# CTGAN for Synthetic Tabular Data: Standalone Python Use

> CTGAN is a Python library for learning from one real table and generating synthetic rows with a conditional GAN. It fits developers who want direct control over preprocessing, model fitting, and sampling, although the README labels it Pre-Alpha.

**sdv-dev/CTGAN** — Conditional GAN for generating synthetic tabular data.

- Repository: https://github.com/sdv-dev/CTGAN
- Stars: 1,577 · Forks: 333
- Language: Python
- License: NOASSERTION
- Published: 2026-10-16 · Updated: 2026-10-16 · Language: en
- Canonical page: https://hysenlabs.com/projects/sdv-dev-ctgan

## What CTGAN Does With One Table

CTGAN addresses a specific data problem: generating synthetic records for a single table whose columns may include both continuous and discrete values. The README describes it as a collection of deep learning based synthetic data generators for single-table data. That scope is narrower than a full synthetic data platform. It does not describe multi-table workflows, time-series modeling, or data discovery in this standalone project.

The intended audience is developers working in Python who already understand the shape of their table and need to call a generator directly. A data scientist might use it to create records for experimentation with systems that expect tabular input, although the README does not claim a particular downstream use. It is also relevant to researchers who need access to the CTGAN model rather than a higher-level wrapper.

The library implements CTGAN and TVAE, both described as models from the paper "Modeling Tabular data using Conditional GAN," presented at NeurIPS 2019. The repository topics identify the project with data generation, generative adversarial networks, synthetic data, and synthetic data generation. Those descriptions explain the intended domain, but they do not establish that generated output will satisfy a particular quality, fairness, or privacy threshold. The README does not document a formal evaluation procedure for a specific dataset.

## The CTGAN Fit and Sampling Interface

The documented workflow is short and explicit. The example loads the built-in Adult Census dataset, identifies discrete columns by name, constructs `CTGAN(epochs=10)`, calls `fit(real_data, discrete_columns)`, and then calls `sample(1000)` to request 1,000 synthetic rows. This is the clearest confirmed data flow. An input table and a list of discrete column names enter the model, and a sampled table comes back.

The interface separates continuous and discrete columns through metadata rather than asking the example to convert every value into one representation. The README still requires preprocessing before fitting. Continuous data must be represented as floats, discrete data as ints or strings, and the table must contain no missing values. Those are input conditions, not optional suggestions.

The repository description calls CTGAN a conditional GAN, and the README says the library implements the model described in the cited paper. The public documentation does not expose the internal network layout, loss functions, sampling implementation, or preprocessing sequence. A team needing exact architecture details or debugging access to training internals would need to inspect the `ctgan/` package and its tests rather than relying on the README alone. The example demonstrates API usage, not the full internal architecture.

## Installing CTGAN and Running the First Example

The README offers two documented installation routes for the standalone library. The first uses `pip`, which installs the package into the active Python environment.

```bash
pip install ctgan
```

The second route uses Conda channels for PyTorch and conda-forge.

```bash
conda install -c pytorch -c conda-forge ctgan
```

After installation, the README's example imports `CTGAN` and `load_demo`, loads the Adult Census data, lists the discrete columns, fits the model for 10 epochs, and samples 1,000 rows.

```python
from ctgan import CTGAN
from ctgan import load_demo

real_data = load_demo()
discrete_columns = [
    'workclass', 'education', 'marital-status', 'occupation',
    'relationship', 'race', 'sex', 'native-country', 'income'
]

ctgan = CTGAN(epochs=10)
ctgan.fit(real_data, discrete_columns)
synthetic_data = ctgan.sample(1000)
```

The expected result is a generated table held in `synthetic_data`. The README does not specify a command-line interface, a particular output file format, or a documented way to inspect the generated rows after this example. The package metadata requires Python `>=3.9,<3.15`, with dependency ranges that vary by Python version.

## Preprocessing Constraints Before CTGAN Training

The standalone package places more responsibility on the caller than the SDV route. There is no automatic missing-value handling documented for standalone use. Continuous columns should be floats. Discrete columns should be ints or strings. A column selected in `discrete_columns` should therefore correspond to the categorical fields that the application expects the model to treat as discrete.

This creates an operational boundary. A table containing nulls, mixed numeric representations, or unexpected category values will not meet the documented input conditions before training. The README does not state whether `fit` raises an error, imputes values, or applies a particular encoding for every invalid case. It also does not document categorical vocabularies, unseen-category handling, or schema validation. Those points matter when the same generator will run in a batch pipeline.

The SDV route is explicitly presented as the recommended option for people getting started with synthetic data. According to the README, SDV supplies wrappers for preprocessing and usability features such as constraints. Standalone CTGAN is still useful when the caller wants the model directly, but that choice means accepting more preparation work and a narrower documented feature set.

