**Scientific Tools for Python**

SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world's leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!

**Python and Scientific Computing**

NumPy and SciPy are two of many open-source packages for scientific computing that use the Python programming language. This website, together with other subdomains of the scipy.org domain, serves as a portal for all scientific computing with Python, not just NumPy and SciPy. The index under Topical Software in the navigation bar lists these domains and other destinations for scientific software using Python.

Good places to start to learn more about SciPy:

This is a community effort. We seek volunteers at all levels of ability to work on the project, from coding and packaging to documentation, tutorials, recipes, and the web site. Visit the Developer Zone if you are interested in helping out.
SciPy is sponsored by Enthought, Inc.

SciPy optimization on Ubuntu Linux

NumPy is the fundamental package needed 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.

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