36fde17034b
MD5 b3cd37f5e91a40ec74e2198473c60fee
BLAKE2b-256 ba3af5bd821836b2b73850fc464a4b696524c532c518b781fc758c98376f07b9

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 33f2cde98e14ccbea37b29c34630bd34d74d19e8432a21b689005ec0a24b586a
MD5 5df4c5fdb99f954955bca1fd8ce48ccf
BLAKE2b-256 8d39dddfc166cd99cbdacb2dc7fabf4d5d47a22adf97ed3403d9c28d2f56dd2c

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 bac05ae845488c7e1d99dea998ffd0094aa021f1fd09a8af32cd4cad683929be
MD5 dd0d3fc1ab5f826cc82b5a081cbb4866
BLAKE2b-256 838555915cd31da009a27ab12b99130062c0819f2a4f115fc8a84bd0a69fea16

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1d639e5bbb27373ec754a797ebf42c889e8a2159800fb5204f5ba2f6e86790e5
MD5 18aa187c88d24cc0b0a06518df30aadb
BLAKE2b-256 bbdf5f9639e7e5df006cda9504a909f74d2e02529a27812b9408afe3b4ad39e7

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 58a91bed96a07a33803346c9d7353515edd475c6c45a087f8fedc95b7f9c0a6f
MD5 69de38c1365b95d156eb33f6025e4eeb
BLAKE2b-256 d264a2b22f73e8768cbfce9a50523803f82f6e778a4b8c6da3c473728a9a7244

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1887db0a8dad949ddfffa921c6731e3af31b1692a62a5b364375d589772d2709
MD5 85d89489e5596fc98abcb709ab5ad4c8
BLAKE2b-256 e68ebcf34cfb1deba69f68697a233f1a85bf3e4fbb0b17a9451fa9ae54e9f4e2

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-pp37-pypy37_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-pp37-pypy37_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 573204b8e5cf92888fb9b48b2842ae0bc9a025c7d17e7ec2ba77b52bf8d782e7
MD5 8b246cd666269c7fce79857d96382ef7
BLAKE2b-256 d3c6ea448cb3f41b782b0ee5401bfcd7f9da307fd92f0ea107ac0dd8803f8feb

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: awkward-1.10.1-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 15.8 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for awkward-1.10.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 1ffec27e0889080f686a6ab3fa06de753c2e91d732cfae56c13d1bc25f7e4fcd
MD5 d6c902a3a1d79f2ebd2d37d319c9a0e0
BLAKE2b-256 991fdabf694800bb7aa9d2ff5e7092272e4a8a980ab1595e50cd34ee96058bac

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp311-cp311-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp311-cp311-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 2526dbda27d895be49c53a93be54545075870b8d83fdf1ebe1c0839eeef11990
MD5 edb93ad61ea46be0bf4978d813ad0026
BLAKE2b-256 8ead30232044ba6bd0b830520344a9f3ddc35f092a0948161f294d6754f02dd2

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp311-cp311-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp311-cp311-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 554b03e7787e79919af9d68d457fa99ebae6a66e4745c4b3dc06cfc0c090b02a
MD5 fc6cd43979d339d408878ecf41d67e19
BLAKE2b-256 67045f9871a4f666cae4ddd585fb9011984c0e40ef22b27b4ad884689b3ddff3

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1d054e3edc20987507fef0d9e2fbf221b21f384b2f7b3921fe6e3b0d32054df2
MD5 5f1c0c0ebd3166ca273890c1acffc71b
BLAKE2b-256 caed39a3ce5817f45b739bb82c7f5cf950f733f45780dd2782bb8c9d5691db54

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 1d9d4eed8fe636477f60580b42ef9cac5a1d44a2c1ca8401e874fe56eaddac0f
MD5 8df6c137aebee4bc6f94db9e8fd2a7d2
BLAKE2b-256 9022e79e778399577ae3a169dc33fd364545dc39e8f85af4cdee1d6bb08e1b07

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3268545a4195557dd82073d4b562de30dc076e5a370d1cca9b1e93c42ae6d05f
MD5 97fcd1a1dac984878564cadc614ec604
BLAKE2b-256 4fb0810ce47b900d68d63b5e10fa273f07501471f280ccb00cd9d102f0921b28

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp311-cp311-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp311-cp311-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 3fa7b0bd01e21e772a17b37daee7c2637c7fcefe74729a082cd3614bac59e2c0
MD5 e088372904e38fa3a86ea75896703dcd
BLAKE2b-256 0e93e3e7fb245e43486add0b8136e3eedfb22b9f367a786eefb390b3fa53dad3

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: awkward-1.10.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 15.8 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for awkward-1.10.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 e7098af3a9387ed77414bc123ec10903f43c0f55ef454fdf624ad88af7fbab8c
MD5 da448efb75e416850b1627effed2af43
BLAKE2b-256 c506fb2327db3ed82c15706ad7bb3211bc90d386e2e65a7bf7dbea6de5b9ed7a

