.. _sphx_glr_auto_examples_preprocessing_ex_one_hot.py:


================
One-hot encoding
================

Demonstrates how to use the DummyEncoder. For a more comprehensive explanation,
take a look at the
`demo on alkaline-ml.com <https://alkaline-ml.com/2018-06-18-skoot-intro>`_.

.. raw:: html

   <br/>





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

 Out::

    Test transformation:
           age  ...  native-country_Vietnam
    14160   27  ...                     0.0
    27048   45  ...                     0.0
    28868   29  ...                     0.0
    5667    30  ...                     0.0
    7827    29  ...                     0.0

    [5 rows x 99 columns]

    Applied on a row with a new native-country:
           age  ...  native-country_Vietnam
    14160   27  ...                     0.0

    [1 rows x 99 columns]




|


.. code-block:: python

    print(__doc__)

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

    from skoot.datasets import load_adult_df
    from skoot.preprocessing import DummyEncoder
    from skoot.utils.dataframe import get_categorical_columns
    from sklearn.model_selection import train_test_split
    import pandas as pd

    # #############################################################################
    # load & split the data
    adult = load_adult_df(tgt_name="target")
    y = adult.pop("target")

    # we don't want this column
    _ = adult.pop("education-num")

    X_train, X_test, y_train, y_test = train_test_split(adult, y, random_state=42,
                                                        test_size=0.2)

    # #############################################################################
    # Fit a dummy encoder
    obj_cols = get_categorical_columns(X_train).columns
    encoder = DummyEncoder(cols=obj_cols, handle_unknown='ignore', n_jobs=4)
    encoder.fit(X_train, y_train)

    # #############################################################################
    # Apply to the test set
    print("Test transformation:")
    print(encoder.transform(X_test).head())

    # #############################################################################
    # Show we can work with levels we've never seen before
    test_row = X_test.iloc[0]
    test_row.set_value("native-country", "Atlantis")
    trans = encoder.transform(pd.DataFrame([test_row]))
    print("\nApplied on a row with a new native-country:")
    print(trans)

    nc_mask = trans.columns.str.contains("native-country")
    assert trans[trans.columns[nc_mask]].sum().sum() == 0

**Total running time of the script:** ( 0 minutes  1.526 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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