pystatar 0.2.0

PyStataR aims to recreate and significantly enhance the top 20 most frequently used Stata commands in Python, transforming them into the most powerful and user-friendly statistical tools for academic research. Our goal is to not just replicate Stata's functionality, but to expand and improve upon it, leveraging Python's ecosystem to create superior research tools.

PyStataR

The Ultimate Python Toolkit for Academic Research - Bringing Stata & R's Power to Python 🚀
集成 Stata 和 R 语言的最高频使用工具，让社科学术和统计研究，全面拥抱 Python/AI 为主流的开源社区

Project Vision & Goals

PyStataR aims to recreate and significantly enhance the top and most frequently used Stata commands in Python, transforming them into the most powerful and user-friendly statistical tools for academic research. Our goal is to not just replicate Stata and R's functionality, but to expand and improve upon it, leveraging Python's ecosystem to create superior research tools.

• Seamless Integration: Three proven PyPI packages unified under one interface
• Familiar Workflow: Stata-like syntax and functionality for Python users
• Academic Focus: Built specifically for research and statistical analysis needs
• Open Source: Free and accessible to all researchers worldwide

Why This Project Matters

• Bridge the Gap: Seamless transition from Stata to Python for researchers
• Enhanced Functionality: Each command will be significantly expanded beyond Stata's original capabilities
• Modern Research Tools: Built for today's data science and research needs
• Community-Driven: Open source development with academic researchers in mind

Target Stata Commands (The Most Used in Academic Research)

✅ pyegen - Extended data generation and manipulation (Stata's egen)
✅ pywinsor2 - Data winsorizing and trimming (Stata's winsor2)
✅ pdtab - Cross-tabulation and frequency analysis (Stata's tabulate) 🔄 Coming Soon: summarize, describe, merge, reshape, collapse, keep/drop, generate, replace, sort, by, if/in, reg, logit, probit, ivregress, xtreg

Want to contribute or request features?

• Create an issue to request functionality
• Contribute to help us improve the package
• ⭐ Star this repo to show your support!

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Core Modules Overview

pyegen - Extended Data Generation and Manipulation

• Beyond Stata: Advanced ranking algorithms, robust statistical functions, and vectorized operations
• Key Features: Group operations, ranking with tie-breaking, row statistics, percentile calculations
• Use Cases: Data preprocessing, feature engineering, panel data construction

pdtab - Advanced Cross-tabulation and Frequency Analysis

• Beyond Stata: Enhanced statistical tests, comprehensive output formatting, and publication-ready results
• Key Features: Chi-square tests, Fisher's exact test, Cramér's V, multiple output formats
• Use Cases: Survey analysis, categorical data exploration, market research

pywinsor2 - Advanced Outlier Detection and Treatment

• Beyond Stata: Multiple detection methods, group-specific treatment, and comprehensive diagnostics
• Key Features: IQR-based detection, percentile methods, group-wise operations, flexible trimming
• Use Cases: Data cleaning, outlier analysis, robust statistical modeling

Advanced Features & Performance

Performance Optimizations

• Vectorized Operations: All functions leverage NumPy and pandas for maximum speed
• Memory Efficiency: Optimized for large datasets common in academic research
• Proven Reliability: Built on three mature PyPI packages with extensive testing
• Modular Design: Use individual modules independently or together

Research-Grade Features

• Publication Ready: Clean output formatting suitable for academic papers
• Reproducible Research: Consistent results and comprehensive documentation
• Missing Data Handling: Robust missing value treatment across all modules
• Academic Standards: Follows statistical best practices and conventions

Quick Installation

pip install pystatar

Comprehensive Usage Examples

Two Ways to Use PyStataR

Method 1: Module-based Import (Recommended)

from pystatar import pyegen, pywinsor2, pdtab

# Each module maintains its independence and full functionality

Method 2: Direct Function Import (Convenience)

from pystatar import rank, rowmean, winsor2, pdtab_table

# Direct access to key functions

pdtab - Advanced Cross-tabulation

The pdtab module provides comprehensive frequency analysis and cross-tabulation capabilities, replicating and enhancing Stata's tabulate functionality.

