Stata-to-Python Translation Skill

Stata-to-Python translation reference for quantitative social science data analysis. Maps Stata commands and packages (reghdfe, xtreg, ivregress, margins, esttab, svy:, graph twoway) to DAAF Python equivalents (polars, pyfixest, statsmodels, linearmodels, marginaleffects, svy, plotnine). Use when user mentions Stata background, requests Stata-equivalent code comments, needs to understand Python analysis code from a Stata perspective, or wants to translate Stata data analysis concepts to Python. Covers paradigm differences, command-by-command operation translations, regression modeling, causal inference, visualization, and workflow adaptation.

Cross-language translation reference for researchers moving between the Stata and Python data analysis ecosystems. This skill maps Stata commands, idioms, and workflows to their DAAF Python equivalents so that Stata-background users can audit, understand, and learn from DAAF-produced code, and so that code-producing agents can annotate their output with Stata equivalents when directed.

This skill is a routing hub -- it provides overview tables, decision trees, and directs readers to the detailed reference files listed below. The reference files contain the exhaustive command-by-command mappings, code examples, and edge-case documentation.

What This Skill Does

• Maps the Stata command universe to DAAF's Python stack across data management, regression modeling, causal inference, surveys, visualization, and workflow tooling
• Provides a structured annotation protocol for agents to add inline Stata-equivalent comments to Python code
• Identifies paradigm gaps where Stata and Python diverge fundamentally, so users know where to expect friction

Use cases:

1. Stata user auditing DAAF Python code and needing to understand what operations are being performed
2. Agent annotating code with Stata-equivalent comments for a Stata-background researcher
3. Stata user learning Python for data analysis and needing a conceptual bridge
4. Translating a specific Stata command or do-file idiom to its Python equivalent
5. Understanding where Stata commands have no direct Python equivalent (and what the workaround is)

How to Use This Skill

Reference File Structure

Each topic in

./references/

paradigm-differences.md

data-management.md

strings-dates-labels.md

regression-modeling.md

causal-inference.md

visualization.md

survey-spatial-ml.md

workflow-environment.md

external-resources.md

gotchas.md

Reading Order

1. Stata user auditing DAAF code:

   paradigm-differences.md

   then the relevant domain file (e.g.,

   data-management.md

   for wrangling,

   regression-modeling.md

   for models) then

   gotchas.md

2. Agent annotating code with Stata equivalents: Agent Code Annotation Protocol section below, then the relevant domain file for the code being annotated
3. Learning Python from Stata background:

   paradigm-differences.md

   then

   data-management.md

   then

   workflow-environment.md

   then

   external-resources.md

4. Looking up a specific Stata command translation: Quick Decision Trees below, then the relevant reference file

Quick Decision Trees

"How do I do X from Stata in Python?"

What kind of Stata command?
+-  Data management (gen, replace, keep, drop, merge, reshape, collapse)
|   +-- ./references/data-management.md
+-  Group operations (by:, bysort, egen)
|   +-- ./references/data-management.md
+-  Regression / estimation (regress, areg, reghdfe, xtreg, logit, probit)
|   +-- ./references/regression-modeling.md
+-  Post-estimation (margins, test, lincom, nlcom, predict, esttab)
|   +-- ./references/regression-modeling.md
+-  Causal inference (diff, did_multiplegt, rdrobust, teffects, synth)
|   +-- ./references/causal-inference.md
+-  Surveys (svyset, svy:)
|   +-- ./references/survey-spatial-ml.md
+-  Plotting (graph twoway, histogram, graph bar)
|   +-- ./references/visualization.md
+-  String/date manipulation (substr, strpos, date, mdy)
|   +-- ./references/strings-dates-labels.md
+-  Value labels (label define, encode, decode)
|   +-- ./references/strings-dates-labels.md
+-- Programming (local, global, foreach, forvalues, tempvar, preserve)
    +-- ./references/workflow-environment.md

"Why does this Python code look different from Stata?"

