Skip to main content

A python wrapper for toml++

Project description

pytomlpp

Build Status Conda Status PyPI version

This is an python wrapper for toml++ (https://marzer.github.io/tomlplusplus/).

Some points you may want to know before use:

  • Using toml++ means that this module is fully compatible with TOML v1.0.0.
  • We convert toml structure to native python data structures (dict/list etc.) when parsing, this is more inline with what json module does.
  • The binding is using pybind11.
  • The project is tested using toml-test and pytest.
  • We support all major platforms (Linux, Mac OSX and Windows), for both CPython and Pypy and all recent Python versions. You just need to pip install and we have a pre-compiled binaries ready. No need to play with clang, cmake or any C++ toolchains.

Example

In [1]: import pytomlpp                                                                                                                                                                                                                                                                            

In [2]: toml_string = 'hello = "世界"'                                                                                                                                                                                                                                                             

In [3]: pytomlpp.loads(toml_string)                                                                                                                                                                                                                                                                
Out[3]: {'hello': '世界'}

In [4]: type(_)                                                                                                                                                                                                                                                                                    
Out[4]: dict

In [6]: pytomlpp.dumps({"你好": "world"})                                                                                                                 
Out[6]: '"你好" = "world"'

Why bother?

There are some existing python TOML parsers on the market but from my experience they are implemented purely in python which is a bit slow.

Parsing data.toml 1000 times:
  pytomlpp:   0.914 s
     rtoml:   1.148 s ( 1.25x)
     tomli:   4.850 s ( 5.30x)
     qtoml:  11.882 s (12.99x)
   tomlkit:  72.140 s (78.89x)
      toml: Parsing failed. Likely not TOML 1.0.0-compliant.

Test it for yourself using the benchmark script.

Installing

We recommend you to use pip to install this package:

pip install pytomlpp

You can also use conda to install this package, on all common platforms & python versions. If you have an issue with a package from conda-forge, you can raise an issue on the feedstock

conda install -c conda-forge pytomlpp

You can also install from source:

git clone git@github.com:bobfang1992/pytomlpp.git --recurse-submodules=third_party/tomlplusplus --shallow-submodules
cd pytomlpp
pip install .

Alt

Project details


Download files

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

Source Distribution

pytomlpp-1.0.13.tar.gz (1.3 MB view details)

Uploaded Source

Built Distributions

pytomlpp-1.0.13-pp39-pypy39_pp73-win_amd64.whl (183.3 kB view details)

Uploaded PyPy Windows x86-64

pytomlpp-1.0.13-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (221.4 kB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

pytomlpp-1.0.13-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (237.2 kB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

pytomlpp-1.0.13-pp39-pypy39_pp73-macosx_10_9_x86_64.whl (166.0 kB view details)

Uploaded PyPy macOS 10.9+ x86-64

pytomlpp-1.0.13-pp38-pypy38_pp73-win_amd64.whl (183.4 kB view details)

Uploaded PyPy Windows x86-64

pytomlpp-1.0.13-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (222.1 kB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

pytomlpp-1.0.13-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (237.7 kB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

pytomlpp-1.0.13-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (166.2 kB view details)

Uploaded PyPy macOS 10.9+ x86-64

pytomlpp-1.0.13-pp37-pypy37_pp73-win_amd64.whl (183.1 kB view details)

Uploaded PyPy Windows x86-64

pytomlpp-1.0.13-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (224.3 kB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

pytomlpp-1.0.13-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl (240.1 kB view details)

Uploaded PyPy manylinux: glibc 2.17+ i686

pytomlpp-1.0.13-pp37-pypy37_pp73-macosx_10_9_x86_64.whl (165.6 kB view details)

Uploaded PyPy macOS 10.9+ x86-64

pytomlpp-1.0.13-cp311-cp311-win_amd64.whl (183.7 kB view details)

Uploaded CPython 3.11 Windows x86-64

pytomlpp-1.0.13-cp311-cp311-musllinux_1_1_x86_64.whl (3.4 MB view details)

