Metadata-Version: 2.4
Name: scikit-sampling
Version: 1.0.2
Summary: A set of python modules for dataset sampling
Author-email: Leonardo Moraes <leomaurodesenv@users.noreply.github.com>
License-Expression: MIT
Project-URL: homepage, https://github.com/leomaurodesenv/scikit-sampling
Project-URL: source, https://github.com/leomaurodesenv/scikit-sampling
Project-URL: tracker, https://github.com/leomaurodesenv/scikit-sampling/issues
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: scipy<2,>=1.15.0
Provides-Extra: build
Requires-Dist: setuptools<90,>=80.9.0; extra == "build"
Requires-Dist: uv<0.8,>=0.7.12; extra == "build"
Requires-Dist: python-semantic-release~=10.0; extra == "build"
Dynamic: license-file

# 🧪 Scikit-Sampling

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Scikit-Sampling (or `sksampling`) is a Python library for dataset sampling techniques. It provides a unified API for common sampling strategies, making it easy to integrate into your data science and machine learning workflows.

## Installation

You can install `sksampling` using pip:

```bash
pip install scikit-sampling
```

## Features

`sksampling` offers a range of sampling methods, including:

- `sample_size`: Computes the ideal sample size based confidence level and interval.

## Usage

`sksampling` follows the `scikit-learn` API, making it intuitive to use.

```python
from sksampling import sample_size

# Example usage
population_size: int = 100_000
confidence_level: float = 0.95
confidence_interval: float = 0.02
sample_size(population_size, confidence_level, confidence_interval) # approx 2345
```
