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.
Python 3.10 New Features and Improvements
Python 3.10 Performance Improvements
Here is a complete Python 3.10 review covering Python 3.10 updates, changes, and more.
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.
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.
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.
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.
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:
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.
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.
With the new type Union operator, you can simplify the syntax to this:
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 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 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.
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)
print(list(zipped_lists))
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.
Input:
fruits = [‘Mango’, 'Kiwi', 'Banana']
weights = [2, 5]
for fruit, weight in zip(fruits, weights):
print(fruit, weight)
Output:
Mango 2
Kiwi 5
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 = re.search(pattern, "aab")
if match:
print("Match found:", match.group())
else:
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 = re.search(pattern, "aab")
if match:
print("Match found:", match.group())
else:
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 = re.search(pattern, "XXXXXX…”)
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.
Here are other benefits Python 3.10 offers to developers:
Here is a table comparing Python 3.10 and Python 3.9, with a focus on the differences:
Feature/Change |
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 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.