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.
Examples
>>> 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])