May 2017
Intermediate to advanced
270 pages
6h 18m
English
Content preview from Python High Performance - Second Edition
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Designing for High Performance
In the earlier chapters, we learned how to use the vast array of tools available in Python's standard library and third-party packages to assess and improve the performance of Python applications. In this chapter, we will provide some general guidelines on how to approach different kinds of applications as well as illustrate some good practices that are commonly adopted by several Python projects.
In this chapter, we will learn the following:
- Picking the right performance technique for generic, number crunching, and big data applications
- Structuring a Python project
- Isolating Python installations with virtual environments and containerization
- Setting up continuous integration with Travis CI
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.