.. contents:: **DataProperty**
:backlinks: top
:local:
Summary
=======
A Python library for extract property from data.
.. image:: https://badge.fury.io/py/DataProperty.svg
:target: https://badge.fury.io/py/DataProperty
:alt: PyPI package version
.. image:: https://anaconda.org/conda-forge/DataProperty/badges/version.svg
:target: https://anaconda.org/conda-forge/DataProperty
:alt: conda-forge package version
.. image:: https://img.shields.io/pypi/pyversions/DataProperty.svg
:target: https://pypi.org/project/DataProperty
:alt: Supported Python versions
.. image:: https://img.shields.io/pypi/implementation/DataProperty.svg
:target: https://pypi.org/project/DataProperty
:alt: Supported Python implementations
.. image:: https://github.com/thombashi/DataProperty/actions/workflows/ci.yml/badge.svg
:target: https://github.com/thombashi/DataProperty/actions/workflows/ci.yml
:alt: CI status of Linux/macOS/Windows
.. image:: https://coveralls.io/repos/github/thombashi/DataProperty/badge.svg?branch=master
:target: https://coveralls.io/github/thombashi/DataProperty?branch=master
:alt: Test coverage
.. image:: https://github.com/thombashi/DataProperty/actions/workflows/github-code-scanning/codeql/badge.svg
:target: https://github.com/thombashi/DataProperty/actions/workflows/github-code-scanning/codeql
:alt: CodeQL
Installation
============
Installation: pip
------------------------------
::
pip install DataProperty
Installation: conda
------------------------------
::
conda install -c conda-forge dataproperty
Installation: apt
------------------------------
::
sudo add-apt-repository ppa:thombashi/ppa
sudo apt update
sudo apt install python3-dataproperty
Usage
=====
Extract property of data
------------------------
e.g. Extract a ``float`` value property
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: python
>>> from dataproperty import DataProperty
>>> DataProperty(-1.1)
data=-1.1, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=1, extra_len=1
e.g. Extract a ``int`` value property
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: python
>>> from dataproperty import DataProperty
>>> DataProperty(123456789)
data=123456789, type=INTEGER, align=right, ascii_width=9, int_digits=9, decimal_places=0, extra_len=0
e.g. Extract a ``str`` (ascii) value property
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: python
>>> from dataproperty import DataProperty
>>> DataProperty("sample string")
data=sample string, type=STRING, align=left, length=13, ascii_width=13, extra_len=0
e.g. Extract a ``str`` (multi-byte) value property
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: python
>>> from dataproperty import DataProperty
>>> str(DataProperty("吾輩は猫である"))
data=吾輩は猫である, type=STRING, align=left, length=7, ascii_width=14, extra_len=0
e.g. Extract a time (``datetime``) value property
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: python
>>> import datetime
>>> from dataproperty import DataProperty
>>> DataProperty(datetime.datetime(2017, 1, 1, 0, 0, 0))
data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0
e.g. Extract a ``bool`` value property
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: python
>>> from dataproperty import DataProperty
>>> DataProperty(True)
data=True, type=BOOL, align=left, ascii_width=4, extra_len=0
Extract data property for each element from a matrix
----------------------------------------------------
``DataPropertyExtractor.to_dp_matrix`` method returns a matrix of ``DataProperty`` instances from a data matrix.