Uploaded CPython 3.11 musllinux: musl 1.1+ x86-64

pytomlpp-1.0.13-cp311-cp311-musllinux_1_1_i686.whl (3.4 MB view details)

Uploaded CPython 3.11 musllinux: musl 1.1+ i686

pytomlpp-1.0.13-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

pytomlpp-1.0.13-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl (2.7 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ i686

pytomlpp-1.0.13-cp311-cp311-macosx_11_0_arm64.whl (179.6 kB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

pytomlpp-1.0.13-cp311-cp311-macosx_10_9_x86_64.whl (188.0 kB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

pytomlpp-1.0.13-cp311-cp311-macosx_10_9_universal2.whl (369.5 kB view details)

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

pytomlpp-1.0.13-cp310-cp310-win_amd64.whl (183.7 kB view details)

Uploaded CPython 3.10 Windows x86-64

pytomlpp-1.0.13-cp310-cp310-musllinux_1_1_x86_64.whl (3.4 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ x86-64

pytomlpp-1.0.13-cp310-cp310-musllinux_1_1_i686.whl (3.4 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ i686

pytomlpp-1.0.13-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

pytomlpp-1.0.13-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl (2.7 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ i686

pytomlpp-1.0.13-cp310-cp310-macosx_11_0_arm64.whl (179.6 kB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

pytomlpp-1.0.13-cp310-cp310-macosx_10_9_x86_64.whl (188.0 kB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

pytomlpp-1.0.13-cp310-cp310-macosx_10_9_universal2.whl (369.5 kB view details)

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

pytomlpp-1.0.13-cp39-cp39-win_amd64.whl (183.8 kB view details)

Uploaded CPython 3.9 Windows x86-64

pytomlpp-1.0.13-cp39-cp39-musllinux_1_1_x86_64.whl (3.4 MB view details)

Uploaded CPython 3.9 musllinux: musl 1.1+ x86-64

pytomlpp-1.0.13-cp39-cp39-musllinux_1_1_i686.whl (3.4 MB view details)

Uploaded CPython 3.9 musllinux: musl 1.1+ i686

pytomlpp-1.0.13-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

pytomlpp-1.0.13-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl (2.7 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ i686

pytomlpp-1.0.13-cp39-cp39-macosx_11_0_arm64.whl (179.7 kB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

pytomlpp-1.0.13-cp39-cp39-macosx_10_9_x86_64.whl (188.2 kB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

pytomlpp-1.0.13-cp39-cp39-macosx_10_9_universal2.whl (369.8 kB view details)

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

pytomlpp-1.0.13-cp38-cp38-win_amd64.whl (183.6 kB view details)

Uploaded CPython 3.8 Windows x86-64

pytomlpp-1.0.13-cp38-cp38-musllinux_1_1_x86_64.whl (3.4 MB view details)

Uploaded CPython 3.8 musllinux: musl 1.1+ x86-64

pytomlpp-1.0.13-cp38-cp38-musllinux_1_1_i686.whl (3.4 MB view details)

Uploaded CPython 3.8 musllinux: musl 1.1+ i686

pytomlpp-1.0.13-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

pytomlpp-1.0.13-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl (2.7 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ i686

pytomlpp-1.0.13-cp38-cp38-macosx_11_0_arm64.whl (179.4 kB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

pytomlpp-1.0.13-cp38-cp38-macosx_10_9_x86_64.whl (187.9 kB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

pytomlpp-1.0.13-cp38-cp38-macosx_10_9_universal2.whl (369.3 kB view details)

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

pytomlpp-1.0.13-cp37-cp37m-win_amd64.whl (185.0 kB view details)

Uploaded CPython 3.7m Windows x86-64

pytomlpp-1.0.13-cp37-cp37m-musllinux_1_1_x86_64.whl (3.5 MB view details)

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

pytomlpp-1.0.13-cp37-cp37m-musllinux_1_1_i686.whl (3.4 MB view details)

Uploaded CPython 3.7m musllinux: musl 1.1+ i686

pytomlpp-1.0.13-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

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

pytomlpp-1.0.13-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl (2.8 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ i686

pytomlpp-1.0.13-cp37-cp37m-macosx_10_9_x86_64.whl (186.8 kB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

pytomlpp-1.0.13-cp36-cp36m-win_amd64.whl (192.6 kB view details)

