Advanced Python: Memory Management and Garbage Collection

🐍 Advanced Python Tutorial 6: Memory Management & Garbage Collection

🔍 Introduction

Python manages memory automatically with its garbage collector. However, as advanced developers, understanding how it works can help us write more efficient code and debug memory-related issues.

📦 Python Memory Model

• Python objects are stored in a private heap.
• All memory allocation happens behind the scenes via Python’s memory manager.
• Each object has a reference count.

🗑️ What is Garbage Collection?

Python uses **reference counting** and a **cyclic garbage collector** to remove unused objects from memory.

🧪 Reference Counting Example

import sys
a = []
b = a
print(sys.getrefcount(a)) # ➤ Shows reference count

🔁 Circular References

Circular references can’t be collected with reference counting alone.

class A:
def __init__(self):
self.b = None

class B:
def __init__(self):
self.a = None

a = A()
b = B()
a.b = b
b.a = a

⚙️ Forcing Garbage Collection

You can manually invoke garbage collection using Python's gc module.

import gc
gc.collect() # ➤ Forces garbage collection

📋 Best Practices

• ✅ Avoid circular references if possible.
• ✅ Use del to delete unneeded references early.
• ✅ Use gc.collect() in large scripts if needed.

✅ Summary

• Python manages memory automatically via reference counting and GC.
• Understanding memory helps write efficient, bug-free code.
• Use sys and gc modules for deeper memory insight.