Python is a powerful and versatile language, but with power comes great responsibility — the responsibility to write efficient code that doesn’t hog system resources.
In this article, we’ll explore how Python’s built-in collections can help you optimize memory usage and improve the overall performance of your applications.
Understanding Memory Usage in Python
Before we dive into collections, let’s take a moment to understand how Python manages memory. Python uses a private heap to store objects, and each object occupies a certain amount of memory based on its type and size.
When you create a new object, Python allocates memory for it on the heap. When the object is no longer needed, Python’s garbage collector reclaims the memory for future use.
However, memory management in Python is not always straightforward. For example, consider the following code:
a = [1, 2, 3, 4, 5]
b = aIn this case, both a and b are references to the same list object in memory. If you modify b, you're also modifying a. This behavior can lead to unintended consequences and memory leaks if…
