.. 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

Dependency	Optional
mbstrdecoder	false
typepy	false
loguru	true
pytest	true
pytest-md-report	true
tcolorpy	true

Package profile

DataProperty

Releases

Release table

PyPI Downloads

Dependencies

Details

Project URLs

Dependencies

Package description