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/ contains focused documentation:

File	Purpose	When to Read
paradigm-differences.md	Core language and paradigm differences (single-dataset model, missing values, value labels, macros, by:/_n/_N)	Encountering fundamental Stata-vs-Python confusion
data-management.md	gen/replace/keep/drop/sort/merge/append/reshape/collapse/egen to polars	Reading or writing data manipulation code
strings-dates-labels.md	String functions, date epoch, value labels, encode/decode	Working with string, date, or categorical columns
regression-modeling.md	regress/areg/reghdfe/xtreg/ivregress/logit/probit/margins/test/esttab to pyfixest/statsmodels/linearmodels	Reading or writing regression code
causal-inference.md	DiD/RDD/IV/event studies/synthetic control/matching	Working with causal inference methods
visualization.md	graph twoway/bar/box/histogram to plotnine/plotly	Reading or writing visualization code
survey-spatial-ml.md	svy: commands, spatial data, machine learning	Working with surveys, spatial data, or ML
workflow-environment.md	Do-files/log/macros/ado/ssc to Python/DAAF execution model	Adapting to DAAF's execution model
external-resources.md	Curated guides and tutorials with provenance	Seeking additional learning materials
gotchas.md	Common Stata-user mistakes in Python	Debugging or reviewing code from Stata perspective

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

Stata Command(s)	Python Package	Fidelity	Key Difference
regress, areg, reghdfe	pyfixest	High	Near-identical formula syntax; | for FE absorption
xtreg, fe	pyfixest	High	No xtset needed; FE specified in formula
xtreg, re	linearmodels	Medium	Requires pandas MultiIndex for panel structure
ivregress, ivreg2, ivreghdfe	pyfixest / linearmodels	High	Three-part formula for IV in pyfixest
logit, probit, ologit, mlogit	statsmodels	Medium	Requires .fit(); C() for categoricals in formulas
poisson, ppmlhdfe	statsmodels / pyfixest fepois	High	fepois for Poisson with multi-way FE
margins, marginsplot	marginaleffects	Medium	Separate package; different function names
esttab, outreg2	pyfixest etable()	High	pf.etable([m1, m2]) produces publication tables
gen, replace, drop, keep, sort	polars	Low	Expression system vs imperative commands; immutable DataFrames
merge, append	polars .join(), pl.concat()	Medium	No automatic _merge diagnostic
collapse, egen	polars .group_by().agg(), .over()	Medium	Must choose aggregation vs window explicitly
reshape long/wide	polars .unpivot(), .pivot()	Medium	No in-place reshape; column naming differs
graph twoway, histogram, graph bar	plotnine / plotly	Low	Declarative grammar of graphics vs imperative graph syntax
svyset, svy:	svy	Medium	Explicit Design/Sample objects instead of persistent svyset
rdrobust, rdplot	rdrobust (Python)	Very High	Same authors; nearly identical API
binscatter, binsreg	binsreg (Python)	Very High	Same authors; nearly identical API; not pre-installed
synth	scpi	High	Same authors; includes prediction intervals; not pre-installed
local, global, foreach, forvalues	Python variables, f-strings, for loops	Low	Fundamentally different paradigm (text substitution vs value binding)

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.

Python Package	DAAF Version	Stata Equivalent	Stata Version
polars	1.39.3	Data management commands (gen, replace, merge, etc.)	Stata 18
pyfixest	0.60.0	regress, areg, reghdfe, ivreghdfe, ppmlhdfe, esttab	Stata 18 + reghdfe 6.x
statsmodels	0.14.6	regress, logit, probit, poisson, nbreg, glm	Stata 18
linearmodels	7.0	xtreg, sureg, ivregress	Stata 18
plotnine	0.15.3	graph twoway, graph bar, graph box, histogram	Stata 18
plotly	6.5.2	(no direct Stata equivalent; interactive charts)	N/A
svy	0.19.0	svyset, svy: prefix commands	Stata 18
marginaleffects	0.5.0	margins, marginsplot, lincom, nlcom	Stata 18
rdrobust	1.3.0	rdrobust, rdplot, rdbwselect	rdrobust (SSC)
binsreg	not pre-installed	binsreg, binscatter	binsreg (SSC)
scpi	not pre-installed	synth, synth_runner	synth (SSC)
scikit-learn	1.8.0	(limited; teffects, psmatch2 partially)	Stata 18
marimo	0.19.11	(no equivalent; replaces do-file + log workflow)	N/A

