Python’s control flow tools include if/elif/else statements, for and while loops, break/continue statements, and the else clause for loops, providing powerful ways to control program execution.
Python’s built-in functions like map(), filter(), zip(), and enumerate() provide powerful, memory-efficient ways to process data without explicit loops.
The any() and all() functions can short-circuit evaluation - any() returns True as soon as it finds one truthy value, making them incredibly efficient for large datasets.
Python classes bundle data and functionality together, supporting inheritance, method overriding, and special methods for creating powerful, reusable object-oriented code.
Python’s “everything is an object” philosophy means even classes are objects - you can inspect class attributes, pass classes as arguments, and create classes dynamically at runtime.
PEP 343 introduced the ‘with’ statement for automatic resource management, ensuring cleanup code runs even when exceptions occur, making file handling and resource management safer and cleaner.
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 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.
PEP 572 introduced the walrus operator (:=) in Python 3.8, allowing assignment within expressions to reduce code duplication and improve readability.
The walrus operator gets its name from its resemblance to a walrus face - the colon represents the eyes and the equals sign represents the tusks!
Python’s built-in data structures - lists, dictionaries, sets, and tuples - provide powerful tools for organizing data with different performance characteristics and use cases.
PEP 498 introduced f-strings (formatted string literals) in Python 3.6, allowing direct expression embedding in strings with f"Hello {name}" syntax, making string formatting more readable and performant.
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.
PEP 8 defines Python’s official coding style guide, emphasizing readability with 4-space indentation, descriptive naming conventions, and the principle that “code is read much more often than it is written.”