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
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
GraphIis 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,
GraphIis designed for you to work on graph data instead of pre-sliced storybook data.
GraphIimplements 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,
GraphIuses 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
GraphIas a whole.