scipy.stats.trimboth(a, proportiontocut, axis=0) function slices off the portion of elements in the array from both the ends.
Parameters :
arr : [array_like] Input array or object to trim.
axis : Axis along which the mean is to be computed. By default axis = 0.
proportiontocut : Proportion (in range 0-1) of data to trim of each end.Results : trimmed array elements from both the ends in the given proportion.
Code #1: Working
# stats.trimboth() method   import numpy as np from scipy import stats     arr1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]     print ("\narr1 : ", arr1)   print ("\nclipped arr1 : \n", stats.trimboth(arr1, proportiontocut = .3)) print ("\nclipped arr1 : \n", stats.trimboth(arr1, proportiontocut = .1)) |
Output :
arr1 : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] clipped arr1 : [3 4 5 6] clipped arr1 : [1 3 2 4 5 6 7 8]
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Code #2:
# stats.trimboth() method   import numpy as np from scipy import stats       arr1 = [[0, 12, 21, 3, 14],         [53, 16, 37, 85, 39]]   print ("\narr1 : ", arr1)   print ("\nclipped arr1 : \n",        stats.trimboth(arr1, proportiontocut = .3))   print ("\nclipped arr1 : \n",        stats.trimboth(arr1, proportiontocut = .1))   print ("\nclipped arr1 : \n",        stats.trimboth(arr1, proportiontocut = .1, axis = 1))   print ("\nclipped arr1 : \n",        stats.trimboth(arr1, proportiontocut = .1, axis = 0)) |
Output :
arr1 : [[0, 12, 21, 3, 14], [53, 16, 37, 85, 39]] clipped arr1 : [[ 0 12 21 3 14] [53 16 37 85 39]] clipped arr1 : [[ 0 12 21 3 14] [53 16 37 85 39]] clipped arr1 : [[ 0 3 12 14 21] [16 37 39 53 85]] clipped arr1 : [[ 0 12 21 3 14] [53 16 37 85 39]]
