# Return the variance of the masked array elements along row axis

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To return the variance of the masked array elements, use the ma.MaskedArray.var() in Numpy. The axis is set using the axis parameter. The axis is set to 1, for row axis

Returns the variance of the 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.

The “axis” parameter is the axis or axes along which the variance is computed. The default is to compute the variance of the flattened array. If this is a tuple of ints, a variance is performed over multiple axes, instead of a single axis or all the axes as before. The dtype is the type to use in computing the variance. For arrays of integer type the default is float64; for arrays of float types it is the same as the array type.

If “keepdims” 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.

## Steps

At first, import the required library −

import numpy as np
import numpy.ma as ma

Create an array with int elements using the numpy.array() method −

arr = np.array([[55, 85, 68, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 99, 85]])
print("Array...\n", arr)
print("\nArray type...\n", arr.dtype)

Get the dimensions of the Array −

print("\nArray Dimensions...\n",arr.ndim)


Create a masked array and mask some of them as invalid −

maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]])
print("\nOur Masked Array type...\n", maskArr.dtype)

Get the dimensions of the Masked Array −

print("\nOur Masked Array Dimensions...\n",maskArr.ndim)


Get the shape of the Masked Array −

print("\nOur Masked Array Shape...\n",maskArr.shape)

Get the number of elements of the Masked Array −

print("\nElements in the Masked Array...\n",maskArr.size)


To return the variance of the masked array elements, use the ma.MaskedArray.var() in Numpy. The axis is set using the axis parameter. The axis is set to 1, for row axis −

res = maskArr.var(axis = 1)
print("\nResult..\n.", res)

## Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[55, 85, 68, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 99, 85]])
print("Array...\n", arr)
print("\nArray type...\n", arr.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...\n",arr.ndim)

# Create a masked array and mask some of them as invalid
maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]])

# Get the dimensions of the Masked Array

# Get the shape of the Masked Array

# Get the number of elements of the Masked Array

# To return the variance of the masked array elements, use the ma.MaskedArray.var() in Numpy
# The axis is set using the axis parameter
# The axis is set to 1, for row axis
res = maskArr.var(axis = 1)
print("\nResult..\n.", res)

## Output

Array...
[[55 85 68 84]
[67 33 39 53]
[29 88 51 37]
[56 45 99 85]]

Array type...
int64

Array Dimensions...
2

[[-- -- 68 84]
[67 33 -- 53]
[29 88 51 --]
[56 -- 99 85]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(4, 4)

Elements in the Masked Array...
16

Result..
. [ 64. 194.66666667 592.66666667 320.66666667]