GraphI - Python Graph Interface and Types¶
GraphI
is a lightweight graph library - it is suitable to model networks, connections and other relationships.
Compared to other graph libraries, GraphI
aims for being as pythonic as possible.
If you are comfortable using list
, dict
or other types, GraphI
is intuitive and straight-forward to use.
# create a graph with initial nodes
airports = Graph("New York", "Rio", "Tokyo")
# add connections between nodes
airports["New York":"Rio"] = timedelta(hours=9, minutes=50)
airports["New York":"Tokyo"] = timedelta(hours=13, minutes=55)
At its heart, GraphI
is built to integrate with Python’s data model.
It natively works with primitives, iterables, mappings and whatever you need.
For example, creating a multigraph is as simple as using multiple edge values:
# add multiple connections between nodes
airports["Rio":"Tokyo"] = timedelta(days=1, hours=2), timedelta(days=1, hours=3)
With its general-purpose design, GraphI
makes no assumptions about your data.
You are free to use whatever is needed to solve your problem, not please data structure.
Frequently Asked Questions¶
- Yet another graph library?
- The goal of
GraphI
is not to be another graph library, but to provide an intuitive graph interface. Working with complex graphs should be as easy for you as working with any other primitive type. - Where are all the algorithms?
First and foremost,
GraphI
is designed for you to work on graph data instead of pre-sliced storybook data.GraphI
implements only algorithms that- are fundamental building blocks for advanced algorithms, and/or
- benefit from knowledge of internal data structures.
- What about performance?
At its core,
GraphI
uses Python’s native, highly optimized data structures. For any non-trivial graph algorithm, the provided performance is more than sufficient.From our experience, performance critical code is best run with PyPy. This will not just optimize isolated pieces, but the actual combination of your algorithm and
GraphI
as a whole.