numba

compiling Python code using LLVM

numba Permissive license BSD Active v0.67.0 released 2026-08-11

Install

numba on PyPI

pip

pip install numba

uv

uv add numba

poetry

poetry add numba

Package facts

License	BSD (permissive)
Python support	supports the current Python release (>=3.10)
Install friction	medium — platform-specific wheel
Runtime dependencies	2 — llvmlite, numpy
Maintenance	actively maintained — 2 days since the last release
First released	2012-08-15
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: numba-0.67.0-cp310-cp310-macosx_12_0_arm64.whl; numba-0.67.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; numba-0.67.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; numba-0.67.0-cp310-cp310-win_amd64.whl; numba-0.67.0-cp311-cp311-macosx_12_0_arm64.whl; numba-0.67.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; numba-0.67.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; numba-0.67.0-cp311-cp311-win_amd64.whl; numba-0.67.0-cp312-cp312-macosx_12_0_arm64.whl; numba-0.67.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; numba-0.67.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; numba-0.67.0-cp312-cp312-win_amd64.whl; numba-0.67.0-cp313-cp313-macosx_12_0_arm64.whl; numba-0.67.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; numba-0.67.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; numba-0.67.0-cp313-cp313-win_amd64.whl; numba-0.67.0-cp314-cp314-macosx_12_0_arm64.whl; numba-0.67.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; numba-0.67.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; numba-0.67.0-cp314-cp314t-macosx_12_0_arm64.whl

Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Software Development :: Compilers

About numba

from the package's own PyPI description — quoted content, verbatim

Numba

.. image:: https://img.shields.io/badge/discuss-on%20discourse-blue :target: https://numba.discourse.group/ :alt: Discourse

.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.4343230.svg :target: https://doi.org/10.5281/zenodo.4343230 :alt: Zenodo

.. image:: https://img.shields.io/pypi/v/numba.svg :target: https://pypi.python.org/pypi/numba/ :alt: PyPI

.. image:: https://dev.azure.com/numba/numba/_apis/build/status/numba.numba?branchName=main :target: https://dev.azure.com/numba/numba/_build/latest?definitionId=1?branchName=main :alt: Azure Pipelines

A Just-In-Time Compiler for Numerical Functions in Python

Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax.

Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.

For more information about Numba, see the...

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AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

Numba is a just-in-time compiler that accelerates numerically-focused Python code by compiling it to machine code via LLVM, with support for NumPy functions, loop parallelization, and GPU code generation.

Medium install friction due to compiled dependencies (llvmlite and numpy); however, prebuilt wheels cover Python 3.10–3.14 across macOS (ARM64), Linux (x86_64, aarch64), and Windows (amd64), and the package is actively maintained with a release just 2 days old.

BSD permissive license allows commercial and private use with minimal restrictions; you may use, modify, and distribute numba provided you retain the license notice.

Usage

pip install numba
import numba

@numba.jit
def add(a, b):
    return a + b

result = add(1, 2)

Requires Python ≥3.10; llvmlite and numpy must be installed (pulled in automatically as runtime dependencies).

Verdict: Numba is a mature, actively maintained compiler for numerical Python with no known vulnerabilities, permissive licensing, and broad platform coverage. Medium install friction is offset by strong maintenance signals and top-1000 popularity, making it a solid choice for performance-critical numerical workloads.

Needs verification

Whether the latest release (0.67.0, dated 2026-08-11) is a stable production release or a pre-release candidate.
Specific performance gains typical for common numerical workloads (e.g., array operations, nested loops).
GPU support availability and requirements for CUDA/ROCm backends.

jit compiler pythonnumpy accelerationpython performance optimizationgpu code generationllvm python compilernumerical python speeduploop parallelization python