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 with numba/JAX/GPU backends
• 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 0.40.0, the major release aligning with R fixest 0.13. Breaking changes from earlier versions:

• 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

• fixef_rm

  default changed from

  "none"

  to

  "singleton"

  — singletons now dropped by default
• Multicollinearity tolerance reduced from 1e-10 to 1e-09

How to Use This Skill

Reference File Structure

Each topic in

./references/

quickstart.md

fixed-effects.md

instrumental-variables.md

difference-in-differences.md

tables-and-plots.md

advanced-inference.md

integration.md

gotchas.md

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

Related Skills

data-scientist

statsmodels

linearmodels

svy

svy

polars

plotnine

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 with fixed effects error → ./references/gotchas.md
├─ numba installation problems → ./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

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

agent_reference/INLINE_AUDIT_TRAIL.md

# INTENT:

# REASONING:

# ASSUMES:

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

pf.feols("Y ~ X | fe", data=df)

pf.fepois("Y ~ X | fe", data=df)

pf.feols("Y ~ X2 | fe | X1 ~ Z1", data=df)

pf.did2s(data, yname, first_stage, second_stage, treatment, cluster)

pf.event_study(data, yname, idname, tname, gname, estimator)

pf.lpdid(data, yname, idname, tname, gname)

Formula Syntax Quick Reference

Y ~ X1 + X2

"wage ~ educ + exper"

Y ~ X | fe1 + fe2

"wage ~ educ | state + year"

Y ~ X | fe | endog ~ inst

"wage ~ exper | state | educ ~ college_prox"

i(factor, ref=val)

"Y ~ i(year, ref=2000) | state"

sw(X1, X2)

"Y ~ sw(educ, exper) | state"

csw0(X1, X2)

"Y ~ csw0(educ, exper) | state"

Y1 + Y2 ~ X

"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

./references/quickstart.md

./references/quickstart.md

./references/quickstart.md

./references/quickstart.md

./references/fixed-effects.md

./references/fixed-effects.md

./references/fixed-effects.md

./references/fixed-effects.md

./references/fixed-effects.md

./references/instrumental-variables.md

./references/instrumental-variables.md

./references/instrumental-variables.md

./references/difference-in-differences.md

./references/difference-in-differences.md

./references/difference-in-differences.md

./references/difference-in-differences.md

./references/difference-in-differences.md

./references/difference-in-differences.md

./references/difference-in-differences.md

./references/tables-and-plots.md

./references/tables-and-plots.md

./references/tables-and-plots.md

./references/tables-and-plots.md

./references/advanced-inference.md

./references/advanced-inference.md

./references/advanced-inference.md

./references/advanced-inference.md

./references/advanced-inference.md

./references/integration.md

./references/integration.md

./references/integration.md

./references/integration.md

./references/integration.md

./references/integration.md

./references/integration.md

./references/gotchas.md

./references/quickstart.md

./references/difference-in-differences.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./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