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NumPy provides fast numerical computing capabilities from "summary" of Python for Data Analysis by Wes McKinney
NumPy is a fundamental package for numerical computing in Python. It provides comprehensive support for efficient array operations, particularly concerning mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation, and so forth. NumPy excels at providing an easy-to-use and flexible interface for manipulating large arrays and matrices of numeric data. It is not surprising that NumPy is at the core of many other scientific computing packages in Python. NumPy's primary object is the homogeneous multidimensional array, a table of elements (usually numbers), all of the same type, indexed by a tuple of nonnegative integers. In NumPy, dimensions are called axes. The number of axes is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each axis. NumPy arrays have a fixed size at...
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