TheAlgorithms/Python: A Living Textbook of Classical Algorithms

The Mission Is Education

TheAlgorithms/Python is not a Python package and not a benchmarking project. The README is one screen long, the disclaimer is one sentence, and both point in the same direction: "Implementations are for learning purposes only. They may be less efficient than the implementations in the Python standard library. Use them at your discretion." There is no installable distribution on PyPI, no compiled extension, no service binary. The unit of work is one .py file that a learner can read, run, modify, and copy into another project.

The educational stance shapes every constraint that follows. A production library can hide internals behind opaque interfaces; an educational reference cannot. So the codebase is flat, self-contained, and deliberately redundant: quick_sort.py reimplements partitioning even though sorts/ contains seven other sorting algorithms that could have shared a helper. The redundancy is the point. Open one file, see one algorithm, end to end.

The Contribution Gate Holds the Quality

What separates this repository from a folder full of tutorial code is the contribution checklist in CONTRIBUTING.md. Every merged file must have descriptive names (greatest_common_divisor, not gcd), Python type hints on every parameter and return, docstrings with URL references to the source paper or Wikipedia article, and doctests that exercise valid and invalid input. The contribution guide closes the loop: "identical implementation of an existing implementation is not allowed," which keeps the catalog from drifting into noise.

The CI gate enforces what the prose asks for. pyproject.toml declares requires-python = ">=3.14" and configures pytest --doctest-modules --showlocals, which means every >>> example in every docstring runs on every push. ruff check applies about forty rule groups including flake8-bugbear, pep8-naming, pylint, and flake8-bandit. mypy checks the type hints. The pre-commit hook runs ruff and codespell before any commit reaches the remote.

Doctests Are Documentation

The decision that gives the repository its character is doctests-as-validation. A docstring like the one in sorts/insertion_sort.py opens with a paragraph of prose, then drops into a sequence of >>> insertion_sort([0, 5, 3, 2, 2]) followed by the expected output. Pytest reads the docstring, runs the example, and fails the build if the output drifts. This means the example a reader sees in a function's docstring is one a contributor proved correct, not one they wrote and forgot to verify.

For a learner the consequence is concrete: the docstrings can be trusted. For a contributor the consequence is unavoidable: a new algorithm without doctests will not pass review. The pattern repeats across the 1,381 files in the repository. Even one-page implementations carry a docstring with a Wikipedia URL and at least three doctest examples, including one that proves the function raises ValueError on bad input.

Forty-Three Categories, One Pattern

The repository's 43 algorithm directories are listed in DIRECTORY.md and again in pyproject.toml's autoapi_dirs array. The categories cover what an algorithms textbook covers: sorting, searching, graphs, dynamic programming, divide-and-conquer, greedy methods, backtracking, ciphers, hashes, data structures, strings, matrix and linear algebra, machine learning and neural networks, bit manipulation, geometry, conversions, physics formulas, and Project Euler problem solutions. Each directory is independent. Adding a new sort means dropping one file into sorts/, not registering it anywhere.

The naming rules are strict. Filenames are snake_case, function names expand acronyms, classes are CamelCase, constants are UPPERCASE. Files end in .py; Jupyter notebooks go to a sibling repository. Contributors are asked not to create new top-level directories, which keeps the catalog from fragmenting as the file count grows past a thousand.

What This Project Is Not

It is not a production library. The disclaimer at the top of the README spells that out, and the license file confirms it: MIT-licensed code, used at the reader's discretion. There is no shared abstraction layer, no public API surface, no semantic versioning, no release tags that mean anything beyond a snapshot in time. Two implementations of the same algorithm in different categories will diverge in style because they were merged years apart by different contributors against the same checklist.

It is also not a benchmark. The implementations are written for clarity and pedagogical fidelity to the original algorithm, not for cache efficiency or vectorization. The README disclaimer is direct on this: the standard library is faster. What the repository offers instead is a uniform reading experience across about 1,381 algorithms: type-hinted, doctested, MIT-licensed, sourced. That uniformity is the product, and the contribution gate is what produces it.