Python High Performance Programming by Unknown

Author:Unknown
Language: eng
Format: epub
Publisher: Packt Publishing

By simply copying the expressions using numexpr we were able to obtain a 4.5x increase in performance in a real-world scenario over standard NumPy. The numexpr package can be used every time you need to optimize a NumPy expression that involves large arrays and complex operations, and you can do so with minimal changes in the code.

Summary

In this chapter, we learned how to manipulate NumPy arrays and how to write fast mathematical expressions using array broadcasting. This knowledge will help you to design better programs while obtaining massive performance gains. We also introduced the numexpr library to further increase the speed of our calculations with a minimal amount of effort.

NumPy works very well when handling independent sets of inputs, but it's not suitable when the expressions grow complex and cannot be split in element-wise operations. In such cases, we can leverage Python capabilities as a glue language by interfacing it with C using the Cython package.

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