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Tools for data analysis and data generation

When faster numerical computation and/or greater dimensional abstraction than native Python affords is needed, NumPy is a "standard" by which other tools can be judged.

Name   NumPy
Description   NumPy (Numeric Python) is an extensive array data processing tools. It contains useful procedures for numerical computation. It can be used for fast manipulation of multi-dimensional arrays.
random random generation tools
linalg linear algebra tools
dft FFT (fast Fourier transform) routines
testing Scipy (Scientific Python) testing tools
NumPy also supports a Fortran-to-Python interface generator and other Fortran compiler tools.
Principal Reference   The SciPy (scientific python) website:
// has a comprehensive list of documents.
update 20090127: Oliphant, T.E. 2006. Guide to NumPy. This book has been released into the public domain after three years of restricted distribution.
The SciPy community is quite active and they maintain a good range of downloads. Make sure you get the right version for your hardware, Operating System and SoniPy's current Python version.
Version   1.0.3
License OSI-Approved Open Source software
Dependencies   None
Warnings   The correct version for HW and SW is important.
Initial Comments   Many scientific routines have been written using these tools. Over its evolution, NumPy has also been known as both Numeric and Numarray and references to it as such can be found both in the documentation and in third-party code. Earlierthis year (2007) there was some discussion on the Python SIG about reasons for a Numeric package still being available, however I believe that for those using it for the first time, NumPy is the correct package. In fact some of the code examples in the package download still try to "import numeric". I have yet to find an example which doesn't run if "numeric" is replaced with "numpy" in the python code.

Do you have other suggestions for tools in this category? For example, there there is also a Python interface to Matlab called PyLab. See details on the matplotlib home page. If you'd like to contribute your experience, contact us via the FORUM link.

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