stable-stats 0.1.1

A Python package for beautifying statistical outputs into clean tables

Stable

A Python package for beautifying statistical outputs from scipy, statsmodels, and other libraries into clean, publication-ready tables.

Features

• Automatic Detection: Recognizes common statistical tests (t-tests, ANOVA, chi-square, regression, etc.)
• Multiple Export Formats: Markdown, Excel, HTML, and pandas DataFrame
• Pretty Formatting: Rounded decimals, significance stars, confidence intervals
• Flexible Input: Works with scipy.stats and statsmodels results
• Easy to Use: Simple API with methods like .to_markdown(), .to_excel()

Installation

From PyPI (recommended)

pip install stable-stats

From source

git clone https://github.com/Chris-R030307/StaTable.git
cd StaTable
pip install -e .

Development installation

git clone https://github.com/Chris-R030307/StaTable.git
cd StaTable
pip install -e ".[dev,test]"

Dependencies

The package requires:

• Python 3.8+
• numpy >= 1.19.0
• pandas >= 1.3.0
• scipy >= 1.7.0
• statsmodels >= 0.12.0
• openpyxl >= 3.0.0 (for Excel export)

Quick Start

from scipy import stats
from stable import Stable

# Run a statistical test
result = stats.ttest_ind(group1, group2)

# Beautify the results
table = Stable(result)

# Export to different formats
print(table.to_markdown())  # Pretty table in console
table.to_excel("results.xlsx")  # Export to Excel
html_output = table.to_html()  # Get HTML string

Examples

T-test

import numpy as np
from scipy import stats
from stable import Stable

# Generate sample data
np.random.seed(42)
group1 = np.random.normal(100, 15, 30)
group2 = np.random.normal(110, 15, 30)

# Run t-test
result = stats.ttest_ind(group1, group2)

# Beautify
stable = Stable(result)
print(stable.to_markdown())

Output:

## Independent t-test

**Sample Size:** 30

## Results

| Statistic | Value | p-value | Significance |
|-----------|-------|---------|--------------|
| Test Statistic | -2.108 | 0.039* | * |

Effect Size: -2.108

ANOVA

# Generate data for 3 groups
group_a = np.random.normal(50, 10, 25)
group_b = np.random.normal(55, 10, 25)
group_c = np.random.normal(60, 10, 25)

# Run ANOVA
result = stats.f_oneway(group_a, group_b, group_c)

# Beautify
stable = Stable(result)
print(stable.to_markdown())

Linear Regression

import pandas as pd
import statsmodels.api as sm
from statsmodels.formula.api import ols

# Generate sample data
x = np.random.normal(0, 1, 100)
y = 2 * x + np.random.normal(0, 0.5, 100)
df = pd.DataFrame({'x': x, 'y': y})

# Run regression
model = ols('y ~ x', data=df).fit()

# Beautify
stable = Stable(model)
print(stable.to_markdown())

Direct Analysis Methods

# Direct t-test
stable = Stable.from_ttest(group1, group2)

# Direct ANOVA
stable = Stable.from_anova(group_a, group_b, group_c)

# Direct chi-square
observed = [20, 30, 25, 25]
expected = [25, 25, 25, 25]
stable = Stable.from_chi2(observed, expected)

Supported Statistical Tests

Scipy.stats

• t-tests (independent, paired, one-sample)
• ANOVA (one-way)
• Chi-square tests
• Kolmogorov-Smirnov tests
• Mann-Whitney U test
• Wilcoxon signed-rank test
• Kruskal-Wallis test
• Friedman test

Statsmodels

• Linear regression
• ANOVA
• t-tests
• F-tests
• Contrast tests

Export Formats

Markdown

markdown_output = stable.to_markdown(title="My Analysis")
print(markdown_output)

Excel

stable.to_excel("results.xlsx", sheet_name="Analysis")

HTML

html_output = stable.to_html(title="My Analysis", include_css=True)

Pandas DataFrame

df = stable.to_dataframe()

API Reference

Stable Class

Methods

• to_markdown(title=None): Export to Markdown format
• to_excel(filename, sheet_name="Statistical Results"): Export to Excel
• to_html(title=None, include_css=True): Export to HTML
• to_dataframe(): Export to pandas DataFrame
• summary(): Get brief summary of results
• is_supported(): Check if result type is supported

Properties

• get_test_name(): Get human-readable test name
• get_statistic(): Get test statistic(s)
• get_p_value(): Get p-value(s)
• get_effect_size(): Get effect size(s)
• get_confidence_interval(): Get confidence interval
• get_sample_size(): Get sample size information
• get_degrees_of_freedom(): Get degrees of freedom
• get_coefficients(): Get coefficient information (regression)
• get_model_info(): Get model information (regression)

Class Methods

• Stable.from_ttest(group1, group2, **kwargs): Direct t-test
• Stable.from_anova(*groups, **kwargs): Direct ANOVA
• Stable.from_chi2(observed, expected=None, **kwargs): Direct chi-square
• Stable.from_regression(model_result): From regression result

Package Structure

stable/
├── __init__.py              # Main package interface
├── core.py                  # Core Stable class
├── utils.py                 # Helper functions
├── adapters/                # Input adapters
│   ├── scipy_adapter.py     # Scipy.stats adapter
│   └── statsmodels_adapter.py # Statsmodels adapter
└── exporters/               # Output exporters
    ├── markdown.py          # Markdown exporter
    ├── excel.py             # Excel exporter
    └── html.py              # HTML exporter

Requirements

• Python 3.7+
• numpy >= 1.19.0
• pandas >= 1.3.0
• scipy >= 1.7.0
• statsmodels >= 0.12.0
• openpyxl >= 3.0.0 (for Excel export)

Development

Setup Development Environment

git clone <repository-url>
cd stable
pip install -e ".[dev]"

Run Tests

pytest

Run Example

python example_usage.py

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Future Features

• Support for more statistical libraries (pingouin, sklearn)
• Interactive tables (Plotly dashboards)
• Custom templates (APA style, clinical reports)
• LaTeX export
• More effect size calculations
• Power analysis integration