parameters: (data, proportiontocut=0.2, inclusive=(True,True), axis=None)
return_statement: return trimr(data, limits=(proportiontocut,proportiontocut),
                   inclusive=inclusive, axis=axis)
docstring: Trims the smallest and largest data values.
  
      Trims the `data` by masking the ``int(proportiontocut * n)`` smallest and
      ``int(proportiontocut * n)`` largest values of data along the given axis,
      where n is the number of unmasked values before trimming.
  
      Parameters
      ----------
      data : ndarray
          Data to trim.
      proportiontocut : float, optional
          Percentage of trimming (as a float between 0 and 1).
          If n is the number of unmasked values before trimming, the number of
          values after trimming is ``(1 - 2*proportiontocut) * n``.
          Default is 0.2.
      inclusive : {(bool, bool) tuple}, optional
          Tuple indicating whether the number of data being masked on each side
          should be rounded (True) or truncated (False).
      axis : int, optional
          Axis along which to perform the trimming.
          If None, the input array is first flattened.
docstring_summary: Trims the smallest and largest data values.
docstring_tokens: Trims the smallest and largest data values .
function: def trimboth(data, proportiontocut=0.2, inclusive=(True,True), axis=None):
      """
      Trims the smallest and largest data values.
  
      Trims the `data` by masking the ``int(proportiontocut * n)`` smallest and
      ``int(proportiontocut * n)`` largest values of data along the given axis,
      where n is the number of unmasked values before trimming.
  
      Parameters
      ----------
      data : ndarray
          Data to trim.
      proportiontocut : float, optional
          Percentage of trimming (as a float between 0 and 1).
          If n is the number of unmasked values before trimming, the number of
          values after trimming is ``(1 - 2*proportiontocut) * n``.
          Default is 0.2.
      inclusive : {(bool, bool) tuple}, optional
          Tuple indicating whether the number of data being masked on each side
          should be rounded (True) or truncated (False).
      axis : int, optional
          Axis along which to perform the trimming.
          If None, the input array is first flattened.
  
      """
      return trimr(data, limits=(proportiontocut,proportiontocut),
                   inclusive=inclusive, axis=axis)
function_tokens: def trimboth ( data , proportiontocut = 0.2 , inclusive = ( True , True ) , axis = None ) : return trimr ( data , limits = ( proportiontocut , proportiontocut ) , inclusive = inclusive , axis = axis )