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The NumPy Array: A Structure for Efficient Numerical Computation
March/April 2011 (vol. 13 no. 2)
pp. 22-30
Stéfan van der Walt, Stellenbosch University
S. Chris Colbert, Enthought, Inc.

In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.

1. F. Perez and B.E. Granger, "IPython: A System for Interactive Scientific Computing," Computing in Science & Eng., vol. 9, no. 3, 2007, pp. 21–29.
2. J. Bergstra, "Optimized Symbolic Expressions and GPU Metaprogramming with Theano," Proc. 9th Python in Science Conf. (SciPy2010); forthcoming; http://conference.scipy.orgproceedings.html .

Index Terms:
Python, NumPy, scientific programming, numerical computations, programming libraries
Citation:
Stéfan van der Walt, S. Chris Colbert, Gaël Varoquaux, "The NumPy Array: A Structure for Efficient Numerical Computation," Computing in Science and Engineering, vol. 13, no. 2, pp. 22-30, March-April 2011, doi:10.1109/MCSE.2011.37
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