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

Calculating the assortativity coefficient of a graph


In graph theory, similarity is measured by the degree distribution. Degree is the number of connections a node has to other nodes. In a directed graph, we have incoming and outgoing connections and corresponding indegree and outdegree. Friends tend to have something in common. In graph theory, this tendency is measured by the assortativity coefficient. This coefficient is the Pearson correlation coefficient between a pair of nodes, as given in the following equation:

qk (distribution of the remaining degree) is the number of connections leaving node k. ejk is the joint probability distribution of the remaining degrees of the node pair.

Getting ready

Install NetworkX with the instructions from the Introduction section.

How to do it...

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

  1. The imports are as follows:

    import networkx as nx
    import dautil as dl
  2. Load the Facebook SPAN data into a NetworkX graph:

    fb_file = dl.data.SPANFB...