Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
Online Resources

Chapter 12.  Performance Tuning, Profiling, and Concurrency

 

"Premature optimization is the root of all evil"

 
 --Donald Knuth, a renowned computer scientist and mathematician

For real-world applications, performance is as important as features, robustness, maintainability, testability, and usability. Performance is directly proportional to the scalability of an application. Ending this book without looking at performance enhancement was never an option. In fact, we delayed discussing the topic of performance until the last chapter of the book to avoid premature optimization. In this chapter, we will give hints on improving performance using profiling as the key technique. We will also discuss the relevant frameworks for multicore, distributed systems. 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...