Overview

Prerequisites

• Prior topics: 4 · Just Enough Python -- every example in this topic is Python, and this topic assumes you can already read and write functions, lists, dicts, and loops the way that primer taught them.
• Tools & environment: a macOS/Linux terminal; Python 3.x (python3 --version) with pytest installed in a venv for the capstone's test suite.
• Assumed knowledge: basic Python syntax from Just Enough Python. No prior algorithms background is required -- this topic is where that background starts.

Why this exists -- the big idea

This is topic 7 of the journey, sitting between the language fundamentals Pass 1 has already built and the object-oriented design the very next topic introduces. The problem before the solution: the same task can run instantly or crawl depending on how you store the data -- choosing the wrong structure is a bug you only feel at scale, long after the code first "worked." The one idea worth keeping if you forget everything else: pick the structure that makes the common operation cheap -- the data-structure choice is the real decision, and the algorithm you reach for often follows directly from it.

Cross-cutting big idea: abstraction-and-its-cost -- every structure in this topic trades one operation's cost for another. A hash map (dict) buys O(1) average lookup and charges you ordering; a plain list buys insertion order and charges you O(n) search; a heap buys O(log n) access to the smallest element and charges you the ability to peek at anything else cheaply. Nothing here is free -- the skill this topic teaches is knowing exactly which cost you are choosing to pay, and why.

This topic covers the usable slice: the structures and algorithms a working engineer reaches for daily -- arrays, stacks, queues, hash maps, trees, heaps, graphs, searching, sorting, recursion, and memoization -- verified against Python 3.14.x and CLRS 4th edition. Deeper paradigms (formal amortized/Θ/Ω rigor, the full graph/DP/greedy families) are deliberately deferred to a later, dedicated Advanced Algorithms topic; this topic's job is the everyday foundation those build on.

How this topic is organized

• Learning -- 82 runnable, heavily annotated Python examples across Beginner (Examples 1-28: lists, stacks, queues, hash maps, sets, linear search, sorting, recursion, type hints, linked lists), Intermediate (Examples 29-60: linked-list algorithms, binary and bisect-based search, heaps and priority queues, the classic comparison sorts, binary trees, binary search trees, and graph traversal), and Advanced (Examples 61-82: memoized and iterative Fibonacci, dynamic-programming-flavored recursion, BST deletion and balance checks, Dijkstra's algorithm, topological sort, cycle detection, quickselect, two-pointer/sliding-window techniques, an LRU cache built from scratch, and a prefix trie) -- plus a capstone job scheduler that ties a heap, a graph, and cycle detection together in one runnable program.

Every example is a complete, self-contained .py file colocated under learning/code/, runnable with python3 example.py and self-checked with assert statements -- there is no pseudocode anywhere in this topic.

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