# Python Numpy Scipy

> Publicly sourced profile assembled by Optimly, in the Optimly AI Brand Index. Last refreshed 2026-10-04.

- **Business Profile:** https://optimly.ai/brand/python-numpy-scipy
- **Dataset:** https://optimly.ai/brand/python-numpy-scipy.json
- **Markdown:** https://optimly.ai/brand/python-numpy-scipy/llms.txt
- **Official website:** https://numpy.org
- **Slug:** python-numpy-scipy
- **Categories:** developer-tools

## Overview

- **Tagline:** Provides powerful and versatile N-dimensional arrays; de-facto standard for array computing concepts (vectorization, indexing, broadcasting); offers comprehensive mathematical capabilities; interoperable with various hardware and computing platforms; performant due to well-optimized C code with Python's flexibility; easy to use high-level syntax; forms the basis for a vast ecosystem of scientific and data analysis libraries.
Source: Brand's website (https://numpy.org) · 2026-10-04

- **Description:** NumPy is the fundamental open-source package for scientific computing with Python. It provides powerful N-dimensional array objects and a suite of tools for numerical computing, including comprehensive mathematical functions, random number generators, linear algebra routines, and Fourier transforms. It is known for its performance, versatility, and ease of use, serving as a core component for data science, machine learning, and various scientific domains.
Source: Brand's website (https://numpy.org) · 2026-10-04

## Offerings

- **Core capabilities:**
  - Powerful N-dimensional arrays; comprehensive numerical computing tools including mathematical functions
  - random number generators
  - linear algebra routines
  - and Fourier transforms.
Source: Brand's website (https://numpy.org) · 2026-10-04

- **Pricing:** Open-source, free (distributed under a liberal BSD license).
Source: Brand's website (https://numpy.org) · 2026-10-04

## Audience

- **Target industry:** Python developers, data scientists, machine learning engineers, and researchers across scientific and engineering disciplines (e.g., quantum computing, statistical computing, signal processing, image processing, astronomy, bioinformatics, mathematical analysis, chemistry, geoscience, GIS, robotics).
Source: Brand's website (https://numpy.org) · 2026-10-04

Page last updated 2026-10-17.