Develop branch:
Master branch:
This is very much a work in progress.
Neo4J's graph query language, "Cypher", seems to be emerging as the de facto standard. This is a project to provide an implementation of Cypher for Python. The plan is for it to consist of two parts. The first will be a parser that matches Neo4J's Cypher implementation, which produces a syntax tree from arbitrary Cypher queries. The second will be a bridge allowing graphs (such as might be generated from NetworkX) to be queried in Cypher.
Use this to query graphs using the Cypher language. The project is very young, but under active development. Up-to-date information will be kept on the project wiki.
In the beginning, we had tables. Tables are perfect for a very wide range of data. But increasingly, we're getting more interested in representing data that is not table-like. The canonical example of this is a social network, which consists of people who have various relationships to each other. For this sort of data, it's much cleaner and more intuitive to use graphs with vertices (nodes) and edges (arrows), representing the various objects and their relationships.
Several excellent graph databases have emerged. In my opinion, the dominant player in the graph database space is Neo4J. A large part of its success is due to its excellent query language, which is called "Cypher". Cypher allows you to build queries that actually look like the structures you're trying to find. For example, if you want to find all the people who like the move "Star Wars", you might end up with a query that looks like this:
MATCH (p:Person)-[:LIKES]->(m:Movie {title:"Star Wars"}) RETURN p
This syntax is very nice and clear, and the learning curve is not steep, especially for Cypher's more basic types of queries. So far, so good.
The trouble is that it would be awfully nice to be able to use Cypher to query other graph implementations. For example, I frequently end up loading a graph-like structure in memory using a library like NetworkX. Searching the graph requires me to write specialized little functions that are customized for my application. When I want to do a different kind of search, I have to write a new function.
A much better solution would be to have a generic set of functions that translates Cypher queries into searches for NetworkX or other graph systems. Then I could stop writing one-off ad-hoc code and concentrate on the real work. That's what "Cypher for Python" is meant to do. You simply provide your Cypher query as a string, and pass it to your graph. You get back the results of that Cypher query with no extra futzing around.
The roadmap is to get a basic parser written that supports a small subset of Cypher, and build out additional features incrementally. As of now, it supports queries involving MATCH, CREATE, RETURN, WHERE, directed edges (which may contain labels), and nodes with class names and nested JSON documents. I'm trying to design everything so that it will be perfectly straightforward to design subclasses that will work with arbitrary graph storage schemes other than NetworkX.
Check the github wiki for the project's current status, roadmap, bugs, etc. Sphinx documentation will be available right here on Github as the project develops.