What looks unfamiliar?
+-  Expression syntax (pl.col().method().alias())
|   +-- ./references/paradigm-differences.md
+-  Missing values (None vs NaN vs null vs .)
|   +-- ./references/paradigm-differences.md
+-  No single "dataset" -- multiple DataFrames everywhere
|   +-- ./references/paradigm-differences.md
+-  Value labels missing from output
|   +-- ./references/paradigm-differences.md
+-  Regression output structure (model objects vs e()/r())
|   +-- ./references/regression-modeling.md
+-  No `by:` prefix -- .over() and .group_by() instead
|   +-- ./references/paradigm-differences.md
+-- Import statements and namespacing
    +-- ./references/gotchas.md

"I want to translate a Stata do-file to Python"

What does the do-file do?
+-  Loads and wrangles data (use, gen, replace, keep, merge, collapse)
|   +-- ./references/data-management.md
+-  Runs regressions (regress, xtreg, reghdfe, ivregress)
|   +-- ./references/regression-modeling.md
+-  Creates tables (esttab, outreg2, margins)
|   +-- ./references/regression-modeling.md
+-  Creates plots (graph twoway, histogram)
|   +-- ./references/visualization.md
+-  Uses survey weights (svyset, svy:)
|   +-- ./references/survey-spatial-ml.md
+-  Multiple of the above
|   +-- Start with ./references/paradigm-differences.md, then each relevant file
+-- Uses macros, loops, or programs
    +-- ./references/workflow-environment.md

"Something isn't working and I think it's a Stata habit"

What went wrong?
+-  Missing values behaving differently than expected
|   +-- ./references/paradigm-differences.md
+-  gen/replace pattern not translating
|   +-- ./references/gotchas.md
+-  Merge producing wrong results (no _merge diagnostic)
|   +-- ./references/gotchas.md
+-  Model output looks different from Stata
|   +-- ./references/regression-modeling.md
+-  Off-by-one error (0-indexed vs 1-indexed)
|   +-- ./references/gotchas.md
+-  `by:` / `_n` / `_N` not available
|   +-- ./references/paradigm-differences.md
+-- Macro syntax not working
    +-- ./references/gotchas.md

"Which Python package replaces my Stata command?"

Which Stata command?
+-  regress / areg / reghdfe -> pyfixest
|   +-- ./references/regression-modeling.md
+-  xtreg (fe/re) -> pyfixest (FE) / linearmodels (RE)
|   +-- ./references/regression-modeling.md
+-  ivregress / ivreg2 / ivreghdfe -> pyfixest / linearmodels
|   +-- ./references/regression-modeling.md
+-  logit / probit / ologit / mlogit -> statsmodels
|   +-- ./references/regression-modeling.md
+-  poisson / nbreg / ppmlhdfe -> statsmodels / pyfixest fepois
|   +-- ./references/regression-modeling.md
+-  margins / marginsplot -> marginaleffects
|   +-- ./references/regression-modeling.md
+-  esttab / outreg2 -> pf.etable()
|   +-- ./references/regression-modeling.md
+-  test / lincom / nlcom -> pyfixest .wald_test() / marginaleffects hypotheses()
|   +-- ./references/regression-modeling.md
+-  gen / replace / drop / keep / sort -> polars
|   +-- ./references/data-management.md
+-  merge / append -> polars .join() / pl.concat()
|   +-- ./references/data-management.md
+-  collapse / egen -> polars .group_by().agg() / .over()
|   +-- ./references/data-management.md
+-  reshape long/wide -> polars .unpivot() / .pivot()
|   +-- ./references/data-management.md
+-  graph twoway / histogram / graph bar -> plotnine / plotly
|   +-- ./references/visualization.md
+-  svyset / svy: -> svy package
|   +-- ./references/survey-spatial-ml.md
+-  rdrobust -> rdrobust (Python, same authors)
|   +-- ./references/causal-inference.md
+-  binscatter -> binsreg (Python, same authors)
|   +-- ./references/causal-inference.md
+-  synth -> scpi (Python, same authors)
|   +-- ./references/causal-inference.md
+-- ado-file / ssc install -> pip install
    +-- ./references/workflow-environment.md

Command Mapping Overview

regress

areg

reghdfe

|

xtreg, fe

xtset

xtreg, re

ivregress

ivreg2

ivreghdfe

logit

probit

ologit

mlogit

.fit()

C()

poisson

ppmlhdfe

fepois

fepois

margins

marginsplot

esttab

outreg2

etable()

pf.etable([m1, m2])

gen

replace

drop

keep

sort

merge

append

.join()

pl.concat()

_merge

collapse

egen

.group_by().agg()

.over()

reshape long/wide

.unpivot()

.pivot()

graph twoway

histogram

graph bar

svyset

svy:

Design

Sample

svyset

rdrobust

rdplot

binscatter

binsreg

synth

local

global

foreach

forvalues

Fidelity key: Very High = same authors, near-identical API. High = same capability, similar syntax. Medium = same capability, different API patterns. Low = fundamentally different paradigm requiring conceptual remapping.

