policyengine-python-client

name	policyengine-python-client
description	ONLY use this skill when users explicitly ask about the PolicyEngine Python package installation, REST API endpoints, API authentication, rate limits, or policyengine.py client library. DO NOT use for household benefit/tax calculations — ALWAYS use policyengine-us or policyengine-uk instead. This skill is about the API/client tooling itself, not about calculating benefits or taxes.

PolicyEngine Python Client

IMPORTANT: Always use the current year (2026) in situation dictionaries and calculate() calls, not 2024 or 2025.

This skill covers programmatic access to PolicyEngine for analysts and researchers.

Installation

# Install the Python client
pip install policyengine

# Or for local development
pip install policyengine-us  # Just the US model (offline)

Quick Start: Python Client

from policyengine import Simulation

# Create a household
household = {
    "people": {
        "you": {
            "age": {"2026": 30},
            "employment_income": {"2026": 50000}
        }
    },
    "households": {
        "your household": {
            "members": ["you"],
            "state_name": {"2026": "CA"}
        }
    }
}

# Run simulation
sim = Simulation(situation=household, country_id="us")
income_tax = sim.calculate("income_tax", "2026")

For Users: Why Use Python?

Web app limitations:

• ✅ Great for exploring policies interactively
• ❌ Can't analyze many households at once
• ❌ Can't automate repetitive analyses
• ❌ Limited customization of charts

Python benefits:

• ✅ Analyze thousands of households in batch
• ✅ Automate regular policy analysis
• ✅ Create custom visualizations
• ✅ Integrate with other data sources
• ✅ Reproducible research

For Analysts: Common Workflows

Workflow 1: Calculate Your Own Taxes

from policyengine import Simulation

# Your household (more complex than web app)
household = {
    "people": {
        "you": {
            "age": {"2026": 35},
            "employment_income": {"2026": 75000},
            "qualified_dividend_income": {"2026": 5000},
            "charitable_cash_donations": {"2026": 3000}
        },
        "spouse": {
            "age": {"2026": 33},
            "employment_income": {"2026": 60000}
        },
        "child1": {"age": {"2026": 8}},
        "child2": {"age": {"2026": 5}}
    },
    # ... entities setup (see policyengine-us-skill)
}

sim = Simulation(situation=household, country_id="us")

# Calculate specific values
federal_income_tax = sim.calculate("income_tax", "2026")
state_income_tax = sim.calculate("state_income_tax", "2026")
ctc = sim.calculate("ctc", "2026")
eitc = sim.calculate("eitc", "2026")

print(f"Federal income tax: ${federal_income_tax:,.0f}")
print()
()
()

Workflow 2: Analyze a Policy Reform

from policyengine import Simulation

# Define reform (increase CTC to $5,000)
reform = {
    "gov.irs.credits.ctc.amount.base_amount": {
        "2026-01-01.2100-12-31": 5000
    }
}

# Compare baseline vs reform
household = create_household()  # Your household definition

sim_baseline = Simulation(situation=household, country_id="us")
sim_reform = Simulation(situation=household, country_id="us", reform=reform)

ctc_baseline = sim_baseline.calculate("ctc", "2026")
ctc_reform = sim_reform.calculate("ctc", "2026")

print(f"CTC baseline: ${ctc_baseline:,.0f}")
print(f"CTC reform: ${ctc_reform:,.0f}")
print(f"Increase: ${ctc_reform - ctc_baseline:,.0f}")

Workflow 3: Batch Analysis

import pandas as pd
from policyengine import Simulation

# Analyze multiple households
households = [
    {"income": 30000, "children": 0},
    {"income": 50000, "children": 2},
    {"income": 100000, "children": 3},
]

results = []
for h in households:
    situation = create_household(income=h["income"], num_children=h["children"])
    sim = Simulation(situation=situation, country_id="us")

    results.append({
        "income": h["income"],
        "children": h["children"],
        "income_tax": sim.calculate("income_tax", "2026"),
        "ctc": sim.calculate("ctc", "2026"),
        "eitc": sim.calculate("eitc", "2026")
    })

df = pd.DataFrame(results)
print(df)

