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NumPy

by NumPy developers (NumFOCUS) · Library

Foundational Python library for n-dimensional arrays, vectorized math, linear algebra, random numbers and the array API used by the scientific stack

BSD-3-Clause licenseOpen sourceFreeLocalSelf-hostable
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Checked Nov 2, 2026

Overview

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.

Almost 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.

Capabilities

  • n-dimensional ndarray with dtypes, strides and views
  • Broadcasting and vectorized universal functions
  • Linear algebra, FFT and polynomial modules
  • Random Generator API with multiple bit generators
  • Array API standard compliance and interoperability protocols
  • C API for extension modules

Typical workflows

  1. Numerical computation underneath pandas, SciPy and scikit-learn
  2. Matrix algebra for econometric estimators
  3. Preprocessing arrays before handing them to PyTorch, JAX or MLX

Strengths

  • Universal interchange format for numerical Python
  • Compiled performance for vectorized operations
  • Stable, well-documented API and BSD license

Limitations

  • CPU-only and single-process
  • Arrays must fit in memory
  • Loops in Python remain slow; vectorization required

Prerequisites

  • Python 3

Who it is for

  • Scientists and engineers
  • Students
  • Library developers

Alternatives & comparison

  • JAX

    Python library for NumPy-style array computing with composable transformations (jit, grad, vmap, shard_map) compiled through XLA to CPU, GPU and TPU

    Open sourceOpen source
  • MLX

    Apple's open-source array framework for machine learning on Apple Silicon, with NumPy-like Python, C++ and Swift APIs and unified CPU/GPU memory

    Open sourceOpen source
  • pandas

    The standard Python DataFrame library for labeled tabular data, time series and I/O to CSV, Excel, SQL, Parquet and more

    Open sourceOpen source
  • Polars

    Rust-based DataFrame library with Python, Rust, Node and R bindings offering lazy query optimization, multithreading and Arrow-native memory

    Open sourceOpen source
  • PyTorch

    Open-source deep-learning framework with eager tensors, autograd, torch.compile and distributed training, governed by the PyTorch Foundation and the base of most LLM tooling

    Open sourceOpen source
  • QuantLib

    Open-source C++ library for quantitative finance covering instruments, term structures, pricing engines and calendars, with Python and other bindings

    Open sourceOpen source
  • SciPy

    Core open-source Python library for scientific computing on NumPy arrays: optimization, integration, interpolation, linear algebra, sparse matrices, statistics, signal processing

    Open sourceFree
Compare NumPy with its alternatives →

Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

  • License
    NumPy LICENSE.txt (BSD 3-Clause)

    https://github.com/numpy/numpy/blob/main/LICENSE.txt

    Supports: license_spdx, source_model

    checked Nov 1, 2026
  • Official documentation
    NumPy documentation

    https://numpy.org/doc/stable

    Supports: capabilities

    checked Nov 1, 2026
  • Official site
    NumPy

    https://numpy.org/

    Supports: name, summary

    checked Nov 1, 2026
  • Repository
    numpy/numpy

    https://github.com/numpy/numpy

    Supports: repo_url

    checked Nov 1, 2026
Confidence
High
Reviewed
Nov 1, 2026
Links
Broken · Nov 2, 2026

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