# Return the standard deviation of the masked array elements along column axis in NumPy

To return the standard deviation of the masked array elements, use the ma.MaskedArray.std() in Numpy. The axis is set using the axis parameter. The axis is set to 0, for column axis.

Returns the standard deviation, a measure of the spread of a distribution, of the array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis.

The axis parameter is the axis or axes along which the standard deviation is computed. The default is to compute the standard deviation of the flattened array. If this is a tuple of ints, a standard deviation 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 standard deviation. For arrays of integer type the default is float64, for arrays of float types it is the same as the array type.

## 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...", arr)
print("Array type...", arr.dtype)

Get the dimensions of the Array −

print("Array Dimensions...",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("Our Masked Array type...", maskArr.dtype)

Get the dimensions of the Masked Array −

print("Our Masked Array Dimensions...",maskArr.ndim)


Get the shape of the Masked Array −

print("Our Masked Array Shape...",maskArr.shape)

Get the number of elements of the Masked Array −

print("Elements in the Masked Array...",maskArr.size)


To return the standard deviation of the masked array elements, use the ma.MaskedArray.std() in Numpy. The axis is set using the axis parameter. The axis is set to 0, for column axis −

res = maskArr.std(axis = 0)
print("Result...", 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...", arr)
print("Array type...", arr.dtype)

# Get the dimensions of the Array
print("Array Dimensions...",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 standard deviation of the masked array elements, use the ma.MaskedArray.std() in Numpy
# The axis is set using the axis parameter
# The axis is set to 0, for column axis
print("Result...", 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]]

int64

. [15.965240019770729 27.5 19.871811414385174 14.854853303438128]