Python 2 vs Python 3 – Which to Learn?

If you're getting started with Python, one of the first questions you might have is whether to learn Python 2 or Python 3. The answer is straightforward: learn Python 3. Python 2 was officially retired in 2020, meaning it no longer receives updates, bug fixes, or security patches. While you may encounter legacy Python 2 code in older projects, all modern development, tools, libraries, and tutorials are focused on Python 3.

Why Python 3 is the clear choice

Python 3 was introduced in 2008 as a significant overhaul of the language. The goal was to fix fundamental design flaws and inconsistencies in Python 2, making the language cleaner, more consistent, and better equipped for the future. While this meant some breaking changes, the improvements were well worth it.

Here are some of the key advantages of Python 3 over Python 2:

• Active development and support: Python 3 is continuously improved with new features, optimizations, and security updates.
• Modern language features: Python 3 includes syntax and library improvements that make code more readable and efficient.
• Unicode by default: Strings are Unicode by default in Python 3, simplifying working with text from different languages.
• Better iterators: Functions like range(), zip(), and map() return iterators instead of lists, improving memory efficiency.
• Type annotations: Python 3 supports optional type hints, making larger codebases easier to maintain and understand.
• Community and ecosystem: All major libraries and frameworks have moved to Python 3, and new tools are built exclusively for it.

Key differences in syntax and behavior

While Python 3 is largely similar to Python 2, there are a few important differences you should be aware of. Let’s look at some of the most common ones.

Print is a function, not a statement

In Python 2, print was a statement, but in Python 3, it’s a function. This change allows for more flexibility, such as specifying separators and writing to files.

# Python 2
print "Hello, World!"

# Python 3
print("Hello, World!")

Integer division behavior

Division with integers behaves differently. In Python 2, dividing two integers truncates the result to an integer, while in Python 3, it returns a float.

# Python 2
print 5 / 2   # Output: 2

# Python 3
print(5 / 2)  # Output: 2.5

For integer division in Python 3, use the // operator.

print(5 // 2)  # Output: 2

Unicode support

In Python 2, strings are ASCII by default, and you need to explicitly define Unicode strings with u''. In Python 3, all strings are Unicode by default.

# Python 2
text = u"Hello, 世界"

# Python 3
text = "Hello, 世界"

xrange() is gone, use range()

In Python 2, range() returned a list, and xrange() returned an iterator. In Python 3, range() behaves like xrange(), returning an efficient iterator.

# Python 2
for i in xrange(5):
    print i

# Python 3
for i in range(5):
    print(i)

Handling the transition from Python 2 to Python 3

If you ever need to work with older Python 2 code, it’s useful to know how to make it compatible with Python 3. Many of the changes can be handled with the __future__ imports in Python 2 or by using tools like 2to3.

Using __future__ imports

You can make Python 2 code behave more like Python 3 by using certain __future__ imports.

# At the top of a Python 2 script
from __future__ import print_function
from __future__ import division

print(5 / 2)  # Now outputs 2.5 in Python 2

The 2to3 tool

Python includes a tool called 2to3 that automatically converts Python 2 code to Python 3. While it doesn’t handle everything, it takes care of many of the common changes.

2to3 your_script.py

This will show you the changes it recommends. You can apply them with:

2to3 -w your_script.py

Performance and library support

Python 3 has seen significant performance improvements over the years. While early versions were sometimes slower than Python 2, modern Python 3 is faster in most cases thanks to optimizations like better memory handling and more efficient built-in functions.

Library availability

In the early days of Python 3, some libraries were slow to update, but today, all major libraries support Python 3. Popular frameworks like Django, Flask, NumPy, pandas, and TensorFlow have long since dropped support for Python 2. If you’re starting a new project, you should always use Python 3.

Should you ever learn Python 2?

In most cases, no. However, there are a few scenarios where knowledge of Python 2 might be useful:

• You're maintaining a legacy system that hasn’t been upgraded.
• You're working in an environment where old scripts are still in use.
• You're curious about the history and evolution of Python.

Even in these cases, it’s better to learn Python 3 first and then understand the differences, rather than starting with an outdated version.

Getting started with Python 3

If you’re new to Python, here’s how to get started with Python 3:

1. Download Python 3: Visit the official Python website and download the latest version.
2. Use a good editor or IDE: Tools like VS Code, PyCharm, or Jupyter Notebook are great for writing Python code.
3. Practice with tutorials: There are countless free resources online for learning Python 3.
4. Build projects: Apply what you learn by working on small projects that interest you.
5. Join the community: Participate in forums like Stack Overflow or Reddit’s r/learnpython.

Example of a simple Python 3 script

Here’s a basic example that uses some Python 3 features:

# Simple number guessing game
import random

number = random.randint(1, 10)
guess = int(input("Guess a number between 1 and 10: "))

if guess == number:
    print("You got it!")
else:
    print(f"Sorry, it was {number}.")

Note the use of input() (which returns a string in Python 3, unlike raw_input() in Python 2) and the f-string for formatted output.

Conclusion

While Python 2 was a great language in its time, Python 3 is the present and future of Python. It offers better performance, more features, and full support from the community and ecosystem. If you’re learning Python today, focus on Python 3. You’ll be learning a modern, powerful language that is widely used in web development, data science, automation, and more.