# best-of-ml-python: a ranked list of 920 Python machine learning libraries > best-of-ml-python is a curated, automatically ranked index of 920 open source Python machine learning projects across 34 categories. It is a discovery and comparison aid, not a library you install into a model pipeline. **lukasmasuch/best-of-ml-python** — 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. - Repository: https://github.com/lukasmasuch/best-of-ml-python - Website: https://ml-python.best-of.org - Stars: 23,836 · Forks: 3,151 - Language: Unknown - License: CC-BY-SA-4.0 - Published: 2026-09-10 · Updated: 2026-09-10 · Language: en - Canonical page: https://hysenlabs.com/projects/lukasmasuch-best-of-ml-python ## What best-of-ml-python actually is, and who it is for This repository is a list, not a library. Its README describes it as "a ranked list of awesome machine learning Python libraries. Updated weekly", and the current index holds 920 projects grouped into 34 categories, from Machine Learning Frameworks and Data Visualization to Vector Similarity Search and GPU Utilities. The audience is narrow but real: engineers and data scientists who are about to pick a dependency and want a defensible shortlist instead of whatever surfaced first in a search engine. The problem it solves is selection, not execution. Python's machine learning ecosystem has thousands of packages, and the useful ones are spread across categories that do not share vocabulary. A team choosing an experiment tracker, a hyperparameter optimizer, and a graph library is making three unrelated decisions. This list puts all three in one place with a consistent scoring method applied to each. It is worth being precise about what that buys you. The ranking is a filter, not a verdict. A high position means a project scores well on metrics collected from GitHub and package managers, which correlates with adoption and maintenance but says nothing about whether the API fits your problem. ## How the project-quality score is built and what it hides Every entry carries a combined project-quality score, shown with medals in the README. The README states the score is "calculated based on various metrics automatically collected from GitHub and different package managers". The legend spells out which signals feed the entry display: star count, contributor count, fork count, open issue count, the last update timestamp on the package manager, download count, and the number of dependent projects. That is a maintenance-and-adoption composite. It rewards projects that are widely installed and recently published, and it penalizes ones that have gone quiet. The README's own badges make the staleness thresholds explicit: a project with six months of no activity is marked inactive, and twelve months marks it dead. Those thresholds are the most useful part of the scoring model, because they are absolute rather than relative. A project does not look healthy just because its neighbours are worse. What the score does not measure is anything about code quality, API stability, or whether the library does what its description claims. Download counts also favour packages that are pulled in transitively by larger frameworks, which can inflate a small utility's position. Treat the number as a tiebreaker between candidates you have already vetted, not as the vetting itself. ## Installing best-of-ml-python and reading the list for the first time There is nothing to install in the usual sense. The README does not document a setup procedure for the list itself; it points readers at the hosted version at ml-python.best-of.org and at the projects.yaml file in the repository. If you want the data locally, clone the repository, which is the only retrieval step the README describes. ```bash git clone https://github.com/ml-tooling/best-of-ml-python ``` After cloning you get the repository layout: projects.yaml at the top level, plus config/, history/, latest-changes.md, and README.md. The YAML file is the source of truth for the entries; the README is generated from it. If you want to see what moved in a given week, latest-changes.md is the file to read, and the releases are dated by week, for example 2025.10.30 and 2025.10.23. For a first real use, pick one category and read it end to end rather than skimming the whole index. The category headings are specific enough to make this practical: Hyperparameter Optimization & AutoML has 52 projects, Model Interpretability has 55, and Workflow & Experiment Tracking has 40. Each entry links out to the project's GitHub repository and its package manager pages, and where the README shows an install command it is the indexed project's own, not this list's. For example, the Tensorflow entry in the Machine Learning Frameworks category shows the upstream commands. ```bash pip install tensorflow ``` ```bash conda install -c conda-forge tensorflow ``` Those commands belong to Tensorflow. The list only tells you that Tensorflow sits at the top of its category and why. ## The ranking is a popularity signal, not a quality guarantee The clearest limitation is baked into the method. Stars, forks, and downloads measure attention. Attention and correctness are different properties, and the score cannot distinguish a library that is popular because it is good from one that is popular because it was early. The README is candid about the composite but does not publish weights, so you cannot tell how much of a given score comes from downloads versus contributor