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))) |
Trimmed Standard error : 1.32287565553 Trimmed Standard error by setting limit : 0.912870929175
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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)) |
Trimmed Standard error is with default axis = 0 : 27.1476974115
