scipy.stats.

trim_mean#

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

Return mean of array after trimming a specified fraction of extreme values

Removes the specified proportion of elements from each end of the sorted array, then computes the mean of the remaining elements.

Parameters:

aarray_like: Input array.
proportiontocutfloat: Fraction of the most positive and most negative elements to remove. When the specified proportion does not result in an integer number of elements, the number of elements to trim is rounded down.
axisint or None, default: 0: Axis along which the trimmed means are computed. If None, compute over the raveled array.

Returns:

trim_meanndarray: Mean of trimmed array.

See also

trimboth: Remove a proportion of elements from each end of an array.
tmean: Compute the mean after trimming values outside specified limits.

Notes

For 1-D array a, trim_mean is approximately equivalent to the following calculation:

import numpy as np
a = np.sort(a)
m = int(proportiontocut * len(a))
np.mean(a[m: len(a) - m])

Array API Standard Support

trim_mean has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	⛔
PyTorch	⛔	⛔
JAX	⛔	⛔
Dask	⛔	n/a

See Support for the array API standard for more information.

Examples

>>> import numpy as np
>>> from scipy import stats
>>> x = [1, 2, 3, 5]
>>> stats.trim_mean(x, 0.25)
2.5

When the specified proportion does not result in an integer number of elements, the number of elements to trim is rounded down.

>>> stats.trim_mean(x, 0.24999) == np.mean(x)
True

Use axis to specify the axis along which the calculation is performed.

>>> x2 = [[1, 2, 3, 5],
...       [10, 20, 30, 50]]
>>> stats.trim_mean(x2, 0.25)
array([ 5.5, 11. , 16.5, 27.5])
>>> stats.trim_mean(x2, 0.25, axis=1)
array([ 2.5, 25. ])