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Skill v1.0.0
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PublishedJuly 29, 2026 at 12:05 PM
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version: "1.0.0" 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
bash
uv pip install sdv
Single-Table (CTGAN)
python
from sdv.single_table import CTGANSynthesizerfrom sdv.datasets.demo import load_demodata, 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
python
from sdv.multi_table import HMA1Synthesizersynth = HMA1Synthesizer(multi_table_metadata)synth.fit(multi_table_data)synthetic = synth.sample(scale=0.5)
Privacy Evaluation
python
from sdv.evaluation import evaluate# Statistical similarityreport = 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}")