Bytes are the unit of data which is 8 bits in length that is basically used to store the data in the memory of a computer depending on which architecture the CPU uses. In Python, there is a library called Numpy which is basically used for working with arrays, and there is a method provided by Numpy called byteswap() which can be used for byte swapping in Numpy.

In the article “Byte Swapping in NumPy”, we will discuss the need for byte swapping, how byte swapping can be done, and lastly, and implementation of byte swapping.

Numpy Library in Python

In the article “Byte Swapping in NumPy”, we will get some idea about the Numpy library because we are going to use the Numpy library in the concept of byte swapping. So, Numpy is the library provided by Python that basically works on the array operations, which results in faster execution time than the generic Python lists.

We are going to see an example in which we will create the Numpy array and reverse it because we will need to create it while doing byte swapping and we are doing reversing because internally byte swapping does the same:

In the above code, the array() function is used from the library Numpy where the list is passed of 4 elements. Then reversing the array is done by creating two variables, i and j, for the start and end of the array. Then an iteration is done till i is lesser than j, and swapping of the element at i and j is done. Then i and j are incremented and decremented by 1. Lastly, we printed the reversed Numpy array.

Byte Ordering

In the article “Byte Swapping in NumPy”, we will discuss the byte ordering. Computers read the byte in the byte stream from left to right or the same as the humans see words. In byte, the least-significant bit is at index zero, and the most-significant bit is at index seven. So, It gets very important to know which order is followed by the CPU.

CPU can follow two systems that are, big-endian and little-endian data representations which are explained below:

Big-Endian System

When the most significant is in the smallest address, or we can say that if the bytes are placed from left to right, then the data representation will be known as a big-endian system.

For example, if the value of the 32-bit unsigned integer is 12345678, then this value will be represented in the big-endian system as 123455678.

Little-Endian System

When the least significant is in the smallest address, or we can say that if the bytes are placed from right to left, then the data representation will be known as a little-endian system.

For example, if the value of the 32-bit unsigned integer is 12345678, then this value will be represented in the big-endian system as 87654321.

Need of Byte Swapping

In this section of the article “Byte Swapping in NumPy”, we will discuss what is needed for byte swapping. Byte swapping is required while working on a computer with a little-endian CPU, the data can be loaded on the computer with a big-endian CPU.

So, there can be differences when you want to view an array that is not of the same byte ordering as the computer on which you are running Python. There are two CPUs that are little-endian and big-endian; simply, we can say least significant and most significant in the smallest address.

Working of Byte Swapping with Numpy library

In this section of the article “Byte Swapping in NumPy”, we will discuss how byte swapping can be done with Numpy Library. For byte swapping, the Numpy library offers the method called “byteswap()”, which is basically used to swap the byte order of the data stored in memory or one endianness to another. In simpler words, the byteswap() function is used to switch between low-endian and big-endian data representations, but this function does not work with the string arrays.

There are several data types that can be used with this function, such as integers, floating point numbers, and binary data.

Syntax of byteswap() function

Here is the syntax below by which the byteswap() function can be used:

ndarray.byteswap(inplace=False)

You can also try this code with Online Python Compiler

In the above syntax of the polyfit() method, there is only one parameter that can be either True or False, which shows if the swapping bytes in-place will work or not.

Return Type of byteswap() function

As we know byteswap() function can work with all the data types like integers, floating points, and long. So the byteswap() function returns the new value of the same data type with the byte order swapped.

Implementation of Byte Swapping

In this section of the article “Byte Swapping in NumPy”, we will discuss the implementation of byte swapping with the Numpy library. Here are some of the examples by which we can learn how to do byte swapping:

Byte Swapping on Integer Numpy Arrays

In this example, we will do byte swapping on the Numpy arrays using the array() function in Python.

Python

Python

# Importing numpy library import numpy as np

# Numpy array created using array() function a = np.array([1, 256, 8755], dtype = np.int16)

# Printing given numpy array print('Given Numpy array:') print(a)

# Printing the numpy array after doing byte swapping on it print('Applying the byteswap() function on Numpy array:') print(a.byteswap(True))

You can also try this code with Online Python Compiler

In the above code, to create an array, we used the function array() of numpy, which will create the Numpy arrays. The byteswap() function is then used on this Numpy array with the value True which defines in-place.

Byte Swapping on Array of Strings

In this example, we will do byte swapping on the Numpy array of strings using the array() function.

Python

Python

# Importing numpy library

import numpy as geek

# a is an array of strings

a = geek.array(["Ninja1","Ninja2","Ninja3"], dtype = np.int16)

# Printing the numpy array after doing byte swapping on it

print(a.byteswap(True))

You can also try this code with Online Python Compiler

In the above code, first, the necessary library called numpy is imported. we used the function array() of numpy, which will create the Numpy array of strings. But unfortunately, the byteswap() function does not work with the array of strings like in the above example.

Frequently Asked Questions

Why to use Numpy arrays over Python lists?

Numpy arrays offer faster execution time and contiguous memory allocation, they can also be used with various libraries, and several methods are provided for ease of use.

How the arithmetic operations on Numpy arrays can be done?

There are several methods like add(), subtract(), multiply(), divide(), power(), and mod() provided by Numpy which can be used for the arithmetic operations that can be applied to the arrays.

What are little-endian and big-endian CPUs?

There are two CPUs that are little-endian which is the least significant in the smallest address, and big-endian, which is the most significant in the smallest address.

What is Byte Ordering?

Byte Ordering is the order of the bytes, or we can say the 8-bits of data which can be of different data types such as integers, floating point numbers, and characters.

Conclusion

Byte Swapping is done to swap the byte order of the data stored in memory or one endianness to another. In Python, the Numpy library provides the function called “byteswap()”, which can be used to swap the bytes.

In the article “Byte Swapping in NumPy”, we have discussed the Numpy Library, what is the need for byte swapping, how byte swapping can be done, and lastly, we discussed the implementation of byte swapping. Here are more articles that are recommended to read: