Python 3.11 vs Python 3.10: Why Developers Love the Latest Version More?

Developers are eagerly trying to differentiate between Python 3.11 and 3.10. On average, Python 3.11 is 14% faster than Python 3.10.

Python is one of the most popular programing languages out there. But the only problem with this program language is with the speed as it is no secret that Python isn't the fastest programming language out there. But that is about to change with Python 3.11 but what makes Python 3.11 better than its 3.10 version? We will find out in this article. The Python Software Foundation (PSF) maintains Python, develops the language, and is always working on new ways to improve it. On average, Python 3.11 is 14% faster than Python 3.10. The new version is marginally slower on some benchmarks, but on the others, it's up to 64% faster.

Features of Python 3.10

Better Error Messages

Python is often lauded for being a user-friendly programming language. While this is true, there are certain parts of Python that could be friendlier. Python 3.10 comes with a host of more precise and constructive error messages. One of the more impactful improvements in Python 3.10 is better and more precise error messages for many common issues.

Structural Pattern Matching

Structural pattern matching is one of the biggest features in Python 3.10, probably both in terms of controversy and potential impact, is structural pattern matching. Its introduction has sometimes been referred to as a switch case coming to Python, but you'll see that structural pattern matching is much more powerful than that.

You'll see three different examples that together highlight why this feature is called structural pattern matching and show you how you can use this new feature:

• Detecting and deconstructing different structures in your data
• Using different kinds of patterns
• Matching literal patterns

Structural pattern matching is a comprehensive addition to the Python language. To give you a taste of how you can take advantage of it in your own projects, the next three subsections will dive into some of the details. You'll also see some links that can help you explore in even more depth if you want.

Deconstructing Data Structures

At its core, structural pattern matching is about defining patterns to which your data structures can be matched. In this section, you'll study a practical example where you'll work with data that are structured differently, even though the meaning is the same. You'll define several patterns, and depending on which pattern matches your data, you'll process your data appropriately.

This section will be a bit light on explanations of the possible patterns. Instead, it will try to give you an impression of the possibilities. The next section will step back and explain the patterns in more detail.

Features in Python 3.11

CPython Opimizations

CPython is the reference implementation of the Python programming language. Written in C and Python, CPython is the default and most widely used implementation of the Python language. In version 3.11, the CPython interpreter is much more optimized and much faster than in version 3.10. CPython 3.11 is on average 1.22x faster than CPython 3.10 when measured with the performance benchmark suite, and compiled with GCC on Ubuntu Linux. Depending on your workload, the speedup could be up to 10–60% faster. In Python 3.11, the developers have mostly focused on faster startup and faster runtime as has been stated in the documentation.

Python 3.11: I am Speed

Every new version comes with lots of improvements and the same goes with Python 3.11. One of the features (Speed) that every developer was waiting for is finally here. Since an object's type rarely changes, the interpreter now attempts to analyze running code and replace general bytecodes with type-specific ones. For instance, binary operations (add, subtract, etc.) can be replaced with specialized versions for integers, floats, and strings.

Python function calls also require less overhead in Python 3.11. Stack frames for function call now use less memory and are more efficiently designed. Also, while recursive calls aren't tail-optimized (which probably isn't possible in Python, anyway), they are more efficient than in previous versions. The Python interpreter itself also starts faster, and core modules needed for the Python runtime are stored and loaded more efficiently.

According to the official Python benchmark suite, Python 3.11 runs around 1.25 times faster than version 3.10. Note that this speedup is an aggregate measure: some things are much faster, but many others are only slightly faster or about the same. Still, the best part about these improvements is that they come for free. You don't need to make any code changes for Python programs to take advantage of 3.11's speedups.