## Standalone CTGAN Against the SDV Wrapper

The real alternative described by the README is the SDV library, not another independent GAN project. SDV exposes user-friendly APIs for accessing CTGAN and adds preprocessing wrappers and constraints. Its approach is higher level: the caller works through SDV interfaces while the underlying generator remains CTGAN. The standalone CTGAN library exposes the model directly and leaves data preparation to the caller.

That difference affects adoption decisions. A team with a controlled preprocessing pipeline may prefer standalone CTGAN because the model interface is small and explicit. A team that receives raw business tables, needs constraints, or wants the broader synthetic data ecosystem may find SDV more suitable. The README links to SDV documentation for those workflows, but it does not compare installation size, runtime, model defaults, evaluation behavior, or license terms between the two packages.

The repository also lists an R interface for CTGAN and CTGAN Server CLI. The README explicitly says these projects are external to the SDV ecosystem and are not affiliated with or maintained by DataCebo. They are therefore references for further investigation, not maintained components of this repository. The project also does not document rollback or a compatibility policy for saved models in the available files.

## Maintenance, Releases, and BUSL-1.1

The repository is not archived, and its last push was on 2026-10-13. Recent releases include `v0.12.1` on 2026-02-13, `v0.12.0` on 2026-01-27, and `v0.11.1` on 2025-10-30. The package metadata identifies the development status as `Development Status :: 2 - Pre-Alpha`, and the README repeats that designation. These facts indicate repository activity without removing the stated pre-alpha status.

The `pyproject.toml` file declares `license = 'BUSL-1.1'` and identifies `LICENSE` as the license file. The repository context classifies the license as Other because GitHub cannot classify the custom license. This is a material adoption constraint, particularly for organizations that need to know whether a release can be used, modified, redistributed, or offered as a service. This review does not interpret the license or provide legal advice. Review the actual `LICENSE` file and compare it with the organization's approved license list before deployment.

Upgrade cost is not quantified in the README. The changing release line and Python-version ranges mean that dependency compatibility should be checked before upgrading an environment that already contains Torch, NumPy, pandas, and related packages. The project has recent pushes and releases, but adoption decisions should account for its Pre-Alpha classification.

## Choosing CTGAN for a Production Data Workflow

CTGAN is a focused choice for developers who want to instantiate the model themselves and already control table preparation. The README example provides a complete basic path from demo data to a sampled table, and the project cites the original conditional GAN research. It does not, however, promise privacy guarantees, exact preservation of marginal distributions, constraint enforcement in standalone mode, or production support.

The wrong choice is a dataset with missing values that the caller cannot preprocess, or a workflow that requires automatic business constraints and higher-level data preparation. SDV is the documented alternative for that need. CTGAN may also be a poor fit when a team needs multi-table relationships or time-series dependencies, since the README defines this library around single-table generators.

Adopt CTGAN when the team can inspect and evaluate every generated column, understand the BUSL-1.1 terms, and accept a Pre-Alpha library with a PyTorch-centered dependency stack. Do not adopt it merely because the model can produce rows. First verify the exact input schema, the license, and whether the generated data passes the project's own fidelity, usefulness, and privacy checks.

## Conclusion

CTGAN is a reasonable choice for Python developers who want direct access to a conditional tabular GAN and can prepare clean, single-table input themselves. It is a weaker fit for users who need built-in preprocessing, constraints, or a less experimental interface, because the README recommends SDV for those cases. Verify the BUSL-1.1 license, confirm the supported Python version, and evaluate the generated table against the project's fidelity and privacy requirements before adoption.

## FAQ

### How do I install CTGAN for Python?

The README gives `pip install ctgan` for a standalone installation. It also documents `conda install -c pytorch -c conda-forge ctgan` for Conda users. The package metadata requires Python `>=3.9,<3.15`.

### How do I generate synthetic rows with CTGAN?

Load a table, list its discrete column names, create `CTGAN(epochs=10)`, call `fit(real_data, discrete_columns)`, and call `sample(1000)`. The README says continuous data should be floats, discrete data should be ints or strings, and missing values should be removed.

### Should I use CTGAN or SDV for synthetic tabular data?

The README recommends SDV for users getting started with synthetic data because it provides user-friendly APIs, preprocessing wrappers, and usability features such as constraints. Standalone CTGAN is the direct alternative when the developer wants to use the CTGAN library itself and handle preprocessing manually.

## Sources

- [Issues](https://github.com/sdv-dev/CTGAN/issues)
- [README](https://github.com/sdv-dev/CTGAN/blob/main/README.md)
- [Releases](https://github.com/sdv-dev/CTGAN/releases)
- [sdv-dev/CTGAN on GitHub](https://github.com/sdv-dev/CTGAN)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/sdv-dev-ctgan