.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "glr_examples/view/plot_pairwise_corr.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_glr_examples_view_plot_pairwise_corr.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_glr_examples_view_plot_pairwise_corr.py:


===================================
Plot pairwise features
===================================

creates a pairwise comparison between features. 

.. GENERATED FROM PYTHON SOURCE LINES 8-11

.. code-block:: Python

    # Author: L.Kouadio 
    # Licence: BSD-3-clause








.. GENERATED FROM PYTHON SOURCE LINES 12-14

Plot the numerical pairwise correlation 
It shows ['pearson'|'spearman'|'covariance'] correlation. 

.. GENERATED FROM PYTHON SOURCE LINES 14-28

.. code-block:: Python

    from watex.datasets import fetch_data 
    from watex.view import ExPlot 
    from watex.utils import bi_selector 
    data = fetch_data ('bagoue original').get('data=dfy1') 
    # we can use the biselector for selectng the categorical and numerical
    # feature for plotting 
    num_data, cat_data = bi_selector (data, return_frames= True )
    p= ExPlot(tname='flow', fig_size=(7, 5)).fit(num_data)
    p.plotpairwisecomparison(fmt='.2f',  pkg ='yb',
    								 annot=True, 
    								 cmap='RdBu_r', 
    								 vmin=-1, 
    								 vmax=1 )




.. image-sg:: /glr_examples/view/images/sphx_glr_plot_pairwise_corr_001.png
   :alt: Pearson Ranking of 9 Features
   :srcset: /glr_examples/view/images/sphx_glr_plot_pairwise_corr_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <'ExPlot':xname=None, yname=None , tname='flow'>



.. GENERATED FROM PYTHON SOURCE LINES 29-32

Plot the categorical pairwise correlation 
For visulizing the categorical features 
using the spearman rho correlation 

.. GENERATED FROM PYTHON SOURCE LINES 32-41

.. code-block:: Python


    p= ExPlot(tname='geol', fig_size=(7, 5)).fit(cat_data)
    p.plotpairwisecomparison(fmt='.2f', 
                             corr='spearman', 
                             pkg ='yb',
    								 annot=True, 
    								 cmap='RdBu_r', 
    								 vmin=-1, 
    								 vmax=1 )



.. image-sg:: /glr_examples/view/images/sphx_glr_plot_pairwise_corr_002.png
   :alt: Spearman Ranking of 4 Features
   :srcset: /glr_examples/view/images/sphx_glr_plot_pairwise_corr_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/watex/envs/0.3.2/lib/python3.10/site-packages/scipy/stats/_stats_py.py:112: RuntimeWarning:

    The input array could not be properly checked for nan values. nan values will be ignored.


    <'ExPlot':xname=None, yname=None , tname='geol'>




.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.346 seconds)


.. _sphx_glr_download_glr_examples_view_plot_pairwise_corr.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/watex/watex/0.3.X?urlpath=lab/tree/notebooks/glr_examples/view/plot_pairwise_corr.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

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

    .. container:: sphx-glr-download sphx-glr-download-python

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


.. only:: html

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

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