Python Panel Data
- 138 installs
- 590 repo stars
- Updated June 23, 2026
- meleantonio/awesome-econ-ai-stuff
Load longitudinal firm, household, or country panels, set entity-time indexes, run fixed effects models, and export tidy tables for papers or APIs.
About
Guides Python panel-data econometrics: loading long-format datasets, setting multi-index entity-time keys, running fixed-effects and related estimators, validating coverage, and exporting regression tables for research pipelines and downstream visualization skills.
- Panel index setup
- Fixed effects workflows
- Balance and missing checks
- Tidy regression exports
- Reproducible econ pipelines
Python Panel Data by the numbers
- 138 all-time installs (skills.sh)
- +6 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #87 of 290 Python skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/meleantonio/awesome-econ-ai-stuff --skill python-panel-dataAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 138 |
|---|---|
| repo stars | ★ 590 |
| Last updated | June 23, 2026 |
| Repository | meleantonio/awesome-econ-ai-stuff ↗ |
What it does
Load longitudinal firm, household, or country panels, set entity-time indexes, run fixed effects models, and export tidy tables for papers or APIs.
Files
Python Panel Data
Purpose
This skill helps economists run panel data models in Python using pandas, statsmodels, and linearmodels, with correct fixed effects, clustering, and diagnostics.
When to Use
- Estimating fixed effects or random effects models
- Running difference-in-differences on panel data
- Creating regression tables and plots in Python
Instructions
Follow these steps to complete the task:
Step 1: Understand the Context
Before generating any code, ask the user:
- What is the unit of observation and panel identifiers?
- Which outcomes and regressors are required?
- What fixed effects or time effects are needed?
- How should standard errors be clustered?
Step 2: Generate the Output
Based on the context, generate Python code that:
1. Loads and cleans the data with pandas 2. Sets a MultiIndex for panel structure 3. Fits the model using linearmodels.PanelOLS or RandomEffects 4. Outputs results in a readable table and optional LaTeX
Step 3: Verify and Explain
After generating output:
- Interpret key coefficients
- Note assumptions (strict exogeneity, parallel trends, etc.)
- Suggest robustness checks (alternative clustering, placebo tests)
Example Prompts
- "Run a two-way fixed effects model with firm and year effects"
- "Estimate a DiD using state and year fixed effects"
- "Export panel regression results to LaTeX"
Example Output
# ============================================
# Panel Data Analysis in Python
# ============================================
import pandas as pd
from linearmodels.panel import PanelOLS
# Load data
df = pd.read_csv("panel_data.csv")
# Set panel index
df = df.set_index(["firm_id", "year"])
# Create treatment indicator
df["treat_post"] = df["treated"] * df["post"]
# Two-way fixed effects model
model = PanelOLS.from_formula(
"outcome ~ 1 + treat_post + EntityEffects + TimeEffects",
data=df
)
results = model.fit(cov_type="clustered", cluster_entity=True)
print(results.summary)Requirements
Software
- Python 3.10+
Packages
pandaslinearmodelsstatsmodels
Install with:
pip install pandas linearmodels statsmodelsBest Practices
1. Always verify panel identifiers and balanced vs unbalanced panels 2. Cluster standard errors at the appropriate level 3. Check for missing data before estimation
Common Pitfalls
- Failing to set a proper panel index
- Using pooled OLS when fixed effects are required
- Misinterpreting coefficients without accounting for fixed effects
References
- linearmodels documentation
- statsmodels documentation
- Wooldridge (2010) Econometric Analysis of Cross Section and Panel Data
Changelog
v1.0.0
- Initial release
Python Panel Data
Purpose
This skill helps economists run panel data models in Python using pandas, statsmodels, and linearmodels, with correct fixed effects, clustering, and diagnostics.
When to Use
- Estimating fixed effects or random effects models
- Running difference-in-differences on panel data
- Creating regression tables and plots in Python
Instructions
Follow these steps to complete the task:
Step 1: Understand the Context
Before generating any code, ask the user:
- What is the unit of observation and panel identifiers?
- Which outcomes and regressors are required?
- What fixed effects or time effects are needed?
- How should standard errors be clustered?
Step 2: Generate the Output
Based on the context, generate Python code that:
1. Loads and cleans the data with pandas 2. Sets a MultiIndex for panel structure 3. Fits the model using linearmodels.PanelOLS or RandomEffects 4. Outputs results in a readable table and optional LaTeX
Step 3: Verify and Explain
After generating output:
- Interpret key coefficients
- Note assumptions (strict exogeneity, parallel trends, etc.)
- Suggest robustness checks (alternative clustering, placebo tests)
Example Prompts
- "Run a two-way fixed effects model with firm and year effects"
- "Estimate a DiD using state and year fixed effects"
- "Export panel regression results to LaTeX"
Example Output
# ============================================
# Panel Data Analysis in Python
# ============================================
import pandas as pd
from linearmodels.panel import PanelOLS
# Load data
df = pd.read_csv("panel_data.csv")
# Set panel index
df = df.set_index(["firm_id", "year"])
# Create treatment indicator
df["treat_post"] = df["treated"] * df["post"]
# Two-way fixed effects model
model = PanelOLS.from_formula(
"outcome ~ 1 + treat_post + EntityEffects + TimeEffects",
data=df
)
results = model.fit(cov_type="clustered", cluster_entity=True)
print(results.summary)Requirements
Software
- Python 3.10+
Packages
pandaslinearmodelsstatsmodels
Install with:
pip install pandas linearmodels statsmodelsBest Practices
1. Always verify panel identifiers and balanced vs unbalanced panels 2. Cluster standard errors at the appropriate level 3. Check for missing data before estimation
Common Pitfalls
- Failing to set a proper panel index
- Using pooled OLS when fixed effects are required
- Misinterpreting coefficients without accounting for fixed effects
References
- linearmodels documentation
- statsmodels documentation
- Wooldridge (2010) Econometric Analysis of Cross Section and Panel Data
Changelog
v1.0.0
- Initial release
Related skills
Python ExecutorExecute Python code and scripts from agents and workflows.71.7k680
Python ExecutorSafely run Python code from within Claude, Cursor or other agents without leaving the IDE.45.7k680
Python Performance OptimizationProfile slow Python code, locate bottlenecks, and apply targeted optimizations that reduce latency and memory usage.30.5k38.5kAsync Python PatternsLearn and apply battle-tested async patterns when writing concurrent Python services, agents, and data pipelines.14.1k38.5kUv Package ManagerManage Python dependencies with maximum speed, reproducibility, and CI/CD compatibility using the uv tool.12.1k38.5k
Python PatternsEnsure every line of Python they write follows idiomatic patterns, PEP 8, and battle-tested practices that keep code readable and maintainable.7.8k238k