Python Memory Profiling Tutorial – Complete Guide

Welcome to this exciting journey into Python memory profiling! This tutorial aims to simplify the complex task of understanding how Python utilizes memory and how you can gain control over it. Despite its technical nature, we promise to keep it digestible, engaging, and beneficial. Whether you are starting out or an experienced coder willing to delve deeper into Python, this tutorial is crafted just for you!

What is Python Memory Profiling?

Python Memory Profiling refers to the process of monitoring and analyzing the memory usage of a Python program. This is done with the help of specific tools and modules designed for this particular purpose.

Python memory profiling is essential for optimizing the performance of your Python programs. By having a good grip on how your program uses memory, you can:

  • Identify inefficient code blocks that are eating up a lot of memory
  • Spot memory leaks or circular references that take up unnecessary memory space
  • Maintain peak software performance by keeping memory usage to a minimum

Why Should I Learn It?

Understanding and excelling Python Memory Profiling allows you to optimize your code, making it faster, more efficient, and more scalable. Mastering this skill would not only help you significantly in application development tasks but also make you a stand-out Python developer!

To provide a complete understanding of Python Memory Profiling, we’ll now jump into the code and examples starting in the next section.

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Python Memory Profiling: The Basics

Let’s dive into how we can track and optimize memory usage using Python. To ease this process, we are going to explore the `memory-profiler` module.

First, we need to install `memory-profiler` using pip. Open your terminal and type in the following:

pip install -U memory-profiler

Using @profile Decorators

The simplest way to use `memory-profiler` is to use the `@profile` decorator. Here’s a simple example:

from memory_profiler import profile

def my_func():
    a = [1] * (10 ** 6)
    b = [2] * (2 * 10 ** 7)
    del b
    return a

Running this will get you an output detailing memory usage line by line.

Using memory_profiler From Command Line

You can also use memory-profiler directly from the command line. The syntax is:

mprof run

You can then view graphical output using:

mprof plot

Checking Memory Usage of a Function

`memory_profiler` exposes a function called `memory_usage` that takes a function as argument and returns the memory consumption over time.

from memory_profiler import memory_usage

def my_func():
    a = [1] * (10 ** 6)
    b = [2] * (2 * 10 ** 7)
    del b
    return a

mem_usage = memory_usage(my_func)

Stay tuned; our journey of exploring Python Memory Profiling isn’t over yet! In the next section, we’ll delve more into different techniques and strategies for efficient memory usage in Python.

Tracking Specific Portions of the Code

Using `memory_usage` function, you can also track specific parts of your code. Here’s how:

from memory_profiler import memory_usage
import time

def my_func():
    a = [1] * (10 ** 6)
    time.sleep(1)  # Simulates long execution time
    b = [2] * (2 * 10 ** 7)
    del b
    return a

start_time = time.time()
mem_usage = memory_usage(my_func, interval=0.1, timeout=1)
end_time = time.time()
print('Elapsed time:', end_time - start_time)

This measures memory usage every 0.1 seconds and times out after 1 second.

Using With Statement Instead of Decorators

You can also use the `with` statement to profile a block of code. This is particularly useful when you want to profile a segment within a function. Here’s how you use it:

from memory_profiler import profile

def my_func():
    a = [1] * (10 ** 6)
    with profile(precision=2):
        b = [2] * (2 * 10 ** 7)  # This is the line we're interested in
    del b
    return a

The line of code inside the `with` statement will be profiled.

Profiling Classes and Methods

The `@profile` decorator can also be used to profile classes and methods. Here’s an example:

from memory_profiler import profile

class MyClass:
    def my_method(self, n):
        a = [1] * n
        return a
mc = MyClass()

In this case, the `my_method` method of the `MyClass` is profiled.

Working with muppy

Another great tool for memory profiling in Python is `muppy`. To use `muppy`, you need to first install it:

pip install -U Pympler

To see how it works, here’s an example:

from pympler import muppy
from pympler import summary

my_list = [1] * (10 ** 6)
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)

`muppy` returns the memory usage of all objects, and the `summary` module presents these data in an understandable format.

Congratulations! By now, you should have a good understanding of Python memory profiling, and be more confident in controlling the memory usage of your Python programs. We encourage you to continue experimenting with the tools and techniques discussed here and take your Python proficiency to the next level!

Where To Go Next?

Congratulations, you’ve taken a big leap into understanding Python Memory Profiling! But as any successful coder will tell you, the journey doesn’t end here. In the vast universe of Python, there is still so much more to discover and master.

One key aspect of continuing your Python journey could be our Python Mini-Degree. This is a comprehensive collection of Python programming courses aimed at taking your Python skills to the next level. The mini-degree covers various essential topics, including coding basics, algorithms, object-oriented programming, game development, and even app development. The training here encourages hands-on learning. As a result, you’ll get the chance to build exciting projects such as games, bots, and real-world apps.

The courses in this mini-degree are designed with flexibility in mind to allow you to learn at your own pace, making it suitable for both beginners and more experienced learners. Our dedicated and certified instructors, backed by industry giants like Unity Technologies and CompTIA, provide quality education tailored to industry needs.

The skills you acquire here can be effectively applied in various industries, opening up numerous opportunities for developers like you.

For a broader exploration of Python, you can also check out our extensive Python courses. We continually update our course material to keep you updated with the industry’s advanced trends.


It doesn’t matter whether you’re just beginning your journey into Python or you’re already an experienced developer; learning Python memory profiling is an essential step towards becoming a versatile and in-demand professional. This knowledge will give you fine control over your Python programs’ performance, allowing you to ensure your applications are as efficient as they can be.

And remember, Zenva is here to help you through every step of this journey. With our comprehensive Python courses and Python Mini-Degree, you can build the skills you need to tackle any project with confidence and precision. Let’s optimize your learning and maximize your potential today! Happy coding!

Did you come across any errors in this tutorial? Please let us know by completing this form and we’ll look into it!

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