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 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 Issues (bug reports go here)
- Old Numpy Trac (no longer used)
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¶
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.