June 2018
Intermediate to advanced
248 pages
5h 27m
English
Content preview from Mastering Numerical Computing with NumPy
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Summary
Knowing NumPy's internals is crucially important when you are working with scientific operations. Efficiency is key since many scientific computations are compute and memory intensive. Hence, if your code is not written efficiently, computations will take much longer than they need and this will hurt your research and development timeline.
In this chapter, you have seen some of the internals and performance aspects of the NumPy library and also learned about the vprof library, which helps you inspect the performance of your python programs.
Code profiling will help you a lot to inspect your programs line by line and there are different ways of looking at the same data, as you have seen previously. Once you have identified the most ...
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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.