Package availability: The core mappings above (polars, pyfixest, statsmodels, plotnine, svy) plus linearmodels (7.0), marginaleffects (0.5.0), and rdrobust (1.3.0) are version-pinned in DAAF's Dockerfile and pre-installed. binsreg and scpi are NOT pre-installed, and runtime installs are blocked in DAAF (pip install is refused both at the command line and inside executed scripts — see CLAUDE.md § Runtime Package Installation). If one is genuinely required, escalate to the user to add it to the Dockerfile (user additions block) and rebuild before use. Translations reference the documented APIs 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) are noted in the reference files.

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.

#	Friction Point	Stata Way	Python Way	Reference
1	Single-dataset model	One dataset in memory; commands implicit	Multiple DataFrames as variables; must specify which	paradigm-differences.md
2	Missing values	. = +infinity; 27 types (.a-.z)	null excluded from comparisons; one null type; NaN distinct	paradigm-differences.md
3	Value labels	Three-layer system (data, variable, value labels)	No built-in equivalent; dictionaries or Enum types	paradigm-differences.md
4	by:/_n/_N system	bysort group: gen x = _N	.over("group") (window) vs .group_by().agg() (collapse)	paradigm-differences.md
5	In-place modification	replace var = expr modifies data directly	df = df.with_columns(...) returns new DataFrame	paradigm-differences.md
6	Macro system	`local' and $global text substitution	Python variables + f-strings	paradigm-differences.md
7	.fit() required	regress y x auto-fits and prints	smf.ols("y ~ x", data=df).fit() -- two steps	regression-modeling.md
8	Expression system	gen z = x * 2 (bare column names)	df.with_columns((pl.col("x") * 2).alias("z"))	data-management.md
9	Merge diagnostics	_merge variable (1/2/3) automatic	No automatic merge indicator; must add explicitly	gotchas.md
10	1-based vs 0-based indexing	_n starts at 1; var[1] = first obs	Python indices start at 0	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", keep(3) nogen
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

Skill	Relationship
polars	Python-side data wrangling -- detailed API reference for the gen/replace/merge/collapse equivalent
pyfixest	Python-side fixed effects regression -- detailed API for the regress/reghdfe/ivregress equivalent
plotnine	Python-side static visualization -- detailed API for the graph twoway equivalent
plotly	Python-side interactive visualization -- no direct Stata equivalent
statsmodels	Python-side general modeling -- covers logit, probit, poisson, nbreg, glm equivalents
linearmodels	Python-side panel/IV models -- covers xtreg, sureg, ivregress equivalents
scikit-learn	Python-side ML -- covers limited teffects/matching equivalents
svy	Python-side survey analysis -- covers svyset and svy: prefix command equivalents
geopandas	Python-side spatial data -- covers Stata spmap/spregress equivalents
marimo	Python-side notebooks -- replaces do-file + log workflow
r-python-translation	Parallel skill for R-background users -- shares the same Python target stack
stata-r-translation	Reverse direction -- maps Stata commands to R equivalents for Stata-background users working in R

