What it's for: Scientific computing and mathematical work, including statistics, linear algebra, matrix math, financial operations, and tons more.
Why it's great: Quants and bean counters already know about NumPy and love it, but the range of applications for NumPy outside math 'n' stats is broader than you think. For example, it's one of the easiest, most flexible ways to add support for multidimensional arrays to Python, which newcomers from other languages often complain about. If you want the total and complete Python science-and-math enchilada, though, get the SciPy library and environment, which includes NumPy as a standard-issue item. For more sophisticated data analysis built on top of NumPy, check outPandas.