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

Creating a process pool with multiprocessing


Multiprocessing is a standard Python module that targets machines with multiple processors. Multiprocessing works around the Global Interpreter Lock (GIL) by creating multiple processes.

Note

The GIL locks Python bytecode so that only one thread can access it.

Multiprocessing supports process pools, queues, and pipes. A process pool is a pool of system processes that can execute a function in parallel. Queues are data structures that are usually used to store tasks. Pipes connect different processes in such a way that the output of one process becomes the input of another.

Note

Windows doesn't have an os.fork() function, so we need to make sure that only imports and def blocks are defined outside the if __name__ == "__main__" block.

Create a pool and register a function as follows:

   p = mp.Pool(nprocs) 

The pool has a map() method that is the parallel equivalent of the Python map() function:

p.map(simulate, [i for i in xrange(10, 50)]) 

We will simulate...