> I've heard of NumPy, PyPy, SciPy, Pandas, matplotlib, and now Numba. I don't particularly know what these do or how they overlap or interact. Which is kind of the point: to a complete outsider, the world of Python scientific computing feels like a wild west where everybody is happily proclaiming that their setup is just right.
It's not terribly complicated once you spend a little time working with the libraries. Working with arrays? import numpy. Machine learning? import sklearn. Plotting? import matplotlib. Need to do some interpolation, integration, or work with some strange orthogonal polynomials, (etc...)? import scipy. Sure, there's some minor overlap between scipy and numpy, but nothing that causes any problems in my experience.
What I love about using python is that, in addition to all the great math and science libraries, you have all the other python tools at your disposal. Working with xml? import xml. Web-scraping? import urllib2 (or whatever people use now), PyQuery. Additionally, there's all the file system business work that's a joy to do in python using os, sys, etc... libraries.
It's not terribly complicated once you spend a little time working with the libraries. Working with arrays? import numpy. Machine learning? import sklearn. Plotting? import matplotlib. Need to do some interpolation, integration, or work with some strange orthogonal polynomials, (etc...)? import scipy. Sure, there's some minor overlap between scipy and numpy, but nothing that causes any problems in my experience.
What I love about using python is that, in addition to all the great math and science libraries, you have all the other python tools at your disposal. Working with xml? import xml. Web-scraping? import urllib2 (or whatever people use now), PyQuery. Additionally, there's all the file system business work that's a joy to do in python using os, sys, etc... libraries.