Book Image

Graph Data Modeling in Python

By : Gary Hutson, Matt Jackson
Book Image

Graph Data Modeling in Python

By: Gary Hutson, Matt Jackson

Overview of this book

Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you’ll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis. Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you’ll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you’ll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you’ll also get to grips with adapting your network model to evolving data requirements. By the end of this book, you’ll be able to transform tabular data into powerful graph data models. In essence, you’ll build your knowledge from beginner to advanced-level practitioner in no time.
Table of Contents (16 chapters)
1
Part 1: Getting Started with Graph Data Modeling
4
Part 2: Making the Graph Transition
7
Part 3: Storing and Productionizing Graphs
11
Part 4: Graphing Like a Pro

Ingestion considerations

Now that we have decided on a schema for our graph, we can begin to move data from our MySQL relational database to an igraph graph. The subsequent steps detail how to achieve this:

  1. The first thing we must do is extract the data from our MySQL database and move it over to Python. We can do this using the query_mysql() method we wrote previously in this chapter. Now that we know more about the data, and that we have designed a graph schema, we can extract only the columns we need to create our graph:
    play_query = 'SELECT id, game_name, hours FROM steam_play'
    play_data = query_mysql(play_query, password=PASSWORD)
    print(play_data[:10])
    purchase_query = 'SELECT id, game_name FROM steam_purchase'
    purchase_data = query_mysql(purchase_query,password=PASSWORD )
    print(purchase_data[:10])

In play_data, we have information on users, the games they have played, and the time they have spent playing each game. In purchase_data, we only need...