Write Memory-Efficient Python Code
Learn how to write Python code that uses less memory using generators, slots, array types, and streaming patterns.
Learn Python programming from scratch
Learn how to write Python code that uses less memory using generators, slots, array types, and streaming patterns.
Learn practical techniques to speed up Python loops: hoist invariants, use comprehensions, cache lookups, and choose the right data structures.
Learn how to speed up Python functions with functools.cache and lru_cache, when caching helps, and how to control cache size and eviction.
Learn to speed up file I/O and data processing in Python with buffering, binary modes, streaming, and memory-mapped files.
Identify the most common Python performance bottlenecks: slow loops, repeated attribute lookups, wrong data structures, excessive I/O, and the GIL.
Learn which Python collection is fastest for your use case: list vs set vs dict vs deque, plus performance tips for sorting, filtering, and membership testing.
Learn when to optimize Python code and when to leave it alone. Avoid premature optimization, recognize true bottlenecks, and time your effort for maximum impact.
A practical checklist of Python performance best practices: what to measure, which tools to use, when to stop, and how to keep optimized code maintainable.
Walk through a real Python optimization: profile a slow data processing script, find the bottlenecks, apply fixes, and measure the improvements step by step.
Learn how Python's object model works under the hood: what objects are, how names reference them, and why every value in Python is an object.
Learn how Python object identity works with `id()` and `is`, the difference between mutable and immutable types, and how mutability affects your programs.
Learn practical techniques for writing Python code that is easy to read, understand, and maintain, from function design to variable naming and control flow clarity.