This repository is a collection of educational Python programs and Jupyter notebooks by Peter Norvig, focused on algorithmic problem-solving, puzzle-solving, and data analysis. It is not a deployable application or a formal research pipeline, but rather a set of self-contained explorations that demonstrate a wide range of programming techniques: constraint propagation, recursive search, dynamic programming, Monte Carlo simulation, n-gram language models, and symbolic computation. The code is well-structured, clearly documented, and often includes inline tests and performance benchmarks. While each notebook or script is independent, many contain reusable utility functions and algorithmic patterns that could be extracted for other projects. The repository includes data files (word lists, n-gram counts, bike ride logs, Sudoku puzzles) that are used as inputs for the explorations. The primary value is educational: it teaches how to think algorithmically and write clean, efficient Python code.
This repository is a massive, community-driven collection of algorithm and data structure implementations in Python, explicitly designed for education and reference. It contains over 1,700 files organized into topical directories such as maths, sorts, graphs, strings, ciphers, dynamic programming, machine learning, and Project Euler solutions. Each module is self-contained, typically with extensive docstrings, type hints, and doctests, making it easy to study or extract individual algorithms. The code is written for clarity and learning, not production performance, and many implementations include references to Wikipedia or academic sources. Despite its educational focus, the repository exhibits some technical drift. A handful of files still use Python 2 syntax (e.g., `except ValueError, EOFError:`), which will cause SyntaxError in modern Python. Several web-scraping scripts depend on the non-standard `httpx2` library and external APIs that may break. The project targets Python 3.15 (including free-threading), which is bleeding-edge and may cause compatibility issues with some tools. However, the majority of the codebase uses modern Python features (type hints, dataclasses, PEP 695 generics) and is actively maintained with CI workflows, pre-commit hooks, and regular contributions. This is not a production application or a data research pipeline; it is a reference library for learning and teaching algorithms. The sheer breadth and consistency of the implementations make it a valuable resource for students, educators, and developers looking for clear examples of classic algorithms. The most reusable components are the standalone data structures (e.g., kd-tree, suffix tree, Fenwick tree) and algorithm implementations (e.g., Dinic max flow, Huffman coding, federated averaging) that can be extracted with minimal modification.