The gzip module provides seamless compression and decompression of files using the gzip format, reducing file sizes by 60-90% while maintaining a simple interface compatible with standard file operations.
The textwrap module provides functions for formatting text blocks with intelligent line wrapping, indentation control, and paragraph formatting - perfect for creating clean output, documentation, and user interfaces.
The struct module converts between Python values and C structs represented as bytes objects, enabling binary data processing for network protocols, file formats, and hardware communication with precise control over byte layout.
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 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 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 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 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 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.
The glob module finds all pathnames matching a Unix shell-style pattern using wildcards like * (any characters), ? (single character), and [seq] (character ranges), making file discovery and batch operations simple.
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 heapq module provides heap queue (priority queue) operations using a binary heap implemented as a list, where heappush() and heappop() maintain the heap invariant with O(log n) complexity.
The argparse module provides powerful command-line argument parsing with automatic help generation, type checking, subcommands, and error handling for building professional CLI applications.
The csv module provides csv.reader(), csv.writer(), csv.DictReader(), and csv.DictWriter() for robust CSV file processing with automatic dialect detection and proper handling of quotes, delimiters, and line endings.
Python’s standard library provides essential modules for OS operations, file handling, math, networking, dates, and performance measurement - “batteries included”!
Python’s standard library is so comprehensive that many tasks don’t require external dependencies - from web requests to compression, it’s all built-in!
The subprocess module provides subprocess.run(), subprocess.Popen(), and subprocess.PIPE for secure process execution with proper input/output handling, error management, and cross-platform command execution.
The unittest module provides TestCase classes, assertion methods, test discovery, and mock objects for comprehensive unit testing with setUp/tearDown methods and test runners.
The logging module provides flexible, configurable logging with different levels (DEBUG, INFO, WARNING, ERROR, CRITICAL), handlers for various outputs, and formatters for customized log messages.
The os module provides os.listdir(), os.makedirs(), os.environ, os.path operations, and process management functions for cross-platform system interactions and file operations.
The os module automatically adapts path separators for different operating systems - os.path.join() uses backslashes on Windows and forward slashes on Unix, making your code truly cross-platform without any changes.
The random module provides functions for generating random numbers, sampling from sequences, shuffling data, and seeding for reproducible randomness in simulations and games.
Asyncio enables asynchronous programming with async/await syntax, allowing single-threaded concurrent execution perfect for I/O-bound tasks like web requests and file operations.
Asyncio can handle thousands of concurrent connections with minimal memory overhead - a single asyncio application can often outperform traditional threaded servers by avoiding context switching costs.
The enum module provides Enum, IntEnum, Flag, and auto() for creating robust enumerations that replace magic numbers and strings with type-safe, self-documenting constants.
The functools module provides utilities for functional programming including partial(), lru_cache(), singledispatch(), and reduce() for creating reusable, optimized higher-order functions.
The @lru_cache decorator can dramatically speed up recursive functions like Fibonacci calculations by memoizing results - turning an O(2^n) algorithm into O(n) with just one line!
The pathlib module provides object-oriented path handling with the Path class, replacing string-based os.path operations with intuitive methods for cross-platform file and directory manipulation.
The math module provides mathematical functions like sin(), cos(), sqrt(), and constants like pi and e for scientific and mathematical computations.
The math.isclose() function solves floating-point comparison issues by checking if two numbers are “close enough” rather than exactly equal, essential for robust numerical code.
The re module provides regular expression operations for pattern matching, searching, and text manipulation with functions like search(), match(), findall(), and sub().
The itertools module provides memory-efficient iterator building blocks like chain(), combinations(), cycle(), and count() for creating powerful iteration patterns.
The itertools.product() function can generate Cartesian products infinitely - perfect for nested loops without the nesting complexity.
The json module provides simple methods for converting between Python objects and JSON strings using loads() to parse JSON and dumps() to create JSON, essential for web APIs and data storage.
The datetime module provides classes for manipulating dates and times, including datetime, date, time, and timedelta objects with timezone support and flexible formatting options.
The collections module provides specialized container datatypes like Counter for counting, defaultdict for missing keys, namedtuple for structured data, and deque for efficient queue operations.