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.

Getting Started
---------------

- `Getting Numpy <http://www.scipy.org/Download>`_
- `Installing NumPy and SciPy <http://www.scipy.org/Installing_SciPy>`_
- `NumPy and SciPy documentation page <http://docs.scipy.org/doc/>`_
- `NumPy Tutorial <http://www.scipy.org/Tentative_NumPy_Tutorial>`_
- `NumPy for MATLAB© Users <http://www.scipy.org/NumPy_for_Matlab_Users>`_
- `NumPy functions by category <http://www.scipy.org/Numpy_Functions_by_Category>`_
- `NumPy Mailing List <http://www.scipy.org/Mailing_Lists>`_

More Information
----------------

- `NumPy Sourceforge Home Page <http://sourceforge.net/projects/numpy/>`_
- `SciPy Home Page <http://www.scipy.org/>`_
- `Interfacing with compiled code <http://www.scipy.org/Topical_Software#head-7153b42ac4ea517c7d99ec4f4453555b2302a1f8>`_
- :doc:`Older python array packages </old_array_packages>`

.. toctree::
   :hidden:

   old_array_packages