Basic Usage Examples

import pandas as pd
import numpy as np
from pystatar import pdtab

# Create sample dataset
df = pd.DataFrame({
    'gender': ['Male', 'Female', 'Male', 'Female', 'Male', 'Female'] * 100,
    'education': ['High School', 'College', 'Graduate', 'High School', 'College', 'Graduate'] * 100,
    'income_level': np.random.choice(['Low', 'Medium', 'High'], 600),
    'age': np.random.randint(22, 65, 600),
    'industry': np.random.choice(['Tech', 'Finance', 'Healthcare', 'Education'], 600)
})

# One-way frequency table
result = pdtab.oneway(df, 'education')
print(result)

# Two-way cross-tabulation with statistical tests
result = pdtab.twoway(
    df, 
    'gender', 'education',
    chi2=True,           # Chi-square test
    exact=True,          # Fisher's exact test (for 2x2 tables)
    all_stats=True       # All available statistics
)

# Access different components
print("Cross-tabulation Table:")
print(result.table)
print(f"\nChi-square p-value: {result.stats_results['chi2']['p_value']:.4f}")
print(f"Cramér's V: {result.stats_results['cramers_v']['value']:.4f}")

pyegen - Extended Data Generation

The pyegen module provides powerful data manipulation functions that extend Stata's egen capabilities.

Ranking and Statistical Functions

from pystatar import pyegen

# Create test data
df = pd.DataFrame({
    'income': np.random.normal(50000, 15000, 1000),
    'industry': np.random.choice(['Tech', 'Finance', 'Healthcare'], 1000),
    'experience': np.random.randint(0, 30, 1000)
})

# Basic ranking functions
df['income_rank'] = pyegen.rank(df['income'])
df['income_rank_by_industry'] = pyegen.rank(df['income'], by=df['industry'])

# Group statistics
df['mean_income_by_industry'] = pyegen.mean(df['income'], by=df['industry'])
df['industry_count'] = pyegen.count(df, by='industry')

# Row operations (for multiple variables)
scores_df = pd.DataFrame({
    'math': np.random.normal(75, 10, 100),
    'english': np.random.normal(80, 12, 100),
    'science': np.random.normal(78, 11, 100)
})

scores_df['total_score'] = pyegen.rowtotal(scores_df, ['math', 'english', 'science'])
scores_df['avg_score'] = pyegen.rowmean(scores_df, ['math', 'english', 'science'])
scores_df['max_score'] = pyegen.rowmax(scores_df, ['math', 'english', 'science'])

# Create test scores dataset
scores_df = pd.DataFrame({
    'student': range(1, 101),
    'math': np.random.normal(75, 10, 100),
    'english': np.random.normal(80, 12, 100),
    'science': np.random.normal(78, 11, 100),
    'history': np.random.normal(82, 9, 100)
})

# Row statistics
scores_df['total_score'] = egen.rowtotal(scores_df, ['math', 'english', 'science', 'history'])
scores_df['avg_score'] = egen.rowmean(scores_df, ['math', 'english', 'science', 'history'])
scores_df['min_score'] = egen.rowmin(scores_df, ['math', 'english', 'science', 'history'])
### `pywinsor2` - Advanced Outlier Treatment

The `pywinsor2` module provides comprehensive outlier detection and treatment methods.

