npgc 0.1.0

Non-Parametric Gaussian Copula synthesizer for tabular data

NPGC

npgc is a lightweight Python package for fitting a non-parametric Gaussian copula to tabular data and generating synthetic samples from the learned distribution.

Installation

pip install npgc

Quick Start

import pandas as pd

from npgc import NPGC

df = pd.DataFrame(
    {
        "age": [21, 34, 45, 52],
        "income": [42000, 68000, 91000, 120000],
        "segment": ["A", "B", "B", "C"],
    }
)

model = NPGC()
model.fit(df, random_state=42)

synthetic = model.sample(100, seed=42)
print(synthetic.head())

Features

• Works directly with pandas DataFrames
• Supports numeric and categorical columns
• Preserves cross-column dependence with a Gaussian copula
• Includes model save/load helpers for reuse

Development

Install development dependencies with:

uv sync --group dev

Run the test suite with:

.\.venv\Scripts\python -m pytest

Build distributions locally with:

$env:UV_CACHE_DIR='.uv-cache'
uv build

Release

After building, upload the artifacts in dist/ to PyPI:

uv publish

Or with Twine:

python -m twine upload dist/*