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Module « scipy.stats »

Fonction trim1 - module scipy.stats

Signature de la fonction trim1

def trim1(a, proportiontocut, tail='right', axis=0) 

Description

help(scipy.stats.trim1)

Slice off a proportion from ONE end of the passed array distribution.

If `proportiontocut` = 0.1, slices off 'leftmost' or 'rightmost'
10% of scores. The lowest or highest values are trimmed (depending on
the tail).
Slice off less if proportion results in a non-integer slice index
(i.e. conservatively slices off `proportiontocut` ).

Parameters
----------
a : array_like
    Input array.
proportiontocut : float
    Fraction to cut off of 'left' or 'right' of distribution.
tail : {'left', 'right'}, optional
    Defaults to 'right'.
axis : int or None, optional
    Axis along which to trim data. Default is 0. If None, compute over
    the whole array `a`.

Returns
-------
trim1 : ndarray
    Trimmed version of array `a`. The order of the trimmed content is
    undefined.

Examples
--------
Create an array of 10 values and trim 20% of its lowest values:

>>> import numpy as np
>>> from scipy import stats
>>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> stats.trim1(a, 0.2, 'left')
array([2, 4, 3, 5, 6, 7, 8, 9])

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 an array of 10 values will
return an array of 8 values:

>>> b = np.arange(10)
>>> stats.trim1(b, 1/4).shape
(8,)

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.trim1(d, 0.8, axis=0).shape
(1, 10)
>>> stats.trim1(d, 0.8, axis=1).shape
(3, 2)
>>> stats.trim1(d, 0.8, axis=None).shape
(6,)

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