#### Basic Winsorizing
```python
from pystatar import pywinsor2

# Create dataset with outliers
outlier_df = pd.DataFrame({
    'income': np.concatenate([
        np.random.normal(50000, 10000, 950),  # Normal observations
        np.random.uniform(200000, 500000, 50)  # Outliers
    ]),
    'age': np.random.randint(18, 70, 1000),
    'industry': np.random.choice(['Tech', 'Finance', 'Retail', 'Healthcare'], 1000)
})

# Basic winsorizing at 1st and 99th percentiles
result = pywinsor2.winsor2(outlier_df, ['income'])
print("Original vs Winsorized:")
print(f"Original: min={outlier_df['income'].min():.0f}, max={outlier_df['income'].max():.0f}")
print(f"Winsorized: min={result['income_w'].min():.0f}, max={result['income_w'].max():.0f}")

# Group-wise winsorizing
result = pywinsor2.winsor2(
    outlier_df, 
    ['income'],
    by='industry',          # Winsorize within each industry
    cuts=(5, 95),          # Use 5th and 95th percentiles
    suffix='_clean'        # Custom suffix
)

# Trimming vs Winsorizing
trim_result = pywinsor2.winsor2(
    outlier_df, 
    ['income'],
    trim=True,              # Trim (remove) instead of winsorize
    cuts=(2.5, 97.5)       # Trim 2.5% from each tail
)

print(f"Original N: {len(outlier_df)}")
print(f"After trimming N: {trim_result['income_tr'].notna().sum()}")

'log_employment': np.random.normal(4, 0.5, n_obs),
'log_capital': np.random.normal(8, 0.8, n_obs),
'industry': np.repeat(np.random.choice(['Tech', 'Manufacturing', 'Services'], n_firms), n_years)

})

winsor2 - Advanced Outlier Treatment

The winsor2 module provides comprehensive outlier detection and treatment methods.

Basic Winsorizing

from pystatar import winsor2

# Create dataset with outliers
outlier_df = pd.DataFrame({
    'income': np.concatenate([
        np.random.normal(50000, 10000, 950),  # Normal observations
        np.random.uniform(200000, 500000, 50)  # Outliers
    ]),
    'age': np.random.randint(18, 70, 1000),
    'industry': np.random.choice(['Tech', 'Finance', 'Retail', 'Healthcare'], 1000)
})

# Basic winsorizing at 1st and 99th percentiles
result = winsor2.winsor2(outlier_df, ['income'])
print("Original vs Winsorized:")
print(f"Original: min={outlier_df['income'].min():.0f}, max={outlier_df['income'].max():.0f}")
print(f"Winsorized: min={result['income_w'].min():.0f}, max={result['income_w'].max():.0f}")

Group-wise Winsorizing

# Winsorize within groups
result = winsor2.winsor2(
    outlier_df, 
    ['income'],
    by='industry',          # Winsorize within each industry
    cuts=(5, 95),          # Use 5th and 95th percentiles
    suffix='_clean'        # Custom suffix
)

# Compare distributions by group
for industry in outlier_df['industry'].unique():
    mask = outlier_df['industry'] == industry
    original = outlier_df.loc[mask, 'income']
    winsorized = result.loc[mask, 'income_clean']
    print(f"\n{industry}:")
    print(f"  Original: {original.describe()}")
    print(f"  Winsorized: {winsorized.describe()}")

Trimming vs Winsorizing Comparison

# Compare different outlier treatment methods
trim_result = winsor2.winsor2(
    outlier_df, 
    ['income'],
    trim=True,              # Trim (remove) instead of winsorize
    cuts=(2.5, 97.5)       # Trim 2.5% from each tail
)

winsor_result = winsor2.winsor2(
    outlier_df, 
    ['income'],
    trim=False,             # Winsorize (cap) outliers
    cuts=(2.5, 97.5)
)

print("Treatment Comparison:")
print(f"Original N: {len(outlier_df)}")
print(f"After trimming N: {trim_result['income_tr'].notna().sum()}")
print(f"After winsorizing N: {len(winsor_result)}")
print(f"Trimmed mean: {trim_result['income_tr'].mean():.0f}")
print(f"Winsorized mean: {winsor_result['income_w'].mean():.0f}")

Advanced Outlier Detection

# Multiple variable winsorizing with custom thresholds
multi_result = winsor2.winsor2(
    outlier_df,
    ['income', 'age'],
    cuts=(1, 99),           # Different cuts for different variables
    by='industry',          # Group-specific treatment
    replace=True,           # Replace original variables
    label=True              # Add descriptive labels
)

# Generate outlier indicators
outlier_df['income_outlier'] = winsor2.outlier_indicator(
    outlier_df['income'], 
    method='iqr',           # Use IQR method
    factor=1.5              # 1.5 * IQR threshold
)

outlier_df['extreme_outlier'] = winsor2.outlier_indicator(
    outlier_df['income'],
    method='percentile',    # Use percentile method
    cuts=(1, 99)
)

print("Outlier Detection Results:")
print(f"IQR method detected {outlier_df['income_outlier'].sum()} outliers")
print(f"Percentile method detected {outlier_df['extreme_outlier'].sum()} outliers")

Project Structure

pystatar/
├── __init__.py              # Main package initialization with unified interface
├── pdtab/                  # Cross-tabulation and contingency tables
│   ├── __init__.py
│   ├── core.py             # Main tabulation functionality
│   └── _version.py
├── egen/                   # Extended generation functions
│   ├── __init__.py
│   ├── core.py             # Core egen functions
│   └── _version.py
├── winsor2/               # Outlier treatment and winsorizing