Uploaded CPython 3.6m Windows x86-64

pytomlpp-1.0.13-cp36-cp36m-musllinux_1_1_x86_64.whl (3.4 MB view details)

Uploaded CPython 3.6m musllinux: musl 1.1+ x86-64

pytomlpp-1.0.13-cp36-cp36m-musllinux_1_1_i686.whl (3.4 MB view details)

Uploaded CPython 3.6m musllinux: musl 1.1+ i686

pytomlpp-1.0.13-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ x86-64

pytomlpp-1.0.13-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl (2.7 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ i686

pytomlpp-1.0.13-cp36-cp36m-macosx_10_9_x86_64.whl (186.9 kB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

File details

Details for the file pytomlpp-1.0.13.tar.gz.

File metadata

  • Download URL: pytomlpp-1.0.13.tar.gz
  • Upload date:
  • Size: 1.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for pytomlpp-1.0.13.tar.gz
Algorithm Hash digest
SHA256 a0bd639a8f624d1bdf5b3ea94363ca23dbfef38ab7b5b9348881a84afab434ad
MD5 1004717454bb9bd875686265b802e204
BLAKE2b-256 406dabdccdcd9182ffaf4da11a5871762045b2f444c09bc3d8360456475188ea

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp39-pypy39_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp39-pypy39_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 dbc9208ac58ea2a9d5ebb77e69d54d146744007f4a704a3f4e56d9881d41ee1c
MD5 6e1917e8abca7fcd62c85b5049b4eeb7
BLAKE2b-256 4a61167ed31468a6364fa7821a0ad573faa980c2f95862437037f8171b59af2c

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 35225c1d9d674df87b4682f04af97856049351c38822455b78258248d9309363
MD5 ccb5b59c5d5f9399c289f2952099db22
BLAKE2b-256 ce28880f26d7148c3abd45e3619b3f435ce53a72e08fc8fd8776be173eb0763b

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 ac06ca7683f5a2737b3888ea1e38d6968abb24fab703bc7ceccbe589d5420e0c
MD5 20347f5aaa7f0b0bdd296a240a2c9573
BLAKE2b-256 7ce02183caea3aa9da1506c06c7ce7fe0c10e344eab9a6e65c80ad807dbf29d1

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp39-pypy39_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 0e0b34b7a132856567714342e9a622f7be0b4c9bac561a6252f0f85626c1aa4b
MD5 4d77aff940647f32269e2e5b9b9239cd
BLAKE2b-256 1564cb561fd90661a54a5283a1d54747f1c797b31418b426296a113d8f95d95d

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp38-pypy38_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp38-pypy38_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 aad6ae19c056ea62a43fec82427ad4675b5c773dc255c4bdcf6da659cd7edff6
MD5 ca6d5664b43fc7a10c555b3f32107921
BLAKE2b-256 a41a4e841529c144032d013d60ac270fb8fee3a38760c0a4c3f294ef697aa682

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e285aca948b419301fdda1927723287ef28482752782c44c9ee8c57eae7a1dc8
MD5 35e8d50072088356a04717662fc4796b
BLAKE2b-256 9393a876b9360c0f1df5e0ad7c3dbca6245dcc8a99a3235d5b578bcf51146883

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 9f87f6c958309e4c2358b778902c80bd33611d1c392f1abe2c226e3a62909ca4
MD5 a2014960d51cacf28e243fcf65074c43
BLAKE2b-256 0c5f3756c4ca9a58237d6dce97d141e227ea3a5745597afae67f526607b09bda

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp38-pypy38_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 19dbded2995370e802105fa6dce54ed60f79e58b4eb35fee7ef33f1fb5958f6c
MD5 949df7f7e21ed92a1814303328e318d5
BLAKE2b-256 5592277f9c4e54c18853875fa6221ea1299a8cf855f76edfcb7fdde5c72e51cf

