Learn Python programming from scratch
Learn what iterables and iterators are in Python, how they power for loops under the hood, and why understanding them unlocks cleaner data processing patterns.
Learn how Python's iterator protocol works with the __iter__() and __next__() methods, and understand the contract every iterator must follow.
Learn how to build your own iterators in Python by implementing the __iter__() and __next__() methods, with practical examples and reusable patterns.
Learn how Python's built-in iter() function works, its single-argument and two-argument sentinel forms, and how to use it with custom objects and external data sources.
Learn how Python's built-in next() function retrieves items from iterators, how to handle exhaustion with default values, and how manual iteration works under the hood.
Learn what Python generators are, how they produce values lazily with the yield keyword, and why they are the preferred tool for memory-efficient data processing.
Learn how to write generator functions in Python using the yield keyword, with practical examples ranging from simple counters to data transformation pipelines.
Learn how to send values into Python generators using the send() method, and how generators can communicate bidirectionally with their callers beyond simply producing values.
Learn how to write compact, memory-efficient generator expressions in Python using comprehension-like syntax, and when to choose them over list comprehensions.
Learn what lazy evaluation means in Python, how iterators and generators enable it, and why computing values on demand transforms memory usage for large datasets.
Understand the performance advantages of Python generators: reduced memory usage, faster startup, pipeline composition, and when generators outperform list-based approaches.
Build a complete resource management utility in Python that combines decorators and context managers to handle file operations, temporary resources, timers, and retries.
