sdv

name	sdv
description	Synthetic Data Vault (SDV) — generate synthetic tabular data. Single-table, multi-table, and sequential data synthesis. CTGAN, TVAE, CopulaGAN, GaussianCopula. Privacy metrics and evaluation.
tags	["sdv","synthetic-data","data-generation","privacy","ctgan","tabular-data","zorai"]

Overview

The Synthetic Data Vault (SDV) generates synthetic tabular data that preserves statistical properties while protecting privacy. Supports single-table, multi-table, and sequential data generation with CTGAN, TVAE, CopulaGAN, and GaussianCopula models.

Installation

uv pip install sdv

Single-Table (CTGAN)

from sdv.single_table import CTGANSynthesizer
from sdv.datasets.demo import load_demo

data, metadata = load_demo(dataset="census")

synth = CTGANSynthesizer(metadata)
synth.fit(data)
synthetic = synth.sample(num_rows=500)

print(synthetic.head())
print(f"Original columns: {data.shape}, Synthetic: {synthetic.shape}")

Multi-Table

from sdv.multi_table import HMA1Synthesizer

synth = HMA1Synthesizer(multi_table_metadata)
synth.fit(multi_table_data)
synthetic = synth.sample(scale=0.5)

Privacy Evaluation

from sdv.evaluation import evaluate

# Statistical similarity
report = evaluate(synthetic, data, metadata)
print(f"Overall score: {report.get_score():.3f}")
print(f"Column shapes: {report.get_property('Column Shapes'):.3f}")
print(f"Column pairs: {report.get_property('Column Pair Trends'):.3f}")

References

• SDV docs
• SDV GitHub