# pdtab: Pandas-based Tabulation Library
[](https://badge.fury.io/py/pdtab)
[](https://pypi.org/project/pdtab/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/brycewang-stanford/pdtab)
[](https://pypi.org/project/pdtab/)
**pdtab** is a comprehensive Python library that replicates the functionality of Stata's `tabulate` command using pandas as the backend. This library provides powerful one-way, two-way, and summary tabulations with statistical tests and measures of association.
## Overview
Stata's `tabulate` command is one of the most widely used tools for creating frequency tables and cross-tabulations in statistical analysis. **pdtab** brings this functionality to Python, offering:
- **Complete Stata compatibility**: Replicates all major features of Stata's tabulate command
- **Statistical tests**: Chi-square tests, Fisher's exact test, likelihood-ratio tests
- **Association measures**: Cramér's V, Goodman and Kruskal's gamma, Kendall's τb
- **Flexible output**: Console tables, HTML, and visualization options
- **Weighted analysis**: Support for frequency, analytic, and importance weights
- **Missing value handling**: Comprehensive options for dealing with missing data
## Integration with Broader Ecosystem
**pdtab** is part of a comprehensive econometric and statistical analysis ecosystem:
### [PyStataR](https://github.com/brycewang-stanford/PyStataR)
The **pdtab** library will be integrated into **PyStataR**, a comprehensive Python package that bridges Stata and R functionality in Python. PyStataR aims to provide Stata users with familiar commands and workflows while leveraging Python's powerful data science ecosystem.
### [StasPAI](https://github.com/brycewang-stanford/StasPAI)
For users interested in AI-powered econometric analysis, **StasPAI** offers a related project focused on integrating statistical analysis with artificial intelligence methods. StasPAI provides advanced econometric modeling capabilities enhanced by machine learning approaches.
These projects together form a unified toolkit for modern econometric analysis, combining the best of Stata's user-friendly interface, R's statistical capabilities, and Python's machine learning ecosystem.
## Installation
```bash
pip install pdtab
```
Or install from source:
```bash
git clone https://github.com/brycewang-stanford/pdtab.git
cd pdtab
pip install -e .
```
## Requirements
- Python 3.8+
- pandas >= 1.0.0
- numpy >= 1.18.0
- scipy >= 1.4.0
- matplotlib >= 3.0.0 (for plotting)
- seaborn >= 0.11.0 (for enhanced plotting)
## 🎯 Design Philosophy
**pdtab** is designed as a **pure Python library** focused exclusively on providing Stata's tabulate functionality through a clean, programmatic API.
### Key Design Decisions:
- **No Command-Line Interface**: pdtab is intentionally designed as a library-only package to maintain simplicity and focus on the Python ecosystem
- **Jupyter-First Approach**: Optimized for data science workflows in Jupyter notebooks and Python scripts
- **Programmatic Access**: All functionality accessible through Python functions with comprehensive options
- **Integration Ready**: Designed to integrate seamlessly with pandas, matplotlib, and the broader PyData ecosystem
This design ensures pdtab remains lightweight, maintainable, and perfectly suited for modern data science workflows.
