scipy.stats.tmean(array, limits=None, inclusive=(True, True))
calculates the trimmed mean of the array elements along the specified axis of the array.
It’s formula –
Parameters :
array: Input array or object having the elements to calculate the trimmed mean.
axis: Axis along which the trimmed mean is to be computed. By default axis = 0.
limits: Lower and upper bound of the array to consider, values less than the lower limit or greater than the upper limit will be ignored. If limits is None [default], then all values are used.Returns : Trimmed mean of the array elements based on the set parameters.
Code #1:
# Trimmed Mean from scipy import stats
import numpy as np
# array elements ranging from 0 to 19 x = np.arange( 20 )
print ( "Trimmed Mean :" , stats.tmean(x))
print ( "\nTrimmed Mean by setting limit : " ,
stats.tmean(x, ( 2 , 10 )))
|
Trimmed Mean : 9.5 Trimmed Mean by setting limit : 6.0
Code #2: With multi-dimensional data, axis() working
# Trimmed Mean from scipy import stats
import numpy as np
arr1 = [[ 1 , 3 , 27 ],
[ 5 , 3 , 18 ],
[ 17 , 16 , 333 ],
[ 3 , 6 , 82 ]]
# using axis = 0 print ( "\nTrimmed Mean is with default axis = 0 : \n" ,
stats.tmean(arr1, axis = 1 ))
|
Trimmed Mean is with default axis = 0 : 42.8333333333