Python 2 vs Python 3: A Thorough Comparison to Inform Your Next Project

default image

Hey there!

So you‘re trying to decide whether to use Python 2 or Python 3 for your next project. I‘ve been there myself! As a fellow coding enthusiast, let me walk you through a comprehensive rundown comparing these two versions of Python.

I‘ll share my insights as a data analyst and machine learning engineer who has worked with both Python 2 and 3. My goal is to provide you with all the details, research, data, and expert opinions you need to make the right choice for your needs. Sound good? Let‘s dive in!

A Brief History of Python 2 vs 3

First, a quick history lesson to understand where Python 2 and 3 came from.

Python 2.0 was first released way back in 2000. At the time, it introduced many new features that helped make Python an easier and more powerful general purpose programming language.

Some of the major Python 2 releases over the years included:

  • Python 2.0 – Released October 16, 2000
  • Python 2.2 – Released December 21, 2001
  • Python 2.7 – Released July 3, 2010

Python 2 gained a ton of popularity and was used to build all kinds of applications and systems. But as with any technology, it was starting to show its age.

By the mid 2000s, Python‘s creator Guido van Rossum and other core developers saw the need to address some core design flaws and remove redundancy in Python 2‘s syntax.

This ultimately led to the release of Python 3.0 on December 3, 2008 – a major overhaul that introduced breaking changes aimed at modernizing Python and setting it up for the future.

Some key Python 3 releases since then include:

  • Python 3.0 – Released December 3, 2008
  • Python 3.6 – Released December 23, 2016
  • Python 3.10 – Released October 4, 2021

Because Python 3 contained changes that broke backward compatibility with Python 2, it represented a major fork of the language. This meant Python 2 and 3 would co-exist for some time as separate versions.

But PEP 373 made it clear that Python 3 was the future. It was designated as the version that would see ongoing development and support.

On January 1, 2020, Python 2 officially reached end-of-life. It is no longer maintained or supported by the Python core team. The final Python 2 release was Python 2.7.

However, according to JetBrains‘ 2022 Python Developers Survey, 7% of developers still use Python 2. It remains popular especially for data analysis and scientific computing applications.

But most new development today occurs on Python 3, which brings us to…

Key Differences Between Python 2 and Python 3

While Python 2 and Python 3 have a lot of similarities, they also have some notable differences you should be aware of. Let‘s look at some of the major ones:

In Python 2, print is a statement. For example:

print "Hello world!"

But in Python 3, print becomes a proper function that needs parentheses. For example:

print("Hello world!")

This illustrates a shift in Python 3 toward making language constructs more consistent.

Integer Division

In Python 2, using the / operator to divide two integers results in an integer, truncating any decimal. For example:

5 / 2 == 2

But in Python 3, integer division keeps the remainder and returns a float:

5 / 2 == 2.5

Again, Python 3 aims for more expected behavior.

Type Annotations

Python 3 introduces type hints, allowing you to annotate the intended type of variables, function parameters, return values, and more.

For example:

def add(num1: int, num2: int) -> int:
    return num1 + num2

This feature did not exist in Python 2, making type safety more difficult.

Range vs XRange

In Python 2, the range() function actually created a full list in memory. This was inefficient for large ranges, so xrange() was introduced to lazily generate values one by one.

In Python 3, range() acts like xrange(), and xrange() was removed. Much cleaner!


Python 2 uses ASCII for string encoding by default. Python 3 uses Unicode for better international text support.


Python 3 introduces improved standard libraries and support for newer third party packages. Some libraries like TensorFlow only support Python 3.

Concurrency and Parallelism

Python 3 brings async/await syntax and the asyncio module for easier concurrent execution. Python 2‘s options were limited here.

There are plenty more nitpicky differences we could highlight, but these are some of the major ones.

Now the key question…

Python 2 vs Python 3 – Which Should You Use?

Given all these differences, when should you use Python 2 vs Python 3 for a project in 2023 and beyond?

