class_docstr: A two-way contingency table.
  
  Parameters
  ----------
  table : array_like
      A contingency table.
  shift_zeros : bool
      If True and any cell count is zero, add 0.5 to all values
      in the table.
  
  Attributes
  ----------
  table_orig : array_like
      The original table is cached as `table_orig`.
  
  See Also
  --------
  statsmodels.graphics.mosaicplot.mosaic
  scipy.stats.chi2_contingency
  
  Notes
  -----
  The inference procedures used here are all based on a sampling
  model in which the units are independent and identically
  distributed, with each unit being classified with respect to two
  categorical variables.
  
  References
  ----------
  Definitions of residuals:
      https://onlinecourses.science.psu.edu/stat504/node/86
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docstr: Returns the cumulative odds ratios for a contingency table.
  
  See documentation for cumulative_log_oddsratio.
docstr_tok: ["Returns","the","cumulative","odds","ratios","for","a","contingency","table",".","See","documentation","for","cumulative_log_oddsratio","."]
code: def cumulative_oddsratios(self):
      """
      Returns the cumulative odds ratios for a contingency table.
  
      See documentation for cumulative_log_oddsratio.
      """
  
      return np.exp(self.cumulative_log_oddsratios)
  
imports: import warnings
  import numpy
  import pandas
  from scipy import stats
  from statsmodels import iolib
  from statsmodels.tools import sm_exceptions
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 15 for calling the Table obj's underlying member method code with example usage: obj.cumulative_oddsratios() and returns: np