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NumPy is the fundamental package for scientific computing with Python.
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, enabling reuse with few restrictions.
To install NumPy, we strongly recommend using a scientific Python distribution. See Installing the SciPy Stack 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. We also recommend the SciPy Lecture Notes 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.
The most up-to-date NumPy documentation can be found at Latest (development) version. 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.