January 17, 2018
Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Construct, analyze, and visualize networks with networkx, a Python language module and Complex Network Analysis in Python: Recognize → Construct → Visualize → Analyze → Interpret, now in print and shipping from pragprog.com/book/dzcnapy. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially.
Come and get 'em!
Starting from the ground up, learn new syntax (or how to reuse older syntax) to transform code from clunky bug-susceptible scripts to clear and elegant programs that are easy to read and easy to extend.
Create a foundation for readable code with simple variable declarations that reduce side effects and subtle bugs. Select collections with clear goals instead of defaulting to objects or arrays. See how to simplify iterations from complex loops to single line array methods. Master techniques for writing flexible and solid code ranging from high-order functions, to reusable classes, to patterns for architecting large applications creating applications that will last through rounds of refactoring and changing requirements.
The best part is there's no need to read this book straight through. Jump around and incorporate new functionality at will. Most importantly, understand not just what the new syntax is, but when and how to use it. Start writing better code from the first page.
Now in beta from pragprog.com/book/es6tips.
Complex Network Analysis in Python: Recognize → Construct → Visualize → Analyze → Interpret
Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience.
Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive—such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics.
Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer.
Now in print and shipping shortly from pragprog.com/book/dzcnapy.
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- Getting Clojure: Build Your Functional Skills One Idea at a Time in beta
- Functional Web Development with Elixir, OTP, and Phoenix in print
- Domain Modeling Made Functional: Tackle Software Complexity with Domain-Driven Design and F# in print
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