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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

2. NumPy Arrays FREE CHAPTER

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

Integrating SWIG and NumPy

C is a widespread programming language developed around 1970. Various C dialects exist, and C has influenced other programming languages. C is not object-oriented. This led to the creation of C++, which is an object-oriented language with C features, since C is a subset of C++. C and C++ are compiled languages. We need to compile source code to create so-called object files. After that, we must link the object files to create dynamically shared libraries.

The good thing about integrating C and Python is that a lot of options are available to us. The first option is Simplified Wrapper and Interface Generator (SWIG). SWIG adds an additional step in the development process, which is the generation of glue code between Python and C (or C++). Download SWIG from http://www.swig.org/download.html. At the time of writing, the latest SWIG version is 3.0.12. A prerequisite to installing SWIG is to install Perl Compatible Regular Expressions (PCRE). PCRE is a C regular expressions...

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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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