pypath VS pathway

If you are just looking for edgelists of any kind you can download them from a number of resources, like OmniPath (https://omnipathdb.org/). You can query data a lot of ways, through python, R, read them directly into Cytoscape, or use the webservice (https://github.com/saezlab/pypath/blob/master/webservice.rst) like the following example, where the first two columns contain the source and target nodes:

We understood how streaming databases differ from traditional databases, stream processing engines, conventional analytics databases, or OLAP databases. Now let’s focus on when and why we can use stream data processing frameworks for Python as an alternative to streaming databases. Python is the go-to language for data science and machine learning. There are some stream-processing libraries and frameworks in Python such as Bytewax, Quix, GlassFlow, Pathway. They have been developed to cope with the challenges Python Engineers face with Apache Kafka or Flink since they do not natively support Python.

Maintain a data snapshot to observe variations in sales prices over time, as Pathway provides a built-in feature to compute differences between two alterations.

Pathway (https://github.com/pathwaycom/pathway) is a data processing framework we are developing that unifies stream and batch processing of large datasets. It lets developers concentrate on writing the data processing logic, without worrying about tracking changes to data and updating the results. The same code can then be run on batch data (e.g. during testing) or on real-time data streams (i.e. online query processing)

In the LLM app, Pathway allows concentrating on prompt building and querying the LLM APIs as if the corpus of documents were static, while all updates to it are handled by the framework itself.