Return a view of the masked array with axes transposed in NumPy

To return a view of the array with axes transposed, use the ma.MaskedArray.transpose() method in Numpy. For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D array into a 2D column vector, an additional dimension must be added. np.atleast2d(a).T achieves this, as does a[:, np.newaxis]. For a 2-D array, this is a standard matrix transpose.

The axes can be,

• None or no argument − reverses the order of the axes.

• tuple of ints − i in the j-th place in the tuple means a’s i-th axis becomes a.transpose()’s j-th axis.

• n ints − same as an n-tuple of the same ints

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 axes transposed, use the ma.MaskedArray.transpose() method in Numpy −

print("\nResult...<br>",maskArr.transpose())

Example

# Python ma.MaskedArray - Return a view of the array with axes transposed

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[78, 85, 51], [56, 33, 97]])
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], [ 0, 0, 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 axes transposed, use the ma.MaskedArray.transpose() method in Numpy
print("\nResult...<br>",maskArr.transpose())

Output

Array...
[[78 85 51]
[56 33 97]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[78 -- 51]
[56 33 97]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(2, 3)

Elements in the Masked Array...
6

Result...
[[78 56]
[-- 33]
[51 97]]