## Quick Start
### Basic One-way Tabulation
```python
import pandas as pd
import pdtab
# Create sample data
data = pd.DataFrame({
'gender': ['Male', 'Female', 'Male', 'Female', 'Male', 'Female'],
'education': ['High School', 'College', 'Graduate', 'High School', 'College', 'Graduate'],
'income': [35000, 45000, 75000, 40000, 55000, 80000]
})
# One-way frequency table
result = pdtab.tabulate('gender', data=data)
print(result)
```
```
gender Freq Percent Cum
Male 3 50.00 50.00
Female 3 50.00 100.00
Total 6 100.00 100.00
```
### Two-way Cross-tabulation with Statistics
```python
# Two-way table with chi-square test
result = pdtab.tabulate('gender', 'education', data=data, chi2=True, exact=True)
print(result)
```
### Summary Tabulation
```python
# Summary statistics by group
result = pdtab.tabulate('gender', data=data, summarize='income')
print(result)
```
```
Summary of income by gender
gender Mean Std. Dev. Freq
Male 55000.0 20000.0 3
Female 55000.0 20000.0 3
Total 55000.0 18257.4 6
```
## Main Functions
### `tabulate(varname1, varname2=None, data=None, **options)`
Main tabulation function supporting:
**One-way options:**
- `missing=True`: Include missing values as a category
- `sort=True`: Sort by frequency (descending)
- `plot=True`: Create bar chart
- `nolabel=True`: Show numeric codes instead of labels
- `generate='prefix'`: Create indicator variables
**Two-way options:**
- `chi2=True`: Pearson's chi-square test
- `exact=True`: Fisher's exact test
- `lrchi2=True`: Likelihood-ratio chi-square
- `V=True`: Cramér's V
- `gamma=True`: Goodman and Kruskal's gamma
- `taub=True`: Kendall's τb
- `row=True`: Row percentages
- `column=True`: Column percentages
- `cell=True`: Cell percentages
- `expected=True`: Expected frequencies
**Summary options:**
- `summarize='variable'`: Variable to summarize
- `means=False`: Suppress means
- `standard=False`: Suppress standard deviations
- `freq=False`: Suppress frequencies
### `tab1(varlist, data=None, **options)`
Create one-way tables for multiple variables:
```python
results = pdtab.tab1(['gender', 'education'], data=data)
for var, result in results.items():
print(f"\n{var}:")
print(result)
```
### `tab2(varlist, data=None, **options)`
Create all possible two-way tables:
```python
results = pdtab.tab2(['gender', 'education', 'region'], data=data, chi2=True)
for (var1, var2), result in results.items():
print(f"\n{var1} × {var2}:")
print(result)
```
### `tabi(table_data, **options)`
Immediate tabulation from supplied data:
```python
# From string (Stata format)
result = pdtab.tabi("30 18 \\ 38 14", exact=True)
# From list
result = pdtab.tabi([[30, 18], [38, 14]], chi2=True)
```
## Visualization
Create plots directly from tabulation results:
```python
# Bar chart for one-way table
result = pdtab.tabulate('gender', data=data, plot=True)
# Heatmap for two-way table
result = pdtab.tabulate('gender', 'education', data=data)
fig = pdtab.viz.create_tabulation_plots(result, plot_type='heatmap')
```
## Statistical Tests
### Supported Tests
1. **Pearson's Chi-square Test**: Tests independence in contingency tables
2. **Likelihood-ratio Chi-square**: Alternative to Pearson's chi-square
3. **Fisher's Exact Test**: Exact test for small samples (especially 2×2 tables)
### Association Measures
1. **Cramér's V**: Measure of association (0-1 scale)
2. **Goodman and Kruskal's Gamma**: For ordinal variables (-1 to 1)
3. **Kendall's τb**: Rank correlation with tie correction (-1 to 1)
## Weighted Analysis
Support for different weight types:
```python
# Frequency weights
result = pdtab.tabulate('gender', data=data, weights='freq_weight')
# Analytic weights
result = pdtab.tabulate('gender', data=data, weights='analytic_weight')
```