Using the REST API Directly

Authentication

Public access:

• 100 requests per minute (unauthenticated)
• No API key needed for basic use

Authenticated access:

• 1,000 requests per minute
• Contact [e-mail] for API key

Key Endpoints

Calculate household impact:

import requests

url = "https://api.policyengine.org/us/calculate"
payload = {
    "household": household_dict,
    "policy_id": reform_id  # or None for baseline
}

response = requests.post(url, json=payload)
result = response.json()

Get policy details:

# Get policy metadata
response = requests.get("https://api.policyengine.org/us/policy/12345")
policy = response.json()

Get parameter values:

# Get current parameter value
response = requests.get(
    "https://api.policyengine.org/us/parameter/gov.irs.credits.ctc.amount.base_amount"
)
parameter = response.json()

For Full API Documentation

OpenAPI spec: https://api.policyengine.org/docs

To explore:

# View all endpoints
curl https://api.policyengine.org/docs

# Test calculate endpoint
curl -X POST https://api.policyengine.org/us/calculate \
  -H "Content-Type: application/json" \
  -d '{"household": {...}}'

Limitations and Considerations

Rate Limits

Unauthenticated:

• 100 requests/minute
• Good for exploratory analysis

Authenticated:

• 1,000 requests/minute
• Required for production use

Data Privacy

• PolicyEngine does not store household data
• All calculations happen server-side and are not logged
• Reform URLs are public (don't include personal info in reforms)

Performance

API calls:

• Simple household: ~200-500ms
• Population impact: ~5-30 seconds (varies by reform)
• Use caching for repeated calculations

Local simulation (policyengine-us):

• Faster for batch analysis
• No rate limits
• No network dependency
• Limited to one country per package

Choosing Local vs API

Use Local (policyengine-us package)

When:

• Batch analysis of many households
• Need offline capability
• Analyzing parameter sweeps (axes)
• Development/testing

Install:

pip install policyengine-us  # US only
pip install policyengine-uk  # UK only

Example:

from policyengine_us import Simulation

# Works offline
sim = Simulation(situation=household)

Use API (policyengine or requests)

When:

• Multi-country analysis
• Using latest model version
• Don't want to manage dependencies
• Integration with web services

Example:

import requests

# Requires internet
response = requests.post("https://api.policyengine.org/us/calculate", ...)

For Contributors: Understanding the Client

Repository: PolicyEngine/policyengine.py

To see implementation:

# Clone the client
git clone https://github.com/PolicyEngine/policyengine.py

# See the Simulation class
cat policyengine/simulation.py

# See API integration
cat policyengine/api.py

Architecture:

• Simulation class wraps API calls
• calculate() method handles caching
• Transparent fallback between API and local

Advanced: Direct Country Package Usage

For maximum control and performance, use country packages directly:

from policyengine_us import Simulation

# Full control over situation structure
situation = {
    # Complete situation dictionary
    # See policyengine-us-skill for patterns
}

sim = Simulation(situation=situation)
result = sim.calculate("variable_name", 2026)

Benefits:

• No API dependency
• Faster (no network)
• Full access to all variables
• Use axes for parameter sweeps

See policyengine-us-skill for detailed patterns.

Examples and Tutorials

PolicyEngine documentation:

• US: https://policyengine.org/us/docs
• UK: https://policyengine.org/uk/docs

Example notebooks:

• Repository: PolicyEngine/analysis-notebooks
• See policyengine-analysis-skill for analysis patterns

Community examples:

• Blog posts: policyengine.org/us/research
• GitHub discussions: github.com/PolicyEngine discussions

Getting Help

For usage questions:

• GitHub Discussions: https://github.com/PolicyEngine/policyengine-us/discussions

For bugs:

• File issues in appropriate repo (policyengine-us, policyengine.py, etc.)

For collaboration:

• Email: [e-mail]