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  2. sciPy function stats.trimboth () | python

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]]