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp310-cp310-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 93e07a4a4fc0ed90e1ba12123d6042eed6a2742fb9565405a674a9763c0ad27e
MD5 126672159af804351517b332e283618c
BLAKE2b-256 2be9aae9369ff1eb3f637d0f49c37935c15e885823a9105ed2958bf3fc7baddd

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp310-cp310-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp310-cp310-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 112e9c9e8dddaf3c3916db56ca99b3215b388bcc01adf5a59b82d99b0d9edf7d
MD5 5093d532f2d9b19a0c9ab35bcf4b0990
BLAKE2b-256 2dd77e8bc729344bec525901a7bd3325375b6f0addb72cc0c1014c14b96083bd

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c7d5d71527a15e7e93ec61d079ac3b7308c18b1fc516a69a5160fb93f66848d0
MD5 d04f55dc76f486e3914ad90ed8885078
BLAKE2b-256 319c94bd5c8b9ebe53903a74f104646423dc31cbac266b6371f0be614eb1efc1

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ec863b5e41b227d196e765e59888015d09e52cd3544d476ceb2e690314516d31
MD5 b34f5045f58e55b62b88d9baabe88e2b
BLAKE2b-256 367e291681c90ec50dffef619e8521aa1c5aec42c6ee8707d9df7a48933c6e4f

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 62c910db4c8330f58d14fad22e35aee79c4a9ed1709eb76b69cbbd529f62a18e
MD5 5d8d645152c795e0ee048df30371d5c5
BLAKE2b-256 a018787cd2b89a2d5a34989a48378778e891d7074da7646b29006f5d85b71a14

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 fdb2692418ccb9b415914416b46cc1dbe9a91f2bf0897a804d18ddc7b9248ac1
MD5 29adb1f1e48874a4498dcce4e3d415c9
BLAKE2b-256 bc0b16aff90e0362ec3f8371e8c3d529e430cb44d946a8986d671e29edc4bfa4

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: awkward-1.10.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 15.8 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for awkward-1.10.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 71d00818839e75611953f06ace7b420d0cfee64286bd4fd67f38081c74f3ff21
MD5 d8f44cf6397929a7f0f8dac7f30ee6d3
BLAKE2b-256 6ec8b4402d67a1677182e563be05047e97fd31dd6d7e1c45379495708f4ce58a

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp39-cp39-win32.whl.

File metadata

  • Download URL: awkward-1.10.1-cp39-cp39-win32.whl
  • Upload date:
  • Size: 12.5 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for awkward-1.10.1-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 297c36953d591f254741a8248063fbeb6d9d83bbf159f2fd5985b9fc827412a9
MD5 ee814547653d20734b92479b791c880b
BLAKE2b-256 ef73c6cefb860c9ce9ebd41fa3b6b89ce1031c157fe51f284b3fde55885ffd98

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp39-cp39-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 2a4eb065d3758e4968c6386fa4324ed5923337325582b84d5687f748586aa2df
MD5 cdf60dbe4b9a12b99d356238d1e1531a
BLAKE2b-256 6dfc5e59ea84c3c64ea85cada97089dd56712a0ffd7f4fce755a09be425dc049

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp39-cp39-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp39-cp39-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 ba74ed94509de546c8c9f3726c3e8a9d5ab23cb9fc099f737051c74b79aa64eb
MD5 105d2fd8ba10e2123a30504effa9cafd
BLAKE2b-256 12bb524696d083d3a41d07d4370324c48863045209f5b5097848e65b7f59ecbe

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9c2a6003b2e04acaa112f82cd8a4ee008b987a8153d1e951b10ce0c0213a82a7
MD5 ab7c2dfcf494da2caf55cd288eaa9861
BLAKE2b-256 b485296217a9216dd09c2496470d3858a953355a3b582a443a2115303ad0bdae

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 219a59f51bbd10caad2c9204a7b82a3a7b665061784a7389a8e9204943f6cb21
MD5 1229f7717ab56c82727a8d48e21a6b54
BLAKE2b-256 5a3e50b0a00855c14c914cb6bf28a3452ca0f4ecce7c9d18b74bf84252cba9f7

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9482310f90b73d0db9d162f08f0eab48beeaea4c44f732af5b5b8a28d1cf71c6
MD5 da71cc94ef29ea8ea7feb00b28fa3d57
BLAKE2b-256 a182fe844675c9bed73f3dcdbcdb4a047dcb818618bf79e73df1a856baaacd28

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 b8c065a1e65826946a51d76c6e2d997725a56b7e9c215f657b13d5c03e9ebbb0
MD5 5ef5dc9233fd747fe00afa1487281d44
BLAKE2b-256 ce2779cfbabc96d83f42b700ba2e9d43a92c5bb771c3650f80aec0549f6b32d4

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: awkward-1.10.1-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 15.8 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for awkward-1.10.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 267d6b0444a47efbf87870a6dc9026206a4f397b8ab01c2048349eacc18ef020
MD5 a7ec76f4b47e8830a884236622e56d8a
BLAKE2b-256 225c5bd0fbf61cd1bfd063a40b668bdbe3d5f7126862b5f70bbe295a76e480b6

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp38-cp38-win32.whl.

