Return a view of the masked array with axis1 and axis2 interchanged in Numpy

To return a view of the array with axis1 and axis2 interchanged, use the ma.MaskedArray.swapaxes() method in Numpy.

For NumPy >= 1.10.0, if a is an ndarray, then a view of a is returned; otherwise a new array is created. For earlier NumPy versions a view of a is returned only if the order of the axes is changed, otherwise the input array is returned.

A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.

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([[49, 85, 45], [67, 33, 59]])
print("Array...<br>", arr)
print("\nArray type...<br>", arr.dtype)

Get the dimensions of the Array −

print("Array Dimensions...<br>",arr.ndim)

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

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

Get the dimensions of the Masked Array −

print("\nOur Masked Array Dimensions...<br>",maskArr.ndim)

Get the shape of the Masked Array −

print("\nOur Masked Array Shape...<br>",maskArr.shape)

Get the number of elements of the Masked Array −

print("\nElements in the Masked Array...<br>",maskArr.size)

Return a view of the array with axis1 and axis2 interchanged, use the ma.MaskedArray.swapaxes() method −

print("\nResult...<br>",np.swapaxes(maskArr, 0 , 1))

Example

# Python ma.MaskedArray - Return a view of the array with axis1 and axis2 interchanged

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[[15], [30], [45]]])
print("Array...<br>", arr)
print("\nArray type...<br>", arr.dtype)

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

# Create a masked array and mask some of them as invalid
maskArr = ma.masked_array(arr, mask =[[0, 1, 0]])
print("\nOur Masked Array<br>", maskArr)
print("\nOur Masked Array type...<br>", maskArr.dtype)

# Get the dimensions of the Masked Array
print("\nOur Masked Array Dimensions...<br>",maskArr.ndim)

# Get the shape of the Masked Array
print("\nOur Masked Array Shape...<br>",maskArr.shape)

# Get the number of elements of the Masked Array
print("\nElements in the Masked Array...<br>",maskArr.size)

# To return a view of the array with axis1 and axis2 interchanged, use the ma.MaskedArray.swapaxes() method in Numpy
print("\nResult...<br>",np.swapaxes(maskArr, 0 , 1))

Output

Array...
[[[15]
[30]
[45]]]

Array type...
int64

Array Dimensions...
3

Our Masked Array
[[[15]
[--]
[45]]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
3

Our Masked Array Shape...
(1, 3, 1)

Elements in the Masked Array...
3

Result...
[[[15]]

[[--]]

[[45]]]