Numpy MaskedArray.average() function | Python Last Updated : 13 Oct, 2019 Comments Improve Suggest changes Like Article Like Report numpy.MaskedArray.average() function is used to return the weighted average of array over the given axis. Syntax : numpy.ma.average(arr, axis=None, weights=None, returned=False) Parameters: arr :[ array_like] Input masked array whose data to be averaged. Masked entries are not taken into account in the computation. axis :[ int, optional] Axis along which to average arr. If None, averaging is done over the flattened array. weights : [array_like, optional] The importance that each element has in the computation of the average. If weights=None, then all data in arr are assumed to have a weight equal to one. If weights is complex, the imaginary parts are ignored. returned :[ bool, optional] It indicates whether a tuple (result, sum of weights) should be returned as output (True), or just the result (False). Default is False. Return : [ scalar or MaskedArray] The average along the specified axis. When returned is True, return a tuple with the average as the first element and the sum of the weights as the second element. Code #1 : Python3 # Python program explaining # numpy.MaskedArray.average() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array in_arr = geek.array([[1, 2], [ 3, -1], [ 5, -3]]) print ("Input array : ", in_arr) # Now we are creating a masked array. # by making entry as invalid. mask_arr = ma.masked_array(in_arr, mask =[[1, 0], [ 1, 0], [ 0, 0]]) print ("Masked array : ", mask_arr) # applying MaskedArray.average # methods to masked array out_arr = ma.average(mask_arr) print ("normal average of masked array : ", out_arr) Output: Input array : [[ 1 2] [ 3 -1] [ 5 -3]] Masked array : [[-- 2] [-- -1] [5 -3]] normal average of masked array : 0.75 Code #2 : Python3 # Python program explaining # numpy.MaskedArray.average() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array in_arr = geek.array([[1, 2], [ 3, -1], [ 5, -3]]) print ("Input array : ", in_arr) # Now we are creating a masked array. # by making entry as invalid. mask_arr = ma.masked_array(in_arr, mask =[[1, 0], [ 1, 0], [ 0, 0]]) print ("Masked array : ", mask_arr) # applying MaskedArray.average # methods to masked array out_arr = ma.average(mask_arr, weights =[[0, 1], [ 0, 2], [ 3, 1]]) print ("weighted average of masked array : ", out_arr) Output: Input array : [[ 1 2] [ 3 -1] [ 5 -3]] Masked array : [[-- 2] [-- -1] [5 -3]] weighted average of masked array : 1.7142857142857142 Comment More infoAdvertise with us Next Article Numpy MaskedArray.average() function | Python jana_sayantan Follow Improve Article Tags : Python Python-numpy Python numpy-arrayManipulation Practice Tags : python Similar Reads Numpy MaskedArray.argmax() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. 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