An example data set and the result are as follows:
:Sample Code:
.. code:: python
import datetime
from dataproperty import DataPropertyExtractor
dp_extractor = DataPropertyExtractor()
dt = datetime.datetime(2017, 1, 1, 0, 0, 0)
inf = float("inf")
nan = float("nan")
dp_matrix = dp_extractor.to_dp_matrix([
[1, 1.1, "aa", 1, 1, True, inf, nan, dt],
[2, 2.2, "bbb", 2.2, 2.2, False, "inf", "nan", dt],
[3, 3.33, "cccc", -3, "ccc", "true", inf, "NAN", "2017-01-01T01:23:45+0900"],
])
for row, dp_list in enumerate(dp_matrix):
for col, dp in enumerate(dp_list):
print("row={:d}, col={:d}, {}".format(row, col, str(dp)))
:Output:
::
row=0, col=0, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
row=0, col=1, data=1.1, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
row=0, col=2, data=aa, type=STRING, align=left, ascii_width=2, length=2, extra_len=0
row=0, col=3, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
row=0, col=4, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
row=0, col=5, data=True, type=BOOL, align=left, ascii_width=4, extra_len=0
row=0, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0
row=0, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0
row=0, col=8, data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0
row=1, col=0, data=2, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
row=1, col=1, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
row=1, col=2, data=bbb, type=STRING, align=left, ascii_width=3, length=3, extra_len=0
row=1, col=3, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
row=1, col=4, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
row=1, col=5, data=False, type=BOOL, align=left, ascii_width=5, extra_len=0
row=1, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0
row=1, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0
row=1, col=8, data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0
row=2, col=0, data=3, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
row=2, col=1, data=3.33, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=2, extra_len=0
row=2, col=2, data=cccc, type=STRING, align=left, ascii_width=4, length=4, extra_len=0
row=2, col=3, data=-3, type=INTEGER, align=right, ascii_width=2, int_digits=1, decimal_places=0, extra_len=1
row=2, col=4, data=ccc, type=STRING, align=left, ascii_width=3, length=3, extra_len=0
row=2, col=5, data=True, type=BOOL, align=left, ascii_width=4, extra_len=0
row=2, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0
row=2, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0
row=2, col=8, data=2017-01-01T01:23:45+0900, type=STRING, align=left, ascii_width=24, length=24, extra_len=0
Full example source code can be found at *examples/py/to_dp_matrix.py*
Extract properties for each column from a matrix
------------------------------------------------------
``DataPropertyExtractor.to_column_dp_list`` method returns a list of ``DataProperty`` instances from a data matrix. The list represents the properties for each column.
An example data set and the result are as follows:
Example data set and result are as follows:
:Sample Code:
.. code:: python
import datetime
from dataproperty import DataPropertyExtractor
dp_extractor = DataPropertyExtractor()
dt = datetime.datetime(2017, 1, 1, 0, 0, 0)
inf = float("inf")
nan = float("nan")
data_matrix = [
[1, 1.1, "aa", 1, 1, True, inf, nan, dt],
[2, 2.2, "bbb", 2.2, 2.2, False, "inf", "nan", dt],
[3, 3.33, "cccc", -3, "ccc", "true", inf, "NAN", "2017-01-01T01:23:45+0900"],
]
dp_extractor.headers = ["int", "float", "str", "num", "mix", "bool", "inf", "nan", "time"]
col_dp_list = dp_extractor.to_column_dp_list(dp_extractor.to_dp_matrix(dp_matrix))
for col_idx, col_dp in enumerate(col_dp_list):
print(str(col_dp))
:Output:
::
column=0, type=INTEGER, align=right, ascii_width=3, bit_len=2, int_digits=1, decimal_places=0
column=1, type=REAL_NUMBER, align=right, ascii_width=5, int_digits=1, decimal_places=(min=1, max=2)
column=2, type=STRING, align=left, ascii_width=4