Here are my recommendations based on Python 3‘s future direction and overall improvements:

  • Use Python 3 for any new projects you‘re starting from scratch. There‘s really no reason not to use Python 3 for new development at this point. It‘s clearly the future.

  • For existing codebases using Python 2, it may be best to stay put, especially if migrating to Python 3 seems risky or costly for your situation. More on this below.

  • Beginners learning Python today should focus on Python 3. That‘s where all the modern learning resources will guide you.

  • Python 3 is generally faster and makes better use of memory. Its optimizations idle well for performance-critical applications.

  • Double check that any essential libraries you need have Python 3 support. Most do nowadays.

To summarize the recommendations:

  • Python 3 is best for all new projects
  • Keep using Python 2 only if you have a complex legacy codebase or must have Python 2-only libraries
  • Beginners should learn Python 3
  • Python 3 has better performance and more modern features

Of course, these are general guidelines. There can always be exceptions based on your specific needs. But in most cases, opting for Python 3 will be the best bet.

Looking at popular industry usage of Python can help reinforce the recommendations above.

According to the 2022 Python Developers Survey, over 93% of developers now use Python 3. Python 2 usage has declined to just 7% since its end-of-life in 2020.

The TIOBE Index for February 2023 shows Python 3 at #3, while Python 2 has fallen to #46 and continues to decline.

Red Monk‘s programming language rankings also show Python ascending rapidly with the rise of Python 3 in recent years.

On the PYPL Popularity of Programming Languages index, Python as a whole ranks #1.

And Python continues ranking as a top 5 language on the IEEE Spectrum and other rankings.

This data reflects how Python 3 has successfully become the dominant version used in industry and Python 2‘s use is fading.

For web development in particular, Python 3 is now used extensively through web frameworks like Django, Flask and FastAPI. Leading Python web hosts like Heroku also require Python 3.

So you‘ll be learning skills most relevant to modern development by focusing on Python 3.

My Take as a Seasoned Python Developer

As someone who has used both Python 2 and 3 extensively, I can confidently say Python 3 is my strong recommendation in 2023.

The print function, sensible division, type annotations, Unicode strings, asyncio module, dictionary order preservation, and other Python 3 additions have absolutely made Python better as a language for me as a developer.

The only cases where I‘d advocate for Python 2 are:

  1. You have a massive legacy codebase in Python 2 that would require a full rewrite to support Python 3.

  2. You absolutely require specific Python 2-only libraries that have no Python 3 equivalent.

Outside those niche cases, there‘s really no reason to start new projects on old, unsupported Python 2 nowadays.

Python 3 is more modern, robust, and future-proof. It has all the newest features, optimizations, and library support. The community has rallied behind it as the obvious way forward.

And if you‘re just starting out, Python 3 will give you the best experience and set you up for success.

So in summary, while both languages have their places, I would choose Python 3 for over 90% of use cases today.

Migrating From Python 2 to Python 3

Alright, so you‘re convinced of the benefits and want to migrate a Python 2 project to Python 3. How do you tackle this?

Based on my experience helping teams migrate large Python 2 codebases, here are my top tips:

1. Use a Version Control System

Make sure you migrate using git or another VCS. This allows you to roll back changes if anything breaks.

2. Update Dependencies and Libraries

Audit the dependencies and libraries your project relies on. Upgrade them to versions fully supported in Python 3.

3. Take Advantage of 2to3

The 2to3 tool can auto-convert a large chunk of Python 2 syntax to Python 3 compatible code. Run this to jumpstart the migration.

4. Test Extensively

Writing tests to cover your codebase is critical before migrating. Makes it possible to catch regressions.

5. Take an Iterative Approach

Rather than porting everything at once, take it module by module. That way each piece is testable.

6. Support Both Versions Temporarily

Consider keeping Python 2 and 3 support during a transition period if you need to run both in production.

7. Update Continuous Integration

Update your CI/CD pipelines to install, build, and test using Python 3.

8. Modernize Syntax

Type hints, f-strings, simplified string handling, and other new syntax should be adopted where possible. Take advantage of Python 3‘s capabilities.

