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sciPy stats.tsem() function | Python
  • Last Updated : 10 Feb, 2019

scipy.stats.tsem(array, limits=None, inclusive=(True, True)) calculates the trimmed standard error of the mean of array elements along the specified axis of the array.

Its formula :-

Parameters :
array: Input array or object having the elements to calculate the trimmed standard error of the mean.
axis: Axis along which the trimmed standard error of the 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 standard error of the mean of array elements based on the set parameters.

Code #1:






# Trimmed Standard error 
   
from scipy import stats
import numpy as np 
   
# array elements ranging from 0 to 19
x = np.arange(20)
    
print("Trimmed Standard error :", stats.tsem(x)) 
   
   
print("\nTrimmed Standard error by setting limit : "
      stats.tsem(x, (2, 10)))
Output:
Trimmed Standard error : 1.32287565553

Trimmed Standard error by setting limit :  0.912870929175

 
Code #2: With multi-dimensional data, axis() working




# Trimmed Standard error 
   
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 Standard error is with default axis = 0 : \n"
      stats.tsem(arr1, axis = 1))
Output:
Trimmed Standard error is with default axis = 0 : 
 27.1476974115

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