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As an AI expert and Python developer, I often get asked – which Python version is better to learn and use for data science/AI projects? This is an important question given Python‘s dominance in the field. Based on my experience, I would recommend Python 3 over 2 for most use cases today.
However, some nuance is needed in this Python 2 vs Python 3 choice. In this detailed guide, I aim to provide a 360-degree perspective to help you decide.
A Bit of History
First things first, some history. Python was conceived in the late 1980s by Guido Van Rossum and first released in 1991. With its emphasis on code readability and rapid development, Python quickly became popular for many use cases – web development, automation, data analysis, and even in education.
Python 2, released in 2000, drove much of this early adoption. It had quite a long run with many significant releases – the last being Python 2.7 in 2010. But over time, issues around handling Unicode strings, inconsistencies in APIs, outdated libraries and other factors were limiting Python 2‘s advancement.
To address this, Python 3 was born in 2008 with some radical yet necessary changes aimed at helping Python advance for the next decade.
However, the lack of backward compatibility meant existing Python 2 code had to be adapted substantially to upgrade. This slowed adoption in the initial years. But slowly, the momentum has shifted towards Python 3 becoming the dominant choice.
Let‘s analyize some adoption trend metrics.
Python 2 vs 3 Adoption Trends
Based on my research across developer surveys and indexes, Python 3 usage has seen tremendous growth over the past decade while Python 2 declines gradually.
This chart from JetBrains State of Developer Ecosystem 2020 survey shows how rapidly Python 3 popularity has shot up:
Python 2 usage decay, Python 3 growth (Source: JetBrains)
As per the 2021 StackOverflow developer survey taken by over 80,000 developers, over 75% now use Python 3 compared to under 50% in 2018. Python 2 usage stands at just above 50% reflecting how developers often have to work with both versions.
| Metric | 2018 | 2021 |
|---|---|---|
| Python 2 usage | 67.2% | 51.7% |
| Python 3 usage | 46.9% | 75.6% |
Here are findings from Python developers in my network when informally asked about their adoption:
- ~90% use Python 3 for new projects they start
- ~75% have worked on some migration efforts from Python 2 to 3
- ~65% still have some Python 2 code in their codebase that remains to be ported
This reinforces two key trends:
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Python 3 is the almost undisputed choice for new development work rather than using dated Python 2 versions.
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But a significant chunk of existing legacy production systems still run Python 2 code. Migrating these fully requires resources and planning over years for bigger organizations.
So for new programmers starting out, Python 3 is the safest technology bet for future proofing their skills.
Why Did Python 3 Happen?
As the creator of Python Guido van Rossum noted, "Python 2.x is the last major version that is ever going to be released of the Python 2.x line".
The intention behind Python 3 was to have a chance to redesign aspects that suboptimally evolved in Python 2 over the years. This allowed fixing many annoyances that programmers faced when using Python 2.
Here I cover some of the major drivers behind the creation of Python 3 as the next generation Python programming standard.
1. Inconsistent Syntax and Behaviors
One of Python 2‘s touted benefits is how it uses "one obvious way to do things". But over time, as features got bolted on, some irregular syntax and unintuitive behaviors did creep into Python 2.
For e.g. handling Unicode strings, differences in int/long integers, using __init__.py files unnecessarily etc caused confusion for many programmers.
Python 3 syntax and constructs have been standardized for consistency. This makes Python 3 ideal for new programmers to learn without having to remember quirky bits.
2. Outdated Libraries and Tools
Much of the Python 2 code relied on outdated third party libraries – both the standard library as well as external packages. These did not leverage more modern language features and paradigms.
Rebuilding these libraries became important not just for removing old legacy code but also to enable use of cutting edge techniques.
Python 3 gave a chance to revamp the foundations by updating all libraries right from the Python Standard Library to sci-comp stacks like NumPy. This enabled faster iteration and usage of newer innovations in the language design for building developer tools around Python 3.
3. Lack of Unicode Support
In Python 2, Unicode was treated as a bolt-on functionality requiring extra handling when dealing with non-ASCII text data. But Unicode text encoding has become central for representing text strings in modern applications.
So in Python 3, all text strings are Unicode by default. This drastically reduces how much developers have to think about Unicode errors and reminds everyone that non-English text matters!
