Python has improvements and modifications in every release, and 3.11 is no different. Every year, a new version of Python is released. The first half of the year is dedicated to a feature-locked beta release, and the second half is devoted to the official release.

On October 24, 2022, Python 3.11 was made available to users. The most recent version of Python is quicker and simpler to use. It has undergone 17 months of development and is now prepared for widespread use.

To see if it functions with their systems and if the performance improvements would benefit their code, the developers are encouraged to test it out on non-production code.

A new Python release is always an occasion to celebrate and thank volunteers for their tireless work.

If you're still new to the world of programming, employing some of the new features of Python 3.11 in real-time projects is the easiest way to comprehend and learn them.

There are a lot of new features in Python 3.11, let's explore them here.

Better error messages

Python is considered to be an easy programming language for beginners due to its readable syntax and robust data structures. However, interpreting the traceback that it displays when an error occurs is a bit difficult.

Python's error messages were considerably enhanced in version 3.10. Python has now added decorative annotations to tracebacks that help you interpret error messages quickly.

To check this, add the following code to a file and name it inverse.py:

#inverse.py

def inverse(number):
    return 1 / number

print(inverse(0))

Use inverse () if you want to calculate the multiplicative inverse of a number. In the above code, we have written 0 and since there is no multiplicative inverse of 0, you will get an error when you run it:

Faster code execution

Python is known for being a slow language. A normal loop, for instance, runs orders of magnitude slower in Python than it would in C. There are numerous strategies to overcome this disadvantage. Code execution speed is frequently less significant than programmer productivity.

Additionally, Python is excellent at encasing libraries created in faster languages. For instance, NumPy calculations are substantially faster than equivalent plain Python calculations. Python is a strong contender in the data science field due to its ease of use in writing code.

The fundamental Python language is still being worked on to make it faster. Mark Shannon proposed several performance enhancements for Python in the fall of 2020. The Shannon Plan is a very ambitious plan that aims to make Python five times faster throughout multiple releases.

Based on the Faster CPython project, Python 3.11 has a lot of enhancements. You will discover more about other improvements in later sections.

Python code is assembled to bytecode before execution. Each line of Python code is split up into many bytecode statements because bytecode contains more fundamental instructions than conventional Python code.

You can use dis to view the bytecode for Python. For example, consider a function that can convert between feet and meters as an illustration:

# feet_to_meters.py
def feet_to_meters(feet):
    return 0.3048 * feet

import dis
dis.dis(feet_to_meters)

Speed improvement

The usual benchmark suite runs about 25% faster than in 3.10, which is the first prominent change that will delight data scientists. According to the Python documentation, 3.11 can occasionally be up to 60% quicker. You must install Docker before you can run the benchmark test and assess speed improvements on your computer. This will help you to compare the speeds of Python 3.10 and 3.11. Run these two commands in the terminal to download two images for the two versions of Python after making sure Docker Desktop is running.

Exception Notes

The ability to add arbitrary notes is an addition to standard exceptions. The usage of these notes to annotate an exception in a piece of code other than the one that first raised it is covered by PEP 678. A testing library like Hypothesis, for instance, can give details about which tests failed. Any exception can have a remark added to it using:

Use .add_note() to add a code to any exception and inspect the.__notes__ attribute to see the current notes:

asyncio Task Groups

Asynchronous programming frequently requires that you start a lot of activities running simultaneously and then take some action once they are finished. For instance, downloading several photos concurrently and then compiling them into a zip file.

You must gather tasks and pass them to asyncio.gather to accomplish this. Here's a simple example of how to use the gather function to run multiple jobs at the same time:

# asd.py
import asyncio


async def simulate_flight(city, departure_time, duration):
         await asyncio.sleep(departure_time)
         print(f"Flight for {city} departing at {departure_time}PM")

         await asyncio.sleep(duration)
         print(f"Flight for {city} arrived.")


flight_schedule = {'Boston': [3, 2], 'Detroit': [7, 4], 'New York': [1, 9],}


async def main():
         tasks = [ ]
         for city, (departure_time, duration) in flight_schedule.items():
               tasks.append(simulate_flight(city, departure_time, duration))
         await asyncio.gather (*tasks)
         print("Simulations done.")

asyncio.run(main())

New typing feature: Self

Statically typed programming languages make your code more readable and easier to debug. Defining the precise types of variables, function inputs, and outputs will help others understand your code more easily and save you hours of troubleshooting effort. When you add typing annotations to your functions, modern IDEs will display function definitions as you type their names, making your functions easier for others to understand.

The robust Python typing module has classes for almost every data type until this point, with the exception of classes that return instances of themselves. The example given below is not possible in the previous versions of Python:

from typing import Self


class Language:

        def __init__(self, name, version, release_date):
                self.name = name
                self.version = version
                self.release_date = release_date

        def change_version(self, version) -> Self:
               self.version = version

               return Language(self.name, self.version, self.release_date)

Python 3.11, however, introduces the Self class, which you can include in the definition of a function if its return value is self or a new instance of the class.

Other benefits of Python 3.11:

1) Namespace dictionaries for Python objects are now built lazily and their dictionaries share keys, so less memory is needed to store them now.

2) Hashes are no longer needed to be stored with all keys being Unicode, which minimizes the dictionary size and improves cache performance.

3) Now there is experimental support for compiling to WebAssembly in the CPython runtime, the standard Python interpreter. This could help with the future development of initiatives like PyScript, which enables the use of a Wasm-compiled Python runtime to function in the browser.

It’s time to upgrade!

According to the official announcement, the new release is 10–60% faster than Python 3.10, depending on your workload. Additionally, error and exception handling in the programming language has been improved.

It brings better efficiency, more exception handling options, a new module for reading TOML files, interpreter enhancements, more types, annotations, and type features, as well as the deprecation and removal of some outdated language baggage. If you’re planning to migrate but not sure how to proceed, it is always better to take help from a professional Python development services provider.