numpy
Fundamental package for array computing in Python
Decision gist · record as of 2026-08-14
Yes. NumPy is essential infrastructure for any scientific, data, or numerical work in Python. It is actively maintained, has no known vulnerabilities, carries permissive licenses, and is the de facto standard that nearly all other scientific Python packages build on. Install it unless you have no need for numerical computing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later; prebuilt wheels available for CPython on macOS, Linux, Windows, and ARM platforms.
- Medium install friction due to compiled wheels, but extremely well-maintained with active development (5 days since last release) and broad platform coverage across macOS, Linux, Windows, and ARM architectures.
License · maintenance · safety
BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0 (permissive) — Multiple permissive licenses (BSD-3-Clause, MIT, Zlib, 0BSD, CC0-1.0) mean you can use NumPy freely in commercial and open-source projects with minimal restrictions.
last release 2026-08-09 (5 days) · last repo commit 2026-08-13 · 32,530 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,143,792,865 downloads/mo, #19 on PyPI
Alternatives
Verify before relying
pip install numpy==2.5.2
import numpy as np
arr = np.array([1, 2, 3])
result = np.sum(arr)- Whether the compiled C/Fortran integration tools are fully functional on all listed platforms without additional system dependencies.
What it is and what it does
NumPy is the foundational library for numerical and scientific computing in Python. It provides a fast, memory-efficient N-dimensional array object (ndarray) and a large collection of mathematical functions that operate on those arrays. The library implements broadcasting—a powerful mechanism for performing operations on arrays of different shapes—and includes tools for linear algebra, discrete Fourier transforms, random number generation, and integration with C, C++, and Fortran code.
Developers use NumPy as the base layer for nearly all scientific Python work: data analysis, machine learning preprocessing, numerical simulations, and statistical computation all depend on it. It has no runtime dependencies and is actively maintained by a large open-source community. The library is production-stable (Development Status 5) and supports current Python versions (3.12 through 3.15).
Use it for
- Build numerical algorithms and scientific simulations using fast array operations and mathematical functions.
- Preprocess and manipulate data for machine learning pipelines and statistical analysis.
- Perform linear algebra computations, matrix decompositions, and eigenvalue problems.
- Generate and work with random numbers and probability distributions for Monte Carlo simulations.
- Integrate legacy C, C++, or Fortran code into Python applications via NumPy's interop tools.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
NumPy is essential infrastructure for any scientific, data, or numerical work in Python. It is actively maintained, has no known vulnerabilities, carries permissive licenses, and is the de facto standard that nearly all other scientific Python packages build on. Install it unless you have no need for numerical computing.
Install
numpy on PyPI
Before you install
Medium install friction due to compiled wheels, but extremely well-maintained with active development (5 days since last release) and broad platform coverage across macOS, Linux, Windows, and ARM architectures.
Requires Python 3.12 or later; prebuilt wheels available for CPython on macOS, Linux, Windows, and ARM platforms.
License in practice
Multiple permissive licenses (BSD-3-Clause, MIT, Zlib, 0BSD, CC0-1.0) mean you can use NumPy freely in commercial and open-source projects with minimal restrictions.
Quickstart
pip install numpy==2.5.2
import numpy as np
arr = np.array([1, 2, 3])
result = np.sum(arr)Verify before relying
- Whether the compiled C/Fortran integration tools are fully functional on all listed platforms without additional system dependencies.
Package facts
| License | BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0 permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 5 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,143,792,865 / month, #19 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: CProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.15Programming Language :: Python :: Implementation :: CPythonTopic :: Scientific/EngineeringTopic :: Software DevelopmentTyping :: Typed |
Evidence: numpy-2.5.2-cp312-cp312-macosx_10_13_x86_64.whl; numpy-2.5.2-cp312-cp312-macosx_11_0_arm64.whl; numpy-2.5.2-cp312-cp312-macosx_14_0_arm64.whl; numpy-2.5.2-cp312-cp312-macosx_14_0_x86_64.whl; numpy-2.5.2-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; numpy-2.5.2-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; numpy-2.5.2-cp312-cp312-musllinux_1_2_aarch64.whl; numpy-2.5.2-cp312-cp312-musllinux_1_2_x86_64.whl; numpy-2.5.2-cp312-cp312-win32.whl; numpy-2.5.2-cp312-cp312-win_amd64.whl; numpy-2.5.2-cp312-cp312-win_arm64.whl; numpy-2.5.2-cp313-cp313-macosx_10_13_x86_64.whl; numpy-2.5.2-cp313-cp313-macosx_11_0_arm64.whl; numpy-2.5.2-cp313-cp313-macosx_14_0_arm64.whl; numpy-2.5.2-cp313-cp313-macosx_14_0_x86_64.whl; numpy-2.5.2-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; numpy-2.5.2-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; numpy-2.5.2-cp313-cp313-musllinux_1_2_aarch64.whl; numpy-2.5.2-cp313-cp313-musllinux_1_2_x86_64.whl; numpy-2.5.2-cp313-cp313-win32.whl
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See also scipy · xarray-einstats · sparse · jenkspy · galois · control · mt2 · xarray · linear-operator · scikit-learn