NumPy

• Website: https://www.numpy.org
• Documentation: http://docs.scipy.org/
• Mailing list: https://mail.python.org/mailman/listinfo/numpy-discussion
• Source code: https://github.com/numpy/numpy
• Contributing: https://www.numpy.org/devdocs/dev/index.html
• Bug reports: https://github.com/numpy/numpy/issues
• Report a security vulnerability: https://tidelift.com/docs/security

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.

Installing Packages

For many users, especially on Windows, the easiest way to begin is to download one of these Python distributions, which include all the key packages:

• Anaconda: A free distribution of Python with scientific packages. Supports Linux, Windows and Mac.
• Enthought Canopy: The free and commercial versions include the core scientific packages. Supports Linux, Windows and Mac.
• Python(x,y): A free distribution including scientific packages, based around the Spyder IDE. Windows and Ubuntu; Py2 only.
• WinPython: Another free distribution including scientific packages and the Spyder IDE. Windows only, but more actively maintained and supports the latest Python 3 versions.
• Pyzo: A free distribution based on Anaconda and the IEP interactive development environment. Supports Linux, Windows and Mac.

Installing via PIP Command in Terminal / Command Prompt

pip install numpy