sciPy stats.tsem() function | Python

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