What's New In Python 3.14

NicePython

— 3 min read

Python 3.14 introduces several new features, performance improvements, and optimizations. Here’s a comprehensive overview of the most notable updates in Python 3.14, along with code examples where applicable:

1. Pattern Matching Enhancements

Python 3.14 expands the functionality of structural pattern matching, which was first introduced in Python 3.10. This feature allows more powerful and intuitive matching of complex data structures.

Example:

def process_shape(shape):
    match shape:
        case {"type": "circle", "radius": radius}:
            print(f"Circle with radius {radius}")
        case {"type": "rectangle", "width": width, "height": height}:
            print(f"Rectangle with width {width} and height {height}")
        case _:
            print("Unknown shape")

shape = {"type": "circle", "radius": 5}
process_shape(shape)

2. Typed Syntax in for and with Statements

Python 3.14 allows the use of type hints directly within for loops and with statements, making it easier to enforce type constraints in more contexts.

Example:

def process_numbers(numbers: list[int]) -> None:
    for number: int in numbers:
        print(number * 2)

process_numbers([1, 2, 3])

3. Speed Improvements to Built-in Functions

Python 3.14 includes performance optimizations for several built-in functions, particularly for map(), filter(), and zip().

Example:

# Optimized built-ins
numbers = [1, 2, 3, 4, 5]
squares = map(lambda x: x ** 2, numbers)  # Now faster in Python 3.14
print(list(squares))

4. Inline if Statements with Pattern Matching

You can now use pattern matching in inline if statements, simplifying common conditional checks.

Example:

def get_color(color: dict):
    return "red" if color == {"name": "red"} else "blue"

print(get_color({"name": "red"}))  # red

5. Faster Start-up Times

The internal handling of modules and imports has been improved, resulting in faster startup times for Python programs. This change is especially noticeable in scripts with many dependencies.

6. More Efficient Integer Operations

Python 3.14 introduces optimizations in integer operations, specifically for small integers, which are now handled more efficiently.

Example:

a = 5
b = 3
result = a * b  # Faster small integer operations
print(result)

7. New random.uniform() Behavior

The random.uniform() function now has better precision and performs faster under certain circumstances.

Example:

import random

# Generates a random floating-point number between 1 and 10
print(random.uniform(1, 10))

8. Buffer Protocol for More Objects

Python 3.14 extends the buffer protocol to support additional types of objects, making it easier to perform memory-efficient operations on various data structures.

9. Positional-only Parameters in Lambdas

Python 3.14 allows the use of positional-only parameters in lambda functions, allowing for greater control over argument passing.

Example:

# Using a positional-only parameter in a lambda function
double = lambda x, /: x * 2
print(double(10))  # Output: 20

10. Improved Error Messages

Python 3.14 continues the trend of improving error messages, particularly around type hints and pattern matching errors, making debugging easier for developers.

Example:

# Improved error message if pattern matching fails
def process_number(n: int) -> None:
    match n:
        case 0:
            print("Zero")
        case _:
            raise ValueError("Unsupported number")

process_number(10)  # Detailed error message if an unsupported number is passed

11. Deprecation of Some Legacy Features

As part of Python's ongoing effort to modernize and simplify the language, Python 3.14 deprecates certain legacy features, including old-style string formatting. These features may be removed in future versions, so developers are encouraged to migrate to modern alternatives like f-strings.

Example (Deprecated):

# Old-style string formatting (will be deprecated)
name = "Alice"
age = 30
print("Name: %s, Age: %d" % (name, age))  # Use f-strings instead

12. Native Support for TOML

Python 3.14 adds native support for parsing TOML (Tom’s Obvious, Minimal Language) files, which are commonly used for configuration files.

Example:

import tomllib

with open("config.toml", "rb") as f:
    config = tomllib.load(f)

print(config)

13. Enhanced Type Hinting

Python 3.14 improves the expressiveness of type annotations, making it easier to represent more complex types. For example, support for constrained generics and variadic generics has been improved.

14. New Math Functions

Python 3.14 introduces new mathematical functions to the math module, expanding the capabilities for numeric operations.

Example:

import math

# New math function in Python 3.14
result = math.exp2(3)  # Equivalent to 2^3
print(result)  # Output: 8

Conclusion

Python 3.14 introduces various performance optimizations, language feature enhancements, and improved error messages. These updates make Python more efficient, user-friendly, and capable of handling complex workflows with better speed and precision.

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