Book Image

Python Data Analysis Cookbook

By : Ivan Idris
Book Image

Python Data Analysis Cookbook

By: Ivan Idris

Overview of this book

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.
Table of Contents (23 chapters)
Python Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Glossary
Index

Computing social network density


Humans are social animals and, therefore, social connections are very important. We can view these connections and the persons involved as a network. We represent networks or a subset as a graph. A graph consists of nodes or points connected by edges or lines. Graphs can be directed or undirected—the lines can be arrows.

We will use the Facebook SPAN data, which we also used in the Visualizing network graphs with hive plots recipe. Facebook started out small in 2004, but it has more than a billion users as of 2015. The data doesn't include all the users, but it is still enough for a decent analysis. The following equations describe the density of undirected (8.1) and directed (8.2) graphs:

In these equations, n is the number of nodes and m is the number of edges.

Getting ready

Install NetworkX with the instructions from the Introduction section.

How to do it...

The code is in the net_density.ipynb file in this book's code bundle:

  1. The imports are as follows:

    import...