# Numpy MaskedArray.average() function | Python

• Last Updated : 13 Oct, 2019

`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 :

 `# 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]]
[-- -1]
[5 -3]]
normal average of masked array :  0.75
```

Code #2 :

 `# 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]]