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# Numpy MaskedArray.mean() function | Python

• Last Updated : 18 Oct, 2019

`numpy.MaskedArray.mean()` function is used to return the average of the masked array elements along given axis.Here masked entries are ignored, and result elements which are not finite will be masked.

Syntax : `numpy.ma.mean(axis=None, dtype=None, out=None)`

Parameters:

axis :[ int, optional] Axis along which the mean is computed. The default (None) is to compute the mean over the flattened array.
dtype : [dtype, optional] Type of the returned array, as well as of the accumulator in which the elements are multiplied.
out : [ndarray, optional] A location into which the result is stored.
-> If provided, it must have a shape that the inputs broadcast to.
-> If not provided or None, a freshly-allocated array is returned.

Return : [mean_along_axis, ndarray] A new array holding the result is returned unless out is specified, in which case a reference to out is returned.

Code #1 :

 `# Python program explaining``# numpy.MaskedArray.mean() 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.mean    ``# methods to masked array``out_arr ``=` `mask_arr.mean() ``print` `(``"mean of masked array along default axis : "``, out_arr) `
Output:
```Input array :  [[ 1  2]
[ 3 -1]
[ 5 -3]]
[-- -1]
[5 -3]]
mean of masked array along default axis :  0.75
```

Code #2 :

 `# Python program explaining``# numpy.MaskedArray.mean() 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``, ``0``, ``3``], [ ``4``, ``1``, ``6``]]) ``print` `(``"Input array : "``, in_arr)``     ` `# Now we are creating a masked array. ``# by making one entry as invalid.  ``mask_arr ``=` `ma.masked_array(in_arr, mask ``=``[[ ``0``, ``0``, ``0``], [ ``0``, ``0``, ``1``]]) ``print` `(``"Masked array : "``, mask_arr) ``    ` `# applying MaskedArray.mean methods ``# to masked array``out_arr1 ``=` `mask_arr.mean(axis ``=` `0``) ``print` `(``"mean of masked array along 0 axis : "``, out_arr1)`` ` `out_arr2 ``=` `mask_arr.mean(axis ``=` `1``) ``print` `(``"mean of masked array along 1 axis : "``, out_arr2)`
Output:
```Input array :  [[1 0 3]
[4 1 6]]
Masked array :  [[1 0 3]
[4 1 --]]
mean of masked array along 0 axis :  [2.5 0.5 3.0]
mean of masked array along 1 axis :  [1.3333333333333333 2.5]
```

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