File metadata

  • Download URL: awkward-1.10.1-cp38-cp38-win32.whl
  • Upload date:
  • Size: 12.5 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for awkward-1.10.1-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 2c261698e0578edc9615a914fe8da1ceb7eb4c4dc627366b716eb0f7b183f0ea
MD5 7dc6d6dcd6aa2278e20ca9587e72cbb3
BLAKE2b-256 a78fb642e35ab298d753f810d96fbe0561b0bcf3433ea6c6e821ca0795b1013c

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp38-cp38-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp38-cp38-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 3d0053d410a71102d69bf0cfa01abc848be510425ab97ab4b091300460cbe291
MD5 d250bf6d79ea9e636221a79526fea79a
BLAKE2b-256 74273e488095489ec19beb70ff49a37832dd4de3f85cb1b5ac469c58b2be1b28

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp38-cp38-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp38-cp38-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 c6dc939c60f0ea4eddc82b2e95204fb258d8a1588b26e1a632b094a04e2c918f
MD5 aec1324efc2fb9eff0f2e1041f758474
BLAKE2b-256 f3e5b57b1e420bd691b857c9ff2a6ebc84c872c7b5cab6d5b45d6c09d748a707

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0a9b0f62a15148419c938e04b8d037e404592016a6a9c3bbfd6365c05c4d3553
MD5 739c52697265b0df9d4d4a7cdaac3da2
BLAKE2b-256 1f4d9171f52d58adddf6fa61b94561477918dc153da24d326cf86401eba5f3ba

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 4b56802a1fff69c12d8185bb9f5e4342a4c618e1865918388851745fa07c769b
MD5 f9f77a1eac2aa32770cf8d8762dbf6ec
BLAKE2b-256 cac9d04e11493c02ef200bae4e231ecb922a11f33d3fa5b3f31f70d54a532b76

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 dfb5e5026cadb8b16d783eeaf4bc5310423e113c97291bcef6a61577aea2d7fe
MD5 0b72778aaa3d2f891f52893ba2dbd140
BLAKE2b-256 f46dfa6b27be110640bb3b4ac717e929edf092aaef3eb708f53d21e5064f0e1d

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 25f551d52e866d2162c66831d9d9347643a10246f518b7ff8ae9c9a002c02dd4
MD5 370ca992f8733cda9c8d2ace1017638d
BLAKE2b-256 a27492f040f2aff86dac9720d33f5403f5c0ccb0588ea964635e302862458fff

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: awkward-1.10.1-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 15.8 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for awkward-1.10.1-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 b0de180b0dc6f53ce8f3a8386a650abddf45928decf3b9c9cfdcbc2b9ef35b2b
MD5 3f9d3c9ad1908e832c506145aab6b687
BLAKE2b-256 adc37717e52ed0727cd083b25f4793a88a96f1442a4ef3f388cea37f0317a48b

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp37-cp37m-win32.whl.

File metadata

  • Download URL: awkward-1.10.1-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 12.5 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for awkward-1.10.1-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 394761e94560a34488aaf1bf2014913f2181f8088e7e52e63193bce2f955c593
MD5 1f2174546612a91fc3f23ab14eb53974
BLAKE2b-256 ce6a38d0cae4833312a9cccb408aa4f70e9dd3a8a40fe9801bcafbdbb0191262

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp37-cp37m-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 54d5ef172d10ee8d645d911577c17716a1992cadba54c00a53f7271b3e095794
MD5 9b070f847184bb13878b7e782a498b10
BLAKE2b-256 6b21eacefa740cc30cd62d34df640db53eaa099d22d4488d02cc299494028bd7

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp37-cp37m-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp37-cp37m-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 4de2d3ec1e9753c2c3d7f82d2f8673efeb31f6d6479ef1aeeca9819f04859e1f
MD5 16854fbbbfd020e2ca3fd7d472eb30b9
BLAKE2b-256 f17ea00a5422319fcfe41f1761e94bde071b8a1bc7fbf7e3082ac45029b3aa06

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3d1fb56085e19a396f82cef869fc0c7a79d93a6e14efd6b924a62e2f79548c98
MD5 715017e233e1372b287b5421486266a1
BLAKE2b-256 808e49e6a01e74ec78fa1dac8606b5441f002014eb4aa51d9ea11bf2be765bd0

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 531c870ee7da426809ff8f69d169a2c37a15c645a8232b13384c9b1dd52fa321
MD5 02cd3e8def04dd95fc373f8619da92ee
BLAKE2b-256 7f7c0c951da811d6328f5fd8be9aa9b30f0b0449f29923b8a20e4edb782957d8

See more details on using hashes here.

File details

Details for the file awkward-1.10.1-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for awkward-1.10.1-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1f430dbfa4e509da379ec165006c44e4e05334df051cf6b0df16a953909c0bc1
MD5 b48d6b180967f99b4c6bfc992f5cd1ac
BLAKE2b-256 e2d32ea790c4006135490f54cea411874ff1d2f9851a465b1750408ceaee9caf

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page