How does it handle survey weights?

It creates weight-aware wrappers around pandas objects, ensuring that operations like .mean(), .median(), and .sum() automatically account for the weights provided in the dataset.

MicroDF is a Python library that provides weighted pandas DataFrames and Series specifically designed for analyzing survey microdata such as income and household surveys.

How do I calculate inequality metrics using this skill?

You can use built-in methods like .gini() or .top_x_pct_share() on weighted income data directly within a MicroDataFrame or MicroSeries.

Is this skill only for PolicyEngine developers?

While optimized for the PolicyEngine ecosystem, it is highly useful for any analyst working with weighted survey data for poverty, inequality, or social policy research.

PolicyEngine Data Science

Name: PolicyEngine Data Science
Author: PolicyEngine

byPolicyEngine

•

Data Science & ML

Analyzes survey microdata using weighted pandas DataFrames to calculate inequality, poverty, and distributional metrics.

About

PolicyEngine Data Science provides specialized tools for analysts and developers to perform sophisticated microsimulation and survey data analysis. By leveraging the MicroDF library, this skill enables Claude to work with weighted pandas DataFrames and Series, ensuring that statistical calculations like Gini coefficients, poverty rates, and income distributions are accurate and weight-aware. It is specifically designed to facilitate the evaluation of tax and benefit policies, allowing for seamless integration between simulation results and deep distributional analysis.

Key Features

Native integration with PolicyEngine US simulation data patterns
2 GitHub stars
Weighted pandas DataFrames and Series (MicroDF)
Automated inequality metrics including Gini coefficients and top income shares
Advanced poverty analysis tools for rates, gaps, and deep poverty calculations
Distributional analysis with weighted quantiles, deciles, and quintiles

Use Cases

Generating weighted statistical reports from household-level microdata
Evaluating the impact of tax and benefit reforms on income distribution
Analyzing poverty rates and gaps across demographic groups using survey data

About

Key Features

Native integration with PolicyEngine US simulation data patterns
2 GitHub stars
Weighted pandas DataFrames and Series (MicroDF)
Automated inequality metrics including Gini coefficients and top income shares
Advanced poverty analysis tools for rates, gaps, and deep poverty calculations
Distributional analysis with weighted quantiles, deciles, and quintiles

Use Cases

Generating weighted statistical reports from household-level microdata
Evaluating the impact of tax and benefit reforms on income distribution
Analyzing poverty rates and gaps across demographic groups using survey data