Statistics for Python is an extension module for Python that implements some commonly used statistical tools. This package is under development; its current version focuses on kernel estimation of probability density functions. As of version 0.21, this extension can be compiled with both Python 2 and Python 3 (thanks to David Joy of the University of Colorado, Boulder).
De Hoon, M.J.L.; Imoto, S.; Kobayashi, K.; Ogasawara, N.; Miyano, S.;
"Predicting the operon structure of Bacillus subtilis using operon length, intergene distance, and gene expression information".
Proceedings of the Pacific Symposium on Biocomputing, 9: pp. 276--287 (2004).
Documentation
The documentation is available in PDF format and in HTML. Note that the latter makes use of MathML to display mathematics. While most browsers are able to display MathML, check the MathML home to find more information if the equations look weird.
Download
statistics-0.22.tar.gz (source distribution for Python 2 and Python 3)
statistics-0.22.win32-py2.6.exe (windows installer for Python 2.6)
statistics-0.22.win32-py2.7.exe (windows installer for Python 2.7)
statistics-0.22.win32-py3.1.exe (windows installer for Python 3.1)
statistics-0.22.win32-py3.2.exe (windows installer for Python 3.2)
Installation
On Windows, you can use the binary installer. On all other platforms, you can compile the module in three steps:
python setup.py config
This step is actually not necessary, and may not work with Python versions
older than 2.3. If you decide to skip it, the loss in precision will be minimal.
This step will determine your computer architecture (IEEE 754, Motorola
IEEE 754, or DEC), and checks your math library. You may see a lot of error messages when running this command. These messages are normal and can be ignored; the error messages are due to failed trial compilations during the configuration step. The configuration results are written to a file config, which is used in the subsequent build step.
python setup.py build
python setup.py install
For the last step, you may need to login as superuser.
Example
For an example of using a kernel estimation of probability density functions from experimentally measured data in computational biology, see
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