 # Numpy MaskedArray.var () Function | python

`numpy.MaskedArray.var()` is used to calculate variance along the specified axis. Returns the variance of masked array elements, a measure of the spread of the distribution. The variance is calculated for a flattened array by default, otherwise along the specified axis.

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

Parameters:

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.
– & 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: [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:

` `

``` # Program Python explaining # numpy.MaskedArray.var () method   # import numy as geek # and numpy.ma module as ma import numpy as geek  import numpy.ma as ma    # create input array in_arr = geek.array ([[ 1 , 2 ], [ 3 , - 1 ], [ 5 , - 3 ]]) print ( "Input array:" , in_arr)    # Now we create a masked array. # invalidating the entry. mask_arr = ma.masked_array (in_arr, mask = [[ 1 , 0 ], [ 1 , 0 ], [ 0 , 0 ]])  print ( "Masked array:" , mask_arr)    # apply MaskedArray.var # methods of the 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]] Masked array: [[- 2] [- -1] [5 -3]] variance of masked array along default axis: 9.1875 `

Code # 2:

` `

``` # Python program explaining # numpy.MaskedArray.var () method   # import numy as geek # and 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)   # Now we create 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 MaskedArray.var methods # to the 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) ```

` ` Exit:

` 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] `