Exporting to Pandas dataframes

Raphtory enables a powerful table → graph → table workflow: ingest data from DataFrames, apply graph algorithms and temporal operations, then export the results back to DataFrames for downstream ML pipelines and data tools.

This makes Raphtory a natural extension of your existing data science workflow. Use to_df() on Nodes and Edges to convert your graph data back into tabular format.

Node Dataframe

To explore the use of to_df() on the nodes we first call the function with default parameters. This exports only the latest property updates and utilises epoch timestamps - the output from this can be seen below.

To demonstrate flags, we call to_df() again, this time enabling include_property_history and convert_datetime. The output for this can also be seen below.

Edge Dataframe

Exporting to an edge dataframe via to_df() generally works the same as for the nodes. However, by default this will export the property history for each edge, split by edge layer. This is because to_df() has an alternative explode flag to view each update individually (which will then ignore include_property_history).

In the below example we first create a subgraph of the monkey interactions, selecting ANGELE and FELIPE as the monkeys we are interested in. This isn't a required step, but helps to demonstrate the export of GraphViews.

Then we call to_df() on the subgraph edges, setting no flags. In the output you can see the property history for each interaction type (layer) between ANGELE and FELIPE.

Finally, we call to_df() again, turning off the property history and exploding the edges. In the output you can see each interaction that occurred between ANGELE and FELIPE.