Library Versions

Translations in this skill reference specific library versions. Python versions are pinned in DAAF's Docker environment (Python 3.12). Stata versions reference the current release as of March 2026. When syntax or behavior has changed between versions, the reference files note the change.

Unpinned packages: linearmodels, marginaleffects, rdrobust, binsreg, and scpi install the latest version at Docker build time. Translations reference their documented API as of March 2026.

Stata version note: Stata 18 is the current release as of March 2026. Most command mappings apply to Stata 15+; version-specific features (frames,

hdidregress

Top 10 Paradigm Differences

These are the friction points Stata users encounter most frequently when reading or writing DAAF Python code. Each is covered in depth in the referenced file.

paradigm-differences.md

.

.a

.z

null

paradigm-differences.md

paradigm-differences.md

by:

_n

_N

bysort group: gen x = _N

.over("group")

.group_by().agg()

paradigm-differences.md

replace var = expr

df = df.with_columns(...)

paradigm-differences.md

`local'

$global

paradigm-differences.md

.fit()

regress y x

smf.ols("y ~ x", data=df).fit()

regression-modeling.md

gen z = x * 2

df.with_columns((pl.col("x") * 2).alias("z"))

data-management.md

_merge

gotchas.md

_n

var[1]

gotchas.md

Agent Code Annotation Protocol

This section defines when and how code-producing agents add inline Stata-equivalent comments to DAAF Python scripts.

When to Annotate

Annotations are added only when the orchestrator explicitly passes a Stata-background directive to the agent. This is not a default behavior.

Trigger conditions (orchestrator activates this when any apply):

• User states they have a Stata background
• User requests Stata-equivalent comments in code
• User asks to understand Python code from a Stata perspective

How the orchestrator passes the directive: The orchestrator adds the following to the agent prompt:

"User has Stata background. Load stata-python-translation skill. Add inline Stata-equivalent comments for non-trivial data operations."

Comment Format

# Stata: gen log_income = log(income)
df = df.with_columns(pl.col("income").log().alias("log_income"))

# Stata: bysort state: egen mean_score = mean(test_score)
df = df.with_columns(
    pl.col("test_score").mean().over("state").alias("mean_score")
)

# Stata: reghdfe wage education experience, absorb(industry year) cluster(state)
fit = pf.feols("wage ~ education + experience | industry + year",
               data=pdf, vcov={"CRV1": "state"})

# Stata: drop if missing(income)
df = df.filter(pl.col("income").is_not_null())

# Stata: merge 1:1 school_id using "districts.dta"
df = df.join(districts, on="school_id", how="inner")

What to Annotate

• Annotate: Data wrangling (polars operations), modeling calls (pyfixest, statsmodels, linearmodels), visualization layer construction (plotnine, plotly), causal inference method calls, survey estimation calls
• Do NOT annotate: Import statements,

  print()

  /

  assert

  validation lines, file I/O boilerplate (

  pl.read_parquet

  ,

  df.write_parquet

  ), config sections, section separator comments

Rules

• One

  # Stata:

  comment per logical operation, placed on the line immediately above the Python code
• Keep annotations to a single line; abbreviate complex Stata command sequences if needed
• Stata annotations are in addition to standard IAT comments (

  # INTENT:

  ,

  # REASONING:

  ,

  # ASSUMES:

  ), not a replacement
• Consumer agents: research-executor, code-reviewer, debugger, data-ingest

Related Skills

polars

pyfixest

plotnine

plotly

statsmodels

linearmodels

scikit-learn

svy

geopandas

marimo

r-python-translation

Note: Individual tool skills contain library-specific usage guidance (syntax, gotchas, performance). This skill provides the Stata-to-Python conceptual bridge -- use both together when a Stata-background user is working with a specific library.

Topic Index

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/data-management.md

./references/data-management.md

./references/data-management.md

./references/data-management.md

./references/data-management.md

./references/data-management.md

./references/data-management.md

./references/data-management.md

./references/strings-dates-labels.md

./references/strings-dates-labels.md

./references/strings-dates-labels.md

./references/strings-dates-labels.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/causal-inference.md

./references/causal-inference.md

./references/causal-inference.md

./references/causal-inference.md

./references/causal-inference.md

./references/causal-inference.md

./references/visualization.md

./references/visualization.md

./references/visualization.md

./references/visualization.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/external-resources.md

./references/external-resources.md

./references/external-resources.md

./references/external-resources.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md