# Numpy MaskedArray.var() function | Python

`numpy.MaskedArray.var() ` function is used to compute the variance along the specified axis.It returns the variance of the masked array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, otherwise over the specified axis.

Syntax : `numpy.ma.var(arr, axis=None, dtype=None, out=None, ddof=0, keepdims=False)`

Parameters:

arr : [ ndarray ] Input masked array.
axis :[ int, optional] Axis along which the variance is computed. The default (None) is to compute the variance 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.
ddof : [int, optional] “Delta Degrees of Freedom”: the divisor used in the calculation is N – ddof, where N represents the number of elements. By default ddof is zero.
keepdims :[ bool, optional] If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Return : [variance_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.var() 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.var     ` `# methods to masked array ` `out_arr ``=` `ma.var(mask_arr)  ` `print` `(``"variance of masked array along default axis : "``, out_arr)  `

Output:

```Input array :  [[ 1  2]
[ 3 -1]
[ 5 -3]]
[-- -1]
[5 -3]]
variance of masked array along default axis :  9.1875
```

Code #2 :

 `# Python program explaining ` `# numpy.MaskedArray.var() 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.var methods  ` `# to masked array ` `out_arr1 ``=` `ma.var(mask_arr, axis ``=` `0``)  ` `print` `(``"variance of masked array along 0 axis : "``, out_arr1) ` ` `  `out_arr2 ``=` `ma.var(mask_arr, axis ``=` `1``)  ` `print` `(``"variace of masked array along 1 axis : "``, out_arr2) `

Output:

```Input array :  [[1 0 3]
[4 1 6]]
Masked array :  [[1 0 3]
[4 1 --]]
variance of masked array along 0 axis :  [2.25 0.25 0.  ]
variace of masked array along 1 axis :  [1.55555556 2.25      ]
```

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