.. _sphx_glr_auto_examples_ex_getting_acquainted.py:


=============================
Getting acquainted with skoot
=============================

This example walks through the package layout and where various
transformers/classes can be located, as well as displays some nuances
between scikit-learn and skoot.

.. raw:: html

   <br/>





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

 Out::

    ['QRDecomposition', 'SelectiveIncrementalPCA', 'SelectiveKernelPCA', 'SelectiveNMF', 'SelectivePCA', 'SelectiveTruncatedSVD', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_dqrsl', '_dqrutl', 'decompose']

       sepal length (cm)  ...  Species
    0                5.1  ...        0
    1                4.9  ...        0
    2                4.7  ...        0
    3                4.6  ...        0
    4                5.0  ...        0

    [5 rows x 5 columns]

    The base class for all Pandas frame transformers.

        Provides the base class for all skoot transformers that require
        Pandas dataframes as input.

        Parameters
        ----------
            cols : array-like, shape=(n_features,), optional (default=None)
            The names of the columns on which to apply the transformation.
            If no column names are provided, the transformer will be fit
            on the entire frame. Note that the transformation will also
            only apply to the specified columns, and any other
            non-specified columns will still be present after
            the transformation.
        
    
            as_df : bool, optional (default=True)
            Whether to return a Pandas ``DataFrame`` in the ``transform``
            method. If False, will return a Numpy ``ndarray`` instead.
            Since most skoot transformers depend on explicitly-named
            ``DataFrame`` features, the ``as_df`` parameter is True by
            default.
        

        Examples
        --------
        The following is an example of how to subclass a BasePDTransformer:
    
            >>> from skoot.base import BasePDTransformer
            >>> class A(BasePDTransformer):
            ...     def __init__(self, cols=None, as_df=None):
            ...             super(A, self).__init__(cols, as_df)
            ...
            >>> A()
            A(as_df=None, cols=None)




|


.. code-block:: python

    print(__doc__)

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

    # #############################################################################
    # Skoot is laid out much like scikit-learn. That is, many of the same modules
    # exist in skoot that are present in scikit. For example:
    from skoot import decomposition
    print(dir(decomposition))  # many are similar to sklearn classes
    print("")

    # #############################################################################
    # Skoot also has a dataset interface, like sklearn. Except it returns
    # dataframes rather than numpy arrays:
    from skoot.datasets import load_iris_df
    df = load_iris_df(include_tgt=True, tgt_name='Species')
    print(df.head())
    print("")

    # #############################################################################
    # All skoot transformers are based on the BasePDTransformer:
    from skoot.base import BasePDTransformer

    print(BasePDTransformer.__doc__)

**Total running time of the script:** ( 0 minutes  0.027 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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