scipy.stats.sem (arr, axis = 0, ddof = 0) is used to compute the standard error of the mean of the input data.

Parameters:
arr: [array_like] Input array or object having the elements to calculate the standard error.
axis: Axis along which the mean is to be computed. By default axis = 0.
ddof: Degree of freedom correction for Standard Deviation.

Results: standard error of the mean of the input data.

Example :

# stats.sem () method import numpy as np from scipy import stats     arr1 = [[[ 20 , 2 , 7 , 1 , 34 ], [ 50 , 12 , 12 , 34 , 4 ]]   arr2 = [ 50 , 12 , 12 , 34 , 4 ]   print (< / code> "arr1:" , arr1) print ( "arr2:" , arr2)   print ( "sem ratio for arr1:" ,  stats.sem (arr1, axis = 0 , ddof = 0 ))   print ( "sem ratio for arr1:" ,  stats.sem (arr1, axis = 1 , ddof = 0 ))   print ( "sem ratio for arr1:" ,  stats.sem (arr2, axis = 0 , ddof = 0 ))

Output:

 arr1: [[20, 2, 7, 1, 34], [50, 12, 12, 34, 4]] arr2: [50, 12, 12, 34, 4] sem ratio for arr1: [10.60660172 3.53553391 1.76776695 11.66726189 10.60660172] sem ratio for arr1: [5.62423328 7.61892381] sem ratio for arr1: 7.618923808517841 <>