A python wrapper for toml++
Project description
pytomlpp
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 withclang
,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 .
Project details
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Source Distribution
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SHA256 | d6f21ee29efcd30bf85a5d1e25055599412940ff3b1773d991a2af387c824047 |
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MD5 | b24c9c2ea2266249b2a76297a3b57349 |
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BLAKE2b-256 | cb5a27de89c394146e3b28b4ec058815334e838d58bab9d941953d3f9626f398 |
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Hashes for pytomlpp-1.0.12-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 54fdbb023426087a4934356ca558842fec3112b409592d7fc1fcbf1060609618 |
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MD5 | 93b07585e8911c57ac8ed9d094e69d4d |
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BLAKE2b-256 | defc66bf3db3f3de487ebf2ffbfc5a6ade95d69220cc04c50da00fa56388ad2f |
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Hashes for pytomlpp-1.0.12-cp38-cp38-macosx_10_9_universal2.whl
Algorithm | Hash digest | |
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SHA256 | e07a2928a1cedd969fce4e1ac1764a3573b54b48d101b74b2fa9d1c011cd1870 |
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MD5 | 9182e013bee81ae4e8bc0cf1795a2f9a |
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BLAKE2b-256 | 3d57a840ed1bd9abebe54a4199764ebb0dc9a00795ddc19d15ce99b47b7b4b61 |
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Hashes for pytomlpp-1.0.12-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
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SHA256 | e534581005c57d1cee57e6f6106e92da7c68a7908a4c14a2571ba7fe6dd580a0 |
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MD5 | 41eb82df493bbb3969f996c6dae7f10b |
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BLAKE2b-256 | cac7fbb39896c57e9ad0b4ec15410ae1487b412f408f02c3d4c259f2cef4ca06 |
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Hashes for pytomlpp-1.0.12-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 4d2f6d2e22a99ebcddb98057886ff0e1e7ae561040f33f468c0fa7e1ceb5c574 |
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MD5 | 8ee8d49c72b10206a3a91e62039f4ba2 |
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BLAKE2b-256 | 476cf7bd8e0fefaa2ca975c5100f2f0ddfdfb2b33dc5c4e735942990d91c521e |
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Hashes for pytomlpp-1.0.12-cp37-cp37m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
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SHA256 | 6a81ed11ab4e51d288f6215ec37efc7776f08377dc39b481ee1df42892b82874 |
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MD5 | 4060c215ac748d3d63c95dfcf4746c20 |
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BLAKE2b-256 | f2be45e8ca7d19feccdaf37e2d2b4fd901ca4d3abc651e3332d1a4e2b23479a9 |
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Hashes for pytomlpp-1.0.12-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 9b41488626518c95075a396f45560b6dd991781b4d99f29e8bf1a959de31fdb6 |
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MD5 | 339ede4357f5964bde90db34720b9bf2 |
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BLAKE2b-256 | b6eda9fda6b3a386d55a3f736abd49cb6b32252b7b47c48a8ad39e07d52f3eed |
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Hashes for pytomlpp-1.0.12-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
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SHA256 | 108a7d95c0ee1c10ac0d01aaea4f7183dd15a2e757f79f4976173eaf4dc108f1 |
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MD5 | 230c02e9fee414253089b42d295d4e03 |
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BLAKE2b-256 | 95e31ebf77fbe8b84275a17184de7a327ec3e0f74c2af4b2e52095cea3b1665f |
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Hashes for pytomlpp-1.0.12-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 5b6fa079ce402795afc1e51f4431ab64f2122759cc00cb459369ed662b0fc5a2 |
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MD5 | f169210428fdb6220ef82e918cda6b27 |
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BLAKE2b-256 | dcbe1653343681b3d3cf2c8a11158506b9ac3a98c7c45dc2a784a1d8dee4e44c |
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Hashes for pytomlpp-1.0.12-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fe99e307821208ac21b53fcabeeb6c95c29ddc2657ffb64d53d5be26ee44eefd |
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MD5 | 3759e53ebeccb1a276348fb6d6023891 |
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BLAKE2b-256 | f9ed60c0d4961be7c6cf45e6a97b6dd736e8663d6aeeb0f0af5f04fc8f7e1d2a |
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Hashes for pytomlpp-1.0.12-cp36-cp36m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 293c24e0141024bf3130f7dd118eae6962e945ed55bc8f05e1f9320251701dd8 |
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MD5 | ccd3bee26a993b05037e26900163aa5f |
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BLAKE2b-256 | 395b740f3fb7920ac1737ca1e501ee4cce09abbbc1f75e5acf7222d1c49e3947 |
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Hashes for pytomlpp-1.0.12-cp36-cp36m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
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SHA256 | 8aed1f28e9584865296b4fa3d1fa9efbe22525849bb928f3e70bcf44f32c78e0 |
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MD5 | c585c2b5490843d5d1300b00c35724f0 |
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BLAKE2b-256 | bc2ad8406622ea6bf3f584c6276e82a95623fa9c176ebae465fc4674f7420f6b |
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Hashes for pytomlpp-1.0.12-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e4442e8a521ebfdb017046801f132c449c94d1a0ec8369facdf9cf836e427cfa |
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MD5 | 1e1be93fe425b880579a57a4994ea60e |
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BLAKE2b-256 | 77ea7b0318a2297537111cdc8b57dbf4a1ae5b6426bf528c0973e7806ca1efda |
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Hashes for pytomlpp-1.0.12-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
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SHA256 | e6086ce54c2df71a9f9f89e7b1f86dcc29439ae2b308f751bdc70a8dea6c82fd |
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MD5 | f9624bc3ae4472f51a71a5dee0415cae |
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BLAKE2b-256 | 41c564a6b1ff6a4792e6ced182aa750255cdf4eac7f78fc123f34dd9f8f5ecc0 |
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Hashes for pytomlpp-1.0.12-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9d7340b35b092f91a7847279b393735f113113b69eeedd1433b8fe079460c2de |
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MD5 | c9facad17c17d65baafafb46addea851 |
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BLAKE2b-256 | 5e1e3596f5b0d1192228c27f9953c2ba7ac50cd549bc6a519d0e25d664d30d1e |