trim1#
- scipy.stats.trim1(a, proportiontocut, tail='right', axis=0)[source]#
- 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:
- aarray_like
- Input array. 
- proportiontocutfloat
- Fraction to cut off of ‘left’ or ‘right’ of distribution. 
- tail{‘left’, ‘right’}, optional
- Defaults to ‘right’. 
- axisint or None, optional
- Axis along which to trim data. Default is 0. If None, compute over the whole array a. 
 
- Returns:
- trim1ndarray
- 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,)