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
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])
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. ])