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Organizations across the globe are starting to use graph approaches and visualization techniques to make sense of complex networks. These networks are present in many industries, ranging from social network analysis (analyzing the connections of people interacting on social networks) to fraud detection (looking at transactions in a network to spot outliers), modeling the stability of systems such as rail and energy grids, and as critical components of recommendation engines that are used in many of your favorite online streaming services, for example, Netflix, Prime, and so on. This book provides you with the tools to get up and running with these methods while working with a familiar language, such as Python. We start by looking at how you can create graphs in igraph NetworkX and how these can be used to carry out sophisticated graph analytics. We will then delve into the world of Neo4j and graph databases, as well as equipping you with the knowledge to query graph databases with the Cypher query language. The primary aim of this book is to assist existing Python developers of an intermediate level who may have the ambition of getting into graph data modeling. Or, if you are a database developer or IT professional, then the graph database section may give you insights into how graph databases are different from traditional databases. In essence, this book is targeted at anyone who loves coding in Python and wants to learn more about how to build graph data pipelines, how to ingest and clean data, various ways to store graph data relationships, how to conduct analytical techniques such as community detection and recommendation engine creation, and how to use Cypher to store these relationships and then query the in-memory graph with Cypher.