column=3, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=(min=0, max=1), extra_len=(min=0, max=1)
column=4, type=STRING, align=left, ascii_width=3, int_digits=1, decimal_places=(min=0, max=1)
column=5, type=BOOL, align=left, ascii_width=5
column=6, type=INFINITY, align=left, ascii_width=8
column=7, type=NAN, align=left, ascii_width=3
column=8, type=STRING, align=left, ascii_width=24
Full example source code can be found at *examples/py/to_column_dp_list.py*
Dependencies
============
- Python 3.9+
- `Python package dependencies (automatically installed) <https://github.com/thombashi/DataProperty/network/dependencies>`__
Optional dependencies
---------------------
- `loguru <https://github.com/Delgan/loguru>`__
- Used for logging if the package installed
Raw data
{
"_id": null,
"home_page": "https://github.com/thombashi/DataProperty",
"name": "DataProperty",
"maintainer": "Tsuyoshi Hombashi",
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "tsuyoshi.hombashi@gmail.com",
"keywords": "data, library, property",
"author": "Tsuyoshi Hombashi",
"author_email": "tsuyoshi.hombashi@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/0b/81/8c8b64ae873cb9014815214c07b63b12e3b18835780fb342223cfe3fe7d8/dataproperty-1.1.0.tar.gz",
"platform": null,
"description": ".. contents:: **DataProperty**\n :backlinks: top\n :local:\n\n\nSummary\n=======\nA Python library for extract property from data.\n\n\n.. image:: https://badge.fury.io/py/DataProperty.svg\n :target: https://badge.fury.io/py/DataProperty\n :alt: PyPI package version\n\n.. image:: https://anaconda.org/conda-forge/DataProperty/badges/version.svg\n :target: https://anaconda.org/conda-forge/DataProperty\n :alt: conda-forge package version\n\n.. image:: https://img.shields.io/pypi/pyversions/DataProperty.svg\n :target: https://pypi.org/project/DataProperty\n :alt: Supported Python versions\n\n.. image:: https://img.shields.io/pypi/implementation/DataProperty.svg\n :target: https://pypi.org/project/DataProperty\n :alt: Supported Python implementations\n\n.. image:: https://github.com/thombashi/DataProperty/actions/workflows/ci.yml/badge.svg\n :target: https://github.com/thombashi/DataProperty/actions/workflows/ci.yml\n :alt: CI status of Linux/macOS/Windows\n\n.. image:: https://coveralls.io/repos/github/thombashi/DataProperty/badge.svg?branch=master\n :target: https://coveralls.io/github/thombashi/DataProperty?branch=master\n :alt: Test coverage\n\n.. image:: https://github.com/thombashi/DataProperty/actions/workflows/github-code-scanning/codeql/badge.svg\n :target: https://github.com/thombashi/DataProperty/actions/workflows/github-code-scanning/codeql\n :alt: CodeQL\n\n\nInstallation\n============\n\nInstallation: pip\n------------------------------\n::\n\n pip install DataProperty\n\nInstallation: conda\n------------------------------\n::\n\n conda install -c conda-forge dataproperty\n\nInstallation: apt\n------------------------------\n::\n\n sudo add-apt-repository ppa:thombashi/ppa\n sudo apt update\n sudo apt install python3-dataproperty\n\n\nUsage\n=====\n\nExtract property of data\n------------------------\n\ne.g. Extract a ``float`` value property\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n.. code:: python\n\n >>> from dataproperty import DataProperty\n >>> DataProperty(-1.1)\n data=-1.1, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=1, extra_len=1\n\ne.g. Extract a ``int`` value property\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n.. code:: python\n\n >>> from dataproperty import DataProperty\n >>> DataProperty(123456789)\n data=123456789, type=INTEGER, align=right, ascii_width=9, int_digits=9, decimal_places=0, extra_len=0\n\ne.g. Extract a ``str`` (ascii) value property\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n.. code:: python\n\n >>> from dataproperty import DataProperty\n >>> DataProperty(\"sample string\")\n data=sample string, type=STRING, align=left, length=13, ascii_width=13, extra_len=0\n\ne.g. Extract a ``str`` (multi-byte) value property\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n.. code:: python\n\n >>> from dataproperty import DataProperty\n >>> str(DataProperty(\"\u543e\u8f29\u306f\u732b\u3067\u3042\u308b\"))\n data=\u543e\u8f29\u306f\u732b\u3067\u3042\u308b, type=STRING, align=left, length=7, ascii_width=14, extra_len=0\n\ne.g. Extract a time (``datetime``) value property\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n.. code:: python\n\n >>> import datetime\n >>> from dataproperty import DataProperty\n >>> DataProperty(datetime.datetime(2017, 1, 1, 0, 0, 0))\n data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0\n\ne.g. Extract a ``bool`` value property\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n.. code:: python\n\n >>> from dataproperty import DataProperty\n >>> DataProperty(True)\n data=True, type=BOOL, align=left, ascii_width=4, extra_len=0\n\n\nExtract data property for each element from a matrix\n----------------------------------------------------\n``DataPropertyExtractor.to_dp_matrix`` method returns a matrix of ``DataProperty`` instances from a data matrix.\nAn example data set and the result are as follows:\n\n:Sample Code:\n .. code:: python\n\n import datetime\n from dataproperty import DataPropertyExtractor\n\n dp_extractor = DataPropertyExtractor()\n dt = datetime.datetime(2017, 1, 1, 0, 0, 0)\n inf = float(\"inf\")\n nan = float(\"nan\")\n\n dp_matrix = dp_extractor.to_dp_matrix([\n [1, 1.1, \"aa\", 1, 1, True, inf, nan, dt],\n [2, 2.2, \"bbb\", 2.2, 2.2, False, \"inf\", \"nan\", dt],\n [3, 3.33, \"cccc\", -3, \"ccc\", \"true\", inf, \"NAN\", \"2017-01-01T01:23:45+0900\"],\n ])\n\n for row, dp_list in enumerate(dp_matrix):\n for col, dp in enumerate(dp_list):\n print(\"row={:d}, col={:d}, {}\".format(row, col, str(dp)))\n\n:Output:\n ::\n\n row=0, col=0, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0\n row=0, col=1, data=1.1, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0\n row=0, col=2, data=aa, type=STRING, align=left, ascii_width=2, length=2, extra_len=0\n row=0, col=3, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0\n row=0, col=4, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0\n row=0, col=5, data=True, type=BOOL, align=left, ascii_width=4, extra_len=0\n row=0, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0\n row=0, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0\n row=0, col=8, data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0\n row=1, col=0, data=2, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0\n row=1, col=1, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0\n row=1, col=2, data=bbb, type=STRING, align=left, ascii_width=3, length=3, extra_len=0\n row=1, col=3, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0\n row=1, col=4, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0\n row=1, col=5, data=False, type=BOOL, align=left, ascii_width=5, extra_len=0\n row=1, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0\n row=1, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0\n row=1, col=8, data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0\n row=2, col=0, data=3, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0\n row=2, col=1, data=3.33, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=2, extra_len=0\n row=2, col=2, data=cccc, type=STRING, align=left, ascii_width=4, length=4, extra_len=0\n row=2, col=3, data=-3, type=INTEGER, align=right, ascii_width=2, int_digits=1, decimal_places=0, extra_len=1\n row=2, col=4, data=ccc, type=STRING, align=left, ascii_width=3, length=3, extra_len=0\n row=2, col=5, data=True, type=BOOL, align=left, ascii_width=4, extra_len=0\n row=2, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0\n row=2, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0\n row=2, col=8, data=2017-01-01T01:23:45+0900, type=STRING, align=left, ascii_width=24, length=24, extra_len=0\n\n\nFull example source code can be found at *examples/py/to_dp_matrix.py*\n\n\nExtract properties for each column from a matrix\n------------------------------------------------------\n``DataPropertyExtractor.to_column_dp_list`` method returns a list of ``DataProperty`` instances from a data matrix. The list represents the properties for each column.