sciPy stats.tsem() function | Python Last Updated : 10 Feb, 2019 Comments Improve Suggest changes Like Article Like Report 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: Python3 # 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 Python3 # 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 Comment More infoAdvertise with us Next Article sciPy stats.tsem() function | Python V vishal3096 Follow Improve Article Tags : Python Python-scipy Python scipy-stats-functions Practice Tags : python Similar Reads sciPy stats.tstd() function | Python scipy.stats.tstd(array, limits=None, inclusive=(True, True)) calculates the trimmed standard deviation of the array elements along the specified axis of the array. It's formula - Parameters : array: Input array or object having the elements to calculate the trimmed standard deviation. axis: Axis alo 2 min read scipy stats.tmin() function | Python scipy.stats.tmin(array, lowerlimit=None, axis=0, inclusive=True) function calculates the trimmed minimum 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 m 2 min read sciPy stats.tmax() function | Python 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 t 2 min read sciPy stats.tvar() function | Python scipy.stats.tvar(array, limits=None, inclusive=(1, 1)) function calculates the trimmed variance of the array elements along with ignoring the values lying outside the specified limits. It's formula - Parameters : array: Input array or object having the elements to calculate the trimmed variance. lim 2 min read sciPy stats.sem() function | Python scipy.stats.sem(arr, axis=0, ddof=0) function 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 : 1 min read Like