Python vs. Python3

Python, celebrated for its elegant syntax and comprehensive libraries, has solidified its role as a cornerstone for data analysis and machine learning. However, the transition from Python2 to Python3, a topic often debated as Python vs. Python3, has introduced substantial changes that have significantly impacted how data scientists work.

While Python2 served the data science community well for a long time, its successor, Python3, brought numerous advancements in performance, syntax, and functionality that are essential for modern data science practices. The sunsetting of Python2 in 2020 further cemented Python3’s position as the preferred language for data-driven projects.

Understanding the nuances of the Python vs. Python3 debate is crucial for data scientists, especially when working with legacy code or collaborating on projects that may still utilize Python2. In this article, we cover the key differences between Python2 and Python3 to provide you with a clearer understanding of how the transition affects coding practices, data analysis, and the overall development experience. 

The Benefits of Python3

While the transition might seem daunting, the benefits of Python3 for data scientists are undeniable. Python3 offers significant performance improvements in various areas, such as faster integer arithmetic and more efficient memory usage. This can be especially beneficial for computationally intensive data analysis tasks.

Furthermore, Python3’s syntax is generally cleaner and more consistent than Python2’s. The removal of redundant features and the introduction of new syntax constructs, such as f-strings for string formatting, enhance code readability and maintainability.

One of the most compelling reasons to embrace Python3 is its access to modern features that can streamline data science workflows. Type hinting, for instance, improves code documentation and allows for early error detection, leading to more robust and reliable data pipelines. Asynchronous programming capabilities in Python3 enable efficient handling of I/O-bound tasks, such as fetching data from remote sources or interacting with APIs, further optimizing data-centric applications.

Key Differences: Python vs. Python3

Python3 wasn’t merely an incremental update—it was a deliberate effort to refine the language and address longstanding issues. While maintaining backward compatibility wasn’t always feasible, the changes introduced in Python3 often lead to cleaner, more efficient code and a more streamlined data science workflow. Let’s delve deeper into some of the critical distinctions.

Print Function

One of the most immediately noticeable changes when transitioning from Python2 to Python3 is the change of the print statement to a function. In Python2, you would output text or variables like this:

print "Hello, world!" 

However, in Python3, parentheses are required:

print("Hello, world!")

While this might seem like a minor syntactic adjustment, it has significant implications for code compatibility. Python2 code using the old print statement will raise a SyntaxError in Python3. 

Division

Another fundamental change in Python3 lies in how it handles division. In Python2, dividing two integers would always result in integer division (truncating the decimal portion). For example:

# Python2
print 3 / 2  # Output: 1

Python3, on the other hand, performs true division by default:

# Python3
print(3 / 2)  # Output: 1.5

This change can have significant ramifications for numerical calculations in data analysis. If your Python2 code relies on integer division behavior, porting it to Python3 may lead to unexpected results, so it’s essential to make the necessary adjustments when migrating code or working in mixed Python2/Python3 environments. The old integer division semantics can be replicated in Python3 via the “floor-division” operator, “//”.

Unicode Support

Python3 introduced enhanced support for Unicode, the universal character encoding standard. In contrast to Python2, which primarily used ASCII for string representation, Python3 treats strings as Unicode by default.

Python3’s comprehensive Unicode support ensures that you can seamlessly handle characters from different languages, emojis, and other special symbols without encountering encoding errors. This is a significant advantage when dealing with international datasets, user-generated content, or any data that extends beyond the limitations of ASCII.

Exception Handling

Python3 introduced subtle yet impactful changes to exception handling syntax. In Python2, the syntax for catching exceptions used a comma to separate the exception type and the variable to store the exception object:

# Python2
try:
    # Code that might raise an exception
except ValueError, e:
    # Exception handling code

Python3 replaced the comma with the keyword as:

# Python3
try:
    # Code that might raise an exception
except ValueError as e:
    # Exception handling code

This seemingly small change results in cleaner and more consistent syntax for handling exceptions, especially in complex data pipelines where error management is crucial. 

Libraries and Tooling

The transition from Python2 to Python3 requires updates and adaptations for many Python libraries and tools. Fortunately, the majority of popular data science libraries have been updated to be fully compatible with Python3. Libraries like NumPy, pandas, matplotlib, and scikit-learn have all embraced Python3, ensuring that data scientists can leverage their powerful features and functionality in their Python3 projects.

While most libraries have made the transition smoothly, it’s worth noting that some older, less maintained libraries might still require adjustments or might not be compatible with Python3 at all. In such cases, exploring alternative libraries or considering code migration strategies may be necessary.

While these changes might not be as immediately impactful as the change to the print function or division behavior, they collectively contribute to a more refined and modern Python experience for data scientists.

Python vs. Python3: A Comparison

The table below summarizes some of the key differences between Python2 and Python3 that data scientists should be aware of:

FeaturePython 2Python 3
PrintStatementFunction
DivisionInteger divisionTrue division
UnicodeLimitedFull support
Exception Syntaxexcept Exception, e:except Exception as e:
LibrariesSome may require updatesMost updated or alternatives
xrange()AvailableReplaced by range()
StringsASCII by defaultUnicode by default
input()Returns stringEvaluates input

This table serves as a quick reference guide for identifying areas where code modifications might be necessary when transitioning from Python2 to Python3. It’s worth noting that this is not an exhaustive list of differences but highlights some of the most relevant ones for data scientists.

Making the Transition (Python2 to Python3): Considerations for Data Scientists

Migrating from Python2 to Python3 is a necessary step for data scientists to harness the full potential of modern Python development. However, this transition requires careful consideration and planning to ensure a smooth and successful experience.

Before starting the Python2 to Python3 journey, data scientists should evaluate the compatibility of their existing codebases. Some Python2 code might run seamlessly in Python3, while other parts might require modifications due to the differences we’ve discussed.

Several tools and resources can aid in the conversion process. The 2to3 utility, included in Python3 distributions, can automatically convert much of your Python2 code to Python3 syntax. However, it’s not foolproof and may require manual intervention for complex cases. Additionally, libraries like six can help maintain compatibility with both Python2 and Python3 during the transition phase.

Thorough testing is key: After converting your code, run comprehensive tests to ensure that its functionality remains intact in the Python3 environment. This step is crucial for identifying and rectifying any unforeseen issues arising from Python vs. Python3 differences.

Exaloop: Accelerating Your Python3 Workflows

Exaloop is a cutting-edge platform designed to empower data scientists working in Python. It accelerates data processing and machine learning workflows by leveraging intelligent code optimizations, parallel processing and GPU acceleration. With Exaloop, data scientists can effortlessly scale their Python code, handling massive datasets and computationally intensive tasks with ease. The platform also provides a streamlined environment for managing dependencies and simplifies the deployment of Python applications to production.  Whether you’re developing complex machine learning models or conducting intricate data analysis, Exaloop’s optimized infrastructure and intuitive tools enable you to unlock the full potential of Python3, including faster results and greater efficiency.

Conclusion

The Python vs. Python3 dialogue highlights the evolution of a powerful language in response to the expanding needs of the data science field. While Python2 laid a strong foundation, Python3 has emerged as the preferred choice for modern data science practices, offering enhanced performance, cleaner syntax, and access to modern features that streamline and optimize data-centric workflows. As the Python community continues to embrace Python3, data scientists are encouraged to use its advancements and unlock new possibilities in their work. Exaloop stands as a valuable ally in this journey, empowering data scientists to leverage Python3 and achieve their goals with ease and efficiency.

Try Exaloop to experience the power of enhanced Python in your data science workflows.