\nAn example data set and the result are as follows:\n\nExample data set and result are as follows:\n\n:Sample Code:\n .. code:: python\n\n import datetime\n from dataproperty import DataPropertyExtractor\n\n dp_extractor = DataPropertyExtractor()\n dt = datetime.datetime(2017, 1, 1, 0, 0, 0)\n inf = float(\"inf\")\n nan = float(\"nan\")\n\n data_matrix = [\n [1, 1.1, \"aa\", 1, 1, True, inf, nan, dt],\n [2, 2.2, \"bbb\", 2.2, 2.2, False, \"inf\", \"nan\", dt],\n [3, 3.33, \"cccc\", -3, \"ccc\", \"true\", inf, \"NAN\", \"2017-01-01T01:23:45+0900\"],\n ]\n\n dp_extractor.headers = [\"int\", \"float\", \"str\", \"num\", \"mix\", \"bool\", \"inf\", \"nan\", \"time\"]\n col_dp_list = dp_extractor.to_column_dp_list(dp_extractor.to_dp_matrix(dp_matrix))\n\n for col_idx, col_dp in enumerate(col_dp_list):\n print(str(col_dp))\n\n:Output:\n ::\n\n column=0, type=INTEGER, align=right, ascii_width=3, bit_len=2, int_digits=1, decimal_places=0\n column=1, type=REAL_NUMBER, align=right, ascii_width=5, int_digits=1, decimal_places=(min=1, max=2)\n column=2, type=STRING, align=left, ascii_width=4\n column=3, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=(min=0, max=1), extra_len=(min=0, max=1)\n column=4, type=STRING, align=left, ascii_width=3, int_digits=1, decimal_places=(min=0, max=1)\n column=5, type=BOOL, align=left, ascii_width=5\n column=6, type=INFINITY, align=left, ascii_width=8\n column=7, type=NAN, align=left, ascii_width=3\n column=8, type=STRING, align=left, ascii_width=24\n\n\nFull example source code can be found at *examples/py/to_column_dp_list.py*\n\n\nDependencies\n============\n- Python 3.9+\n- `Python package dependencies (automatically installed) <https://github.com/thombashi/DataProperty/network/dependencies>`__\n\nOptional dependencies\n---------------------\n- `loguru <https://github.com/Delgan/loguru>`__\n - Used for logging if the package installed\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "Python library for extract property from data.",
"version": "1.1.0",
"project_urls": {
"Changelog": "https://github.com/thombashi/DataProperty/releases",
"Homepage": "https://github.com/thombashi/DataProperty",
"Source": "https://github.com/thombashi/DataProperty",
"Tracker": "https://github.com/thombashi/DataProperty/issues"
},
"split_keywords": [
"data",
" library",
" property"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "21c2e12e95e289e6081a40454199ab213139ef16a528c7c86432de545b05a23a",
"md5": "e94fce4402cc78de28d803e0b7dd9ce2",
"sha256": "c61fcb2e2deca35e6d1eb1f251a7f22f0dcde63e80e61f0cc18c19f42abfd25b"
},
"downloads": -1,
"filename": "DataProperty-1.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "e94fce4402cc78de28d803e0b7dd9ce2",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 27581,
"upload_time": "2024-12-31T14:37:22",
"upload_time_iso_8601": "2024-12-31T14:37:22.657247Z",
"url": "https://files.pythonhosted.org/packages/21/c2/e12e95e289e6081a40454199ab213139ef16a528c7c86432de545b05a23a/DataProperty-1.1.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0b818c8b64ae873cb9014815214c07b63b12e3b18835780fb342223cfe3fe7d8",
"md5": "b10a82d8c35a1e69f58b921aa7d7f063",
"sha256": "b038437a4097d1a1c497695c3586ea34bea67fdd35372b9a50f30bf044d77d04"
},
"downloads": -1,
"filename": "dataproperty-1.1.0.tar.gz",
"has_sig": false,
"md5_digest": "b10a82d8c35a1e69f58b921aa7d7f063",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 42574,
"upload_time": "2024-12-31T14:37:26",
"upload_time_iso_8601": "2024-12-31T14:37:26.033125Z",
"url": "https://files.pythonhosted.org/packages/0b/81/8c8b64ae873cb9014815214c07b63b12e3b18835780fb342223cfe3fe7d8/dataproperty-1.1.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-31 14:37:26",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "thombashi",
"github_project": "DataProperty",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"tox": true,
"lcname": "dataproperty"
}
JSON