## Missing Value Handling
Flexible options for missing data:
```python
# Exclude missing values (default)
result = pdtab.tabulate('gender', data=data)
# Include missing as category
result = pdtab.tabulate('gender', data=data, missing=True)
# Subpopulation analysis
result = pdtab.tabulate('gender', data=data, subpop='analysis_sample')
```
## Export Options
Export results in multiple formats:
```python
result = pdtab.tabulate('gender', 'education', data=data)
# Export to dictionary
data_dict = result.to_dict()
# Export to HTML
html_table = result.to_html()
# Save plot
fig = pdtab.viz.create_tabulation_plots(result)
pdtab.viz.save_plot(fig, 'crosstab.png')
```
## Advanced Examples
### Complex Two-way Analysis
```python
# Comprehensive two-way analysis
result = pdtab.tabulate(
'treatment', 'outcome',
data=clinical_data,
chi2=True, # Chi-square test
exact=True, # Fisher's exact test
V=True, # Cramér's V
row=True, # Row percentages
expected=True, # Expected frequencies
missing=True # Include missing values
)
print(result)
print(f"Chi-square: {result.statistics['chi2']['statistic']:.3f}")
print(f"p-value: {result.statistics['chi2']['p_value']:.3f}")
print(f"Cramér's V: {result.statistics['cramers_v']:.3f}")
```
### Summary Analysis by Multiple Groups
```python
# Income analysis by gender and education
result = pdtab.tabulate(
'gender', 'education',
data=data,
summarize='income',
means=True,
standard=True,
obs=True
)
```
### Immediate Analysis of Published Data
```python
# Analyze a 2×3 contingency table from literature
published_data = """
45 55 60 \\
30 40 35
"""
result = pdtab.tabi(published_data, chi2=True, exact=True, V=True)
print("Published data analysis:")
print(result)
```
## Stata Comparison
pdtab aims for 100% compatibility with Stata's tabulate command:
| Stata Command | pdtab Equivalent |
|---------------|------------------|
| `tabulate gender` | `pdtab.tabulate('gender', data=df)` |
| `tabulate gender education, chi2` | `pdtab.tabulate('gender', 'education', data=df, chi2=True)` |
| `tabulate gender, summarize(income)` | `pdtab.tabulate('gender', data=df, summarize='income')` |
| `tab1 gender education region` | `pdtab.tab1(['gender', 'education', 'region'], data=df)` |
| `tab2 gender education region` | `pdtab.tab2(['gender', 'education', 'region'], data=df)` |
| `tabi 30 18 \\ 38 14, exact` | `pdtab.tabi("30 18 \\\\ 38 14", exact=True)` |
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
git clone https://github.com/brycewang-stanford/pdtab.git
cd pdtab
pip install -e ".[dev]"
pytest
```
## 📄 License
MIT License - see [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- **Stata Corporation** for the original tabulate command design
- **Pandas Development Team** for the excellent data manipulation library
- **SciPy Community** for statistical computing tools
## Related Projects
**pdtab** is part of a broader ecosystem of econometric and statistical tools:
- **[PyStataR](https://github.com/brycewang-stanford/PyStataR)** - Comprehensive Python package bridging Stata and R functionality (pdtab will be integrated into this project)
- **[StasPAI](https://github.com/brycewang-stanford/StasPAI)** - AI-powered econometric analysis toolkit combining statistical methods with machine learning
## Support
- **Documentation**: [https://pdtab.readthedocs.io](https://pdtab.readthedocs.io)
- **Issues**: [GitHub Issues](https://github.com/brycewang-stanford/pdtab/issues)
- **Discussions**: [GitHub Discussions](https://github.com/brycewang-stanford/pdtab/discussions)
---
**pdtab** - Bringing Stata's tabulation power to the Python ecosystem! 🐍
Raw data
