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: create a TrimmedMean instance with a new trimming fraction
  
  This reuses the sorted array from the current instance.
docstr_tok: ["create","a","TrimmedMean","instance","with","a","new","trimming","fraction","This","reuses","the","sorted","array","from","the","current","instance","."]
code: def reset_fraction(self, frac):
      """create a TrimmedMean instance with a new trimming fraction
  
      This reuses the sorted array from the current instance.
      """
      tm = TrimmedMean(
          self.data_sorted, frac, is_sorted=True, axis=self.axis
      )
      tm.data = self.data
      # TODO: this will not work if there is processing of meta-information
      #       in __init__,
      #       for example storing a pandas DataFrame or Series index
      return tm
  
file_path: statsmodels/statsmodels/stats/robust_compare.py
imports: import numbers
  import numpy
label_desc: Use image node_id 10 for calling the TrimmedMean obj's underlying member method code with example usage: obj.reset_fraction(frac) and returns: tm