Are you new to SciPy and NumPy? Do you want to learn it quickly and easily through examples and a concise introduction? Then this is the book for you. You’ll cut through the complexity of online documentation and discover how easily you can get up to speed with these Python libraries.

Ideal for data analysts and scientists in any field, this overview shows you how to use NumPy for numerical processing, including array indexing, math operations, and loading and saving data. You’ll learn how SciPy helps you work with advanced mathematical functions such as optimization, interpolation, integration, clustering, statistics, and other tools that take scientific programming to a whole new level.

The new edition is now available, fully revised and updated in June 2013.

Learn the capabilities of NumPy arrays, element-by-element operations, and core mathematical operations Solve minimization problems quickly with SciPy’s optimization package Use SciPy functions for interpolation, from simple univariate to complex multivariate cases Apply a variety of SciPy statistical tools such as distributions and functions Learn SciPy’s spatial and cluster analysis classes Save operation time and memory usage with sparse matrices

About the Author

Eli Bressert was born in Tucson, Arizona. He worked as a science imager for NASA's Chandra X-ray Space Telescope optimizing science images which are frequently seen on book covers, newspapers, television, and other media. Afterward, Eli obtained his PhD in astrophysics at the University of Exeter and is currently a Bolton Fellow at CSIRO Astronomy and Space Science in Sydney, Australia. For the past six years Eli has been programming in Python and giving Python lectures at Harvard University, the European Space Astronomy Centre, and the European Southern Observatory. He is one of the founding developers for two well-known astrophysics Python packages: ATpy and APLpy.

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