class_docstr: Renders visualization of missingness for datasets using one of the methods specified in `graph_type'.
  
  This visualization depends on :py:class:`~autogluon.eda.analysis.missing.MissingValuesAnalysis` analysis.
  
  See also `missingno <https://github.com/ResidentMario/missingno>`_ documentation
  
  Parameters
  ----------
  graph_type: str, default = 'matrix'
      One of the following visualization types:
      - matrix - nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion
          This visualization will comfortably accommodate up to 50 labelled variables.
          Past that range labels begin to overlap or become unreadable, and by default large displays omit them.
      - bar - visualizes how many rows are non-null vs null in the column. Logarithmic scale can by specifying `log=True` in `kwargs`
      - heatmap - correlation heatmap measures nullity correlation: how strongly the presence or absence of one
          variable affects the presence of another. Nullity correlation ranges from -1
          (if one variable appears the other definitely does not) to 0 (variables appearing or not appearing have no effect on one another)
          to 1 (if one variable appears the other definitely also does).
          Entries marked <1 or >-1 have a correlation that is close to being exactingly negative or positive but is still not quite perfectly so.
      - dendrogram - the dendrogram allows to more fully correlate variable completion, revealing trends deeper than the pairwise ones
          visible in the correlation heatmap. The dendrogram uses a hierarchical clustering algorithm (courtesy of scipy) to bin variables
          against one another by their nullity correlation (measured in terms of binary distance).
          At each step of the tree the variables are split up based on which combination minimizes the distance of the remaining clusters.
          The more monotone the set of variables, the closer their total distance is to zero, and the closer their average distance (the y-axis) is to zero.
  headers: bool, default = False
      if `True` then render headers
  namespace: str, default = None
      namespace to use; can be nested like `ns_a.ns_b.ns_c`
  
  See Also
  --------
  :py:class:`~autogluon.eda.analysis.missing.MissingValuesAnalysis`
code: def _get_operation(self, graph_type):
      return self.__OPERATIONS_MAPPING[graph_type]
  
file_path: autogluon/eda/src/autogluon/eda/visualization/missing.py
imports: import logging
  from typing import Optional
  import matplotlib.pyplot
  import missingno
  from ..None import AnalysisState
  from .base import AbstractVisualization
  from .jupyter import JupyterMixin
label_desc: Use image node_id 6 for calling the MissingValues obj's underlying member method code with example usage: obj._get_operation(graph_type) and returns: self