scipy.stats.trim_mean#

scipy.stats.trim_mean(a, proportiontocut, axis=0)[source]#

Return mean of array after trimming distribution from both tails.

If proportiontocut = 0.1, slices off ‘leftmost’ and ‘rightmost’ 10% of scores. The input is sorted before slicing. Slices off less if proportion results in a non-integer slice index (i.e., conservatively slices off proportiontocut ).

Parameters:

aarray_like: Input array.
proportiontocutfloat: Fraction to cut off of both tails of the distribution.
axisint or None, optional: Axis along which the trimmed means are computed. Default is 0. If None, compute over the whole array a.

Returns:

trim_meanndarray: Mean of trimmed array.

See also

trimboth
tmean: Compute the trimmed mean ignoring values outside given limits.

Examples

>>> import numpy as np
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.trim_mean(x, 0.1)
9.5
>>> x2 = x.reshape(5, 4)
>>> x2
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [12, 13, 14, 15],
       [16, 17, 18, 19]])
>>> stats.trim_mean(x2, 0.25)
array([  8.,   9.,  10.,  11.])
>>> stats.trim_mean(x2, 0.25, axis=1)
array([  1.5,   5.5,   9.5,  13.5,  17.5])