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NumPy, SciPy, Sage, and Pandas. NumPy is a package with a long lineage. It is based on older projects such as 
Numeric and numarray, and at its core it implements a multidimensional array of numbers. In addition to this 
data structure, NumPy has several efficiently implemented functions and operators that work on the entire array so 
that when you use them from Python, the number of function calls is minimized, letting you write highly efficient 
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Though, if you’re writing iterator-laden, functional code and you do want an external boost, you might want to look at CyToolz.

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Appendix A 
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 pedAl to the MetAl: AccelerAting python
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numeric computations without compiling any custom extensions. As a supplement to NumPy, Theano can optimize 
mathematical expressions on numeric arrays. SciPy and Sage are much more ambitious projects (although with 
NumPy as one of their building blocks), collecting several tools for scientific, mathematical, and high-performance 
computing (including some of the ones mentioned later in this appendix). Pandas is more geared toward data 
analysis, but if its data model fits your problem instances, it is both powerful and fast. A related toolkit is Blaze,  
which can help if you’re working with large amounts of semistructured data.