spb/tools-spboucher-ai

1{2  "aliases": [],3  "workflows": [4    "Numerical computation underneath pandas, SciPy and scikit-learn",5    "Matrix algebra for econometric estimators",6    "Preprocessing arrays before handing them to PyTorch, JAX or MLX"7  ],8  "advantages": [9    "Universal interchange format for numerical Python",10    "Compiled performance for vectorized operations",11    "Stable, well-documented API and BSD license"12  ],13  "limitations": [14    "CPU-only and single-process",15    "Arrays must fit in memory",16    "Loops in Python remain slow; vectorization required"17  ],18  "prerequisites": [19    "Python 3"20  ],21  "audiences": [22    "Scientists and engineers",23    "Students",24    "Library developers"25  ],26  "alternatives": [27    "pandas",28    "jax",29    "pytorch",30    "mlx",31    "polars"32  ],33  "tags": [34    "arrays",35    "python",36    "numerical-computing",37    "open-source",38    "apple-silicon"39  ],40  "use_cases": [41    "estimate-causal-effects",42    "fine-tune-models",43    "transform-data"44  ],45  "operating_systems": [46    "macos",47    "linux",48    "windows"49  ],50  "deployment_models": [51    "embedded"52  ],53  "integrations": [54    "pandas",55    "SciPy",56    "scikit-learn",57    "matplotlib",58    "PyTorch",59    "JAX",60    "MLX",61    "Numba"62  ],63  "data_formats": [64    ".npy/.npz",65    "Text/CSV (loadtxt)",66    "Raw binary buffers"67  ],68  "languages": [69    "Python",70    "C"71  ],72  "self_hostable": true,73  "discovery_method": "editorial-research",74  "depth": "standard",75  "editorial_status": "reviewed",76  "visibility": "public",77  "featured": false,78  "author": "batch-D",79  "slug": "numpy",80  "name": "NumPy",81  "vendor": {82    "name": "NumPy developers (NumFOCUS)",83    "slug": "numpy-developers",84    "website": "https://numpy.org/"85  },86  "homepage_url": "https://numpy.org/",87  "docs_url": "https://numpy.org/doc/stable/",88  "repo_url": "https://github.com/numpy/numpy",89  "license_url": "https://github.com/numpy/numpy/blob/main/LICENSE.txt",90  "product_type": "library",91  "product_status": "active",92  "summary": "Foundational Python library for n-dimensional arrays, vectorized math, linear algebra, random numbers and the array API used by the scientific stack",93  "description": "NumPy is the array library at the base of scientific Python, maintained by an open community under NumFOCUS and released under the BSD 3-Clause license. Its ndarray stores homogeneous n-dimensional data in contiguous memory with a dtype and strides, and universal functions apply elementwise operations, broadcasting and reductions in compiled C loops. Submodules provide linear algebra (backed by BLAS and LAPACK), FFTs, polynomial fitting and a modern random-number Generator API. NumPy 2.x cleaned up the public namespace, adopted the Python array API standard and added variable-length string dtypes.\n\nAlmost every numerical package in Python, including pandas, SciPy, scikit-learn, statsmodels, matplotlib and the deep-learning frameworks, consumes or produces NumPy arrays or implements its protocols. On Apple Silicon the wheels link against Accelerate or OpenBLAS. NumPy runs on a single CPU and holds arrays in memory; GPU acceleration, autodiff and distributed arrays belong to JAX, PyTorch, MLX, Dask or CuPy, which deliberately mirror its API.",94  "capabilities": [95    "n-dimensional ndarray with dtypes, strides and views",96    "Broadcasting and vectorized universal functions",97    "Linear algebra, FFT and polynomial modules",98    "Random Generator API with multiple bit generators",99    "Array API standard compliance and interoperability protocols",100    "C API for extension modules"101  ],102  "categories": [103    {104      "slug": "dataframes-arrays",105      "primary": true106    },107    {108      "slug": "math-stats-tools"109    }110  ],111  "execution_modes": [112    "local"113  ],114  "interfaces": [115    "library"116  ],117  "source_model": "open-source",118  "license_spdx": "BSD-3-Clause",119  "pricing_model": [120    "open-source",121    "free"122  ],123  "sources": [124    {125      "url": "https://numpy.org/",126      "type": "official_site",127      "supports": [128        "name",129        "summary"130      ],131      "checked_at": "2026-11-02",132      "title": "NumPy"133    },134    {135      "url": "https://numpy.org/doc/stable/",136      "type": "official_docs",137      "supports": [138        "capabilities"139      ],140      "checked_at": "2026-11-02",141      "title": "NumPy documentation"142    },143    {144      "url": "https://github.com/numpy/numpy/blob/main/LICENSE.txt",145      "type": "license_page",146      "supports": [147        "license_spdx",148        "source_model"149      ],150      "checked_at": "2026-11-02",151      "title": "NumPy LICENSE.txt (BSD 3-Clause)"152    },153    {154      "url": "https://github.com/numpy/numpy",155      "type": "repository",156      "supports": [157        "repo_url"158      ],159      "checked_at": "2026-11-02",160      "title": "numpy/numpy"161    }162  ],163  "confidence": "high"164}165