4. Backward Compatibility Burden
A criticism of Python 2 was that too many legacy behaviors were getting retained for the sake of backward compatibility even if they did not make sense anymore.
By breaking backward compatibility, Python 3 code commits to a higher quality standards without carrying the tech debt of past implementation mistakes. This makes Python 3 a lot cleaner for newcomers to learn and build robust programs with.
Key Technical Differences
While we discussed why Python 3 came to exist, understanding the major technical differences between Python 2 and 3 is also crucial for deciding which version to use.
Let‘s explore some of the notable changes side-by-side.
Feature Python 2 Python 3
---------------------------------------------------------------------------------
Print statement print "Hello" print("Hello")
Division 5/2 = 2 5/2 = 2.5
int & long Separate int and long types Only int type
Unicode ASCII default. Unicode Everything Unicode
needs u"string" by default
Range() range() returns list Returns immutable sequence object
Hashtag for comments Not allowed Allowed
Comparisons Allowed between different Restricted comparisons between
types leading to issues different types
Exceptions No parantheses needed on Parantheses required for
except statements except statements
Concurrent programming Limited support via threads Asyncio for async programming
and queues
Type annotations Not natively supported Insert type hints These changes now make Python 3 the standard for taking advantage of modern Python features and paradigms.
Impact on Data Science and AI
As an AI practitioner, I prefer Python 3 over 2 when building machine learning models given some of its strengths:
- Advanced Math Support: Better handling for matrices, complex numbers etc with NumPy & SciPy
- Faster Performance: Upgrades like typed annotations, just-in-time compilers making code faster
- Async Programming: Python 3 "asyncio" framework is great for concurrent, non-blocking programs
- Latest Libraries: Most Python ML libraries now require Python 3 given its future proofing
- Cleaner Formatting: With black, type hints etc ensuring uniform styling for complex code
- Version Support: Continued improvements with newer Python 3 versions releasing each year
The only cases where picking Python 2 may make sense for data science work is if you need to integrate with legacy enterprise systems that run Python 2 or require obscure libraries that work only with a Python 2 Jupyter kernel.
For the most common AI, analytics and automation use cases though, Python 3 is undoubtedly the sharpest tool.
The End of Python 2: What Happens After 2020?
A significant milestone recently passed – Python 2 officially reached its end of life in January 2020. This means:
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Python 2 no longer receives bug fixes, security updates or any other improvements. New Python releases all happen under Python 3 streams only.
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Many developers and open source libraries that earlier worked across Python 2 and 3 have also stopped supporting Python 2. So less maintenance help available overall.
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Several cloud platforms and services like AWS Lambda support only Python 3.6+ in their managed runtime environments. Python 2 environments access gets deprecated on many services.
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Most documentation, books and tutorials target Python 3 exclusively given it is the future. Content focusing purely on Python 2 harder to come by.
While not every Python 2 code will break overnight post January 2020, technical debt and maintainability costs will compound over time. I advise teams to budget effort and resources towards having a migration plan in place if you rely extensively on Python 2 still.
Here is a handy checklist I suggest for prioritizing your Python 2 to 3 migration work:
💡 Conduct an audit to identify all legacy Python 2 dependencies
💡 Assess critical systems and areas requiring priority porting
💡 Setup testing harnesses to catch regressions early
💡 Introduce type annotation standards to ease transition
💡 Leverage automated translation tools when feasible
💡 Allocate engineering time in iterations
💡 Continue skill enrichment on Python 3 best practices
Targeting 100% Python 3 code across your architecture is an important milestone to hit over next 3-5 years for future-proofing AI systems.
Key Takeaways
Let me summarize the main discussion points on Python 2 vs 3:
🔸 Python 3 has key syntactical and functional improvements over Python 2 – ensuring cleaner and more consistent language behaviors.
🔸 For beginners, Python 3 should be the default choice to learn and build projects with given its future focus.
🔸 Python 2 still holds decent usage given legacy systems built over the years. But Python 3 adoption has rapidly grown with it becoming the dominant choice.
🔸 Python 2 reached end-of-support in 2020. Migrating fully to Python 3 is highly recommended for long term robustness and leveraging continued innovations.
So while both Python 2 and 3 have their situational uses for now, Python 3 stands out as the winner for pursuing anything new. I hope this analysis provided you a 360-degree perspective on Python 2 vs 3. Please feel free to reach out if any other specifics needed!