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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Chapter 12. Performance Tuning, Profiling, and Concurrency

 

"Premature optimization is the root of all evil"

 
 --Donald Knuth, a renowned computer scientist and mathematician

In the real world, there are more important things than performance, such as features, robustness, maintainability, testability, and usability. That's one of the reasons that we delayed discussing the topic of performance until the last chapter of the book. We will give hints on improving performance with profiling as the key technique. For multicore, distributed systems, we will discuss the relevant frameworks too. We will discuss the following topics in this chapter:

  • Profiling the code
  • Installing Cython
  • Calling the C code
  • Creating a pool process with multiprocessing
  • Speeding up embarrassingly parallel for loops with Joblib
  • Comparing Bottleneck to NumPy functions
  • Performing MapReduce with Jug
  • Installing MPI for Python
  • IPython Parallel
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