Swap the bytes of the masked array data inplace in Numpy

NumpyServer Side ProgrammingProgramming

To swap the bytes of the masked array, use the ma.MaskedArray.byteswap() method in Numpy. The parameter "inplace" is set to True i.e. swap bytes in-place.

Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually. It returns the byteswapped array. If inplace is True, this is a view to self.

The numpy.ma.MaskedArray is a subclass of ndarray designed to manipulate numerical arrays with missing data. An instance of MaskedArray can be thought as the combination of several elements −

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([[35, 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)

Swap the bytes of the masked array, use the ma.MaskedArray.byteswap() method in Numpy. The parameter "inplace" is set to True i.e. swap bytes in-place. The default is False −

print("\nAfter Swap...\n",maskArr.byteswap(inplace=True))

Example

# Python ma.MaskedArray - Swap the bytes of the masked array data inplace

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[35, 85, 45], [67, 33, 59]])
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, 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)

# To swap the bytes of the masked array, use the ma.MaskedArray.byteswap() method in Numpy
# The parameter "inplace" is set to True i.e. swap bytes in-place.

# The default is False.
print("\nAfter Swap...\n",maskArr.byteswap(inplace=True))

Output

Array...
[[35 85 45]
[67 33 59]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[35 85 --]
[67 -- 59]]

Our Masked Array type...
int32

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

Elements in the Masked Array...
6

After Swap...
 [[2522015791327477760 6124895493223874560 --]  
 [4827858800541171712 -- 4251398048237748224]]
raja
Updated on 02-Feb-2022 07:09:11

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