# TheAlgorithms/Python: eighteen dependencies, one warning, no releases

> TheAlgorithms/Python is an MIT licensed collection of algorithm implementations kept as teaching material, declared for Python 3.15 and newer. It ships no release, its version field reads 0.0.1, and its single documented caveat is that the code may be slower than the standard library.

**TheAlgorithms/Python** — All Algorithms implemented in Python

- Repository: https://github.com/TheAlgorithms/Python
- Website: https://thealgorithms.github.io/Python/
- Stars: 225,125 · Forks: 51,138
- Language: Python
- License: MIT
- Published: 2026-08-17 · Updated: 2026-08-18 · Language: en
- Canonical page: https://hysenlabs.com/projects/thealgorithms-python

## Python 3.15 is the floor, and 0.0.1 is not a version number

The floor is stated in the packaging metadata, and the same requirement appears in three forms. In pyproject.toml the project is named thealgorithms-python, described as TheAlgorithms in Python, authored by TheAlgorithms Contributors, and the interpreter requirement is spelled out as a lower bound. The classifiers agree: only Python 3, then Python 3.15 specifically, then Free Threading 2 - Beta, which is a claim that the collection is tested against the free-threaded build rather than a statement that free threading is finished. The lint configuration pins the same target with target-version = "py315", and a .python-version file sits at the top level for version managers. So an environment on 3.13 or 3.14 is out, whatever the code itself would tolerate. Meanwhile version = "0.0.1" is a placeholder that no release will ever move, and the repository publishes no GitHub releases, so a commit hash is the only snapshot identifier you get. The last push was on 2026-09-29, so the tree does move; just not under a version name.

## Every core dependency is a lower bound with no ceiling

Eighteen packages sit in the core dependency list, and every one of them is a floor rather than a range: beautifulsoup4>=4.15, cython>=3.1.2, fake-useragent>=1.5.1, httpx2>=2.0.1, imageio>=2.36.1, lxml>=6, matplotlib>=3.9.3, numpy>=2.1.3, pandas>=2.3.3, pillow>=11.3, rich>=13.9.4, scikit-learn>=1.9.1, scipy>=1.18.1, statsmodels>=0.14.4, sympy>=1.13.3 and typing-extensions>=4.12.2, each with its own floor. That is a deliberate fit for a repository where a pull request can raise a floor on any of them, and it has a cost: nothing in the metadata stops a future numpy or scipy from changing behaviour underneath maths/ or machine_learning/. Two details are worth knowing before you read a traceback. The HTTP client here is named httpx2, not httpx, so search results and old advice will not match it. And the euler-validate group re-declares httpx2>=2.0.1 and numpy>=2.1.3, the same two floors already present in the core list, so project_euler/ has its own declared pair to work against.

## opencv-python is quarantined over a missing cp315t wheel

The most instructive dependency sits outside the core list, in a group named cv, and the reason is written in a comment above it. opencv-python has no free-threaded cp315t wheel and fails to build from source under 3.15t, where the failure comes from CMake. The comment says this blocks uv sync for every job, and that keeping the package in an optional group lets the free-threaded CI install everything else and actually run pytest-run-parallel on the pure-Python algorithms. The plan is written down too: re-fold it into the core dependencies once a cp315t wheel ships, with the upstream tracking issue cited as opencv/opencv#27933. The visible consequence is that a default sync for the free-threaded job leaves the vision code unimportable, so computer_vision/ and digital_image_processing/ are the two directories where an ImportError means a missing optional group rather than a broken path in the repository.

## Ruff's selected rules decide what a contribution can look like

The style gate is configured in pyproject.toml rather than described in prose, and the selection list is where the real policy sits. Under [tool.ruff] with target-version = "py315" and output-format = "full", lint.select pulls in A for flake8-builtins, ANN for annotations, ARG for unused arguments, ASYNC, B for bugbear, BLE for blind except, C4 for comprehensions, C90 for McCabe complexity, DJ for django, DTZ for datetime timezone rules and E for pycodestyle. Read together, those mean every parameter and return needs a type annotation, a bare except is a lint failure, and a function that accumulates branches hits a complexity ceiling. One entry is dead weight: DJ selects django rules in a repository that has no Django code, so those rules cost nothing but buy nothing. The same gate is enforced twice over, through .pre-commit-config.yaml on your side and the actions referenced in the badges, and the formatter badge points at the ruff formatter documentation rather than a project specific config.

