Context managers handle setup and cleanup automatically using the with statement, implementing __enter__ and __exit__ methods to guarantee resource cleanup even when exceptions occur.
Python separates syntax errors (caught before running) from exceptions (caught during execution). The try/except/finally system lets you catch errors, recover gracefully, and guarantee cleanup code runs.
Python excels at automation and rapid development with its simple syntax, powerful features, and “batteries included” philosophy - perfect for getting things done quickly.
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!
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!
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.
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.
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.
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 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.
Python’s built-in data structures - lists, dictionaries, sets, and tuples - provide powerful tools for organizing data with different performance characteristics and use cases.