One of the most common ways data scientists are introduced to graphs is via Airflow, which helps you build a directed acyclic graph (DAG) for your data pipeline. Graphs are at the heart of our data modeling tool, dbt, as well. Graphs are much more broadly relevant than simply constructing data pipelines however: nodes and edges turn out to be a great way to model data.
This post uses a common Python graph processing library, NetworkX, to create and draw graphs. NetworkX is an awesome library: we use it to do all of dbt's graph processing. This post is a great intro.
Very useful tool in your tool belt.