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# scipy.stats.mstats.trimboth

`scipy.stats.mstats.``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.