StataFlow 0.1.2

Stata2Python: A Python econometrics toolkit aligned with Stata 17

Statapy

Statapy is a Python library that mirrors a focused subset of common Stata econometrics workflows with a Stata-like command surface and source-backed validation.

This clean open-source package is separated from the original development workspace. It keeps the core library, public examples, validation evidence, public datasets, and release-facing documentation, while excluding most internal planning, review, and task-tracking material.

What is included

• Core package code in src/statapy
• Public examples in examples
• Command support documentation in docs/command-support-matrix
• Validation evidence in docs/validation
• Public datasets and validation artifacts in research/data/public and research/results/validation
• A concise user manual in docs/USER_GUIDE.md

Chinese documentation:

• README.zh-CN.md
• docs/USER_GUIDE.zh-CN.md

Installation

pip install StataFlow

Python 3.10+ required. Dependencies: NumPy, pandas, SciPy, PyYAML.

For development (editable install from source):

git clone https://github.com/ZhenHaoFu810/StataFlow.git
cd StataFlow
pip install -e .

Quick start

import pandas as pd
from statapy.compat.stata import regress, reghdfe

df = pd.read_csv("research/data/public/panel/oos/airfare.csv")

ols_res = regress(
    df=df,
    y="lfare",
    x=["ldist", "y98", "y99"],
    vce="robust",
)

hdfe_res = reghdfe(
    df=df,
    y="lfare",
    x=["ldist", "y98##y99"],
    absorb="id year",
    vce="cluster",
    cluster="id",
)

The compat.stata wrappers return stable result schemas for command-style usage. Lower-level estimators remain available in the core package for programmatic workflows.

Supported command families

Current coverage focuses on validated subsets of:

• regress
• xtreg, fe
• areg
• reghdfe
• ivregress 2sls
• ivreghdfe
• logit
• probit
• poisson
• ppmlhdfe
• did_imputation
• eventstudyinteract
• csdid
• rdrobust

Support is command-specific and subset-specific. Do not assume full Stata parity from command name alone. Check the command matrices:

• docs/command-support-matrix/README.md

Validation evidence

The main public evidence entry points are:

• Validation overview
• Validation summary
• Evidence matrix
• Out-of-sample results summary

The validation policy is strict field-level comparison against Stata 17 for the implemented subset. Synthetic development tests exist in the codebase, but the public evidence book emphasizes real public-data dual runs.

Repository structure

• src: package source
• examples: runnable examples
• docs: user-facing documentation
• scripts/validation: validation runners and summary builders
• research/data/public: public datasets used for validation
• research/results/validation: generated validation artifacts

Known limitations

• Several community commands are validated subsets rather than full command reimplementations.
• Validation evidence is strongest for the documented subset and the included public datasets.
• Some internal development reports were intentionally excluded from this clean package.

For current release notes and known issues:

• docs/release/open-source-alpha-status.md
• docs/release/known-issues.md