Python Garbage Collection Tutorial – Complete Guide

Delving into the world of programming, one often encounters the term “garbage collection”. But what exactly does this mean, especially in the context of Python, a popular programming language loved for its simplicity and versatility? In this comprehensive tutorial, we will take you through the ins and outs of Python garbage collection, turning this seemingly complex phenomenon into an approachable and easily understandable concept.

Unraveling the Mystery of Garbage Collection

Garbage collection, in the broadest sense, is a form of automatic memory management that is employed by many modern programming languages, Python included. It involves the idea of identifying and handling memory that is no longer in use, freeing up valuable resources and optimizing the overall performance of a program or a system.

The Importance of Mastering Garbage Collection

Now, you might ask: “Why is understanding garbage collection valuable?” Well, as a programmer, building your skill set around this concept equips you with several advantages.

  • First of all, memory management is a fundamental aspect of programming. By becoming proficient in this area, you gain awarenss about how your code utilizes resources, and how you can optimize it.
  • Secondly, when dealing with large-scale applications or games, control over memory usage can greatly impact performance and user experience.
  • Lastly, being keen on such low-level details will help you stand out as a developer, with a profound understanding of how software works.

In summary, while the idea of garbage collection might seem complex and intimidating, it is certainly a rewarding concept to conquer. And this is where we step in to guide you on your series of informative coding tutorials aimed at demystifying Python garbage collection.

All right, let’s delve into some hands-on application of Garbage Collection (GC) in Python. Remember, Python automates most GC tasks, freeing you from the duty of manual memory management. However, understanding what goes on behind the scenes can provide valuable insight into your application’s performance.

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Disabling and Enabling Garbage Collection

First, let’s look at how you can disable and enable GC in Python. To do this, we import the gc module, which provides the interface to Python’s garbage collector.

import gc

gc.disable()  # This will disable garbage collection
gc.isenabled()  # This will return False

gc.enable()  # This enables garbage collection again
gc.isenabled()  # This will now return True

Realistically, you won’t need to disable garbage collection often. However, there may be specific instances where manual control can lead to performance benefits.

Explicit Garbage Collection

Even with GC enabled, you may wish to force the garbage collector to run at a specific point in your program. To do this, use the gc.collect() function.

# Create some garbage
a = [1, 2, 3]
del a

# Manually run garbage collection

Inspecting Garbage Collector Contents

You can get the count of objects the garbage collector is tracking using the gc.get_count() function.

import gc

# Print the number of objects being tracked

This can be useful when troubleshooting, as a sudden increase in tracked objects may indicate a memory leak.

Conclusion – Part 2

This wraps up our second part of Python Garbage Collection tutorial. We have familiarized ourselves with the basics of GC and how to interact with it using Python. In the final part of this tutorial, we will explore how to detect and deal with circular references in Python, one of the key tasks of Python garbage collector.

Understanding Circular References in Python

Circular references are scenarios where two or more objects reference each other, causing a ‘loop‘ of dependencies. These can be tricky for the garbage collector to handle without further strategies, as each object appears to still be in use. Let’s illustrate this with Python code:

# Defining a class
class MyClass:

# Creating objects
a = MyClass()
b = MyClass()

# Creating circular references
a.ref = b
b.ref = a

# Checking for circular references

Code like this could potentially lead to memory leaks, but Python’s garbage collector is designed to handle such cases.

Cleaning up Circular References

Python’s garbage collector can detect and clean up circular references automatically once these objects are out of scope. We can run the garbage collector manually and count the number of unreferenced objects it found:

# Setting up circular references
a = MyClass()
a.self = a

# Trying to delete object
del a

# Running collector manually
collected_objects = gc.collect()

However, if the objects in the circular reference have a __del__ finalizer method, the garbage collector won’t collect them automatically, since it can’t determine the order of destruction.

Working with __del__ method and gc.garbage

The __del__ method in Python is like a destructor. It is called when an object is about to be destroyed. If an object with a __del__ method is part of a circular reference, it will be added to gc.garbage list after the execution of gc.collect(). We can take a look at this:

class MyClass:
    def __del__(self):

a = MyClass()
a.ref = a

del a


Note that you usually want to avoid having __del__ methods in your Python code to ensure the cyclic garbage collector can do its job.

Taking the Next Step

Having gained a firm grasp on Python Garbage Collection, where do we go next? We believe that the best way to solidify new knowledge is through continuous learning and practical application. Knowing how to write clean, well-optimized Python code is an in-demand skill in the field of technology.

With this in mind, we would like to steer your attention to our Python Mini-Degree program. This extensive collection is more than just about pushing your boundaries as a programmer – it’s an immersive journey through Python programming.

  • The Mini-Degree covers a broad range of Python-related topics – from algorithms and object-oriented programming to game development and app creation.
  • One of its hallmarks is the project-based learning approach, giving you the chance to apply your new skills on real-world applications such as games and chatbots.
  • The courses cater to programmers of all skill levels, ensuring both beginners and experienced coders can benefit.
  • Upon completion, you’ll have a portfolio of Python projects under your belt, demonstrating your proficiency and preparation for the job market.

Going from beginner to professional is no easy task, but with the Python Mini-Degree, you’re always moving forward. The addition of live coding lessons and the guidance of expert instructors enriches the learning experience.

But we don’t stop at a Mini-Degree. Take a look at our broad collection of Python courses for more opportunities to learn and grow. Each course offers flexibility and is updated regularly, ensuring you’re always learning the most relevant skills in the industry.


Python’s garbage collection may seem like a formidable subject, but with a bit of engagement and patience, you can master this core concept and step up your Python coding game. And the best part? You don’t have to do it alone. At Zenva, we are committed to providing you with the resources, courses, and guide you need to break down complex programming fundamentals into approachable, digestible components.

As a next step, consider diving into our Python Mini-Degree program – a comprehensive, in-depth course designed to make you a Python expert. Remember, the journey to mastery is paved with practice, exploration, and continual learning. Happy coding!

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