I'm trying to rapidly delve right into Python and as such will be going over two separate tutorials, one aimed specifically called Python4Astronomers and the other coming from the quant world. The finance article seems particularly interesting as it is doing actual data analysis whereas the former is mostly doing image processing. It's kind of remarkable how hard it is to find astronomy examples of data processing given the fact that most of the data is freely available and there is lots of it. I guess crunching numbers on stars doesn't pay the same as a hedge fund. Go figure.
The python4astronomers specially uses Sherpa for modelling and fitting while the quant analysis uses the scikits.timeseries module, which I had found in other searches. I would really prefer scikits.timeseries module but it is apparently unavailable for Python3 yet and I'm trying to stick to that for learning. So instead I have found Pandas which specifically supports time-series data, works on Python3, and seems to be gaining in popularity anyway so seems like a good choice. Then again, there is also astropy...
In the interest of actually getting something besides reading done I am just going to go with the Pandas library and get to some coding. Stay tuned.
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