Python 3.10 Unveiled: Exploring the New Features and Improvements for Developers

Python 3.10 Unveiled: Exploring the New Features and Improvements for Developers

Learn about Python 3.10 updates, changes, and features regarding the new release.

Blog Overview: Explore Python 3.10's enhancements, performance boosts, and exciting features, making it ideal for efficient, scalable application development.

1.4% of websites worldwide use Python 3.10 to power speed and performance.

Python is a server-side programming language, and its latest version brings many improvements. Companies choose to hire Python developers for many reasons, and Python 3.10 was released on October 4, 2021.

Python 3.10's scripting capabilities make it excellent for automating system administration tasks. The new version makes it easy to create and maintain scripts. It offers developers ease of use, flexibility, and extensive library support. Python 3.10 is ideal for creating responsive and user-friendly GUIs using libraries like PyQt and Tkinter.

If you want to learn about the newest Python 3.10 features and changes, keep reading as we discuss below.

Table of Contents

Python 3.10 New Features and Improvements

Python 3.10 Performance Improvements

Python 3.10 Benefits

Python 3.10 vs. Python 3.9

Python 3.10 Statistics

Final Words

Python 3.10 New Features and Improvements

Here is a complete Python 3.10 review covering Python 3.10 updates, changes, and more.

1. Improved Error Tracking

Errors are an everyday occurrence for developers in the field of software development. The good news is that Python 3.10 changes deliver simpler syntax and show precise error messages. Improvements have been made to line numbering, offering enhanced error tracking. For example, if you forget to close parentheses in code, Python will display the exact line number to assist you. You can quickly locate errors and correct them for a smoother experience.

2. Structural Pattern Matching

Structural pattern matching lets you match variables against different sets of possible values. It can be used with match statements and case statements of patterns with associated actions.1r805iah

You can combine several literals in a single pattern using the pipe operator. You can match not only with single values but also against patterns of values. For example, tuples and class objects with certain properties set to specific values.  


3. Paranthesized Context Managers

You can now use enclosing parathesis with context managers when you use with statements. You can format a long collection of context managers across multiple lines.

2 (2)

  • New Walrus ‘:=’ Operator

Python's Walrus operator is being hailed as a multitasking hero, and it makes code more concise and readable. It assigns values to variables and is an assignment expression operator that makes code simple.

Here is an example of how it is used:

3 (2)

We use the walrus operator to assign the length of a list to the variable n as part of the if statement. This reduces the need to declare and use n for the print statement in the following line, thus saving time and increasing efficiency.

The walrus operator can also be used to evaluate expressions and is not limited to just assigning values to variables. Here is a conditional if statement that demonstrates how it is used to evaluate if the length of a list is greater than 10.

  • New Type Union Operator

The Python PEP 604 now allows writing union types as X | Y. It proposes a generic syntax that adds typing to parameters, variables, and function returns.

Take the code below.

 4 (2)

With the new type Union operator, you can simplify the syntax to this:

5 (2)

You can also use the Union keyword to specify multi-type attributes. You can choose between two data types using the | operator instead of Union in Python 3.10.

  • Automatic Text Encoding

Automatic text encoding is a unique feature in Python that allows code to run on all machines. When a code works on one machine but doesn't work on another, it is usually the case of an incorrect encoding type. Automatic text encoding explicitly states the encoding type and prevents local code from failing on other machines.

  • Asynchronous Iteration

Asynchronous iteration is an advanced programming paradigm that uses two main built-in functions: alter() and anext(). It makes the code more readable and helps programmers write code efficiently and make other changes.

Asynchronous iteration adds a layer of versatility to coding and makes the code more executable.

  • Stricter Zipping

Python’s zip() function can streamline your programming workflow and use different data types, such as lists, tuples, sets, and strings.

The basic syntax of how to use it is as follows:

list1 = [1, 2, 3]

list2 = [4, 5, 6]

zipped_lists = zip(list1, list2)



Below is the output:

[(1, 4), (2, 5), (3, 6)]

You can create multiple lists and pass them to the zip() function as arguments. Zip () can aggregate elements from each iterable into tuples. It will return an iterator that can generate tuples one by one as you iterate over it. If one or more iterables have more or fewer elements, they are truncated accordingly.

