# Numpy MaskedArray.mean() function | Python

`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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