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

Topic	Reference File
Single-dataset model (one dataset in memory)	./references/paradigm-differences.md
Missing values (. vs None/NaN/null)	./references/paradigm-differences.md
Extended missing values (.a-.z)	./references/paradigm-differences.md
Value labels (label define, label values)	./references/paradigm-differences.md
Variable labels (label variable)	./references/paradigm-differences.md
by: prefix and _n/_N system variables	./references/paradigm-differences.md
In-place modification vs immutable DataFrames	./references/paradigm-differences.md
Macro system (local, global)	./references/paradigm-differences.md
Estimation and e()/r() stored results	./references/paradigm-differences.md
Type system (destring/tostring vs .cast())	./references/paradigm-differences.md
Panel data operators (L./F./D. vs .shift()/.over())	./references/paradigm-differences.md
Package model (ssc install vs pip install)	./references/paradigm-differences.md
gen / replace / rename	./references/data-management.md
drop / keep (variables and observations)	./references/data-management.md
sort / gsort	./references/data-management.md
merge 1:1 / m:1 / 1:m	./references/data-management.md
append	./references/data-management.md
reshape long / reshape wide	./references/data-management.md
collapse (aggregation)	./references/data-management.md
egen functions (mean, sum, count, rowtotal, group, tag)	./references/data-management.md
encode / decode	./references/strings-dates-labels.md
String functions (substr, strpos, subinstr, regexm)	./references/strings-dates-labels.md
Date system (epoch, %td, mdy(), date())	./references/strings-dates-labels.md
destring / tostring	./references/strings-dates-labels.md
regress (OLS)	./references/regression-modeling.md
areg / reghdfe (fixed effects)	./references/regression-modeling.md
xtreg fe / xtreg re (panel models)	./references/regression-modeling.md
ivregress / ivreg2 / ivreghdfe (IV)	./references/regression-modeling.md
logit / probit / ologit / mlogit	./references/regression-modeling.md
poisson / nbreg / ppmlhdfe	./references/regression-modeling.md
sureg (seemingly unrelated regression)	./references/regression-modeling.md
margins / marginsplot (marginal effects)	./references/regression-modeling.md
test / lincom / nlcom (hypothesis testing)	./references/regression-modeling.md
esttab / outreg2 (regression tables)	./references/regression-modeling.md
predict (fitted values, residuals)	./references/regression-modeling.md
Robust and clustered standard errors	./references/regression-modeling.md
Factor variable notation (i., c., #, ##)	./references/regression-modeling.md
diff / did_multiplegt / csdid (DiD)	./references/causal-inference.md
eventstudyinteract / event studies	./references/causal-inference.md
rdrobust / rdplot (regression discontinuity)	./references/causal-inference.md
teffects / psmatch2 / cem (matching)	./references/causal-inference.md
synth / synth_runner (synthetic control)	./references/causal-inference.md
binscatter / binsreg	./references/causal-inference.md
graph twoway scatter / line / area / connected	./references/visualization.md
graph bar / graph box / histogram / kdensity	./references/visualization.md
graph export	./references/visualization.md
coefplot / iplot	./references/visualization.md
svyset / svy: prefix	./references/survey-spatial-ml.md
svy: mean / total / proportion / ratio	./references/survey-spatial-ml.md
svy: regress / logit	./references/survey-spatial-ml.md
Spatial data analysis	./references/survey-spatial-ml.md
Machine learning (teffects, matching workarounds)	./references/survey-spatial-ml.md
Do-file execution model	./references/workflow-environment.md
Log files (log using)	./references/workflow-environment.md
Ado-files and ssc install	./references/workflow-environment.md
Macros in loops (foreach, forvalues)	./references/workflow-environment.md
tempvar / tempfile / preserve / restore	./references/workflow-environment.md
quietly / capture / noisily	./references/workflow-environment.md
Curated Stata-to-Python migration guides	./references/external-resources.md
Textbooks with trilingual code (Stata/R/Python)	./references/external-resources.md
Package documentation links	./references/external-resources.md
Tutorial recommendations with provenance	./references/external-resources.md
gen/replace pattern not translating	./references/gotchas.md
Missing value comparison traps	./references/gotchas.md
drop if -> filter(NOT) negation trap	./references/gotchas.md
Merge without _merge diagnostics	./references/gotchas.md
egen rowtotal missing-value behavior	./references/gotchas.md
.fit() forgotten in statsmodels	./references/gotchas.md
Robust SE syntax differences	./references/gotchas.md
0-based vs 1-based indexing	./references/gotchas.md
Macro syntax in Python context	./references/gotchas.md
Error message translation table	./references/gotchas.md