Astronomer Joshua Bloom gave a talk titled Python as Super Glue for the Modern Scientific Workflow at SciPy2012. Bloom teaches Python for Scientific Computing at Berkeley (available as a podcast). Bloom showcased Pythonic contributions to work on Supernova and machine-learning in astronomy.
Python has solid support for data analysis and scientific computing, starting with Numpy for matrix manipulation and SciPy, which adds diff-eqs, optimization, statistics, etc. and matplotlib for graphics. I keep meaning to check out Pandas, scikit-learn, and Sage.
IPython keeps getting more impressive and appears to be evolving in the direction of Mathematica's Live Notebooks. I have a long-standing thing for mixing prose and code. Fernando Perez’s talk on IPython at SciPy2012 and a the longer version IPython in-depth are in my viewing queue.
Part of the beauty of Python is it's breadth as a general purpose programming language. Libraries for everyday programming tasks like web application and database interaction are well developed in the Python world. There are good arguments in favor of DSLs for math and statistics, the approach embodied by R, Matlab, Mathematica and Julia. On the other hand, some may agree with John Cook, who puts it like this: "I’d rather do math in a general-purpose language than try to do general-purpose programming in a math language."
- Data Mining and Machine-Learning in Time-Domain Discovery & Classification, Bloom and Richards
- BigMACC: Bloom's machine classified catalog of variable stars
- NetworkX, a Python network library