sciPy function stats.trimboth () | python

scipy.stats.trimboth (a, proportional cut, axis = 0) function cuts off part of the elements in the array from both 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: Work

# stats.trimboth () method import numpy as np from scipy import stats   arr1 = [ 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]      print ( "arr1:" , arr1)   print ( "clipped arr1:" , stats.trimboth (arr1, proportiontocut = . 3 )) print ( "clipped arr1:" , 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] 

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 ( "arr1:" , arr1)   print ( "clipped arr1:" ,  stats.trimboth (arr1, proportiontocut = . 3 ))   print ( "clipped arr1:" ,  stats.trimboth (arr1, proportiontocut = . 1 ))    print ( "clipped arr1:" ,  s tats.trimboth (arr1, proportiontocut = . 1 , axis = 1 ))   print ( " clipped arr1: " ,    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]] cli pped arr1: [[0 3 12 14 21] [16 37 39 53 85]] clipped arr1: [[0 12 21 3 14] [53 16 37 85 39]]