policyengine-python-client

Batch policy analysis is enabled through PolicyEngine's Python client by constructing multiple Simulation objects with different household situations and country_id, then invoking calculate to retrieve metrics like income_tax, state_income_tax, ctc, and eitc across all households in a single workflow, scaling far beyond web app capacity.

Yes. Automate reform comparisons by constructing Simulations with a baseline situation and country_id, then running the same simulation with optional reform parameters to calculate and compare outcomes like tax liabilities and credits across scenarios without manual web app steps.

The web app suits interactive exploration; the Python client enables batch analysis, automated workflows, custom visualizations, and integration with external data sources—critical for analyzing thousands of households and repeating analyses programmatically.

The policyengine Python package is the primary dependency; policyengine-us is also required for U.S. policy analysis. Both must be installed to construct Simulations and access the simulation engine's calculation capabilities.

Create a Simulation with the household's situation (income, family composition) and country_id, optionally add a reform, then call calculate to retrieve specific metrics including ctc (Child Tax Credit) and eitc (Earned Income Tax Credit) outcomes.

Yes. Extract calculation results from Simulations and use Python's rich visualization ecosystem to create bespoke charts, dashboards, and reports tailored to your policy analysis needs and distribution analysis requirements.