# Numpy MaskedArray.std() function | Python

`numpy.MaskedArray.std() ` function is used to compute the standard deviation along the specified axis.Here masked entries are ignored. The standard deviation is computed for the flattened array by default, otherwise over the specified axis.

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

Parameters:

arr : [ ndarray ] Input masked array.
axis :[ int, optional] Axis along which the standard deviation is computed.
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 : [standard_deviation_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.std() 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.std     ` `# methods to masked array ` `out_arr ``=` `ma.std(mask_arr)  ` `print` `(``"standard deviation of masked array along default axis : "``, out_arr)  `

Output:

```Input array :  [[ 1  2]
[ 3 -1]
[ 5 -3]]
[-- -1]
[5 -3]]
standard deviation of masked array along default axis :  3.031088913245535
```

Code #2 :

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

Output:

```Input array :  [[1 0 3]
[4 1 6]]
Masked array :  [[1 0 3]
[4 1 --]]
standard deviation of masked array along 0 axis :  [1.5 0.5 0.0]
standard deviation of masked array along 1 axis :  [1.247219128924647 1.5]
```

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