Which Python version is best for machine learning?

Python is a popular language, especially in machine learning and artificial intelligence. It is friendly to people; therefore, using it is not complex, and there are many supportive libraries apart from a great community of developers. Besides, with Python having different versions, many people ask the question: What is the best version of Python to use with machine learning?

In this post, we will go through different Python versions that you are likely to work with, and we are going to help you choose the best version for working with machine learning in your projects.

Python 2.x vs. Python 3.x: What’s the Difference?

Currently, there are two major versions of Python in use: Python 2.x and Python 3.x. Python 2.x was once very widely used but reached its official end-of-life on January 1, 2020. What that means is that the developers no longer provide security updates or bug fixes.

Python 3.x is the future of the language, as it has been equipped with new features, better libraries, and active community support. Therefore, Python 3.x should be used with any new project in general, including machine learning.

Why Python 3.x is Better for Machine Learning

1. Library Support

Probably the single most important reason Python becomes so popular in the machine learning community is due to its diverse libraries. Note that the development of popular libraries such as TensorFlow, PyTorch, and scikit-learn is mostly targeted at Python 3.x. Although some of these packages still work with Python 2.x, the support of Python 2.x is partial and will eventually disappear.

2. Easier Syntax

Python 3.x came with a number of improvements that make coding easier and cleaner. For example, the print function in Python 3.x is simpler because it is a true function now:.

3. Better Performance

Python 3.x versions are faster compared to Python 2.x. These use memory more efficiently and handle multiple tasks a lot more effectively. These are the important performance issues when one works with large datasets and complex machine learning models.

Choosing Between Python 3.6, 3.7, 3.8, and 3.9

Python 3.x itself has a lot of versions, each adding something new. For now, Python 3.10 is the latest, but many projects of machine learning use a bit older version like Python 3.6, 3.7, 3.8, and 3.9. Here's a quick look at these versions:

Python 3.6

Key Features: Added formatted string literals, called f-strings, which can simplify the writing of strings.

Compatibility: Works with most of the libraries in machine learning.

Recommendation: Stable version, though older compared to the new ones.

Python 3.7

Key Features: Added data classes and enhanced async code handling.

Compatibility: Great compatibility with machine learning libraries.

Recommendation: Good mix of stability and new features.

Python 3.8

Key features: Added the walrus operator (:=), which allows for assignments within expressions.

Compatibility: Supported by nearly all major machine learning libraries.

Recommendation: Perfect for anyone who wants to utilize very new features available in Python.

Python 3.9

Features: New dictionary combination features were added, along with improved type hints to make it more user-friendly.

Compatibility: The most libraries support this, but some of the sharpest edge libraries might still be catching up.

Recommendation: Best for developers who want the latest stable features.

Python 3.10 and Beyond

Key Features: Pattern matching and better error messages were introduced.

Compatibility: Not every feature might be implemented at full strength in some libraries.

Recommendation: This is best if you need to use any of the very latest features; otherwise, check on library support first.

How to Choose the Right Python Version

Check for Library Compatibility: Before choosing any version of Python, make sure the libraries you are going to use work with that version. Most machine learning libraries will let you know which version of Python they support.

Think About the Long Run: For a project that is long-term, choose a Python version that will be supported for a couple of years. The Python 3.7 and Python 3.8 versions are good choices.

Stay updated: Sometimes, you can use an older version of Python; take this opportunity to observe new releases, though. You can upgrade and get better performance with more functionalities.

Conclusion

Thus, Python 3.x is the best version that works on machine learning due to its modern features, active support, and compatibility with important libraries. Among Python 3.x, Python 3.8 and Python 3.9 are very good options because they combine very nicely stability and new features. However, your ideal Python version would depend on your specific needs and the libraries you use. Keeping these things in mind will help you to choose the best version and thus set yourself up for success with your machine learning projects.