pyfixest Skill

pyfixest: fast high-dimensional fixed effects estimation for Python. Covers OLS, Poisson, and IV regression with multi-way fixed effects; difference-in-differences estimators (TWFE, did2s, lpdid, Sun-Abraham); clustered standard errors; wild bootstrap; and publication output (etable regression tables, coefplot, iplot event study plots). Use when running fixed effects regressions, difference-in-differences designs, Poisson count models with FE, or producing publication-ready regression tables. For panel random/between effects, use linearmodels; for GLM/time series without FE, use statsmodels.

Comprehensive skill for fixed effects regression, instrumental variables, and difference-in-differences estimation with pyfixest. Use decision trees below to find the right guidance, then load detailed references.

What is pyfixest?

pyfixest is a Python implementation of the R fixest package (Berge, Butts, & McDermott, 2026):

• Fast: Multi-way FE demeaning via alternating projections; since 0.60 the default backend is a compiled Rust extension shipped in the wheel (numba is now optional), with PyTorch/CuPy/SciPy LSMR backends available for GPU/large problems
• Concise formula syntax: Fixed effects after |, IV after second |, multiple estimation via sw()/csw()
• Modern DiD: Built-in did2s, local projections DiD (lpdid), and Sun-Abraham saturated estimator
• Flexible inference: Switch SE types post-estimation; wild bootstrap, randomization inference, CCV
• Publication output: etable() for regression tables, coefplot() and iplot() for coefficient visualization

Version Notes

This skill targets pyfixest 0.60.0 (pre-installed in the DAAF container). Only three release lines sit between the previous target and this one — 0.40.0/0.40.1, 0.50.0/0.50.1, and 0.60.0 (there are no 0.41–0.49 releases). The cumulative changes that matter for existing code, oldest first:

0.40.0 (aligned with R fixest 0.13) — silent result changes:

• Default standard errors changed from "cluster by first FE" to "iid" — old code silently produces different SEs
• ssc() arguments renamed: adj → k_adj, fixef_k → k_fixef, cluster_adj → G_adj, cluster_df → G_df; the old names still work but raise a DeprecationWarning (verified live). The option value "nested" was renamed to "nonnested" — passing "nested" now errors
• fixef_rm default changed from "none" to "singleton" — singletons now dropped by default
• Multicollinearity tolerance reduced from 1e-10 to 1e-09; Gelbach decompose() reworked to return a GelbachDecomposition object (arg param → decomp_var)

0.50.0 — table backend + FE-GLMs:

• etable()/dtable() moved to the maketables backend (the great_tables dependency was replaced). The public etable() API is unchanged and, in this install, the default and type="gt" still return a great_tables.gt.GT object rendered via maketables (verified live)
• feglm() now supports fixed effects (logit/probit/gaussian) — the pre-0.50 "feglm does not support FE" limitation is gone (verified live: pf.feglm("ybin ~ X1 | f1", family="logit") fits)

0.60.0 — Rust demeaner default, numba optional, typed backend API:

• The Rust MAP demeaner is now the default; numba is an optional extra (pyfixest[numba]). numba is still used for MapDemeaner(backend="numba") and fast randomization inference (ritest(..., choose_algorithm="fast")). DAAF's image ships numba transitively (via umap-learn/wildboottest), so the numba paths remain available here
• The loose demeaner_backend, fixef_tol, and fixef_maxiter kwargs are deprecated in favor of a typed demeaner=MapDemeaner(...) / LsmrDemeaner(...) object; the old kwargs still work but raise a DeprecationWarning (verified live)
• etable(type="typst") added; fepois(offset=...) exposure terms added

See gotchas.md for the full cumulative breaking-change detail and migration notes.

How to Use This Skill

Reference File Structure

Each topic in ./references/ contains focused documentation:

File	Purpose	When to Read
quickstart.md	Installation, first regression, formula syntax	Starting with pyfixest
fixed-effects.md	Multi-way FE, SE types, clustering, wild bootstrap	FE models and inference
instrumental-variables.md	IV syntax, first stage, weak instruments	IV/2SLS estimation
difference-in-differences.md	TWFE, did2s, lpdid, Sun-Abraham, event studies	DiD designs
tables-and-plots.md	etable, coefplot, iplot, dtable	Reporting results
advanced-inference.md	Wild bootstrap, randomization inference, MHT corrections, Gelbach	Advanced statistical inference
integration.md	Multiple estimation, Poisson, GLM, marginaleffects, online learning	Advanced features
gotchas.md	Common errors, 0.40→0.60 cumulative breaking changes, fixest vs pyfixest	Debugging issues

Reading Order

1. New to pyfixest? Start with quickstart.md then fixed-effects.md
2. Running DiD? Read quickstart.md, then difference-in-differences.md
3. Need IV? Read quickstart.md, then instrumental-variables.md
4. Making tables? Check tables-and-plots.md
5. Coming from R fixest? Read quickstart.md then gotchas.md

The reference-file routing in this skill applies to advisory and brainstorming turns as much as implementation. Recommending an approach, reviewing a plan, or answering a question that touches a routed topic calls for reading the routed reference file just as much as writing code does — the reference files carry curated caveats and environment-specific constraints that this overview and general knowledge lack.