## sphinx-autoapi makes an undocumented function an invisible one

The published site at https://thealgorithms.github.io/Python/ is built from the docs group, which names myst-parser>=4, sphinx>=8.2 and sphinx-autoapi>=3.4. autoapi works by reading the source tree and the docstrings in it, which has a consequence worth planning around: an implementation with no docstring is still in the repository and still importable, but it will not show up in the rendered API documentation. So the site is not a complete inventory of the collection, and the file tree is. The other index is DIRECTORY.md, which the README points you to for easier navigation and a better overview, alongside an index.md at the top level. Neither the README nor the packaging metadata explains how DIRECTORY.md is kept in step with the directories themselves, so if you add a topic folder, assume you also own the index until something tells you otherwise.

## The usable alternative is already in the dependency list

The project names its own alternative in a single sentence, saying the implementations may be less efficient than those in the Python standard library, and it tells you to use them at your discretion. Take that seriously for the numeric topics. The repository depends on numpy>=2.1.3, scipy>=1.18.1 and pandas>=2.3.3, and it also ships linear_algebra/, matrix/, maths/, machine_learning/ and neural_network/ directories as teaching code. When you need a matrix product, a factorisation or a regression in something that has to finish, the libraries in the dependency list are the destination and the directories are the reading. The difference in approach is the point: these implementations are short, unoptimised and readable, and the surrounding packages are written for throughput and documented interfaces. The one place where that distinction collapses is when you only need the algorithm to be right, not fast, which is exactly the case the collection is good at.

## The ciphers/ directory carries an efficiency warning, not a safety one

The one documented caveat is about speed, and the tree contains directories where speed is the least of your concerns. ciphers/, blockchain/, financial/, quantum/ and computer_vision/ all sit at the top level alongside the more harmless conversions/ and strings/. Nothing in the repository states that the cipher implementations have been reviewed for cryptographic correctness, and a SECURITY.md file governs how to report a vulnerability in the repository, which is a different thing from a claim about the maths in a block cipher. The practical rule follows from that silence: read these as textbook code, and take a cipher, a hash or a trading formula to a vetted library before it touches anything. The same gap applies to correctness in general. The lint rules will reject an unannotated function or a blind except, but no configured rule in lint.select checks an algorithm against a reference result, so passing CI is not evidence that the code is right.

## Two dev environment definitions, and a test group the README never names

The top level carries both a .devcontainer/ directory and a .gitpod.yml, and the README carries a Gitpod badge pointing at gitpod.io/#https://github.com/TheAlgorithms/Python. Two environment definitions in one repository means two sources of truth, and no documentation says which one the contributors and the CI are meant to agree on. What the README does say is procedural: read CONTRIBUTING.md before you contribute, and take questions to the Discord server linked at the-algorithms.com/discord. The test side lives in the metadata rather than the README, with a test group of pytest>=8.4.1 and pytest-cov>=6, a tests/ directory at the top level, an euler-validate group for the project_euler/ material, and a comment that mentions running pytest-run-parallel across the pure-Python algorithms. No test command is documented in the README itself, so a first contribution starts by reading CONTRIBUTING.md and .pre-commit-config.yaml.

## Conclusion

Read TheAlgorithms/Python when you want one short, annotated, reviewable implementation of one algorithm. Do not import maths/, linear_algebra/ or ciphers/ into anything that runs for other people, since the only documented caveat covers efficiency and nothing else. And do not use the version field to identify what you have: check requires-python against your interpreter, then pin a commit, because the last push was on 2026-09-29 and the project publishes no release.

## FAQ

### What is TheAlgorithms/Python, and can I use it in production?

It is a collection of algorithm implementations kept for education, and the README says the implementations may be less efficient than those in the Python standard library and that you should use them at your discretion. Nothing else about production suitability is documented, so treat that line as the project's own warning.

### Which Python version does TheAlgorithms/Python require?

The packaging metadata sets requires-python = ">=3.15" and the classifiers list Python 3.15 along with Free Threading 2 - Beta, while the ruff configuration targets py315. A .python-version file at the top level pins the interpreter for version managers too.

### How do I run the tests in TheAlgorithms/Python?

The README does not give a test command, so start with CONTRIBUTING.md. The metadata declares a test dependency group with pytest>=8.4.1 and pytest-cov>=6, a tests/ directory exists at the top level, and a comment in pyproject.toml refers to running pytest-run-parallel on the pure-Python algorithms.

### Why is opencv-python not in the core dependencies?

A comment in pyproject.toml says opencv-python has no free-threaded cp315t wheel and fails to build from source under 3.15t because of CMake, which blocks uv sync for every job. It sits in an optional cv group so the free-threaded CI can install everything else, with the plan to re-fold it once a cp315t wheel ships.

## Sources

- [Official documentation](https://thealgorithms.github.io/Python/)
- [Official README](https://github.com/TheAlgorithms/Python#readme)
- [Project repository](https://github.com/TheAlgorithms/Python)

---

Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/thealgorithms-python