High Performance Python, 2nd Edition by Ian Ozsvald & Micha Gorelick

Author:Ian Ozsvald & Micha Gorelick [Ian Ozsvald]
Language: eng
Format: epub
Publisher: O'Reilly Media, Inc.
Published: 2020-06-24T16:00:00+00:00

Sage, a Matlab-like environment including an integrated development environment (IDE)

IPython, an interactive Python shell heavily used by scientists and developers

IPython Notebook, a browser-based frontend to IPython, heavily used for teaching and demonstrations

One of Python’s main strengths is that it enables fast prototyping of an idea. Due to the wide variety of supporting libraries it is easy to test if an idea is feasible, even if the first implementation might be rather flaky.

If you want to make your mathematical routines faster, look to numpy. If you want to experiment with machine learning, try scikit-learn. If you are cleaning and manipulating data, then pandas is a good choice.

In general, it is sensible to raise the question, “If our system runs faster, will we as a team run slower in the long run?” It is always possible to squeeze more performance out of a system if enough man-hours are invested, but this might lead to brittle and poorly understood optimizations that ultimately trip the team up.

One example might be the introduction of Cython ([Link to Come]), a compiler-based approach to annotating Python code with C-like types so the transformed code can be compiled using a C compiler. While the speed gains can be impressive (often achieving C-like speeds with relatively little effort), the cost of supporting this code will increase. In particular, it might be harder to support this new module, as team members will need a certain maturity in their programming ability to understand some of the trade-offs that have occurred when leaving the Python virtual machine that introduced the performance increase.

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