Python & R programmatic access

Extend the project without forking the data.

You are not trapped when AddMaple does not have a specialist method. Connect Python or R notebooks to the live project with a product API key, run the analysis you need, and write useful columns back without creating a second copy of the study.

Work in the tools your analysis requires.

Data transfers via Apache Arrow: fast, type-safe, and without a CSV export step. Load data into pandas, Polars, or R tibbles for custom scoring, NLP, statistical models, or batch workflows.

Send the result back into the research workflow.

Persist derived columns on the project so they appear in charts, banners, and Insight Hubs like any other variable. AddMaple remains the shared space for exploration and delivery; Python and R extend it when a specific package or bespoke computation is needed.

Keep specialist work connected to shared interpretation.

Notebook access is useful when a team needs a package, a bespoke model, or batch scoring at scale. It should not create a second, private version of the study. Writing the useful result back to the project lets other analysts inspect it, combine it with existing segments or weights, and carry it into the outputs that stakeholders already use.