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Python Data Analysis, Second Edition

You're reading from Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

Product type Paperback

Published in Mar 2017

Publisher Packt

ISBN-13 9781787127487

Length 330 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Data Analysis

Author (1):

Ivan Idris

View More author details

Table of Contents (16) Chapters

Preface

1. Getting Started with Python Libraries FREE CHAPTER

2. NumPy Arrays

3. The Pandas Primer

4. Statistics and Linear Algebra

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

Installing MPI for Python

The Message Passing Interface (MPI) (see http://en.wikipedia.org/wiki/Message_Passing_Interface) is a standard protocol developed by experts to work on a broad assortment of distributed machines. Originally, in the nineties, MPI was used to write programs in Fortran and C. MPI is independent of hardware and programming languages. MPI functions include the send and receive operations, MapReduce functionality, and synchronization. MPI has point-to-point functions involving two processors, and operations involving all processors. MPI has bindings for several programming languages, including Python. Download MPI from http://www.open-mpi.org/software/ompi/. MPI 2.0.2 was installed and used at the time of writing; we can check on the website whether there is a newer version available. Installing MPI can take a while (nearly 30 minutes). The following are the commands involved, assuming that we install it in the /usr/local directory:

$ ./configure --prefix=/usr/local
...

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Authors (1)

Idris

Idris

Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.

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