# SDV Community

**SDV Community** is our publicly available synthetic data product. Use SDV Community to get started with exploring the benefits of synthetic data.

## Access the powerful SDV platform

SDV Community uses features throughout SDV platform ecosystem. The platform includes a suite of libraries and features that work together to form a one-stop shop for your synthetic data needs. You can also browse and use the platform features in a standalone way.

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><a href="https://docs.sdv.dev/rdt"><strong>RDT</strong></a></td><td>Preprocess and anonymize your data using reversible data transformers.</td><td><a href="https://docs.sdv.dev/rdt">https://docs.sdv.dev/rdt</a></td></tr><tr><td><a href="https://docs.sdv.dev/sdmetrics"><strong>SDMetrics</strong></a></td><td>Evaluate synthetic data for quality and privacy. Visualize &#x26; share results.</td><td><a href="https://docs.sdv.dev/sdmetrics">https://docs.sdv.dev/sdmetrics</a></td></tr><tr><td><a href="https://docs.sdv.dev/sdgym"><strong>SDGym</strong></a></td><td>Benchmark synthetic data generators across SDV and other libraries.</td><td><a href="https://docs.sdv.dev/sdgym">https://docs.sdv.dev/sdgym</a></td></tr></tbody></table>

## Installation

SDV Community is available as a Python SDK that you can install and use on-prem. It is distributed under the [Business Source License](https://github.com/sdv-dev/SDV/blob/main/LICENSE), and has been developed on Python [3.9-3.14](https://www.python.org/). As with most Python libraries, we recommend using a virtual environment (such as [virtualenv](https://virtualenv.pypa.io/en/latest/)) to avoid conflicts with other software on your device.

We recommend downloading SDV Community using [pip](https://pypi.org/project/sdv/) (or alternatively [conda](https://anaconda.org/conda-forge/sdv)).

```bash
pip install sdv
```

Then, open up Python and verify that SDV has installed correctly.

```python
import sdv
print(sdv.version.community)
```

For more information about the latest version of SDV Community, see the [Release Notes](https://github.com/sdv-dev/SDV/releases).

{% hint style="info" %}
**Having trouble?** Visit our [**troubleshooting section**](https://docs.sdv.dev/sdv/support/troubleshooting/installation) to diagnose any issues. You can also ask a question on our [forum](https://forum.datacebo.com/).
{% endhint %}

## What's next?

Once you've installed SDV, you're ready to create synthetic data.

```python
import pandas as pd
from sdv.single_table import GaussianCopulaSynthesizer
from sdv.metadata import Metadata

data = pd.read_csv('my_data_file.csv')
metadata = Metadata.detect_from_dataframe(data)

synthesizer = GaussianCopulaSynthesizer(metadata)
synthesizer.fit(data)
synthetic_data = synthesizer.sample(num_rows=1000)
```

**First time here?** Check out our [**Tutorials**](https://docs.sdv.dev/sdv/tutorials) to explore the features. The tutorials will walk you through creating synthetic data for single table, multi-table, and sequential data.