sciPy stats.tvar() function | Python Last Updated : 07 Feb, 2019 Summarize Comments Improve Suggest changes Share Like Article Like Report 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. 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. inclusive: Decide whether to include the values equal to lower or upper bound, or to exclude them while calculation. Returns : Trimmed variance of the array elements based on the set parameters. Code #1: Python3 # Trimmed Variance from scipy import stats import numpy as np # array elements ranging from 0 to 19 x = np.arange(20) print("Trimmed Variance :", stats.tvar(x)) print("\nTrimmed Variance by setting limit : ", stats.tvar(x, (2, 10))) Output: Trimmed Variance : 35.0 Trimmed Variance by setting limit : 7.5 Code #2: Checking "inclusive" flags Python3 # Trimmed Variance from scipy import stats import numpy as np # array elements ranging from 0 to 19 x = np.arange(20) # Setting limits print("\nTrimmed Variance by setting limit : ", stats.tvar(x, (2, 10))) # using flag print("\nTrimmed Variance by setting limit : ", stats.tvar(x, (2, 10), (False, True))) print("\nTrimmed Variance by setting limit : ", stats.tvar(x, (2, 12), (True, False))) Output: Trimmed Variance by setting limit : 7.5 Trimmed Variance by setting limit : 6.0 Trimmed Variance by setting limit : 9.16666666667 Comment More infoAdvertise with us Next Article sciPy stats.tvar() 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.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. ax 2 min read sciPy stats.variation() function | Python scipy.stats.variation(arr, axis = None) function computes the coefficient of variation. It is defined as the ratio of standard deviation to mean. Parameters : arr : [array_like] input array. axis : [int or tuples of int] axis along which we want to calculate the coefficient of variation. -> axis = 0 2 min read sciPy stats.zscore() function | Python scipy.stats.zscore(arr, axis=0, ddof=0) function computes the relative Z-score of the input data, relative to the sample mean and standard deviation. Its formula: Parameters :arr : [array_like] Input array or object for which Z-score is to be calculated. axis : Axis along which the mean is to be com 2 min read sciPy stats.mean() function | Python scipy.stats.mean(array, axis=0) function calculates the arithmetic mean of the array elements along the specified axis of the array (list in python). It's formula - Parameters : array: Input array or object having the elements to calculate the arithmetic mean. axis: Axis along which the mean is to b 1 min read Like