September 2014
Intermediate to advanced
512 pages
12h 39m
English
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Chapter 4. Profiling and Optimization
In this chapter, we will cover the following topics:
- Evaluating the time taken by a statement in IPython
- Profiling your code easily with cProfile and IPython
- Profiling your code line-by-line with line_profiler
- Profiling the memory usage of your code with memory_profiler
- Understanding the internals of NumPy to avoid unnecessary array copying
- Using stride tricks with NumPy
- Implementing an efficient rolling average algorithm with stride tricks
- Making efficient array selections in NumPy
- Processing huge NumPy arrays with memory mapping
- Manipulating large arrays with HDF5 and PyTables
- Manipulating large heterogeneous tables with HDF5 and PyTables
Introduction
Although Python is generally known (a bit unfairly) as a slow language, ...
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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.