class_docstr: class for trimmed and winsorized one sample statistics
  
  axis is None, i.e. ravelling, is not supported
  
  Parameters
  ----------
  data : array-like
      The data, observations to analyze.
  fraction : float in (0, 0.5)
      The fraction of observations to trim at each tail.
      The number of observations trimmed at each tail is
      ``int(fraction * nobs)``
  is_sorted : boolean
      Indicator if data is already sorted. By default the data is sorted
      along ``axis``.
  axis : int
      The axis of reduce operations. By default axis=0, that is observations
      are along the zero dimension, i.e. rows if 2-dim.
docstr: numpy array of trimmed and sorted data
          
docstr_tok: ["numpy","array","of","trimmed","and","sorted","data"]
code: def data_trimmed(self):
      """numpy array of trimmed and sorted data"""
      # returns a view
      return self.data_sorted[self.sl]
  
file_path: statsmodels/statsmodels/stats/robust_compare.py
imports: import numbers
  import numpy
label_desc: Use image node_id 2 for calling the TrimmedMean obj's underlying member method code with example usage: obj.data_trimmed() and returns: self