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"description": "# pdtab: Pandas-based Tabulation Library\n\n[](https://badge.fury.io/py/pdtab)\n[](https://pypi.org/project/pdtab/)\n[](https://opensource.org/licenses/MIT)\n[](https://github.com/brycewang-stanford/pdtab)\n[](https://pypi.org/project/pdtab/)\n\n**pdtab** is a comprehensive Python library that replicates the functionality of Stata's `tabulate` command using pandas as the backend. This library provides powerful one-way, two-way, and summary tabulations with statistical tests and measures of association.\n\n## Overview\n\nStata's `tabulate` command is one of the most widely used tools for creating frequency tables and cross-tabulations in statistical analysis. **pdtab** brings this functionality to Python, offering:\n\n- **Complete Stata compatibility**: Replicates all major features of Stata's tabulate command\n- **Statistical tests**: Chi-square tests, Fisher's exact test, likelihood-ratio tests\n- **Association measures**: Cram\u00e9r's V, Goodman and Kruskal's gamma, Kendall's \u03c4b\n- **Flexible output**: Console tables, HTML, and visualization options\n- **Weighted analysis**: Support for frequency, analytic, and importance weights\n- **Missing value handling**: Comprehensive options for dealing with missing data\n\n## Integration with Broader Ecosystem\n\n**pdtab** is part of a comprehensive econometric and statistical analysis ecosystem:\n\n### [PyStataR](https://github.com/brycewang-stanford/PyStataR)\nThe **pdtab** library will be integrated into **PyStataR**, a comprehensive Python package that bridges Stata and R functionality in Python. PyStataR aims to provide Stata users with familiar commands and workflows while leveraging Python's powerful data science ecosystem.\n\n### [StasPAI](https://github.com/brycewang-stanford/StasPAI)\nFor users interested in AI-powered econometric analysis, **StasPAI** offers a related project focused on integrating statistical analysis with artificial intelligence methods. StasPAI provides advanced econometric modeling capabilities enhanced by machine learning approaches.\n\nThese projects together form a unified toolkit for modern econometric analysis, combining the best of Stata's user-friendly interface, R's statistical capabilities, and Python's machine learning ecosystem.\n\n## Installation\n\n```bash\npip install pdtab\n```\n\nOr install from source:\n\n```bash\ngit clone https://github.com/brycewang-stanford/pdtab.git\ncd pdtab\npip install -e .\n```\n\n## Requirements\n\n- Python 3.8+\n- pandas >= 1.0.0\n- numpy >= 1.18.0\n- scipy >= 1.4.0\n- matplotlib >= 3.0.0 (for plotting)\n- seaborn >= 0.11.0 (for enhanced plotting)\n\n## \ud83c\udfaf Design Philosophy\n\n**pdtab** is designed as a **pure Python library** focused exclusively on providing Stata's tabulate functionality through a clean, programmatic API. \n\n### Key Design Decisions:\n\n- **No Command-Line Interface**: pdtab is intentionally designed as a library-only package to maintain simplicity and focus on the Python ecosystem\n- **Jupyter-First Approach**: Optimized for data science workflows in Jupyter notebooks and Python scripts\n- **Programmatic Access**: All functionality accessible through Python functions with comprehensive options\n- **Integration Ready**: Designed to integrate seamlessly with pandas, matplotlib, and the broader PyData ecosystem\n\nThis design ensures pdtab remains lightweight, maintainable, and perfectly suited for modern data science workflows.