sdv

library1.37.1pypypi✓ verified 85d ago

SDV (Synthetic Data Vault) is a Python library that allows users to generate synthetic data for various data types, including single tables, multi-table relational datasets, and sequential data. It provides a range of models and tools to create high-quality synthetic data that preserves the statistical properties and privacy of the original data. As of version 1.36.0, it continues to be actively developed, with a regular release cadence to add new features and improve existing models.

data ai-ml

pip install sdv

github ↗package ↗homepage ↗changelog ↗

INSTALL

IMPORT

SIG · SDV

sdv

datapythonv1.37.1

Install

84.8s avg

Import

13958ms

Disk

5248MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.37.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 96.6s

py 3.11

✕ build_error

✓ 86s

py 3.12

✕ build_error

✓ 82.1s

py 3.13

✕ build_error

✓ 74.6s

py 3.9

✕ build_error

✕ timeout

5248MB installed

● package 5248MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

GaussianCopulaSynthesizer

✓ from sdv.single_table import GaussianCopulaSynthesizer

✗ from sdv.models import GaussianCopulaSynthesizer

Synthesizers were moved out of 'sdv.models' in v0.17.0. Use 'sdv.single_table', 'sdv.multi_table', or 'sdv.sequential' depending on your data type.

SingleTablePreset

✓ from sdv.single_table.preset import SingleTablePreset

✗ from sdv.lite import SingleTablePreset

The 'sdv.lite' module was deprecated and removed. Presets are now found directly under the respective data type modules.

load_dataset

✓ from sdv.datasets.demo import load_dataset

This quickstart demonstrates how to load a demo dataset, initialize a `GaussianCopulaSynthesizer` with the dataset's metadata, fit the synthesizer to the real data, and then sample synthetic data. This is a common workflow for single-table synthetic data generation.

import pandas as pd from sdv.single_table import GaussianCopulaSynthesizer from sdv.datasets.demo import load_dataset # 1. Load a demo dataset (returns an SDVData object with data and metadata) real_data = load_dataset('PUMS') # 2. Initialize a synthesizer, passing the metadata synthesizer = GaussianCopulaSynthesizer(metadata=real_data.metadata) # 3. Fit the synthesizer to the real data synthesizer.fit(real_data.data) # 4. Sample synthetic data synthetic_data = synthesizer.sample(num_rows=len(real_data.data)) print("Original data head:") print(real_data.data.head()) print("\nSynthetic data head:") print(synthetic_data.head())

Debug

Known issues

breakingSynthesizer import paths were changed in SDV v0.17.0. The `sdv.models` and `sdv.tabular` modules were removed.

fix

Update your imports. For single-table synthesizers, use `from sdv.single_table import ...`. For multi-table, `from sdv.multi_table import ...`, and for sequential, `from sdv.sequential import ...`.

affects: <0.17.0

gotchaWhile SDV can infer metadata, explicit metadata definition is often crucial for higher quality synthetic data, especially with complex schemas or specific data types (e.g., primary keys, relationships, sensitive columns).

fix

Manually create and pass `SingleTableMetadata` or `MultiTableMetadata` objects. Define primary keys, relationships, data types, and potentially privacy considerations explicitly for best results.

affects: All versions

gotchaGenerating synthetic data for very large datasets (millions of rows) or complex multi-table schemas can be memory-intensive and time-consuming.

fix

Consider downsampling your data for initial experimentation. Ensure your environment has sufficient RAM. For production-scale needs, explore SDV's performance optimization features or consider distributed processing frameworks if applicable.

affects: All versions

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'sdv.models'

Attempting to import a synthesizer from an old module path that was removed in SDV v0.17.0.

fix

Change your import statement from `from sdv.models import SynthesizerName` to `from sdv.single_table import SynthesizerName` (or `multi_table`/`sequential` as appropriate).

ValueError: The column '...' contains unsupported data types. Supported data types are numeric, boolean, datetime, and categorical.

SDV synthesizers have limitations on the types of data they can process directly (e.g., complex objects, nested lists, mixed types), or metadata inference incorrectly assigned a type.

fix

Preprocess your data to convert unsupported columns into one of the supported types. Explicitly define column types in your `sdv.metadata` object to guide the synthesizer.

NotEnoughDataError: Not enough data for synthesizer to learn from. Expected at least X rows but got Y rows.

The input dataset provided to `synthesizer.fit()` has too few rows for the selected synthesizer to effectively learn the underlying data patterns and statistical distributions.

fix

Ensure your training data has a sufficient number of rows (typically several dozens or hundreds at minimum, depending on complexity) to provide enough statistical information for the model. SDV is not designed for extremely small datasets.

Upgrade

Version history

1.37.1latest on PyPI · released Jun 11, 2026

Audit

Dependencies

No dependency data recorded yet.

Agent activity

15 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/sdv-dev/SDV ↗

packagehttps://pypi.org/project/sdv/ ↗

homepagehttps://docs.sdv.dev ↗

changeloghttps://github.com/sdv-dev/SDV/blob/main/HISTORY.md ↗