scipy.stats.mstats.trimboth(data, proportiontocut=0.20000000000000001, inclusive=(True, True), axis=None)
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 : {0.2, 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:


inclusive : {(True, True) tuple} optional

Tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False).

axis : {None, integer}, optional

Axis along which to perform the trimming. If None, the input array is first flattened.

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