{"id":57685,"url":"https://github.com/KennethanCeyer/awesome-numpy","name":"awesome-numpy","description":"Awesome list of NumPy","projects_count":32,"last_synced_at":"2026-08-04T09:00:27.345Z","repository":{"id":66098271,"uuid":"345527869","full_name":"KennethanCeyer/awesome-numpy","owner":"KennethanCeyer","description":"Awesome list of NumPy","archived":false,"fork":false,"pushed_at":"2023-02-07T11:05:03.000Z","size":43,"stargazers_count":14,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-06-27T19:03:46.990Z","etag":null,"topics":["atlas","awesome","awesome-list","blas","cupy","data-science","lapack","numba","numpy","numpy-library","numpy-tutorial","python","python-package","scipy"],"latest_commit_sha":null,"homepage":"https://github.com/KennethanCeyer/awesome-numpy","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/KennethanCeyer.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2021-03-08T04:15:11.000Z","updated_at":"2025-11-05T14:21:12.000Z","dependencies_parsed_at":"2023-04-13T05:19:42.768Z","dependency_job_id":null,"html_url":"https://github.com/KennethanCeyer/awesome-numpy","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/KennethanCeyer/awesome-numpy","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KennethanCeyer%2Fawesome-numpy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KennethanCeyer%2Fawesome-numpy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KennethanCeyer%2Fawesome-numpy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KennethanCeyer%2Fawesome-numpy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/KennethanCeyer","download_url":"https://codeload.github.com/KennethanCeyer/awesome-numpy/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KennethanCeyer%2Fawesome-numpy/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35529729,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-16T02:00:06.687Z","response_time":83,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-04-08T00:00:23.421Z","updated_at":"2026-08-04T09:00:27.346Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Ecosystem","Community","Materials"],"sub_categories":["Core","Conference","JIT Compiler","Extended library","Books","AI/ML","Channels","Roadmap \u0026 Milestones","Videos","Reading Materials","Hardware acceleration"],"readme":"# Awesome NumPy [![Awesome](https://awesome.re/badge.svg)](https://github.com/sindresorhus/awesome)\nAwesome list of NumPy\n![Awesome NumPy - Awesome list of NumPy](./cover.png)\n\n## Contents\n\n- [Ecosystem](#ecosystem)\n   - [Core](#core)\n   - [Hardware acceleration](#hardware-acceleration)\n   - [Jit Compiler](#jit-compiler)\n   - [AI/ML](#aiml)\n- [Community](#community)\n   - [Conference](#conference)\n   - [Channels](#channels)\n   - [Roadmap \u0026 Milestones](#roadmap-milestones)\n- [Materials](#materials)\n   - [Books](#books)\n   - [Videos](#videos)\n   - [Reading Materials](#reading-materials)\n\n## Ecosystem\n\n### Core\n\n- [NumPy](https://numpy.org/) - Main project of NumPy.\n- [SciPy](https://scipy.org/) - Extends NumPy for data science purpose.\n\n#### Core dependencies\n\n- [ATLAS](http://math-atlas.sourceforge.net/) - Automatically Tuned Linear Algebra Software.\n- [BLAS](http://www.netlib.org/blas/) - Basic Linear Algebra Subprograms.\n- [LAPACK](http://www.netlib.org/lapack/) - Linera Algebra Package.\n- [FFT](https://www.fftw.org/) - library for efficient computation of the discrete Fourier transform.\n\n### Extended library\n\n- [Blaze](https://blaze.readthedocs.io/en/latest/index.html) - A high-level library, It provides compatible interface with NumPy.\n- [Numexpr](https://github.com/pydata/numexpr) - Fast numerical expression evaluator for NumPy.\n\n### Hardware acceleration\n\n- [CuPy](https://github.com/cupy/cupy) - NumPy-compatible library for GPU-accelerated computing.\n\n### Distributed Computing\n\n- [Dask](https://dask.org/) - Distributed computing platform for NumPy and Pandas.\n\n### JIT Compiler\n\n- [Numba](https://numba.pydata.org/) - JIT compiler that translates a subset of NumPy.\n\n### AI/ML\n\n- [JAX](https://github.com/google/jax) - Extends NumPy by ML purpose, [Autograd](https://github.com/hips/autograd) and [XLA](https://www.tensorflow.org/xla) project.\n- [AutoGrad](https://github.com/HIPS/autograd) - Efficiently computes derivatives of numpy code.\n- [TensorFlow NumPy](https://www.tensorflow.org/guide/tf_numpy) - A subset of the NumPy API implemented in TensorFlow.\n- [NumPy ML](https://github.com/ddbourgin/numpy-ml) - Collection of ML models, algorithms, \u0026 tools using NumPy and Python standard lib.\n\n## Community\n\n### Conference\n\n- [SciPy US](http://conference.scipy.org/)\n- [EuroSciPy](https://www.euroscipy.org/)\n- [SciPy India](https://scipy.in/2021)\n- [PyData](https://pydata.org/event-schedule/)\n- [PyCon](https://pycon.org/)\n- [O'Reilly AI Conference](https://www.oreilly.com/conferences/)\n\n### Channels\n\n- [NumPy Slack](https://numpy-team.slack.com/)\n\n### Roadmap \u0026 Milestones\n\n- [The Scope of NumPy](https://numpy.org/neps/scope.html)\n- [Roadmap](https://numpy.org/neps/)\n- [Wish List](https://github.com/numpy/numpy/issues?q=is%3Aopen+is%3Aissue+label%3A%2223+-+Wish+List%22)\n\n## Materials\n\n### Books\n\n- [Guide to NumPy - Travis E. Oliphant, PhD](http://web.mit.edu/dvp/Public/numpybook.pdf)\n- [From Python to Numpy - Nicolas P. Rougier](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)\n- [Elegant SciPy - Juan Nunez-Iglesias](https://books.google.com/books?id=4tOdjgEACAAJ)\n- [Numerical Python - Robert Johansson](https://books.google.com/books?id=N4-dvAEACAAJ)\n\n### Videos\n\n- [Inside NumPy - Barkeley Institute for Data Science (BIDS)](https://www.youtube.com/watch?v=fowHwlpGb34)\n\n### Reading Materials\n\n- [NumPy Cheat Sheet: Data Analysis in Python](https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python)\n- [Faster Python calculations with Numba: 2 lines of code, 13× speed-up](https://pythonspeed.com/articles/numba-faster-python/)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/kennethanceyer%2Fawesome-numpy/projects"}