Coding with AI

Code Faster. Think Smarter. Ship Better—with AI.

Stop fighting boilerplate and busywork. Coding with AI shows professional Python developers how to use AI tools to accelerate design, coding, testing, debugging, and documentation—without sacrificing quality or control. Learn proven prompts, real workflows, and practical techniques you’ll use on the job every day.

Explore the book ->


The Power of Slicing in Python for List Manipulation

In this article, we will discuss the power of Python’s list slicing and show how it can be used for splitting lists. …

Updated October 8, 2023

In this article, we will discuss the power of Python’s list slicing and show how it can be used for splitting lists.

List slicing is a powerful feature of Python that allows us to break down or split larger lists into smaller ones. It works by specifying start and end indices and includes an optional step parameter. The start index is inclusive, while the end index is exclusive. If no start index is provided, it defaults to 0. Likewise, if no end index is given, it goes up to the end of the list.

Here’s how you can use list slicing to split a list:

list_name = ['item1', 'item2', 'item3', 'item4']
sublist1 = list_name[:2]  # It includes items from index 0 up to but not including 2, i.e., item1 and item2
sublist2 = list_name[2:]  # It includes items starting from index 2 to the end of the list

Similarly, you can slice a list in between two indices:

sublist3 = list_name[1:3]  # This will include item2 and item3

In addition, you can add a step parameter to the slicing operation. If it is set as -1, Python will return a reversed list:

rev_list = list_name[::-1]  # This returns the reversed list

When used with other lists, slicing can be a very efficient way to break down large problems into smaller manageable ones. It also helps in debugging when we want to analyze only part of the data.

Coding with AI

AI Is Changing Software Development. This Is How Pros Use It.

Written for working developers, Coding with AI goes beyond hype to show how AI fits into real production workflows. Learn how to integrate AI into Python projects, avoid hallucinations, refactor safely, generate tests and docs, and reclaim hours of development time—using techniques tested in real-world projects.

Explore the book ->