data_describe.core.correlation
=====================================

.. py:module:: data_describe.core.correlation





.. autoapisummary::

   data_describe.core.correlation.correlation_matrix


.. py:class:: CorrelationWidget(association_matrix=None, cluster_matrix=None, categorical=None, viz_data=None, **kwargs)

   Bases: :class:`data_describe._widget.BaseWidget`

   Container for correlation calculation and visualization.

   This class (object) is returned from the ``correlation_matrix`` function. The
   attributes documented below can be accessed or extracted.

   .. attribute:: association_matrix

      The combined association matrix i.e. correlation and other
      categorical-numeric or categorical-categorical associations.

   .. attribute:: cluster_matrix

      The clustered association matrix.

   .. attribute:: categorical

      True if association matrix contains categorical values.

      :type: bool

   .. attribute:: viz_data

      The final data to be visualized.

   .. method:: show(self, viz_backend=None, **kwargs)


      The default display for this output.

      Displays the correlation matrix heatmap.

      :param viz_backend: The visualization backend.
      :param \*\*kwargs: Keyword arguments.

      :raises ValueError: Computed data is missing.

      :returns: The correlation matrix plot.



.. function:: correlation_matrix(data, cluster=False, categorical=False, compute_backend=None, viz_backend=None, **kwargs) -> CorrelationWidget

   Computes correlations (associations) and visualizes as a heatmap.

   This feature combines measures of association for pairs of variables:
       * Numeric-numeric pairs: Pearson correlation
       * Categorical-numeric pairs: Correlation ratio
       * Categorical-categorical pairs
           * More than 2 levels: Cramer's V
           * Only 2 levels for both variables: Point-biserial coefficient

   :param data: A data frame
   :type data: DataFrame
   :param cluster: If True, use clustering to reorder similar columns together
   :type cluster: bool
   :param categorical: If True, include categorical associations using Cramer's
                       V, Correlation Ratio, and Point-biserial coefficient (a.k.a. Matthews
                       correlation coefficient). All associations (including Pearson correlation)
                       are scaled to be in the range [0, 1].
   :type categorical: bool
   :param compute_backend: The compute backend.
   :param viz_backend: The visualization backend.
   :param \*\*kwargs: Keyword arguments.

   :raises ValueError: Invalid data input type.

   :returns: CorrelationWidget