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# About NumPy

NumPy is the fundamental package needed for scientific computing with Python. This package contains:

- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- basic linear algebra functions
- basic Fourier transforms
- sophisticated random number capabilities
- tools for integrating Fortran code
- tools for integrating C/C++ code

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 a successor for two earlier scientific Python libraries: NumPy derives from the old Numeric code base and can be used as a replacement for Numeric. It also adds the features introduced by Numarray and can also be used to replace Numarray.

## NumPy community

Numpy is a distributed, volunteer open-source project. You can help us make it better; if you believe something should be improved either in functionality or in documentation, don’t hesitate to contact us — or even better, contact us and participate in fixing the problem.

Our main means of communication are:

- scipy.org website
- Mailing lists
- Numpy Trac (bug “tickets” go here)

More information about the development of Numpy can be found at http://scipy.org/Developer_Zone

If you want to fix issues in this documentation, the easiest way is to participate in our ongoing documentation marathon.

# About this documentation

## Conventions

Names of classes, objects, constants, etc. are given in boldface font. Often they are also links to a more detailed documentation of the referred object.

This manual contains many examples of use, usually prefixed with the Python prompt `>>>` (which is not a part of the example code). The examples assume that you have first entered:

    >>> import numpy as np

before running the examples.

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