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp37-pypy37_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp37-pypy37_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 09e716c0f462d15f2334cecc736957777dd30f8a5bfa5cf8150679da7577d2fd
MD5 8c352f308018686c35f295ec81fb0286
BLAKE2b-256 e0aa7ebf8150c491bdbc8903c3c8c43ff4b52add448734ee4a2d2e09208aa8a3

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 252e31a5e013a74b898784f4ffb8aa8068e136b910ad11f2af1ee8a5700e6e1e
MD5 975dc2bdd6684398538878aec28d09b4
BLAKE2b-256 4a6003da4e014808f92943f0d85621795e295e843e30baf3e3bbd8e38ac54e23

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 b59acc12339a992404289ab7294f28ba06c7df3c2562e81d316a0e744ab4103b
MD5 dd708f8cd1062494daf410217cdc8f05
BLAKE2b-256 c8f3b21c1eb4ccf9f0b173da1e838aa5ab3da57ae58b5de99fdf16bcffd23436

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-pp37-pypy37_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-pp37-pypy37_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4710c72456c10a90e58084174312abef8f9652b0f91c240c008903c1bd99814d
MD5 55f92f6882f2226c915439ff7cb8afb6
BLAKE2b-256 3e13d917c4564b8ed07bc21a9c53dd3134a704b4aa6815f9e5d9f8e2251a6d33

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 1843cd3816e25453bfcac5468106f9f39e766bda198bd69d41e09184a6062497
MD5 c0d374601dc85283a8a1072f38d31282
BLAKE2b-256 43d5c3858213aa3185f3400a5ca2d52190a0229bbc1a04cb76d751603293ffae

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp311-cp311-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp311-cp311-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 b606dea8b05d2f5915a4d6f88e7772eb4772ed8a65c113b15bff5754982f48df
MD5 114a114f3bab00411c76384008f3c8b7
BLAKE2b-256 2fd7ec804254100f3313a9fe995efce1121646e7b4de56e2e65f72a4fe4f1060

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp311-cp311-musllinux_1_1_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp311-cp311-musllinux_1_1_i686.whl
Algorithm Hash digest
SHA256 3398853a6ab98ccb5e722b9d2f1ac127ea2a82d267dcff8ff7dc98a472f70ad0
MD5 2ab71684c8336903a762f5a99990d495
BLAKE2b-256 b1db0efbbb2bb47ce7ee1ee909e472f3ac9699a9c1ab2fd1d06584efd28c835c

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7d2e7be5ddf349fbcfdd1cfcee630c4ad33031411a9dded93a96d186f2086038
MD5 18a727f15c66e7c1a0759fe3bf28ef90
BLAKE2b-256 ba3beb65b8896fd8a9baf0c6f0bd223a5effb254d334eeee13cd5152588df4be

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 d4549130097ca8b7aada5f6e8440db3dbc36f0f3df24231b3521297a5e3cecf1
MD5 01f942726519a441723847e6937439c8
BLAKE2b-256 9b1f5002903b80aaf674d2819f67daf98397886048ecb8cc642c999a412a32b7

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 aa016e89a76c3ed58ea7a1eb2cb795ee1b1aa2831bb47c724ae787cb03dcf790
MD5 d4d2ca80f23c169ef04fcda4f2a9b294
BLAKE2b-256 a9b3852392d4f3af192aa95690395d87d9aadc07593da2442542240d9f8c6807

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4d7c0e8a1e21249df4e60f07a728129f33899b7548ff391343b244e1222296b1
MD5 6b521d23780f60dd9dea711bf043c492
BLAKE2b-256 fa1b282e961426b64ecd9e9256cd2a4356c6d34fe8e1a1d3f7eeb2ed3db3299a

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp311-cp311-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp311-cp311-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 47684714f9ed1a880e66b2e183789519a9ad7b55f2da9e30b65090a42342ef83
MD5 41fc0dc738448656174e98cb5fcb551f
BLAKE2b-256 ac17f8129789e66cdb6db6993348ec2904e72f8f5d50979fb650f92a453a8138

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 fab6e69a754d3be5d8580753feac56fa70d2f4f9388f1d7a30c719ceca2d3ff2
MD5 4e530f603fdcb4e4bc83d68d709c7cdc
BLAKE2b-256 1eecb0c1b681256664f7e10de94b0faaaea1258b8d113be82222b6ef53e7bf63

