scipy.stats.tmax(array, lowerlimit=None, axis=0, inclusive=True)
function calculates the trimmed maximum of the array elements along with ignoring the values lying outside the specified limits, along the specified axis.
Parameters :
array: Input array or object having the elements to calculate the trimmed maximum.
axis: Axis along which the statistics 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.
inclusive: Decide whether to include the values equal to lower or upper bound, or to exclude them while calculation.Returns : Trimmed maximum of the array elements based on the set parameters.
Code #1:
# Trimmed Maximum from scipy import stats
import numpy as np
# array elements ranging from 0 to 19 x = [ 1 , 3 , 27 , 56 , 2 , 4 , 13 , 3 , 6 ]
print ( "Trimmed Maximum :" , stats.tmax(x))
print ( "\nTrimmed Maximum by setting limit : " ,
stats.tmax(x, ( 5 )))
|
Trimmed Maximum : 56 Trimmed Maximum by setting limit : 4
Code #2: With multi-dimensional data
# Trimmed Maximum from scipy import stats
import numpy as np
# array elements ranging from 0 to 19 x = [[ 1 , 3 , 27 ],
[ 3 , 4 , 7 ],
[ 7 , 6 , 3 ],
[ 3 , 6 , 8 ]]
print ( "Trimmed Maximum :" , stats.tmax(x))
# setting axis print ( "\nTrimmed Maximum by setting axis : " ,
stats.tmax(x, axis = 1 ))
print ( "\nTrimmed Maximum by setting axis : " ,
stats.tmax(x, axis = 0 ))
# setting limit print ( "\nTrimmed Maximum by setting limit : " ,
stats.tmax(x, ( 5 ), axis = 1 ))
print ( "\nTrimmed Maximum by setting limit : " ,
stats.tmax(x, ( 5 ), axis = 0 ))
|
Trimmed Maximum : [ 7 6 27] Trimmed Maximum by setting axis : [27 7 7 8] Trimmed Maximum by setting axis : [ 7 6 27] Trimmed Maximum by setting limit : [3 4 3 3] Trimmed Maximum by setting limit : [3 4 3]