Module 14 Overview

Module 14: Memory Management pulls back the curtain on how Python handles data storage. Unlike C or C++, where you must manually allocate and free memory, Python does it for you. But “automatic” doesn’t mean “magic.”

In this module, we will explore reference counting, the garbage collector, and how to find memory leaks or performance bottlenecks.

Learning Objectives

By the end of this module, you will be able to:

• Explain how Reference Counting works as Python’s primary memory management strategy.
• Understand the role of the Garbage Collector in cleaning up cyclic references.
• Use Profiling Tools to identify slow code or memory-hungry objects.
• Optimize your applications for better performance.

Module Contents

Reference Counting How Python tracks object usage.

Garbage Collection Cleaning up circular references.

Profiling & Optimization Measuring performance and fixing bottlenecks.