expdpy

Explore, analyze and learn panel data — interactively, in Python.

expdpy pairs composable functions that return interactive Plotly figures and publication-quality Great Tables with fixest-style econometrics, a built-in teaching layer that interprets and explains every result, and three no-code apps. It is built for students, teachers and applied researchers alike.

🔍 Explore

Describe and visualize your panel: tables, distributions, missing-value maps, time trends, group comparisons, scatter plots, within/between variation and panel dynamics.

🚀 Launch app · ▶ Open in Colab

🧮 Analyze

Estimate models: fixed / random / correlated random effects, FWL, the Hausman test, robust inference, event-study / DiD, β/σ/club convergence and the Kuznets-waves curve.

🚀 Launch app · ▶ Open in Colab

📚 Learn

See the ideas behind the methods: 9 runnable concept sandboxes where you tune a known truth, a 27-topic explainer index, and a plain-language reading on every result.

🚀 Launch app · ▶ Open in Colab

Try the apps in your browser

No install, no code — the three ExPdPy apps run the whole workflow in your browser: a sample pipeline, point-and-click analysis, sortable tables, and reproducible notebook export. Each is the no-code companion to a docs case study.

🔍 Explore app · 🧮 Analyze app · 📚 Learn app

What’s inside

Explore — descriptive / correlation / extreme-observation tables, histograms and bar charts, time and quantile trends, by-group bar / violin / trend views, a missing-value heatmap, scatter plots with an optional LOESS smoother, the within/between (xtsum) decomposition, per-unit trajectories, panel-structure diagnostics, distribution & transition dynamics, and treat_outliers.

Analyze — OLS with multi-way fixed effects and clustered standard errors via pyfixest; a richer analyze_estimation (stepwise / multiple-outcome, Newey–West & Driscoll–Kraay SEs); pooled / between / fixed / random effects and the correlated-random-effects (Mundlak) estimator; the Hausman test; post-estimation (fixed-effect plots, predictions, Wald joint tests); robust inference (randomization inference, wild cluster bootstrap); Frisch–Waugh–Lovell and coefficient plots; modern event-study / staggered difference-in-differences (did2s, Sun–Abraham, LP-DiD, dynamic TWFE); β-, σ- and club convergence; and the Kuznets-waves curve under pooled / between / within estimators.

Learn — every result speaks plain language: .interpret() gives an associational reading (never a causal claim unless the design supports it) and .explain() / explain(topic) / list_topics() browse 27 concept explainers. Nine concept sandboxes simulate data so you can see and tune a known truth — the first-differences ≈ demeaning ≈ dummies identity, fixed effects, clustering, omitted-variable bias, β/σ/club convergence, and the Kuznets wave.

Bundled datasets — expdpy.data ships ready-to-explore panels: kuznets (the flagship N-shaped Kuznets-curve demo), gapminder, staggered_did (event-study / DiD), productivity and bolivia112_gdppc (convergence). See the kuznets dataset page for the data dictionary.

Installation

Install the latest release from PyPI (random effects, CRE and the Hausman test work out of the box; the apps need the streamlit extra):

pip install expdpy
pip install "expdpy[streamlit]"   # the no-code ExPdPy apps (Streamlit)

Using uv:

uv pip install expdpy
uv pip install "expdpy[streamlit]"

For the latest unreleased version, install straight from the main branch:

pip install "git+https://github.com/cmg777/expdpy.git"

Requires Python 3.10+.

At a glance

The lead example throughout these docs is the bundled kuznets panel (80 countries × 2015–2025): a synthetic dataset whose regional inequality traces an N-shaped Kuznets curve in income — rising, falling, then rising again at very high income.

import expdpy as ex
from expdpy.data import load_kuznets

df = load_kuznets()
# The N-shaped regional Kuznets curve: regional inequality vs (log) GDP per capita
ex.explore_scatter_plot(
    df, x="log_gdp_pc", y="gini_regional", color="continent", size="population", loess=1
).fig

Run a regression and let it explain itself — two-way fixed effects, clustered standard errors, a plain-language reading, and a coefficient plot:

res = ex.analyze_regression_table(
    df,
    dvs="gini_regional",
    idvs=["log_gdp_pc", "log_gdp_pc_sq", "log_gdp_pc_cu"],
    feffects=["country", "year"],
    clusters=["country"],
)
print(res.interpret())            # plain-language, associational reading
ex.analyze_coefficient_plot(res)  # themed coefficient plot with confidence intervals

Learn as you go — concept sandboxes and explainers:

ex.learn_first_differences()        # first differences ≈ demeaning ≈ dummy variables
print(ex.explain("fixed_effects"))  # a concept explainer; ex.list_topics() lists all 27

Head to Explore, Analyze and Learn to see every function in action, or the kuznets dataset page for the data dictionary.

Built on

expdpy stands on the modern Python data and econometrics stack:

• Plotly — interactive figures
• pyfixest — fixed-effects and difference-in-differences estimators
• Great Tables — publication-quality tables
• linearmodels — random / between / correlated random effects and the Hausman test
• Streamlit — the no-code ExPdPy apps

Acknowledgement

expdpy began as a Python port of the excellent ExPanDaR R package by Joachim Gassen and the TRR 266 Accounting for Transparency project, and its foundations remain deeply inspired by that work. Over time, expdpy has grown well beyond the original — fixest-style estimators, event-study / difference-in-differences tools, random- and correlated-random-effects panel models, convergence analysis, and a built-in teaching layer that interprets and explains results — and it will keep evolving.

We are grateful to the ExPanDaR authors. Please cite the original work when using expdpy in research (see CITATION.cff).