{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"CTGAN generates synthetic tabular data by training deep learning models (CTGAN and TVAE) on real datasets, learning patterns to produce high-fidelity synthetic records suitable for testing, privacy-preserving data sharing, or augmentation.","skillfed_tags":["synthetic-data","deep-learning","privacy"],"use_cases":["Generate synthetic test datasets for machine learning model development without exposing real customer or sensitive data.","Create privacy-preserving data samples for sharing with external teams or in research publications.","Augment imbalanced training datasets by synthesizing additional records for underrepresented classes.","Prototype data pipelines and validate ETL logic on realistic synthetic data before running against production.","Evaluate data quality and statistical properties of synthetic data generation models in research contexts."],"what_it_does":"CTGAN is a standalone deep learning library for generating synthetic tabular data using conditional GAN and TVAE models. It learns patterns from real data and produces synthetic records that preserve statistical properties while protecting privacy. The library is part of the Synthetic Data Vault ecosystem but can be used independently; it requires careful data preprocessing (no missing values, proper type handling) and depends on PyTorch for model training.\n\nTypical workflows involve loading real data, specifying which columns are discrete, training a model for a set number of epochs, and sampling synthetic records. The library is in Pre-Alpha stage, meaning its API and behavior may change. It's most suitable for research, testing, and non-production synthetic data needs where you control the licensing implications.","worth_installing":"Yes, with conditions. Install CTGAN if you need to generate synthetic tabular data and accept its Pre-Alpha maturity and BUSL-1.1 licensing restrictions. The library is actively maintained, has low install friction, and no known vulnerabilities. However, verify that the Business Source License aligns with your use case (commercial use may be restricted), and consider the SDV wrapper library if you need higher-level APIs and preprocessing support. Not recommended for production systems requiring stability guarantees."},"id":"ctgan","links":{"html":"https://skillfed.io/packages/ctgan","md":"https://skillfed.io/packages/ctgan.md","pypi":"https://pypi.org/project/ctgan/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-13","license_spdx":"BUSL-1.1","license_treatment":"unclear","name":"ctgan","python_support":"supports_current","summary":"Create tabular synthetic data using a conditional GAN"},"popularity":{"monthly_downloads":122961,"position":11927,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.12.1"}