`scipy.stats._stats_py:trim_mean`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_stats_py.py:3660

Signature

def trim_mean(a, proportiontocut, axis = 0, *, nan_policy = propagate, keepdims = False)

Summary

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

Extended Summary

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

Parameters

a : array_like

Input array.

proportiontocut : float

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.

axis : int or None, default: 0

If an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output. If None, the input will be raveled before computing the statistic.

nan_policy : {'propagate', 'omit', 'raise'}

Defines how to handle input NaNs.

propagate: if a NaN is present in the axis slice (e.g. row) along which the statistic is computed, the corresponding entry of the output will be NaN.
omit: NaNs will be omitted when performing the calculation. If insufficient data remains in the axis slice along which the statistic is computed, the corresponding entry of the output will be NaN.
raise: if a NaN is present, a ValueError will be raised.

nan_policy='raise' and nan_policy='omit' are not supported by lazy backends, and nan_policy='omit' is not compatible with MArray input. For multidimensional input, nan_policy='omit' is supported only by the NumPy backend. Instead, mask NaN values using MArray; and use the default nan_policy. See Notes for support information.

keepdims : bool, default: False

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Returns

trim_mean : ndarray: Mean of trimmed array.

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])

Beginning in SciPy 1.9, np.matrix inputs (not recommended for new code) are converted to np.ndarray before the calculation is performed. In this case, the output will be a scalar or np.ndarray of appropriate shape rather than a 2D np.matrix. Similarly, while masked elements of masked arrays are ignored, the output will be a scalar or np.ndarray rather than a masked array with mask=False.

Examples

import numpy as np
from scipy import stats
x = [1, 2, 3, 5]

✓

stats.trim_mean(x, 0.25)

✗

np.float64(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)

✗

np.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)
stats.trim_mean(x2, 0.25, axis=1)

✓

array([ 2.5, 25. ])

Aliases

scipy.stats.trim_mean