scipy.stats.

trim_mean#

scipy.stats.trim_mean(a, proportiontocut, axis=0, *, nan_policy='propagate', keepdims=False)[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

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.

keepdimsbool, 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_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])

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.

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. ])
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