Treffer: Numerical Computing in Python.

There is a frequent need for processing large amounts of data in computational science applications. Storing data in lists and traversing lists with plain Python for loops leads to slow code, especially when compared with similar code in compiled languages such as Fortran, C, or C++. Fortunately, there is an extension of Python, commonly called Numerical Python, or abbreviated NumPy, which offers efficient array computations. Numerical Python has a fixed-size, homogeneous (fixed-type), multi-dimensional array type and lots of functions for various array operations. The result is a dynamically typed environment for array computing similar to basic Matlab. Usually, the speed of NumPy operations is quite close to what is obtained in pure Fortran, C, or C++. [ABSTRACT FROM AUTHOR]

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