Benchmarking, Profiling, and Optimizing Your Python Code [Video]

[mitsumi]

Benchmarking, Profiling, and Optimizing Your Python Code:
Easily Identify Bottlenecks and Efficiently Speed Up Your Python Code
by Coen de Groot
English | 2020 | h264, yuv420p, 1280x720 | 44100 Hz, 2channels | 1h 32m | 144 MB

We start by looking at how to measure the speed of a program, so you can                                                                                                                                                                                                         see the impact of your changes. And we will discover where your program spends most of its time to help you focus your efforts. You will see how to create performance graphs and drill down into the detail. Next, we move on to optimizing the code by changing the flow and structure. Some approaches take more processing power than others, and we will look at how to use code complexity to express this and help you choose the best algorithm. We will see some examples of switching to a different algorithm and the impact on the code's speed. You will see some simple techniques to cache the results from your functions. Moving code out of large loops can also have a significant impact. Having restructured the code, you will learn how to speed up individual lines of code. We will look at ten common tasks in Python and compare the different ways to achieve them.

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