activity. The staleness flags are the counterweight, and they are the part I would actually rely on. A project marked dead at twelve months of inactivity is a genuine warning regardless of its score, and the README applies that flag automatically. But the flag only tracks package manager timestamps and repository activity, so a library that is stable and finished will eventually be marked inactive even though nothing is wrong with it. Numerical and statistical libraries in particular can sit unchanged for years without being abandoned. The list is also the wrong tool for anything security-adjacent. It does not audit dependencies, and the only licence signal it carries is a warning marker for missing or risky licences. If your constraint is a permissive licence or an SBOM, this index will point you at candidates but will not answer the question. ## How it compares with hand-maintained awesome lists The obvious alternative is a conventional awesome list, a README of links maintained by hand. The difference is mechanical rather than philosophical. A hand-maintained list accumulates entries and rarely removes them, because removing a link requires someone to notice the project died. This repository regenerates from projects.yaml with metrics collected automatically, so the ranking and the activity flags update on a weekly cadence without a human deciding to revisit each entry. That comes at a cost the hand-maintained list does not pay. An automated list can only rank what its collectors can read, so projects that live outside GitHub or are distributed through channels the collectors do not cover will be absent or under-scored. A curator can include a library because it is the right answer for a niche problem; the scoring script cannot. If your domain is unusual, the categories here will still be useful as a map, but the ordering inside them will be less trustworthy than a specialist's recommendation. The other practical difference is contribution. Here you edit projects.yaml directly or open an issue or pull request, as the README invites. On a hand-maintained list you message a maintainer and wait. For a list of this size, the YAML route is the only one that scales. ## Licence and the cost of following the list over time The repository itself is licensed CC-BY-SA-4.0. That is a content licence, not a software licence, and it applies to the list and its generated output rather than to any project it indexes. If you redistribute the list or a derived version, the share-alike term applies to that derived content. This is not legal advice; check the terms against your own use case. The licences of the 920 indexed projects are a separate matter entirely. Each entry shows the project's own licence identifier where one is available, and the README uses a warning marker for missing or risky licences. That marker is a prompt to look, not a clearance. Nothing here tells you whether a given licence is compatible with your product. Maintenance cost is low if you consume the list and high if you depend on it. The last push to the repository was on 2026-09-10, and releases are cut weekly, so the data is current. But the index changes under you: projects get added, re-ranked, and flagged. If you cite a ranking in an internal document, it will be stale within a month. Pin the week you read, the way the releases are dated, and treat the list as a snapshot rather than a live dependency. ## Conclusion Use best-of-ml-python when you need a starting shortlist of Python machine learning libraries and want the ranking criteria stated openly rather than inferred from search results. Do not use it as a dependency, a benchmark, or a security review: the score is a popularity and maintenance signal, and the README itself flags licenses as warnings rather than clearing them. Before you adopt anything from the list, open the project's own repository and check the license file and the last release date, because those are the two facts the ranking compresses hardest. ## FAQ ### Why is Python considered best for machine learning? The repository does not argue for Python over other languages; it takes Python as the scope and indexes 920 open source machine learning projects written in it. Its premise is that the ecosystem is large enough to need an index. ### Which Python package is best for machine learning? The list does not name a single winner. Tensorflow sits at the top of the Machine Learning Frameworks category with the highest project-quality score, but the index spans 34 categories and the score measures adoption and maintenance rather than fit for a specific problem. ### Which is better for ML, Python or R? The README does not compare Python with R. The repository is scoped to Python libraries only, so it has no position on the question. ### What are the best Python courses for machine learning? Courses are outside the scope of this repository, which indexes open source libraries and frameworks rather than learning material. The README does not mention courses. ## Sources - [License: CC-BY-SA-4.0](https://github.com/lukasmasuch/best-of-ml-python/blob/main/LICENSE) - [lukasmasuch/best-of-ml-python on GitHub](https://github.com/lukasmasuch/best-of-ml-python) - [Project website](https://ml-python.best-of.org) - [README](https://github.com/lukasmasuch/best-of-ml-python/blob/main/README.md) - [Releases](https://github.com/lukasmasuch/best-of-ml-python/releases) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/lukasmasuch-best-of-ml-python