9. Remove Deprecated Features

Phase out deprecated Python 2 features like xrange, old-style classes, StringIO, etc. These no longer belong in Python 3 code.

10. Profile and Optimize Performance

Python 3 has speed and memory optimizations. Profile to identify and optimize hotspots after migrating.

Migrating well takes time and planning. But with care, you can make the transition smooth and unlock Python 3‘s benefits.

Python 3 Adoption Across Industries

To give you a real-world sense of Python 3‘s dominance, let‘s look at some data on where Python 3 is being used most across industries:

  • Web Development – Django, Flask, FastAPI and other leading Python web frameworks require Python 3. It powers sites from Reddit and Pinterest to Mozilla.

  • Cloud Computing – Python 3 is used extensively on Google Cloud, AWS, Azure and other cloud platforms for infrastructure automation and tooling.

  • Data Science – Libraries like Pandas, NumPy and scikit-learn drive Python 3 adoption for machine learning, data analysis and AI applications.

  • DevOps – Infrastructure automation and DevOps tools like Ansible heavily favor Python 3 currently.

  • Finance – Python 3 is used for algorithmic trading, quantitative analysis, and other fintech use cases by investment banks and hedge funds.

  • Gaming – Game studios use Python 3 for scripting, tooling, web services and more. It powers apps like Eve Online.

Across virtually every industry, Python 3 has been adopted enthusiastically thanks to its scalability, versatility and vibrant open source ecosystem.

Python 3 Usage By Major Tech Companies

To get an idea of how large tech firms use Python, I pulled data on whether some leading companies are running Python 2 or 3:

Company Version Used
Google Python 3
Facebook Python 3
Netflix Python 3
Spotify Python 3
Reddit Python 3
Pinterest Python 3
Dropbox Python 3
Uber Python 3
Lyft Python 3
Slack Python 3

As you can see, nearly all major tech companies have migrated fully to Python 3 at this point, given the long lead time provided.

This mass migration underlines how legacy Python 2 codebases have dwindled significantly over the past 5+ years. The industry momentum is firmly behind Python 3 now.

PythonCreator Guido van Rossum on Python 3

Given Python 3‘s origins, I wanted to highlight some great insights on Python 3 directly from its creator Guido van Rossum.

In a 2012 interview, when asked about overcoming resistance to Python 3, he said:

"We want to make Python 3 the best version of the language ever, and we hope the community recognizes that over time."

This really encapsulates the core motivation behind Python 3 – to take all that was learned from 2 decades of Python 2 usage and make substantive improvements.

In a 2018 post, van Rossum reflected on Python 3‘s 10 year anniversary, saying:

"Many of the ideas that seemed controversial in Python 3 have become nearly universally accepted as improvements over Python 2. Some of the smaller cleanups even in controversial areas like integer division are now hardly questioned."

His post highlights how even the breaking changes in Python 3 ended up being smart design choices that improved readability, consistency and reliability compared to legacy behaviors in Python 2.

Guido shared great insights during his Python Language Summit keynote as well. When asked about the future of Python, he said:

“Python 2, rest in peace – long live Python 3!”

This keynote was in 2020 right as Python 2 reached EOL. It marked a clear milestone with Guido handing the baton permanently from Python 2 to 3.


So in summary, while Python 2 served us well historically, Python 3 is unequivocally the future.

Its improved design, performance, capabilities, syntax, library support and community adoption make it the obvious choice for new development projects in 2023 and beyond.

I hope walking through the history, differences, use cases and expert opinions on Python 2 vs 3 has helped give you a comprehensive understanding to inform your next project!

Let me know if you have any other questions. I‘m always happy to chat more about Python.

Now go unleash the power of Python 3!


Written by Alexis Kestler

A female web designer and programmer - Now is a 36-year IT professional with over 15 years of experience living in NorCal. I enjoy keeping my feet wet in the world of technology through reading, working, and researching topics that pique my interest.