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
pytomlpp-1.0.13.tar.gz
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Hashes for pytomlpp-1.0.13-cp38-cp38-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
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SHA256 | 2fe54bcd5911b33410a7a7e9cad66d016b056b331cfdf9e5e9d8b404339b1003 |
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MD5 | 2a0e2ebbe3f16a95898bfcfba37045a4 |
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BLAKE2b-256 | 0ebc37667e74b34965df03472d815d2f4922c21f71d703bab5939811d5c6feae |
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Hashes for pytomlpp-1.0.13-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 73062c46d315e1f3cdf631fb8761e0981dda0df346723ca50355c4311167fbfa |
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MD5 | e18cd94af1f14cb333ee3d60ccd29023 |
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BLAKE2b-256 | 0507681ca123b072fe87a60590968546ed2de7e00f1c78df86806b227fbb4156 |
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Hashes for pytomlpp-1.0.13-cp38-cp38-macosx_10_9_universal2.whl
Algorithm | Hash digest | |
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SHA256 | d50395394963529940703638b422b19d84c5a003a7eb0106ad7c7347ad6f20c0 |
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MD5 | 505aae6c1411fb2dba36e739fd95d3e6 |
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BLAKE2b-256 | e4028b46adda6fd287f8ae679064601d43929e4ef37d302e796ff68b47470e6c |
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Hashes for pytomlpp-1.0.13-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
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SHA256 | 7832307f4bc0518f78f0afc615cb8135400522be8676eff47ece5dfbca240115 |
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MD5 | 34e3009d6abc08c3624c383936d9f7e8 |
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BLAKE2b-256 | 6b5e5d0242c8de753f8e7bb8e2d8f221d854f3868af3822af1b762326cf05c31 |
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Hashes for pytomlpp-1.0.13-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 70b43d955fb53ca9186dafc95fa24ea783870a226bf875b833c25dd5865ed526 |
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MD5 | d5068bca1fc76c013f307f33f848c0f2 |
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BLAKE2b-256 | 63c0fc0e317e190bd1780532e60db32ed0c1c5bdfd514d823d026db5f87100fa |
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Hashes for pytomlpp-1.0.13-cp37-cp37m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
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SHA256 | 2e2a94463207608d1cba0c36ea4f882e0b409e28590e2dbc48961dea59f170c0 |
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MD5 | 3718a80aacc481868a575722251ae522 |
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BLAKE2b-256 | 3afacfa6b906b8fe81fd303d0a9de7f32462d09937fe9732da509b974d7526ab |
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Hashes for pytomlpp-1.0.13-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | e030b4440cc65937ae9701448615ba445ea2c1ff20fa630c149e368e06f9b082 |
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MD5 | 115afbd008b8433c2ab51d4155482cee |
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BLAKE2b-256 | 74b1287c9482cf6722ece42bc83db7ecc7c80b264ac4272221c77764eb53f379 |
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Hashes for pytomlpp-1.0.13-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
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SHA256 | ffae01816f10fa29e0cd30bd8709573de5ba1ec57dd5901ab6e2f8c7c199ed7a |
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MD5 | 3def70d82b712c00d00c16a5d15ad98d |
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BLAKE2b-256 | 167d6a1814e4db8862e726613a2fd1e3e5f769033ba4ff9d5ef432ab202c2fa9 |
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Hashes for pytomlpp-1.0.13-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | c155e2b72844dd11ca0fdfc3bfb44bc41770ba34803b67621e41496f67b70453 |
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MD5 | 4945c5ffff0300879afe161755fe9089 |
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BLAKE2b-256 | 7f9949524953be52adfbd16980f97a9a11fda2b2742b21d3ab87d3c7252eed90 |
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Hashes for pytomlpp-1.0.13-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
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SHA256 | 7443a2dce8f48c0801c1f5b3ea78acbae30fb263480570f8b68b198620041afa |
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MD5 | 9e10d5da61f023cff689412ff146bd3b |
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BLAKE2b-256 | 51042a0e64a38535429654f3305d22651845c4f92286b1cf74e30d0b82b744ac |
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Hashes for pytomlpp-1.0.13-cp36-cp36m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 807f8cfff2195b68fbefe8122561be6ced51009f5c95c422f781bae5d7d90fc4 |
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MD5 | c88fe4febe07100502b96da0c69ebd74 |
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BLAKE2b-256 | afee3fd6a6f336fdc943e6e15dd865adf9af6033da66f349bfe3b84679ad187c |
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Hashes for pytomlpp-1.0.13-cp36-cp36m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
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SHA256 | 988288644025dc20a997a5caa8d6d283ad94bdbc30415a965a12b82bc77026c1 |
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MD5 | 57c13e3ef5899232a09a312d4c7bf031 |
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BLAKE2b-256 | f5149087fa7eceb352d0e819b35469616dd7ae030fe50c26803f1791c078a18d |
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Hashes for pytomlpp-1.0.13-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | a2752caeb649c04dcdfe78dd43d63b9c03db01d41f4e97af133f214cf6ee5f09 |
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MD5 | 6d6c855a61b6e4048abf1bd33a8ca318 |
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BLAKE2b-256 | 389b07506c785dea603107e29e3c56276bd1ebe23c591550f39c7e9a29b1e9d4 |
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Hashes for pytomlpp-1.0.13-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
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SHA256 | 87256e701a6237a178739323394e5abfe3bf5fa5eb0188d8710839412556b56e |
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MD5 | 4bbcb087e728ea400869cc1ad9038a9f |
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BLAKE2b-256 | 992a813c06f641a1a35953d362777166decd722bbf20fea0a94351ce1f22abde |
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Hashes for pytomlpp-1.0.13-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | adfbfb981bcfe31cec4443b2410ae65cea6ec37b13396f7a0a66ffffd418a075 |
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MD5 | a277c350921902e5cdaef49fb0d9a74b |
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BLAKE2b-256 | 8a8b156f80e2551f65b83b5eeb5a185ff01df3e45d9ec3cdf6fc552dedf4fa31 |