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File:NumPy logo.png
Developer(s) community project
Stable release 1.4.0 / December 27, 2009; 130538597 ago
Operating system Cross-platform
Type Technical computing
License BSD-new license

NumPy is an extension to the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. The ancestor of NumPy, Numeric, was originally created by Jim Hugunin. NumPy is open source and has many contributors.



Since the standard Python implementation is an interpreter, mathematical algorithms often run much slower than compiled equivalents, such as those written in C. NumPy addresses this problem for many numerical algorithms by providing multidimensional arrays and lots of functions and operators that operate on arrays. Thus any algorithm that can be expressed primarily as operations on arrays and matrices can run almost as fast as the equivalent C code.[1]

NumPy is a free alternative to MATLAB (PyLab) since they are both interpreted, and they both allow the user to write fast programs as long as most operations work on arrays or matrices instead of scalars. One advantage of MATLAB is the large number of additional toolboxes that are available, including the Simulink packages. NumPy, on the other hand, has the advantage that Python is a more modern and complete programming language and is open source. Other complementary Python packages are available: SciPy is a library that adds more MATLAB-like functionality; Matplotlib is a plotting package that provides MATLAB-like plotting functionality. Internally, both MATLAB and NumPy rely on LAPACK for efficient linear algebra computations.


The following is a simple example of how to do interactive array manipulations and plot a graph with NumPy and Matplotlib.

>>> x = linspace(0, 2*pi, 100)
>>> y = sin(x)
>>> plot(x, y) # call Matplotlib plotting function
>>> show()


NumPy is based on two earlier Python array packages. The original one, Numeric, which is reasonably complete and stable, remains available, but is now obsolete. It was originally written in 1995 largely by Jim Hugunin with the help of many people including Jim Fulton, David Ascher, Paul DuBois[2], and Konrad Hinsen. A newer implementation, Numarray, is a complete rewrite of Numeric but is also deprecated.[3] NumPy is a merge between the two that builds on the code base of Numeric and adds the features of Numarray.

There was a desire to get Numeric into the Python standard library, but Guido van Rossum (the author of Python) was quite clear that the code was not maintainable in its state then. Another problem was that for large arrays Numeric is very slow. As a result, another package called Numarray was created. Numarray is faster for large arrays, but slower for small arrays. For a time both Numeric and Numarray were used, both with different ways to accomplish similar goals. The last version of Numeric v24.2 was released on November 11, 2005 and numarray v1.5.2 was released on August 24, 2006.[4]

In early 2005, Travis Oliphant wanted to reunify the community around a single array package. The Numeric code was adapted to make it more maintainable and flexible enough to implement the novel features of Numarray. This new project was part of SciPy. To avoid installing a whole package just to get an array object, this new package was separated and called NumPy. While the source code is freely available and it contains significant documentation, there is also an extensive official Guide to NumPy.[5] The documentation is built around a unified docstring standard.[6]

NumPy version 1.3.0, released April 5, 2009, supports Python 2.6.[7] Support for Python 3 is planned, but not yet scheduled.

Video Source

There are several videos recorded in the seminars and the conferences. These videos may help beginners learn how NumPy works. 2009 SciPy meeting had several sessions on SciPy and NumPy.[8]

See also


External links

ja:NumPy pt:NumPy ru:NumPy

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