Here is how the zip() function works with lists of different lengths.


fruits = [‘Mango’, 'Kiwi', 'Banana']

weights = [2, 5]

for fruit, weight in zip(fruits, weights):

   print(fruit, weight)



Mango 2

Kiwi 5


  • Atomic Grouping and Speedups for Regex

Atomic grouping treats subexpressions as a single unit and groups them.

The re module supports atomic group and is included with the standard library.

The atomic group's syntax in Python 3.10 is identical to other regex flavors.

Suppose you want to match a string with multiple occurrences of the sequence "ab" but not "aab". Here is how you would do it:

import re

pattern = r"a+b+"

match =, "aab")

if match:

   print("Match found:",


   print("No match found.")

This pattern matches one or more "a" characters, followed by one or more "b" characters.

To exclude matching the “aab” string and prevent backtracking into the group, make the following changes:

import re

pattern = r"(?>(a+)b+)"

match =, "aab")

if match:

   print("Match found:",


   print("No match found.")


Another use case is to improve the performance of regex patterns and prevent catastrophic backtracking.

Here is an example:

import re

pattern = r"(x+|X+)*y"

match =, "XXXXXX…”)


Python 3.10 Performance Improvements

Speed is a crucial factor in programming, and Python 3.10 performance improvements are significant. Developers can build applications lightning-fast, boost responsiveness, and increase success. Python 3.10 is a lot faster than 3.9 and 3.8. Python 3.10 is overall 20% faster than Python 3.9, and it includes various features not included in the previous versions.

Python 3.10 Benefits

Here are other benefits Python 3.10 offers to developers:

  1. The debugging process for Python 3.10 is straightforward. It immediately stops running code if errors are found before execution and points them out.
  1. There is no need to assign any variables or data types before execution manually. Python 3.10 changes take care of assigning value to variables automatically.
  1. Developers will not experience portability issues when running Python 3.10 programs on different platforms.
  1. The Python 3.10 library has a vast collection of executable codes for data analytics, mathematical modeling, Unicode, automation, database queries, visualizations, etc. They work on Linux, macOS, and Windows OS versions.

Python 3.10 vs. Python 3.9

Here is a table comparing Python 3.10 and Python 3.9, with a focus on the differences:


Python 3.9

Python 3.10

Release date

October 2020

Scheduled for release in October 2021

New features and changes

- Improved built-in dict type, with significant performance improvements for certain use cases<br>- New math.isqrt() function for integer square root calculation<br>- New zoneinfo module for timezone support<br>- New typing.Protocol class for structural subtyping<br>- New os.PathLike class for handling file and directory paths

- New Parenthesized context managers syntax<br>- New Structural pattern matching feature<br>- New Error messages for the parser and compiler<br>- New New error codes for sys.last_value<br>- Performance improvements for certain use cases

Deprecated features

- xmlrpc.client module is now considered legacy<br>- distutils package is now deprecated

- _hashlib module is now considered legacy<br>- xmlrpc.client module is now considered legacy


Python 3.10 Statistics

  1. The TIOBE Index shows that Python 3.10 is the world's third most popular programming language, second only to Java and C.
  2. According to a survey by JetBrains, it has a 44% adoption rate; it is used to develop tools for software developers.
  3. Stack Overflow studies show that Python 3.10 has an adoption rate of 39.7%
  4. Python is the most in-demand programming framework for data science. Many industry surveys reveal that career prospects for Python 3.10 developers are promising. The industry has witnessed a 29% year-by-year growth in job postings recently.
  5. According to the US Bureau of Labor Statistics, the demand for Python software developers, quality assurance analysts, and testers is projected to grow 22% from 2019 to 2029. 

Final Words

Python 3.10 is a powerful way to design applications, reduce time-to-market, and scale up projects fast. It is a highly effective approach to object-oriented programming, and its simple syntax makes it the ideal programming language for beginners. You can refer to the official Python 3.10 release notes for the most up-to-date information about updates. For industry updates and news related to Python 3.10, refer to the Python Developer’s Guide and the Python Software Foundation blog, which are credible sources that publish the latest findings and insights.

To hire Python developers, contact Clarion Technologies and get started today.




Vinit Sharma, a seasoned technologist with over 21 years of expertise in Open Source, cloud transformation, DevSecOps strategy, and software architecture, is a Technical Architect leading Open Source, DevOps, and Cloud Computing initiatives at Clarion. Holding certifications as an Architect and Business Analyst professional, he specializes in PHP services, including CMS Drupal and Laravel, contributing significantly to the dynamic landscape of content management and web development.

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