The statistics module provides mathematical statistics functions for calculating averages, spread measures, and correlations without external dependencies.
Unlike floating-point calculations, the statistics module can work with Decimal and Fraction types for exact mathematical precision in statistical calculations!
PEP 492 introduced async def and await keywords, creating native coroutines that make asynchronous programming cleaner and more intuitive than generator-based approaches.
PEP 380 introduced yield from syntax for delegating to subgenerators, simplifying generator composition and enabling generators to return values.
yield from was the foundation that made async/await possible - it established the delegation pattern that coroutines needed!
Python’s interactive interpreter lets you experiment immediately with numbers, strings, and lists using intuitive syntax and meaningful indentation.
Python uses indentation instead of braces to define code blocks - making the visual structure of your code match its logical structure!
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.
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 random module provides functions for generating random numbers, sampling from sequences, shuffling data, and seeding for reproducible randomness in simulations and games.
The Python interpreter provides an interactive REPL (Read-Eval-Print Loop) for testing code, exploring APIs, and rapid prototyping, accessible via python command or enhanced with IPython for advanced features.
PEP 526 introduced variable annotations in Python 3.6, allowing type hints for variables using the syntax variable: type = value, extending PEP 484’s function annotations to all variables for better code documentation and static analysis.
PEP 308 introduced conditional expressions in Python 2.5 with the syntax value_if_true if condition else value_if_false, providing a concise way to assign values based on conditions.
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 logging module provides flexible, configurable logging with different levels (DEBUG, INFO, WARNING, ERROR, CRITICAL), handlers for various outputs, and formatters for customized log messages.
Python provides multiple string formatting methods (f-strings, .format(), manual), file operations with context managers, and JSON serialization for data exchange.
F-strings can execute any Python expression inside the braces - you can even call functions and perform calculations right inside the string!
PEP 484 introduced type hints to Python, enabling static type checking with tools like mypy while maintaining runtime flexibility and improving code documentation and IDE support.
PEP 289 introduced generator expressions in Python 2.4, providing memory-efficient alternatives to list comprehensions using lazy evaluation with syntax (expression for item in iterable if condition).
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.
Python modules are files containing Python code that can be imported and reused, organized into packages (directories with init.py), with various import styles (import, from…import, as) and special attributes like name and all.
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!
Python handles errors through exceptions using try/except/finally blocks, with built-in exception types like ValueError, TypeError, and FileNotFoundError, plus the ability to create custom exceptions and proper error handling patterns.
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.