Python Generators Explained
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 Python programming from scratch
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 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.
Compare Python iterators and generators side by side, understand when to use each, and learn the tradeoffs between custom iterator classes and generator functions.
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.
Explore Python's built-in functions for working with iterators, including map, filter, zip, enumerate, reversed, sorted, and the powerful itertools module.
Avoid the most common pitfalls with Python iterators and generators, including exhaustion, late binding, accidental materialization, and misuse of send().
Understand the performance advantages of Python generators: reduced memory usage, faster startup, pipeline composition, and when generators outperform list-based approaches.
Learn how to build a complete data processing pipeline in Python using generators, from reading input to writing output, with lazy evaluation at every stage.
Learn to write your first Python decorator from scratch, understand the wrapper function pattern, and build a working timing decorator step by step.
Learn how to write Python decorators that work with functions accepting any number of positional and keyword arguments by forwarding them correctly.
Learn why decorators erase function metadata and how functools.wraps preserves the original name, docstring, and attributes of wrapped functions.