NumPy - Byte Swapping

Swapping Axes of Arrays in NumPy

Byte swapping is a process used to convert data between different byte orders, also known as endianness. In computing, different systems might use different byte orders to represent multi-byte data types (e.g., integers, floats). Byte swapping ensures that data is interpreted correctly when transferred between systems with different endianness.

NumPy provides the byteswap() function to swap the bytes of an array. This is particularly useful when you need to convert data to the correct endianness for compatibility with other systems or formats.

Understanding Byte Order

Byte Order (Endianness) is the sequence in which bytes are ordered within larger data types. There are two primary byte orders −

• Little-Endian − The least significant byte is stored at the smallest address. For example, in the number 0x1234, 0x34 would be stored first.
• Big-Endian − The most significant byte is stored at the smallest address. For the same number 0x1234, 0x12 would be stored first.

The numpy.ndarray.byteswap() Function

The numpy.ndarray.byteswap() function is used to swap the byte order of the elements in a NumPy array. This function toggles between the two representations: bigendian and little-endian.

The byteswap() function is used on arrays with specific data types and does not affect the shape or size of the array. Following is the syntax −

numpy.ndarray.byteswap(inplace=False)

Where, if inplace is "True", the array is modified in place. If "False" (default), a new array with swapped bytes is returned.

Example: Swapping Bytes in a Simple Array

In the following example, we are swapping bytes in an array using the byteswap() function in NumPy −

import numpy as np 
a = np.array([1, 256, 8755], dtype = np.int16) 

print ('Our array is:', a)

print ('Representation of data in memory in hexadecimal form:', map(hex,a))
# We can see the bytes being swapped
print ('Applying byteswap() function:', a.byteswap())
print ('In hexadecimal form:', map(hex,a))

Following is the output obtained −

Our array is: [   1  256 8755]
Representation of data in memory in hexadecimal form: <map object at 0x7fdfa46a3370>
Applying byteswap() function: [  256     1 13090]
In hexadecimal form: <map object at 0x7fdff5867190>

Example: Byte Swapping In-Place

We can modify the array in place by setting the "inplace" parameter to "True" in the byteswap() function, swapping the bytes directly within the original array −

import numpy as np

# Creating a NumPy array with 32-bit integers
arr = np.array([1, 256, 65535], dtype=np.int32)

print("Original Array:")
print(arr)

# Perform in-place byte swapping
arr.byteswap()

print("\nArray After In-Place Byte Swapping:")
print(arr)

The result produced is as follows −

Original Array:
[    1   256 65535]

Array After In-Place Byte Swapping:
[    1   256 65535]

When to Use Byte Swapping

We can use Byte swapping in the following scenarios −

• Interoperability − When data is exchanged between systems with different endianness, byte swapping ensures correct interpretation.
• Data Reading/Writing − When dealing with raw binary files or network protocols that use different byte orders, byte swapping is necessary to correctly read or write data.
• Legacy Systems − Working with legacy systems or file formats that use specific byte orders might require byte swapping for correct data handling.