Learn the differences between shallow and deep copy in Python to efficiently manage data structures and avoid unintended side effects. Shallow copies create new objects with references to the original elements, while deep copies create fully independent copies. Explore various methods for creating both shallow and deep copies, including using the copy module, list slicing, and custom functions, to ensure your data is handled correctly for your specific needs.

Decorators in Python offer a flexible way to modify function or class behavior, enabling code extension without permanent changes. This blog post delves into first-class functions, simple and advanced decorators, and their usage with arguments and return values. Examples illustrate how decorators enhance code modularity and maintainability. Learn how to apply decorators to functions and classes effectively.

In this post, explore the crucial concepts of mutable and immutable types in Python. Understand how immutable types like strings and integers ensure data integrity by preventing changes once created, while mutable types like lists and dictionaries allow in-place modifications. Learn with practical examples and see why grasping these concepts is essential for writing robust and efficient Python code.

Discover the fundamentals of Python in this guide. Learn the difference between variables and objects, and understand their core characteristics: value, identity, and type. Understand how variables reference objects in memory and the role of Python's automatic garbage collection. Dive into examples that illustrate these concepts, setting a foundation for more advanced Python topics.

Discover AWS Lambda instances and their start types, focusing on cold starts, handler methods, and instance reuse. Learn how cold starts impact performance and how Python code is executed in these scenarios. Understand the importance of minimizing cold start durations and see practical examples with a simple 'Hello World' lambda function in Python. This article provides insights into optimizing lambda functions for better performance in time-sensitive environments.