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

NumpyServer Side ProgrammingProgramming

To return a view of the array with axes transposed in Python, 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...\n", arr)
print("\nArray type...\n", arr.dtype)

Get the dimensions of the Array −

print("Array Dimensions...\n",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\n", maskArr)
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)

Return a view of the array with axes transposed, use the ma.MaskedArray.transpose() −

print("\nResult...\n",maskArr.transpose((1, 0)))

Example

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

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...\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 =[[0, 1, 0], [ 0, 0, 0]])
print("\nOur Masked Array\n", maskArr)
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 a view of the array with axes transposed, use the ma.MaskedArray.transpose() method in Numpy
print("\nResult...\n",maskArr.transpose((1, 0)))

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]]
raja
Updated on 04-Feb-2022 05:57:32

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