sciPy stats.nanmean() function | Python Last Updated : 11 Feb, 2019 Summarize Comments Improve Suggest changes Share Like Article Like Report scipy.stats.nanmean(array, axis=0) function calculates the arithmetic mean by ignoring the Nan (not a number) values of the array elements along the specified axis of the array. It's formula - Parameters : array : Input array or object having the elements, including Nan values, to calculate the arithmetic mean. axis : Axis along which the mean is to be computed. By default axis = 0. Returns : Arithmetic mean of the array elements (ignoring the Nan values) based on the set parameters. Code #1: Python3 # Arithmetic Mean import scipy import numpy as np arr1 = [1, 3, np.nan, 27] print("Arithmetic Mean using nanmean :", scipy.nanmean(arr1)) print("Arithmetic Mean without handling nan value :", scipy.mean(arr1)) Output : Arithmetic Mean using nanmean : 10.333333333333334 Arithmetic Mean without handling nan value : nan Code #2: With multi-dimensional data Python3 # Arithmetic Mean from scipy import mean from scipy import nanmean import numpy as np arr1 = [[1, 3, 27], [3, np.nan, 6], [np.nan, 6, 3], [3, 6, np.nan]] print("Arithmetic Mean is :", mean(arr1)) print("Arithmetic Mean handling nan :", nanmean(arr1)) # using axis = 0 print("\nArithmetic Mean is with default axis = 0 : \n", mean(arr1, axis = 0)) print("\nArithmetic Mean handling nan with default axis = 0 : \n", nanmean(arr1, axis = 0)) # using axis = 1 print("\nArithmetic Mean is with default axis = 1 : \n", mean(arr1, axis = 1)) print("\nArithmetic Mean handling nan with default axis = 1 : \n", nanmean(arr1, axis = 1)) Output : Arithmetic Mean is : nan Arithmetic Mean handling nan : 6.444444444444445 Arithmetic Mean is with default axis =0 : [nan nan nan] Arithmetic Mean handling nan with default axis =0 : [ 2.33333333 5. 12. ] Arithmetic Mean is with default axis =1 : [10.33333333 nan nan nan] Arithmetic Mean handling nan with default axis =1 : [10.33333333 4.5 4.5 4.5 ] Comment More infoAdvertise with us Next Article sciPy stats.nanmean() function | Python V vishal3096 Follow Improve Article Tags : Python Python-scipy Python scipy-stats-functions Practice Tags : python Similar Reads sciPy stats.nanmedian() function | Python scipy.stats.nanmedian(array, axis=0) function calculates the median by ignoring the Nan (not a number) values of the array elements along the specified axis of the array. Parameters : array : Input array or object having the elements, including Nan values, to calculate the median. axis : Axis along 2 min read sciPy stats.nanstd() function | Python scipy.stats.nanstd(array, axis=0) function calculates the standard deviation by ignoring the Nan (not a number) values of the array elements along the specified axis of the array. It's formula - Parameters : array : Input array or object having the elements, including Nan values, to calculate the st 2 min read scipy stats.normaltest() function | Python scipy.stats.normaltest(array, axis=0) function test whether the sample is different from the normal distribution. This function tests the null hypothesis of the population that the sample was drawn from. Parameters : array : Input array or object having the elements. axis : Axis along which the norm 1 min read scipy stats.kurtosis() function | Python scipy.stats.kurtosis(array, axis=0, fisher=True, bias=True) function calculates the kurtosis (Fisher or Pearson) of a data set. It is the fourth central moment divided by the square of the variance. It is a measure of the "tailedness" i.e. descriptor of shape of probability distribution of a real-va 2 min read statistics mean() function - Python The mean() function from Python’s statistics module is used to calculate the average of a set of numeric values. It adds up all the values in a list and divides the total by the number of elements. For example, if we have a list [2, 4, 6, 8], the mean would be (2 + 4 + 6 + 8) / 4 = 5.0. This functio 4 min read Like