The best new features and feature fixes in Python 3.11

This article brings you relevant knowledge about python, which mainly introduces the best new features and function fixes in version 3.11. The following are the most important new features in Python 3.11 An overview of Python developers and their significance to Python developers. Let’s take a look at them below. I hope it will be helpful to everyone.

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The Python programming language releases a new version every year, and a function-locked version was released in the first half of the year beta version, and the final version will be released at the end of the year. The feature set of Python 3.11 has just been finalized, and a beta version is available for testing. Developers are encouraged to try this latest version on non-production code to verify that it does not conflict with your programs and to understand whether your code will benefit from its performance enhancements.

Here is an overview of the most important new features in Python 3.11 and what they mean for Python developers.

Speed ​​improvements:

There are many individual performance improvements in Python 3.11, but the biggest one is the dedicated adaptive interpreter. Since the type of the object rarely changes, the interpreter now tries to analyze the running code and replace the regular bytecode with the specific type of bytecode. For example, binary operations (addition, subtraction, etc.) can be replaced with specialized versions for integers, floating point, and strings.

In Python 3.11, Python function calls also require less overhead. Stack frames for function calls now use less memory and are designed to be more efficient. Additionally, while recursive calls are not tail-optimized (which is probably not possible in Python anyway), they are more efficient than previous versions. The Python interpreter itself also starts up faster, and the core modules required by the Python runtime are stored and loaded more efficiently.

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

Enhanced error messages:

Another very useful feature in 3.11 is more detailed error messages. Python 3.10 already has better error reporting due to the new parser used in the interpreter. Now, Python 3.11 extends this, providing detailed feedback on which specific part of a given expression caused the error.

Think about the following code that throws the error:

x = [1,2,3]
z = x[1][0]

In Python 3.10, we get the following error message, which is not very helpful: It's clear which int is the uncompilable code, and the error trace in Python 3.11 points to the exact part of the line that generated the error:

  File "C:\Python311\code.py", line 2, in <module>
    z = x[1][0]
TypeError: 'int' object is not subscriptable

Now it's pretty clear where the problem is.

Exception improvements:

Python’s error handling mechanism has gained many new features in Python 3.11:

1. Use the new except* syntax and the new ExceptionGroup exception type Can handle multiple exception problems. This allows elegant handling of problems that may raise multiple errors simultaneously, such as when dealing with asynchronous or concurrent methods, or when handling multiple failures when retrying an operation.

2. "Zero-cost" exceptions: Unless an exception is actually thrown, there is now no memory consumption for the program. This means the default path to try/except blocks is faster and uses less memory.

3. The time required to catch exceptions is reduced by about 10%.

4. Exceptions can be improved through contextual comments to separate exceptions from code blocks.

Type Boost:

Python’s type hint feature makes larger code bases easier to manage and analyze, and since Python 3.5, type hint performance has improved significantly with every revision Increase. Python 3.11 introduces several new type hints.

Self type:

Class methods require a lazy and detailed declaration before returning the self type to take effect. The type .Self pattern makes it easier to declare the return value of a class method. You can obtain useful and predictable results from the analysis tools of these methods.

Arbitrary string literal type:

Previously, type annotations could not define that a given variable must be a string literal, that is, a string defined in the source code. new type. LiteralString annotation fixes this issue. Using new annotations, linters can test whether a variable is a string defined in the source or a new string consisting only of strings defined in the source.

Data Class Conversion:

Since Python 3.7, data classes make it easier to define classes that follow the common pattern of creating properties based on initialization parameters. But there is no standard mechanism that allows something that behaves like a data class (but is not the data class itself) to use type annotations to declare its behavior. Dataclass transformation adds the type.dataclass_transform modifier to prompt the compiler that a given function, class, or metaclass behaves like a data class.

Variable Generics:

The original generics proposal included TypeVar, which is a way to specify a generic function using a single parameterized type, for example, type T can be int or float . Python 3.11 added TypeVarTuple, or "variable generics," which you can use to specify placeholders for not just a type, but a range of types, represented as a tuple. This is especially useful in libraries like NumPy, where you can check for errors ahead of time, such as whether the provided array is of the correct shape.

TOML read-only support in stdlib:

Python uses TOML or Tom's explicit minimalist language as the configuration format (such as pyproject.TOML), but does not expose the ability to read TOML format files is a standard library module. Python 3.11 added tomllib to solve this problem. Note that tomllib does not create or write TOML files; for this, you need a third-party module like Tomli-W or TOML Kit.

Atomic grouping and acceleration of regular expressions:

Python’s re module is used to handle regular expressions, and it lacks some features found in other implementations of regular expressions. One is atomic grouping, which is widely supported in other languages. Python 3.11 now supports this pattern using a common syntax for atomic grouping (e.g., (?>...)). The

re module's pattern matching engine has also been rewritten a bit and runs about 10% faster.

Remove "Bad Batteries" from Standard Library:

PEP 594 initiates an effort to remove many so-called dead batteries, or outdated or unmaintained modules, from the Python standard library . Starting with Python 3.11, these libraries are marked as deprecated but have not yet been removed; they will be completely removed in Python 3.13.

Other new features, fixes, and changes in Python 3.11:

## There are many smaller improvements in #Python 3.11:

1. Python objects require less memory because their namespaces are now created lazily and their namespace dictionaries now share keys whenever possible.

2. Dictionaries with all keys in Unicode no longer need to store hashes, thus reducing the size of the dictionary and allowing for higher cache efficiency.

3. The Python runtime uses The Python interpreter now experimentally supports compilation to WebAssembly. This may help future development of projects such as PyScript, which allows WASM-compiled Python runtimes to run in the browser.

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