## page was renamed from Documentation
This is a center for all additional documentation to !NumPy and !SciPy. The main documentation can be found at http://docs.scipy.org/

Numpy and Scipy are two independent packages, but from historical reasons, the documentation for both of them appears on this page. Unfortunately, there is still a lot of outdated documentation to predecessors (or older versions of !NumPy) floating on the internet. If you are confused about the predecessors of !NumPy, read a ["History of SciPy"] -- a summary of the events that led to !SciPy and !NumPy.

Note also that the [:Developer_Zone/DocMarathon2008: NumPy/SciPy Documentation project] is in progress!  See (and help write) the [http://docs.scipy.org improved docstrings].

== NumPy ==
!NumPy is a standalone package that provides array manipulation tools for python.

 * Official Numpy documentation can be found at http://docs.scipy.org
 * [:Tentative_NumPy_Tutorial:Tutorial]: if you are new to !NumPy, you can start with reading this tutorial.
 * Teaching material covering basics and advanced use of Numpy: http://scipy-lectures.github.com/
 * [http://www.tramy.us/ Guide to NumPy] by Travis Oliphant the lead developer of !NumPy. This e-book is a complete reference to !NumPy, this is a nice documentation to all features of !NumPy.  It '''was''' fee-based but as of Aug 21, 2008 it is in the public domain.  The source to the book is also available at [http://www.tramy.us Trelgol].
 * [http://docs.scipy.org/numpy/Front%20Page Docstrings] (reference documentation for each function, module, and class in !NumPy) and general reference documentation pages.  Since these are in active development, the version on the doc wiki is the most current and may be more informative than what is in the released package.  There is a link on the doc wiki to PDF and HTML versions of the reference manual.
 * [:Numpy_Functions_by_Category:Numpy Functions by Category]: A list of the functions in !NumPy organized by task, with links to the Example List with documentation.
 * [:Numpy_Example_List_With_Doc:Numpy Example List]: large database demonstrating most of the !NumPy functionality, read this if you prefer to learn by examples. A [:Numpy_Example_List:version without doc strings] is also available.
 * [:NumPy for Matlab Users:NumPy for MATLAB® Users]:  An overview of the basics of !NumPy for those familiar with MATLAB® (you can read this even if you never used matlab before, this document contains a review of !NumPy)
 * ["Porting to NumPy"]: Provides stories and examples of porting applications to use !NumPy.
 * [http://projects.scipy.org/scipy/numpy/wiki/DistutilsDoc NumPy Distutils User's Guide]: shows how to write a setup.py for your own project
 * [http://numeric.scipy.org/numpydoc/numdoc.htm Documentation from NumPy's predecessor], Numeric.  Much of it still applies, but replace Numeric with numpy in all import statements.

== Scipy ==
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 or optimization.

 * Official SciPy reference documentation (plus tutorial) can be found at http://docs.scipy.org
 * [http://docs.scipy.org/doc/scipy/reference/tutorial Tutorial] : If you are new to !SciPy, you can start with this tutorial.
 * Teaching material covering basic and advanced aspects of Scipy: http://scipy-lectures.github.com/
 * [:/Astronomy_Tutorial:Interactive data analysis tutorial]: Tutorial on using Scipy for interactive data analysis (on astronomy data, but this is also suitable as a general tutorial)
 * [:/New_SciPy_Tutorial:WikiTutorial]: You can also find useful material in the tutorial on this Wiki.
 * [http://www.scipy.org/Additional_Documentation?action=AttachFile&do=get&target=scipy_tutorial.pdf Tutorial II (pdf)]: This is a very nice tutorial, but it was written in 2004, so it doesn't cover new features.
 * [:scipy_Example_List:Examples]: Very few (at the moment) examples of !SciPy usage
 * ["SciPy packages"]: An overview of the packages available in !SciPy, unfortunately very few are currently documented
 * ["Citing SciPy"]: How to properly cite the !SciPy tools in a paper or presentation
 * ["Trinity"]: An example calculation (computing the energy released by the Trinity atomic bomb test).
== Other ==
Other (mostly unofficial) documentation to !NumPy and !SciPy.

 * ["FAQ"].  Answers to the most frequently-asked questions.
 * [http://www.rexx.com/~dkuhlman/scipy_course_01.html A course on NumPy/SciPy] by Dave Kuhlman
 * A [:wikis/topical software/Tutorial:tutorial focused on interactive data analysis] for astronomy, but of generic utility to most scientific users.
 * A series of [http://johnstachurski.net/lectures/index.html lectures] by John Stachurski on Python, NumPy and SciPy with applications in computational economics, Feb 2009
 * [http://www.nanohub.org/resources/?id=99 Scientific Computing with Python] (registration required) A one day tutorial presented by Eric Jones and Travis Oliphant in October 2005
 * ["Weave"]: inclusion of C/C++ code in Python
 * ["Porting to NumPy"]: Provides stories and examples of porting applications from Numeric/Numarray to !NumPy.
 * ["Converting from numarray"]
 * ["Converting from Numeric"]
 * PerformanceTips. How to maximize the speed of your code using numpy/scipy.
 * [wiki:ProposedEnhancements Proposed improvements to NumPy/SciPy] that need discussion.