.. _sphx_glr_auto_examples_feature_extraction_ex_date_extraction.py:


========================
Extracting date features
========================

Demonstrates how to automatically extract factor-level features from
datetime fields.

.. raw:: html

   <br/>





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

 Out::

    Default features:
       transaction_id  ...  time_hour
    0               1  ...       12.0
    1               2  ...       13.0
    2               3  ...        6.0
    3               4  ...        3.0
    4               5  ...        NaN

    [5 rows x 5 columns]

    +Minutes, +Seconds:
       transaction_id  ...  time_second
    0               1  ...          5.0
    1               2  ...         12.0
    2               3  ...         17.0
    3               4  ...         32.0
    4               5  ...          NaN

    [5 rows x 7 columns]

    Same as above, but retain old time column:
       transaction_id  ... time_second
    0               1  ...         5.0
    1               2  ...        12.0
    2               3  ...        17.0
    3               4  ...        32.0
    4               5  ...         NaN

    [5 rows x 8 columns]




|


.. code-block:: python

    print(__doc__)

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

    from skoot.feature_extraction import DateFactorizer
    import pandas as pd
    from datetime import datetime as dt

    # #############################################################################
    # create data
    data = [
        [1, dt.strptime("06-01-2018 12:00:05", "%m-%d-%Y %H:%M:%S")],
        [2, dt.strptime("06-02-2018 13:19:12", "%m-%d-%Y %H:%M:%S")],
        [3, dt.strptime("06-03-2018 06:04:17", "%m-%d-%Y %H:%M:%S")],
        [4, dt.strptime("06-04-2018 03:56:32", "%m-%d-%Y %H:%M:%S")],
        [5, None]
    ]

    df = pd.DataFrame.from_records(data, columns=["transaction_id", "time"])

    # We can extract a multitude of features from date fields. The default will
    # grab the year, month, day and hour
    print("Default features:")
    print(DateFactorizer(cols=['time']).fit_transform(df))

    # we can specify more if we'd like:
    print("\n+Minutes, +Seconds:")
    print(DateFactorizer(cols=['time'],
                         features=("year", "month", "day",
                                   "hour", "minute", "second")).fit_transform(df))

    # And we can retain the old (pre-transform) time features if we wanted
    print("\nSame as above, but retain old time column:")
    print(DateFactorizer(cols=['time'],
                         drop_original=False,
                         features=("year", "month", "day",
                                   "hour", "minute", "second")).fit_transform(df))

**Total running time of the script:** ( 0 minutes  0.038 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_