scipy.stats.mstats.trimboth(data, proportiontocut=0.2, inclusive=(True, True), axis=None)[source]

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.


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.