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Python Data Analysis Cookbook
book

Python Data Analysis Cookbook

by Ivan Idris
July 2016
Beginner to intermediate
462 pages
9h 14m
English
Packt Publishing
Content preview from Python Data Analysis Cookbook

Chapter 12. Parallelism and Performance

In this chapter, we will cover the following recipes:

  • Just-in-time compiling with Numba
  • Speeding up numerical expressions with Numexpr
  • Running multiple threads with the threading module
  • Launching multiple tasks with the concurrent.futures module
  • Accessing resources asynchronously with the asyncio module
  • Distributed processing with execnet
  • Profiling memory usage
  • Calculating the mean, variance, skewness, and kurtosis on the fly
  • Caching with a least recently used cache
  • Caching HTTP requests
  • Streaming counting with the Count-min sketch
  • Harnessing the power of the GPU with OpenCL

Introduction

The ENIAC, built between 1943 and 1946, filled a large room with eighteen thousand tubes and had a 20-bit memory. We have come a long ...

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Publisher Resources

ISBN: 9781785282287Supplemental Content