Related Skills

Skill	Relationship
data-scientist	Methodology guidance — load for "why and when" behind methods
statsmodels	Complement for non-FE models: GLM, time series, diagnostics
linearmodels	Random effects, GMM, system estimation when pyfixest's FE-only approach is insufficient
svy	Survey-weighted regression with complex survey designs. pyfixest's clustered SEs account for within-group correlation but do NOT handle full survey design features (stratification, unequal probability weights, FPC). If your data comes from a complex probability survey, use svy for design-based inference
polars	Data preparation before estimation (convert to pandas before passing to pyfixest)
plotnine	Custom visualization beyond pyfixest's built-in plots

Quick Decision Trees

"I need to run a regression"

What kind of regression?
├─ OLS with fixed effects → ./references/quickstart.md
├─ OLS without fixed effects → ./references/quickstart.md
├─ IV / 2SLS → ./references/instrumental-variables.md
├─ Poisson (count data) → ./references/integration.md
├─ Logit / Probit → ./references/integration.md
├─ Quantile regression → ./references/integration.md
└─ Multiple models at once → ./references/integration.md

"I need difference-in-differences"

DiD design?
├─ Simple 2x2 DiD (one treatment date) → ./references/difference-in-differences.md
├─ Staggered treatment timing → ./references/difference-in-differences.md
│   ├─ did2s (Gardner imputation) → ./references/difference-in-differences.md
│   ├─ Local projections DiD → ./references/difference-in-differences.md
│   └─ Sun-Abraham saturated → ./references/difference-in-differences.md
├─ Event study plot → ./references/difference-in-differences.md
├─ Visualize treatment patterns → ./references/difference-in-differences.md
└─ Parallel trends assessment → ./references/difference-in-differences.md

"I need to choose standard errors"

What inference?
├─ Heteroskedasticity-robust (HC1) → ./references/fixed-effects.md
├─ Clustered (one-way / two-way) → ./references/fixed-effects.md
├─ Few clusters (<20) → ./references/advanced-inference.md
│   └─ Wild cluster bootstrap → ./references/advanced-inference.md
├─ HAC / Newey-West → ./references/fixed-effects.md
├─ Randomization inference → ./references/advanced-inference.md
├─ Multiple hypothesis testing → ./references/advanced-inference.md
└─ Causal cluster variance (CCV) → ./references/advanced-inference.md

"I need to present results"

Presenting results?
├─ Regression table (multiple models) → ./references/tables-and-plots.md
├─ Coefficient plot → ./references/tables-and-plots.md
├─ Event study plot → ./references/tables-and-plots.md
├─ Descriptive statistics table → ./references/tables-and-plots.md
└─ LaTeX output → ./references/tables-and-plots.md

"Something isn't working"

Having issues?
├─ Different results from old code → ./references/gotchas.md
├─ feglm / GLM with fixed effects → ./references/gotchas.md
├─ numba / demeaner backend questions → ./references/gotchas.md
├─ CRV3 memory issues → ./references/gotchas.md
├─ Poisson convergence → ./references/gotchas.md
├─ Formula parsing errors → ./references/gotchas.md
├─ R fixest vs pyfixest differences → ./references/gotchas.md
└─ Singleton warnings → ./references/gotchas.md

File-First Execution in Research Workflows

Important: In data research pipelines (see CLAUDE.md), pyfixest regressions are executed through script files, not interactively. This ensures auditability and reproducibility.

The pattern:

1. Write regression code to scripts/stage8_analysis/{step}_{task-name}.py
2. Execute via Bash with automatic output capture wrapper script
3. Validation results get automatically embedded in scripts as comments
4. If failed, create versioned copy for fixes

Closely read agent_reference/SCRIPT_EXECUTION_REFERENCE.md for the mandatory file-first execution protocol covering complete code file writing, output capture, and file versioning rules. All regression scripts must follow the Inline Audit Trail (IAT) standard — see agent_reference/INLINE_AUDIT_TRAIL.md. For regression code, document model specification choices (why this estimator, why this clustering level, what identifying assumptions) with # INTENT:, # REASONING:, and # ASSUMES: comments.

See:

• agent_reference/WORKFLOW_PHASE4_ANALYSIS.md — Stage 8 (Analysis & Visualization)
• agent_reference/INLINE_AUDIT_TRAIL.md — IAT documentation standard

The examples below show pyfixest syntax. In research workflows, wrap them in scripts following the file-first pattern.

