PEP 544 introduces Protocol classes that enable structural subtyping (static duck typing) - type checking based on what methods an object has rather than its inheritance hierarchy, making Python’s type system more flexible and duck-typing friendly.
The timeit module provides precise execution time measurement for small code snippets, automatically handling timing complexities and providing both programmatic and command-line interfaces for performance benchmarking.
The tempfile module creates secure temporary files and directories with automatic cleanup, preventing race conditions and ensuring cross-platform compatibility for safe temporary data handling.
The uuid module generates 128-bit universally unique identifiers using multiple methods - random (uuid4), time-based (uuid1), or namespace-based (uuid3/uuid5) - perfect for unique IDs in databases and distributed systems.
The secrets module provides cryptographically secure random generation for passwords, tokens, and security keys - use it instead of the random module for any security-sensitive applications.
The decimal module provides exact decimal arithmetic without floating-point precision errors, essential for financial calculations, scientific computing, and any application requiring precise decimal representation.
The urllib package provides URL handling through four submodules: request (fetch URLs), parse (manipulate URLs), error (exceptions), and robotparser (robots.txt).
Unlike many languages that require external libraries, Python includes full-featured HTTP client capabilities right in the standard library!
The sys module provides access to interpreter variables like sys.argv (command-line arguments), sys.path (module search paths), sys.version, sys.exit(), and functions for interacting with the Python runtime environment.
PEP 604 introduced the | operator for union types, allowing int | str instead of Union[int, str] for cleaner type annotations.
The | operator for union types makes Python’s type hints look more like mathematical set notation - int | str means “integer OR string”!
PEP 570 introduced the / separator to mark positional-only parameters, giving API designers control over function call semantics and preventing keyword argument usage.
PEP 1 defines the purpose, format, and workflow for Python Enhancement Proposals (PEPs) - the formal mechanism for proposing new features, processes, and standards for the Python language and community.
The multiprocessing module creates separate Python processes that bypass the GIL, enabling true parallel execution for CPU-intensive tasks.
Unlike threading, multiprocessing actually uses multiple CPU cores simultaneously - each process has its own Python interpreter and memory space!
The zipfile module provides tools for creating, reading, and extracting ZIP archives with support for multiple compression methods and password protection.
The module supports ZIP64 extensions, allowing you to work with ZIP files larger than 4GB and containing more than 65,535 files - breaking traditional ZIP format limits!
Python excels at automation and rapid development with its simple syntax, powerful features, and “batteries included” philosophy - perfect for getting things done quickly.
After learning Python basics, dive into the Standard Library, explore PyPI packages, join the community, and tackle real-world projects.
Python has one of the most comprehensive standard libraries of any programming language - so much functionality is available without installing anything extra!
The second stdlib tour covers professional-grade modules: precise decimal arithmetic, efficient data structures, logging, threading, and binary data handling.
The decimal module can represent exact monetary values without floating-point errors - crucial for financial applications where precision matters!
The string module provides string constants, Template class for simple substitution, and advanced formatting utilities for text processing.
The string.Template class provides a safer alternative to % formatting - it prevents code injection by not allowing arbitrary expressions!
The pickle module serializes Python objects to bytes and deserializes them back - enabling object persistence and inter-process communication.
Pickle can serialize almost any Python object, including functions, classes, and nested objects - but never unpickle untrusted data as it can execute arbitrary code!
PEP 622 introduced match-case statements for structural pattern matching, enabling elegant destructuring and dispatch based on data shape and values.
Pattern matching brings functional programming elegance to Python - you can destructure complex nested data in a single line and handle different cases cleanly!
PEP 3107 introduced syntax for adding metadata to function parameters and return values, stored in __annotations__ - the foundation for modern type hints.