.. _sphx_glr_auto_examples_preprocessing_ex_date_transformation.py:


================
Date transformer
================

Demonstrates how to automatically transform string date-representation
fields into datetime type fields.

.. raw:: html

   <br/>





.. rst-class:: sphx-glr-script-out

 Out::

    Applied to cols ['b', 'c'] with inferred format:
       a  ...          c
    0  1  ... 2018-06-01
    1  2  ... 2018-06-02
    2  3  ... 2018-06-03
    3  4  ... 2018-06-04
    4  5  ...        NaT

    [5 rows x 3 columns]

    Applied to cols ['b', 'c'] with specified format:
       a  ...          c
    0  1  ... 2018-06-01
    1  2  ... 2018-06-02
    2  3  ... 2018-06-03
    3  4  ... 2018-06-04
    4  5  ...        NaT

    [5 rows x 3 columns]

    Applied to cols ['a', 'b', 'c'] with inferred format:
                                  a  ...          c
    0 1970-01-01 00:00:00.000000001  ... 2018-06-01
    1 1970-01-01 00:00:00.000000002  ... 2018-06-02
    2 1970-01-01 00:00:00.000000003  ... 2018-06-03
    3 1970-01-01 00:00:00.000000004  ... 2018-06-04
    4 1970-01-01 00:00:00.000000005  ...        NaT

    [5 rows x 3 columns]




|


.. code-block:: python

    print(__doc__)

    # Author: Taylor Smith <taylor.smith@alkaline-ml.com>

    from skoot.preprocessing import DateTransformer
    import pandas as pd
    from datetime import datetime as dt

    # #############################################################################
    # create data
    data = [
        [1, "06/01/2018", dt.strptime("06-01-2018", "%m-%d-%Y")],
        [2, "06/02/2018", dt.strptime("06-02-2018", "%m-%d-%Y")],
        [3, "06/03/2018", dt.strptime("06-03-2018", "%m-%d-%Y")],
        [4, None, dt.strptime("06-04-2018", "%m-%d-%Y")],
        [5, "06/05/2018", None]
    ]

    df = pd.DataFrame.from_records(data, columns=["a", "b", "c"])

    # the date transformer will automatically handle existing datetime fields
    # and infer the format of string datetime fields:
    print("Applied to cols ['b', 'c'] with inferred format:")
    print(DateTransformer(cols=['b', 'c']).fit_transform(df))

    # we can also supply the format, if desired:
    print("\nApplied to cols ['b', 'c'] with specified format:")
    print(DateTransformer(cols=['b', 'c'],
                          date_format="%m/%d/%Y").fit_transform(df))

    # Finally, if we wanted to apply the transformer to int types, we can
    # add this to the permitted types
    allowed = DateTransformer.DEFAULT_PERMITTED_DTYPES + ("int64",)
    print("\nApplied to cols ['a', 'b', 'c'] with inferred format:")
    print(DateTransformer(cols=['a', 'b', 'c'],
                          allowed_types=allowed).fit_transform(df))

**Total running time of the script:** ( 0 minutes  0.032 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: ex_date_transformation.py <ex_date_transformation.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: ex_date_transformation.ipynb <ex_date_transformation.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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