NumPy

NumPy is the fundamental package for scientific computing with Python. It contains among other things:

- a powerful N-dimensional array object

- sophisticated (broadcasting) functions

- tools for integrating C/C++ and Fortran code

- useful linear algebra, Fourier transform, and random number capabilities

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

NumPy is licensed under the [BSD license](https://www.numpy.org/license.html), enabling reuse with few restrictions.

## Getting Started

To install NumPy, we strongly recommend using a ***scientific Python distribution***. See [Installing the SciPy Stack](https://www.scipy.org/install.html) for details.

Many high quality online tutorials, courses, and books are available to get started with NumPy. For a quick introduction to NumPy we provide the [NumPy Tutorial](https://www.numpy.org/devdocs/user/quickstart.html). We also recommend the [SciPy Lecture Notes](https://scipy-lectures.org) for a broader introduction to the scientific Python ecosystem.

For more information on the SciPy Stack (for which NumPy provides the fundamental array data structure), see [scipy.org](https://www.scipy.org).

## Documentation

The most up-to-date NumPy documentation can be found at [Latest (development) version](https://www.numpy.org/devdocs). It includes a user guide, full reference documentation, a developer guide, meta information, and “NumPy Enhancement Proposals” (which include the NumPy Roadmap and detailed plans for major new features).

A complete archive of documentation for all NumPy releases (minor versions; bug fix releases don’t contain significant documentation changes) since 2009 can be found at https://docs.scipy.org.