class_docstr: 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.
  
  Attributes:
      association_matrix: The combined association matrix i.e. correlation and other
          categorical-numeric or categorical-categorical associations.
      cluster_matrix: The clustered association matrix.
      categorical (bool): True if association matrix contains categorical values.
      viz_data: The final data to be visualized.
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docstr: Correlation matrix.
  
  Args:
      association_matrix: The combined association matrix i.e. correlation and other
          categorical-numeric or categorical-categorical associations.
      cluster_matrix: The clustered association matrix.
      categorical (bool): True if association matrix contains categorical values.
      viz_data: The final data to be visualized.
      **kwargs: Keyword arguments.
docstr_tok: ["Correlation","matrix",".","Args",":","association_matrix",":","The","combined","association","matrix","i.e",".","correlation","and","other","categorical-numeric","or","categorical-categorical","associations",".","cluster_matrix",":","The","clustered","association","matrix",".","categorical","(","bool",")",":","True","if","association","matrix","contains","categorical","values",".","viz_data",":","The","final","data","to","be","visualized",".","*","*","kwargs",":","Keyword","arguments","."]
code: def __init__(
      self,
      association_matrix=None,
      cluster_matrix=None,
      categorical=None,
      viz_data=None,
      **kwargs,
  ):
      """Correlation matrix.
  
      Args:
          association_matrix: The combined association matrix i.e. correlation and other
              categorical-numeric or categorical-categorical associations.
          cluster_matrix: The clustered association matrix.
          categorical (bool): True if association matrix contains categorical values.
          viz_data: The final data to be visualized.
          **kwargs: Keyword arguments.
      """
      super(CorrelationWidget, self).__init__(**kwargs)
      self.association_matrix = association_matrix
      self.cluster_matrix = cluster_matrix
      self.viz_data = viz_data
      self.categorical = categorical
  
imports: import warnings
  import pandas
  import numpy
  from scipy.cluster import hierarchy
  from sklearn.metrics import matthews_corrcoef
  from scipy.stats import chi2_contingency
  import seaborn
  import matplotlib.pyplot
  import plotly.graph_objs
  import plotly.offline
  from data_describe.config._config import get_option
  from data_describe.misc.colors import get_p_RdBl_cmap, mpl_to_plotly_cmap
  from data_describe._widget import BaseWidget
  from data_describe.compat import _is_dataframe, _in_notebook, _requires
  from data_describe.backends import _get_viz_backend, _get_compute_backend
label_desc: Use image node_id 1 to create a new CorrelationWidget object from inherited base classes: BaseWidget with example: obj = CorrelationWidget(association_matrix, cluster_matrix, categorical, viz_data)