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp310-cp310-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 4d325590d276104e4030c7907dd559148566347e840cb1c3874671ea09491789
MD5 b9233ff93a69c49b0ef4ad1baa38decc
BLAKE2b-256 c4838d16dbe3e785b644e7247b711c90272a6fd1e5340931b8c479a37f32daec

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp310-cp310-musllinux_1_1_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp310-cp310-musllinux_1_1_i686.whl
Algorithm Hash digest
SHA256 5b9a8b2179271abc54c0879cccc880b50918409765074c9ee25d45545f60b2a8
MD5 05092c4f0b99e1711087fc6cc7cbd385
BLAKE2b-256 fb557775c4a5b8d8bc7ce7df23a95fb811b97e2bcf82d290274f55524ba169ad

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 68d1743e03806e67a0b5daefb8f548609ffeec4ab94d91092440325c9e22bc77
MD5 4fd6ff98e84e309f74f7dc6dc7b332cc
BLAKE2b-256 33c1f519be8f1f38c1dc448c6784baf9c43bc5a0c22564b742da9a84545000ba

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 cc31231a57b45499c68b7c6c7c7d176874c1b4d3c236e3e4ecfc005642496815
MD5 8bdccc8375ee7e6cb9b707b6b4b213ad
BLAKE2b-256 44ea1ab7e760bbd6c08899718ce477e7c292df308c30cf1c96fa10661291ae97

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 98ae1451dcabb3449df8a097014b2d5cdaeb8961f16cced83462dfb704b61d12
MD5 65dd9bfbac05dd3d67cf808daa1011c0
BLAKE2b-256 760f2ddb15b31da4c48c96bd4c4520202fa47fddaae84b66c1fa70451c0c300d

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 23d7ac98b01965e17c604bc86c77f751e4f91055c68397a724e7a05cd91c04fd
MD5 861c4923dd5cff51fe071a6150bbbe2f
BLAKE2b-256 43059817cd9b4c29eb69c6bf122972dbbf834e317259a6eebb32bec48addd1ca

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 131644b5c32a0b32667875883251fa01a6a852e0386e5db8d0f70ddf44bebe3b
MD5 d2e89ffa1b4633b03c8e0fe1a8bdd0d0
BLAKE2b-256 e7e6ce9c27141406c7453b8627061a8312b22e49b5edabbb5a6401d8c836653f

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pytomlpp-1.0.13-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 183.8 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for pytomlpp-1.0.13-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 b5e89ea80cd25732a2789d67075b72d64133fdf13490aa058abfe9e4964880e4
MD5 344722deae220482aea3889634e7a481
BLAKE2b-256 8e3c5ccc65372a69a32101c8d651e0e117d302e07b81b84aeaa5b618f4e0c92e

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp39-cp39-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 62150ce6f7a47a135ba4375a8075eb321d1064ea2295f59fd92d5d148b7093f0
MD5 a6e09279fb7518f148a1c81751929271
BLAKE2b-256 f0ad4b72121a9c6dc6d8f7d345988b2f232e8452ea7053cf6824627b9453fa32

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp39-cp39-musllinux_1_1_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp39-cp39-musllinux_1_1_i686.whl
Algorithm Hash digest
SHA256 abbca560310f4bc1f0dea15e77042da03d2a9437ffc239fd949fdcb4302bd85b
MD5 35383b03edf0628a60fa860c58228b6c
BLAKE2b-256 2695224c01a107f3ae6f3a64148018e49e9693dcb880dd8a3b9471a4b6131a36

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 20a1f6584814f7c9db63f1f590868788aff86201e2b49e89f76772346b79606a
MD5 4bfb1bec0660b3e995a2a71f90aeaa06
BLAKE2b-256 de6f4f78dc9028b48bf469309ff49b7babae247f40f8ac680e7d8e588bca214b