\n\n## Quick Start\n\n### Basic One-way Tabulation\n\n```python\nimport pandas as pd\nimport pdtab\n\n# Create sample data\ndata = pd.DataFrame({\n 'gender': ['Male', 'Female', 'Male', 'Female', 'Male', 'Female'],\n 'education': ['High School', 'College', 'Graduate', 'High School', 'College', 'Graduate'],\n 'income': [35000, 45000, 75000, 40000, 55000, 80000]\n})\n\n# One-way frequency table\nresult = pdtab.tabulate('gender', data=data)\nprint(result)\n```\n\n```\ngender Freq Percent Cum\nMale 3 50.00 50.00\nFemale 3 50.00 100.00\nTotal 6 100.00 100.00\n```\n\n### Two-way Cross-tabulation with Statistics\n\n```python\n# Two-way table with chi-square test\nresult = pdtab.tabulate('gender', 'education', data=data, chi2=True, exact=True)\nprint(result)\n```\n\n### Summary Tabulation\n\n```python\n# Summary statistics by group\nresult = pdtab.tabulate('gender', data=data, summarize='income')\nprint(result)\n```\n\n```\nSummary of income by gender\n\ngender Mean Std. Dev. Freq\nMale 55000.0 20000.0 3\nFemale 55000.0 20000.0 3\nTotal 55000.0 18257.4 6\n```\n\n## Main Functions\n\n### `tabulate(varname1, varname2=None, data=None, **options)`\n\nMain tabulation function supporting:\n\n**One-way options:**\n- `missing=True`: Include missing values as a category\n- `sort=True`: Sort by frequency (descending)\n- `plot=True`: Create bar chart\n- `nolabel=True`: Show numeric codes instead of labels\n- `generate='prefix'`: Create indicator variables\n\n**Two-way options:**\n- `chi2=True`: Pearson's chi-square test\n- `exact=True`: Fisher's exact test \n- `lrchi2=True`: Likelihood-ratio chi-square\n- `V=True`: Cram\u00e9r's V\n- `gamma=True`: Goodman and Kruskal's gamma\n- `taub=True`: Kendall's \u03c4b\n- `row=True`: Row percentages\n- `column=True`: Column percentages\n- `cell=True`: Cell percentages\n- `expected=True`: Expected frequencies\n\n**Summary options:**\n- `summarize='variable'`: Variable to summarize\n- `means=False`: Suppress means\n- `standard=False`: Suppress standard deviations\n- `freq=False`: Suppress frequencies\n\n### `tab1(varlist, data=None, **options)`\n\nCreate one-way tables for multiple variables:\n\n```python\nresults = pdtab.tab1(['gender', 'education'], data=data)\nfor var, result in results.items():\n print(f\"\\n{var}:\")\n print(result)\n```\n\n### `tab2(varlist, data=None, **options)`\n\nCreate all possible two-way tables:\n\n```python\nresults = pdtab.tab2(['gender', 'education', 'region'], data=data, chi2=True)\nfor (var1, var2), result in results.items():\n print(f\"\\n{var1} \u00d7 {var2}:\")\n print(result)\n```\n\n### `tabi(table_data, **options)`\n\nImmediate tabulation from supplied data:\n\n```python\n# From string (Stata format)\nresult = pdtab.tabi(\"30 18 \\\\ 38 14\", exact=True)\n\n# From list\nresult = pdtab.tabi([[30, 18], [38, 14]], chi2=True)\n```\n\n## Visualization\n\nCreate plots directly from tabulation results:\n\n```python\n# Bar chart for one-way table\nresult = pdtab.tabulate('gender', data=data, plot=True)\n\n# Heatmap for two-way table \nresult = pdtab.tabulate('gender', 'education', data=data)\nfig = pdtab.viz.create_tabulation_plots(result, plot_type='heatmap')\n```\n\n## Statistical Tests\n\n### Supported Tests\n\n1. **Pearson's Chi-square Test**: Tests independence in contingency tables\n2. **Likelihood-ratio Chi-square**: Alternative to Pearson's chi-square\n3. **Fisher's Exact Test**: Exact test for small samples (especially 2\u00d72 tables)\n\n### Association Measures\n\n1. **Cram\u00e9r's V**: Measure of association (0-1 scale)\n2. **Goodman and Kruskal's Gamma**: For ordinal variables (-1 to 1)\n3. **Kendall's \u03c4b**: Rank correlation with tie correction (-1 to 1)\n\n## Weighted Analysis\n\nSupport for different weight types:\n\n```python\n# Frequency weights\nresult = pdtab.tabulate('gender', data=data, weights='freq_weight')\n\n# Analytic weights \nresult = pdtab.tabulate('gender', data=data, weights='analytic_weight')\n```\n\n## Missing Value Handling\n\nFlexible options for missing data:\n\n```python\n# Exclude missing values (default)\nresult = pdtab.tabulate('gender', data=data)\n\n# Include missing as category\nresult = pdtab.tabulate('gender', data=data, missing=True)\n\n# Subpopulation analysis\nresult = pdtab.tabulate('gender', data=data, subpop='analysis_sample')\n```\n\n## Export Options\n\nExport results in multiple formats:\n\n```python\nresult = pdtab.tabulate('gender', 'education', data=data)\n\n# Export to dictionary\ndata_dict = result.to_dict()\n\n# Export to HTML\nhtml_table = result.to_html()\n\n# Save plot\nfig = pdtab.viz.create_tabulation_plots(result)\npdtab.viz.save_plot(fig, 'crosstab.png')\n```\n\n## Advanced Examples\n\n### Complex Two-way Analysis\n\n```python\n# Comprehensive two-way analysis\nresult = pdtab.tabulate(\n 'treatment', 'outcome', \n data=clinical_data,\n chi2=True, # Chi-square test\n exact=True, # Fisher's exact test\n V=True, # Cram\u00e9r's V\n row=True, # Row percentages\n expected=True, # Expected frequencies\n missing=True # Include missing values\n)\n\nprint(result)\nprint(f\"Chi-square: {result.statistics['chi2']['statistic']:.3f}\")\nprint(f\"p-value: {result.statistics['chi2']['p_value']:.3f}\")\nprint(f\"Cram\u00e9r's V: {result.statistics['cramers_v']:.3f}\")\n```\n\n### Summary Analysis by Multiple Groups\n\n```python\n# Income analysis by gender and education\nresult = pdtab.tabulate(\n 'gender', 'education',\n data=data,\n summarize='income',\n means=True,\n standard=True,\n obs=True\n)\n```\n\n### Immediate Analysis of Published Data\n\n```python\n# Analyze a 2\u00d73 contingency table from literature\npublished_data = \"\"\"\n 45 55 60 \\\\\n 30 40 35\n\"\"\"\n\nresult = pdtab.tabi(published_data, chi2=True, exact=True, V=True)\nprint(\"Published data analysis:\")\nprint(result)\n```\n\n## Stata Comparison\n\npdtab aims for 100% compatibility with Stata's tabulate command:\n\n| Stata Command | pdtab Equivalent |\n|---------------|------------------|\n| `tabulate gender` | `pdtab.tabulate('gender', data=df)` |\n| `tabulate gender education, chi2` | `pdtab.tabulate('gender', 'education', data=df, chi2=True)` |\n| `tabulate gender, summarize(income)` | `pdtab.tabulate('gender', data=df, summarize='income')` |\n| `tab1 gender education region` | `pdtab.tab1(['gender', 'education', 'region'], data=df)` |\n| `tab2 gender education region` | `pdtab.tab2(['gender', 'education', 'region'], data=df)` |\n| `tabi 30 18 \\\\ 38 14, exact` | `pdtab.tabi(\"30 18 \\\\\\\\ 38 14\", exact=True)` |\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n```bash\ngit clone https://github.com/brycewang-stanford/pdtab.git\ncd pdtab\npip install -e \".[dev]\"\npytest\n```\n\n## \ud83d\udcc4 License\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- **Stata Corporation** for the original tabulate command design\n- **Pandas Development Team** for the excellent data manipulation library\n- **SciPy Community** for statistical computing tools\n\n## Related Projects\n\n**pdtab** is part of a broader ecosystem of econometric and statistical tools:\n\n- **[PyStataR](https://github.com/brycewang-stanford/PyStataR)** - Comprehensive Python package bridging Stata and R functionality (pdtab will be integrated into this project)\n- **[StasPAI](https://github.com/brycewang-stanford/StasPAI)** - AI-powered econometric analysis toolkit combining statistical methods with machine learning\n\n## Support\n\n- **Documentation**: [https://pdtab.readthedocs.io](https://pdtab.readthedocs.io)\n- **Issues**: [GitHub Issues](https://github.com/brycewang-stanford/pdtab/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/brycewang-stanford/pdtab/discussions)\n\n---\n\n**pdtab** - Bringing Stata's tabulation power to the Python ecosystem! \ud83d\udc0d\n",
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