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data-analysis-python

Analyze political datasets with Python econometric modeling and publication-ready outputs.

5|1|Updated Feb 10, 2026
Search Tags:#python#pandas#data-analysis#econometrics#causal-inference#political-science#panel-data
One-click install
npx skills add https://github.com/mgaldino/agents-workflow --skill data-analysis-python
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis-python
Source: https://github.com/mgaldino/agents-workflow/tree/main/skills-docs/data-analysis-python
Command: npx skills add https://github.com/mgaldino/agents-workflow --skill data-analysis-python

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a structured workflow to perform data analysis in Python for political science research, covering data import, cleaning, econometric modeling, and results presentation.

Core Features & Use Cases

  • Data loading and cleaning with pandas
  • Econometric modeling with statsmodels, linearmodels, and causalinference
  • Presentation of results via stargazer-style tables and matplotlib/seaborn visualizations
  • Support for cross-sectional and panel-data analyses across political science, IR, and econometrics applications

Quick Start

Provide your dataset path or description and run the predefined analysis workflow to generate results.

Frequently Asked Questions about data-analysis-python

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run panel data econometric modeling in Python for political science research?▼

This Skill performs Python-based data ingestion and cleaning with pandas, then applies econometric modeling using statsmodels and linearmodels to cross-sectional and panel datasets for political science research.

What's the best way to generate publication-ready regression tables from pandas dataframes?▼

Publication-ready regression tables are generated from pandas dataframes using stargazer-style outputs alongside matplotlib and seaborn visualizations, producing presentation-quality results for econometric models.

Can I use statsmodels and linearmodels for causal inference on cross-sectional political data?▼

Statsmodels and linearmodels support causal inference on cross-sectional political data, allowing you to estimate treatment effects and derive replicable insights across political science and international relations datasets.

How do I clean and ingest political science datasets for econometric analysis in Python?▼

Cleaning and ingesting political science datasets for econometric analysis in Python involves using pandas to load raw data, handle missing values, and structure cross-sectional or panel formats for downstream modeling.

Does this Python data analysis workflow support both panel data and cross-sectional econometric models?▼

This Python data analysis workflow supports both panel data and cross-sectional econometric models, applying causalinference libraries and linearmodels to derive replicable insights across political science applications.

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