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Module « scipy.stats »
Signature de la fonction trimboth
def trimboth(a, proportiontocut, axis=0)
Description
help(scipy.stats.trimboth)
Slice off a proportion of items from both ends of an array.
Slice off the passed proportion of items from both ends of the passed
array (i.e., with `proportiontocut` = 0.1, slices leftmost 10% **and**
rightmost 10% of scores). The trimmed values are the lowest and
highest ones.
Slice off less if proportion results in a non-integer slice index (i.e.
conservatively slices off `proportiontocut`).
Parameters
----------
a : array_like
Data to trim.
proportiontocut : float
Proportion (in range 0-1) of total data set to trim of each end.
axis : int or None, optional
Axis along which to trim data. Default is 0. If None, compute over
the whole array `a`.
Returns
-------
out : ndarray
Trimmed version of array `a`. The order of the trimmed content
is undefined.
See Also
--------
trim_mean
Examples
--------
Create an array of 10 values and trim 10% of those values from each end:
>>> import numpy as np
>>> from scipy import stats
>>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> stats.trimboth(a, 0.1)
array([1, 3, 2, 4, 5, 6, 7, 8])
Note that the elements of the input array are trimmed by value, but the
output array is not necessarily sorted.
The proportion to trim is rounded down to the nearest integer. For
instance, trimming 25% of the values from each end of an array of 10
values will return an array of 6 values:
>>> b = np.arange(10)
>>> stats.trimboth(b, 1/4).shape
(6,)
Multidimensional arrays can be trimmed along any axis or across the entire
array:
>>> c = [2, 4, 6, 8, 0, 1, 3, 5, 7, 9]
>>> d = np.array([a, b, c])
>>> stats.trimboth(d, 0.4, axis=0).shape
(1, 10)
>>> stats.trimboth(d, 0.4, axis=1).shape
(3, 2)
>>> stats.trimboth(d, 0.4, axis=None).shape
(6,)
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