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 f931803e4f48df82ac86ddc075a16635d57018bbac779e0258c896544f5e8ec6
MD5 7793cfd3855ee9acf05cfa512c60d3d9
BLAKE2b-256 d389a4dc3810199259823728096186a9a7af4058bfcff9c434685a8f81cf4a7a

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8287ded4f27d9f54d017e996480e95ebcf9b2fd8381d4bc755f39fc0b2f70629
MD5 80691d31bc5fc89c86d20eaa56cf1fb8
BLAKE2b-256 9f9617337d4b27cc1b94db95daa2d826954dc566e19eee1d2533a382216c8543

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 98510ef5b92f8f7c5e42280948301f542f0086c39bf879f140e111a987f521aa
MD5 3fe84f9820a6681a36d220e6afbec9ad
BLAKE2b-256 5a53840d6ac55c8221afb09cfa6b12d3e561439c4e536aa7cf9367dffa5c1021

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 2fea0318339407d0a9a097d63e9c020cc1bdb0b2de90b5ba6cfb3eabfdbbdfd1
MD5 04fc5cb3d4a1caf552a25497a3947d9e
BLAKE2b-256 886c4618510fc2f83cf6a966543773ffd1e93c2e3c9426e78e727cb80a8de4d9

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pytomlpp-1.0.13-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 183.6 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for pytomlpp-1.0.13-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 a43b2be6182d56914e9cf8aea25cd756b6c7415395ed223b7fc270582a0a4fd2
MD5 6d83905819c842939435497285223c37
BLAKE2b-256 1ed68bbc68fa7d95448305e3c4e9d3f5b200d7b0e1c31decb82fd58165af9562

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp38-cp38-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp38-cp38-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 b95933988d11d6b54beb1dbf5d13b7afb4f60c2d014dfaaa5c7df44393e23537
MD5 09316911b3c86be68bae7b5d41d8d6b1
BLAKE2b-256 c593415ee10efd30a4ff794103d8cbc91bb5c79df29a7efe68b2ec957f548b70

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp38-cp38-musllinux_1_1_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp38-cp38-musllinux_1_1_i686.whl
Algorithm Hash digest
SHA256 0cf58c70c6cf97d8f98f1931c168b5a2b4c894e5bfd46bd574f0ea0668e8d2b2
MD5 5a641905a6123dd46942babc3b87ea52
BLAKE2b-256 69311c332aa1145ab6f69b50416822f4190a7988aa1cef8d0bfcd419b5f8553c

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ce78fab1987ff48d42e4f4759d765dbf183824a56862f35f500ce0cfc974b5ef
MD5 efae0e63aff4c7bf9b823df90e00f5db
BLAKE2b-256 9b6da9ba483ce3fda8addd3052a052cfce924666c40f6fdb4853a9522b9a5237

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 88facf9533603b8e2a502f4485641dcb3121d20ccff78f1167b38efc1c7eb9a4
MD5 09ba20f986e846f62a3013337eea794b
BLAKE2b-256 7e41b83f438c7ce61b63511530d2b9fdf014b48155fc53bdc2720df0cf76ffaf

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 2fe54bcd5911b33410a7a7e9cad66d016b056b331cfdf9e5e9d8b404339b1003
MD5 2a0e2ebbe3f16a95898bfcfba37045a4
BLAKE2b-256 0ebc37667e74b34965df03472d815d2f4922c21f71d703bab5939811d5c6feae

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 73062c46d315e1f3cdf631fb8761e0981dda0df346723ca50355c4311167fbfa
MD5 e18cd94af1f14cb333ee3d60ccd29023
BLAKE2b-256 0507681ca123b072fe87a60590968546ed2de7e00f1c78df86806b227fbb4156

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 d50395394963529940703638b422b19d84c5a003a7eb0106ad7c7347ad6f20c0
MD5 505aae6c1411fb2dba36e739fd95d3e6
BLAKE2b-256 e4028b46adda6fd287f8ae679064601d43929e4ef37d302e796ff68b47470e6c

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: pytomlpp-1.0.13-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 185.0 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for pytomlpp-1.0.13-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 7832307f4bc0518f78f0afc615cb8135400522be8676eff47ece5dfbca240115
MD5 34e3009d6abc08c3624c383936d9f7e8
BLAKE2b-256 6b5e5d0242c8de753f8e7bb8e2d8f221d854f3868af3822af1b762326cf05c31