---

Quick Reference

Essential Import

import pyfixest as pf

Core Estimation Functions

Function	Purpose
pf.feols("Y ~ X | fe", data=df)	OLS with fixed effects
pf.fepois("Y ~ X | fe", data=df)	Poisson with fixed effects
pf.feglm("Y ~ X | fe", data=df, family="logit")	GLM (logit/probit/gaussian) with fixed effects (since 0.50)
pf.feols("Y ~ X2 | fe | X1 ~ Z1", data=df)	IV / 2SLS
pf.did2s(data, yname, first_stage, second_stage, treatment, cluster)	Gardner (2022) DiD
pf.event_study(data, yname, idname, tname, gname, estimator)	Unified event study
pf.lpdid(data, yname, idname, tname, gname)	Local projections DiD

Formula Syntax Quick Reference

Pattern	Meaning	Example
Y ~ X1 + X2	No FE	"wage ~ educ + exper"
Y ~ X | fe1 + fe2	With FE	"wage ~ educ | state + year"
Y ~ X | fe | endog ~ inst	FE + IV	"wage ~ exper | state | educ ~ college_prox"
i(factor, ref=val)	Categorical with ref	"Y ~ i(year, ref=2000) | state"
sw(X1, X2)	Stepwise alternatives	"Y ~ sw(educ, exper) | state"
csw0(X1, X2)	Cumulative stepwise	"Y ~ csw0(educ, exper) | state"
Y1 + Y2 ~ X	Multiple outcomes	"wage + hours ~ educ | state"

Post-Estimation Essentials

fit = pf.feols("Y ~ X1 + X2 | fe", data=df)

fit.summary()                          # Print results
fit.tidy()                             # DataFrame of coefficients
fit.vcov("hetero")                     # Re-estimate with robust SEs (requires arg)
fit.vcov({"CRV1": "state"})            # Re-estimate with clustered SEs
fit.coef()                             # Coefficient values
fit.se()                               # Standard errors
fit.confint()                          # Confidence intervals
fit.predict()                          # Fitted values
fit.resid()                            # Residuals
fit.fixef()                            # Dict of FE name → numpy array (not a DataFrame)

Reporting

pf.etable([fit1, fit2, fit3])          # Regression table
pf.coefplot([fit1, fit2])              # Coefficient plot
pf.iplot(fit)                          # Event study / interaction plot
pf.panelview(data, unit, time, treat)  # Treatment pattern visualization

Topic Index

Topic	Reference File
Installation	./references/quickstart.md
First regression	./references/quickstart.md
Formula syntax	./references/quickstart.md
SE comparison table	./references/quickstart.md
Multi-way fixed effects	./references/fixed-effects.md
Standard error types	./references/fixed-effects.md
Clustered SEs	./references/fixed-effects.md
HAC / Newey-West	./references/fixed-effects.md
Backend options	./references/fixed-effects.md
IV formula syntax	./references/instrumental-variables.md
First-stage diagnostics	./references/instrumental-variables.md
Weak instrument tests	./references/instrumental-variables.md
TWFE	./references/difference-in-differences.md
did2s	./references/difference-in-differences.md
Local projections DiD	./references/difference-in-differences.md
Sun-Abraham	./references/difference-in-differences.md
Event study plots	./references/difference-in-differences.md
Parallel trends	./references/difference-in-differences.md
panelview	./references/difference-in-differences.md
etable	./references/tables-and-plots.md
coefplot	./references/tables-and-plots.md
iplot	./references/tables-and-plots.md
dtable	./references/tables-and-plots.md
Wild cluster bootstrap	./references/advanced-inference.md
Randomization inference	./references/advanced-inference.md
Multiple testing corrections	./references/advanced-inference.md
Gelbach decomposition	./references/advanced-inference.md
CCV	./references/advanced-inference.md
Multiple estimation	./references/integration.md
Poisson regression	./references/integration.md
GLM (logit/probit)	./references/integration.md
Quantile regression	./references/integration.md
marginaleffects	./references/integration.md
Online learning	./references/integration.md
Performance tuning	./references/integration.md
Polars DataFrame input	./references/gotchas.md
Polars-to-pandas conversion	./references/quickstart.md
DiD clustering level	./references/difference-in-differences.md
0.40→0.60 breaking changes	./references/gotchas.md
feglm with fixed effects	./references/gotchas.md
numba / Rust demeaner backend	./references/gotchas.md
Formula parsing	./references/gotchas.md
R fixest differences	./references/gotchas.md

Citation

When this library is used as a primary analytical tool, include in the report's Software & Tools references:

Berge, L., Butts, K., & McDermott, G. (2026). pyfixest: Fast high-dimensional fixed effects estimation [Computer software]. Based on fixest (R).

Cite when: pyfixest is used for regression estimation (OLS, Poisson, IV) or difference-in-differences analysis. Do not cite when: Only imported but no estimation performed.

For method-specific citations (e.g., individual DiD estimators or inference techniques), consult the reference files in this skill and agent_reference/CITATION_REFERENCE.md.