 # Numpy MaskedArray.std () Function | python

`numpy.MaskedArray.std()` is used to calculate the standard deviation along the specified axis. Masked entries are ignored here. The standard deviation is calculated for the aligned array by default, otherwise along the specified axis.

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

Parameters:

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.
– & gt; If provided, it must have a shape that the inputs broadcast to.
– & gt; 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:

` `

``` # Program Python explaining # numpy.MaskedArray.std () method   # import numy as geek # and numpy.ma module as ma import numpy as geek  import numpy.ma as ma    # create input array < / p> in_arr = geek.array ([[ 1 , 2 ], [ 3 , - 1 ], [ 5 , - 3 ]]) print ( " Input array: " , in_arr)    # Now we create a masked array. # invalidating the post. mask_arr = ma.masked_array (in_arr, mask < code class = "keyword"> = [[ 1 , 0 ], [ 1 , 0 ], [ 0 , 0 ]])  print ( " Masked array: " , mask_arr)    # apply MaskedArray.std # methods of the masked array out_arr = ma.std (mask_arr)  print ( "standard deviation of masked array along default axi s: " , out_arr)  ```

` ` Output:

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

Code # 2:

 ` # Python program explaining ` ` # numpy.MaskedArray.std () method `   ` # import numy as geek ` ` # and the numpy.ma module as ma ` ` import ` ` numpy as geek ` ` import ` ` numpy.ma as ma `   ` # create input array `  ` in_arr ` ` = ` ` geek.array ([[` ` 1 ` `, ` ` 0 ` `, ` ` 3 ` `], [` ` 4 ` `, ` ` 1 ` `, ` ` 6 ` `]]) ` ` print ` ` (` ` "Input array:" ` `, in_arr) `   ` # We are now creating a masked array. ` ` # invalidating one entry. ` ` mask_arr ` ` = ` ` ma.masked_array (in_arr, mask ` ` = ` ` [[` 0 `, ` ` 0 ` `, 0 ], [ 0 , 0 , 1 ]]) ```` print ( "Masked array:" , mask_arr)    # applying the MaskedArray.std methods # into the masked array out_arr1 = ma.std (mask_arr, axis = 0 )  print ( "standard deviati on 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) ```

Exit:

` 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] `