From the Linux Gazette - Python for scientific use. Part I: Data Visualization LG #114:
A first step towards qualitative understanding and interpretation of scientific data is visualization of the data. Also, in order to reach a quantitative understanding, the data needs to be analyzed, e.g. by fitting a physical model to the data. The raw data may also require some initial processing in order to become useful, e.g. filtering, scaling, calibration etc.
Several open source programs for data analysis and visualization exist: gnuplot, grace, octave, R, and scigraphica. Each of these has its own pros and cons. However, it seems like you always end up using more than one program to cover all the different needs mentioned above, at least if you don't have the programming abilities to write your own custom programs using e.g., Fortran or C.
Recently, I came across Python and found it to be a very powerful tool. In this article, I would like to share my experience and illustrate that even with basic (or less) programming skills it is still possible to create some very useful applications for data analysis and visualization using this language. The article is centered around a few illustrative examples and covers the visualization part — data analysis will be covered in a future article.