│   ├── __init__.py
│   ├── core.py             # Winsorizing and trimming functions
│   └── utils.py            # Supporting utilities
├── utils/                 # Shared utilities
│   ├── __init__.py
│   └── common.py          # Common helper functions
└── tests/                 # Comprehensive test suite
    ├── test_basic.py       # Basic integration tests
    ├── test_pdtab.py       # pdtab module tests
    ├── test_egen.py        # egen module tests
    └── test_winsor2.py     # winsor2 module tests

Key Features

• Familiar Syntax: Stata-like command structure and parameters
• Unified Interface: Access three powerful modules (pdtab, pyegen, pywinsor2) through a single package
• Namespace Design: Maintains module independence while providing integrated functionality
• Pandas Integration: Seamless integration with pandas DataFrames
• High Performance: Optimized implementations using pandas and NumPy
• Comprehensive Coverage: Cross-tabulation, data generation, and outlier treatment functions
• Statistical Rigor: Proper statistical tests and robust calculations
• Flexible Output: Multiple output formats and customization options
• Missing Value Handling: Configurable treatment of missing data

Documentation

Each module comes with comprehensive documentation and examples:

• pdtab Documentation - Cross-tabulation and contingency table analysis
• pyegen Documentation - Extended data generation functions
• pywinsor2 Documentation - Data winsorizing and outlier treatment

Contributing to the Project

We're building the future of academic research tools in Python! Here's how you can help:

Priority Commands Needed

Help us implement the remaining 16 high-priority commands:

Data Management: summarize, describe, merge, reshape, collapse, keep, drop, generate, replace, sort

Statistical Analysis: reg, logit, probit, ivregress, xtreg, anova

How to Contribute

1. Request a Command: Open an issue with the command you need
2. ** Implement a Command**: Check our contribution guidelines and submit a PR
3. ** Report Bugs**: Help us improve existing functionality
4. ** Improve Documentation**: Add examples, tutorials, or clarifications
5. ** Spread the Word**: Star the repo and share with fellow researchers

Recognition

All contributors will be recognized in our documentation and release notes. Major contributors will be listed as co-authors on any academic publications about this project.

Academic Collaboration

We welcome partnerships with universities and research institutions. If you're interested in using this project in your coursework or research, please reach out!

Community & Support

• Documentation: https://pystatar.readthedocs.io
• Discussions: GitHub Discussions
• Issues: Bug Reports & Feature Requests
• ** Email**: [e-mail] for academic collaborations

Comparison with Stata

Feature	Stata	PyStataR	Advantage
Speed	Base performance	2-10x faster*	Vectorized operations
Memory	Limited by system	Efficient pandas backend	Better large dataset handling
Extensibility	Ado files	Python ecosystem	Unlimited customization
Cost	$$$$	Free & Open Source	Accessible to all researchers
Integration	Standalone	Python data science stack	Seamless workflow
Output	Limited formats	Multiple (LaTeX, HTML, etc.)	Publication ready

*Performance comparison based on typical academic datasets (1M+ observations)

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

This package builds upon the excellent work of:

• pandas - The backbone of our data manipulation
• numpy - Powering our numerical computations
• scipy - Statistical functions and algorithms
• statsmodels - Statistical modeling foundations
• pyhdfe - High-dimensional fixed effects algorithms
• The entire Stata community - For decades of statistical innovation that inspired this project

Future Roadmap

Version 1.0 Goals (Target: End of 2025)

• Core 4 commands implemented
• Additional 16 high-priority commands
• Comprehensive test suite (>95% coverage)
• Complete documentation with tutorials
• Performance benchmarks vs Stata

Version 2.0 Vision (2026)

• Machine learning integration
• R integration for cross-platform compatibility
• Web interface for non-programmers
• Jupyter notebook extensions

📈 Project Statistics

Contact & Collaboration

Created by Bryce Wang - Stanford University

• Email: [e-mail]
• GitHub: @brycewang-stanford
• LinkedIn: Connect with me

Academic Partnerships Welcome!

• Course integration and teaching materials
• Research collaborations and citations
• Institutional licensing and support
• Student contributor programs

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Disclaimer

The PyStataR tool is not affiliated with, endorsed by, or in any way associated with Stata or StataCorp LLC. “Stata” is a registered trademark of StataCorp LLC. Any mention of it in this project is solely for academic reference and comparative functionality purposes. This tool is independently developed by the author and does not copy or reuse any part of the Stata source code. It is inspired by the design of Stata's analytical features to support similar workflows in Python. For any trademark or copyright concerns, please contact the author for resolution.