Metadata-Version: 2.4
Name: onekit
Version: 1.3.10
Summary: One-liner utilities for the Python data science stack
Project-URL: Homepage, https://github.com/Tenali-Rama/onekit
Project-URL: Documentation, https://Tenali-Rama.github.io/onekit
Project-URL: Repository, https://github.com/Tenali-Rama/onekit
Project-URL: Issues, https://github.com/Tenali-Rama/onekit/issues
Author-email: Tenali Rama <tenalirama.krishna125@gmail.com>
License: MIT License
        
        Copyright (c) 2026 Satvik Gupta
        
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License-File: LICENSE
Keywords: data science,matplotlib,numpy,pandas,scipy,seaborn,utilities
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.9
Requires-Dist: matplotlib>=3.6.0
Requires-Dist: numpy>=1.23.0
Requires-Dist: pandas>=1.5.0
Requires-Dist: scipy>=1.9.0
Requires-Dist: seaborn>=0.12.0
Requires-Dist: statsmodels>=0.13.0
Provides-Extra: dev
Requires-Dist: black>=23.0; extra == 'dev'
Requires-Dist: jupyter>=1.0; extra == 'dev'
Requires-Dist: mkdocs-material>=9.0; extra == 'dev'
Requires-Dist: mkdocs>=1.5; extra == 'dev'
Requires-Dist: pytest-cov>=4.0; extra == 'dev'
Requires-Dist: pytest>=7.0; extra == 'dev'
Requires-Dist: ruff>=0.1.0; extra == 'dev'
Description-Content-Type: text/markdown

# OneKit
**One-liner utilities for the Python data science stack**
OneKit eliminates repetitive boilerplate that every data scientist rewrites from scratch. It wraps NumPy, Pandas, Matplotlib, Seaborn, and SciPy with smart defaults, transparent reporting, and full parameter control.
## Installation
```bash
pip install onekit
```
## Quick Start
```python
import onekit as dk
import pandas as pd
df = pd.read_csv('sales.csv')
# One‑call EDA
dk.quick_eda(df)
# Clean it up
df = dk.clean_columns(df)
df = dk.fix_dtypes(df)
df = dk.auto_impute(df)
# Visualise
dk.plot_distributions(df)
dk.plot_corr(df)
# Compare two groups
result = dk.compare_groups(
    df[df['region'] == 'North']['revenue'],
    df[df['region'] == 'South']['revenue']
)
print(result['interpretation'])
```
## Features
| Function | What it does | Module |
|----------|--------------|--------|
| `auto_impute()` | Fill / drop missing values intelligently | pandas |
| `profile_df()` | Full dataset summary | pandas |
| `plot_distributions()` | Histogram + boxplot for every numeric column | seaborn |
| `plot_corr()` | Correlation heatmap | seaborn |
| `compare_groups()` | Automatic t‑test vs Mann‑Whitney | scipy |
| `quick_eda()` | Full exploratory pipeline | pipelines |
| … and many more | See [documentation](https://Tenali-Rama.github.io/onekit) | |
## Documentation
Full documentation, including API reference and examples, is available at [https://Tenali-Rama.github.io/onekit](https://Tenali-Rama.github.io/onekit).
## License
MIT