Master Python closures at CPython depth - free variables, cell objects, __closure__, co_freevars, the UnboundLocalError trap, the nonlocal keyword, late binding, factory functions, memoization, and when to use a closure vs a class.
Master Python decorators at full engineering depth - functools.wraps, decorator factories with three-level nesting, class-based decorators, stacking order, production patterns (timing, retry, caching, rate limiting), and how FastAPI/Flask route decorators work under the hood.
Three hands-on functional programming engineering projects for the Python Intermediate module. Build real systems using decorators, generators, closures, composition, and pure functions.
Understand Python generators and yield at engineering depth - frame suspension, the generator state machine, send() and the coroutine protocol, yield from, throw() and close(), memory-efficient pipelines, and the foundation of async/await.
Master Python's immutability toolkit at engineering depth - mutable vs immutable types, shallow vs deep immutability, namedtuple, frozen dataclasses, frozenset, MappingProxyType, and the replace/copy pattern for functional state updates. Covers DDD value objects and Redux-style state in Python.
JAX jit, grad, vmap, pmap - functional transformations for high-performance ML, XLA compilation, and NumPy-compatible ML research.
Understand Python lambda expressions at engineering depth - anonymous function objects, compile-time vs call-time evaluation, the loop-closure trap, late binding, the default-argument fix, and when lambda is and is not appropriate.
Understand Python's map, filter, and reduce at engineering depth - lazy iterators, pipeline composition, functools.reduce and left-fold semantics, performance trade-offs, and when to prefer list comprehensions.
Master Python's functional programming model at engineering depth - lambdas, map/filter/reduce, generators, iterators, decorators, closures, pure functions, immutability, functools, and partial application and currying.
Master partial application and currying at engineering depth - functools.partial internals, inspecting partial objects, the distinction between partial application and currying, implementing currying in Python, the operator module as curried-style operations, function composition with reduce, and real-world usage in Django ORM, sorted(), and data pipelines.
Build a production-quality Python decorator library with retry, rate limiting, timeout, argument validation, and structured call logging. Covers closures, functools.wraps, inspect.signature, and decorator composition.
Build a lazy data processing pipeline in Python that handles datasets larger than RAM. Covers generators, generator chaining, lazy evaluation, immutable pipeline objects, and streaming architecture.
Build a functional-style data transformation system in Python using function composition, pipe, partial, singledispatch, and the Result monad pattern. All functions are pure; error handling avoids exceptions.
Master pure functions at engineering depth - same inputs always produce same outputs with no side effects, referential transparency, how to identify and eliminate side effects, the functional core / imperative shell architecture, and why purity unlocks testability, caching, and thread safety.
Solve 11 Python closures deep dive problems (4 Easy, 4 Medium, 3 Hard). Practice closures deep with hints, runnable code, and solutions.
Solve 11 Python decorators — wrapping callables at engineering depth problems. Covers decorators practice, functools.wraps exercises. Hints and solutions.
Solve 11 Python functools module problems (4 Easy, 4 Medium, 3 Hard). Practice functools module with hints, runnable code, and solutions.
Solve 11 Python generators and yield — suspended execution at engineering depth problems. Covers generators practice, yield exercises, generator state. Hints...
Solve 11 Python immutability strategies problems (4 Easy, 4 Medium, 3 Hard). Practice immutability strategies with hints, runnable code, and solutions.
Solve 11 Python lambda expressions — anonymous functions at engineering depth problems. Covers lambda expressions, lambda exercises, lambda sorting. Hints an...
Solve 11 Python map, filter, reduce — lazy iteration and the pipeline model problems. Covers map filter, map exercises, filter practice. Hints and solutions.
Solve 11 Python partial and currying problems (4 Easy, 4 Medium, 3 Hard). Practice partial and with hints, runnable code, and solutions.
Solve 11 Python pure functions problems (4 Easy, 4 Medium, 3 Hard). Practice pure functions with hints, runnable code, and solutions.
Solve 11 Python the iterator protocol — how python's for loop really works problems. Covers iterator protocol, __iter__ __next__, iterable vs. Hints and solu...
Master the entire functools module at engineering depth - LRU cache internals and eviction, wraps, partial and partialmethod, reduce with operator, total_ordering, cached_property, singledispatch and singledispatchmethod, thread safety considerations, and real-world usage patterns.
Master Python's iterator protocol at engineering depth - __iter__, __next__, StopIteration, the iterable vs iterator distinction, for-loop desugaring, iter() with sentinel, next() with default, and lazy pipelines with itertools.