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp37-cp37m-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 70b43d955fb53ca9186dafc95fa24ea783870a226bf875b833c25dd5865ed526
MD5 d5068bca1fc76c013f307f33f848c0f2
BLAKE2b-256 63c0fc0e317e190bd1780532e60db32ed0c1c5bdfd514d823d026db5f87100fa

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp37-cp37m-musllinux_1_1_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp37-cp37m-musllinux_1_1_i686.whl
Algorithm Hash digest
SHA256 2e2a94463207608d1cba0c36ea4f882e0b409e28590e2dbc48961dea59f170c0
MD5 3718a80aacc481868a575722251ae522
BLAKE2b-256 3afacfa6b906b8fe81fd303d0a9de7f32462d09937fe9732da509b974d7526ab

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e030b4440cc65937ae9701448615ba445ea2c1ff20fa630c149e368e06f9b082
MD5 115afbd008b8433c2ab51d4155482cee
BLAKE2b-256 74b1287c9482cf6722ece42bc83db7ecc7c80b264ac4272221c77764eb53f379

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 ffae01816f10fa29e0cd30bd8709573de5ba1ec57dd5901ab6e2f8c7c199ed7a
MD5 3def70d82b712c00d00c16a5d15ad98d
BLAKE2b-256 167d6a1814e4db8862e726613a2fd1e3e5f769033ba4ff9d5ef432ab202c2fa9

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c155e2b72844dd11ca0fdfc3bfb44bc41770ba34803b67621e41496f67b70453
MD5 4945c5ffff0300879afe161755fe9089
BLAKE2b-256 7f9949524953be52adfbd16980f97a9a11fda2b2742b21d3ab87d3c7252eed90

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: pytomlpp-1.0.13-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 192.6 kB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for pytomlpp-1.0.13-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 7443a2dce8f48c0801c1f5b3ea78acbae30fb263480570f8b68b198620041afa
MD5 9e10d5da61f023cff689412ff146bd3b
BLAKE2b-256 51042a0e64a38535429654f3305d22651845c4f92286b1cf74e30d0b82b744ac

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp36-cp36m-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp36-cp36m-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 807f8cfff2195b68fbefe8122561be6ced51009f5c95c422f781bae5d7d90fc4
MD5 c88fe4febe07100502b96da0c69ebd74
BLAKE2b-256 afee3fd6a6f336fdc943e6e15dd865adf9af6033da66f349bfe3b84679ad187c

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp36-cp36m-musllinux_1_1_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp36-cp36m-musllinux_1_1_i686.whl
Algorithm Hash digest
SHA256 988288644025dc20a997a5caa8d6d283ad94bdbc30415a965a12b82bc77026c1
MD5 57c13e3ef5899232a09a312d4c7bf031
BLAKE2b-256 f5149087fa7eceb352d0e819b35469616dd7ae030fe50c26803f1791c078a18d

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a2752caeb649c04dcdfe78dd43d63b9c03db01d41f4e97af133f214cf6ee5f09
MD5 6d6c855a61b6e4048abf1bd33a8ca318
BLAKE2b-256 389b07506c785dea603107e29e3c56276bd1ebe23c591550f39c7e9a29b1e9d4

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 87256e701a6237a178739323394e5abfe3bf5fa5eb0188d8710839412556b56e
MD5 4bbcb087e728ea400869cc1ad9038a9f
BLAKE2b-256 992a813c06f641a1a35953d362777166decd722bbf20fea0a94351ce1f22abde

See more details on using hashes here.

File details

Details for the file pytomlpp-1.0.13-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pytomlpp-1.0.13-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 adfbfb981bcfe31cec4443b2410ae65cea6ec37b13396f7a0a66ffffd418a075
MD5 a277c350921902e5cdaef49fb0d9a74b
BLAKE2b-256 8a8b156f80e2551f65b83b5eeb5a185ff01df3e45d9ec3cdf6fc552dedf4fa31

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