# Demo Data

The SDV library contains many different demo datasets that you can use to get started. Use the `demo` module to access these datasets.

{% hint style="warning" %}
The `demo` module accesses the SDV's public dataset repository. These methods require an internet connection.
{% endhint %}

### get\_available\_demos

Use this method to get information about all the available demos in the SDV's public dataset repository.

**Parameters**

* (required) `modality`: Set this to the string `'multi_table'` to see all the multi table demo datasets
* *\[private]* `s3_bucket_name`: Additional, private demo datasets may be available in other buckets. If you have been given access to one of these buckets, you can input the name as a string. *This is currently only available to internal, DataCebo users.*
* *\[private]* `credentials`: Provide a dictionary with the SDV credentials in order to access the bucket. The expected keys are `'username'` and `'license_key'`. *This is currently only available to internal, DataCebo users.*

**Returns** A pandas DataFrame object containing the name of the dataset, its size (in MB) and the number of tables it contains.

```python
from sdv.datasets.demo import get_available_demos

get_available_demos(modality='multi_table')
```

```
dataset_name            size_MB        num_tables
Accidents_v1            172.3          3
airbnb-simplified       371.5          2
Atherosclerosis_v1      2.9            4                     
...                     ...            ...
```

### download\_demo

Use this method to download a demo dataset from the SDV's public dataset repository.

**Parameters**

* (required) `modality`: Set this to the string `'multi_table'` to access multi table demo data
* (required) `dataset_name`: A string with the name of the demo dataset. You can use any of the dataset names from the `get_available_demo` method.
* `output_folder_name`: A string with the name of a folder. If provided, this method will download the data and metadata into the folder, in addition to returning the data.
  * (default) `None`: Do not save the data into a folder. The data will still be returned so that you can use it in your Python script.
* *\[private]* `s3_bucket_name`: Additional, private demo datasets may be available in other buckets. If you have been given access to one of these buckets, you can input the name as a string. *This is currently only available to internal, DataCebo users.*
* *\[private]* `credentials`: Provide a dictionary with the SDV credentials in order to access the bucket. The expected keys are `'username'` and `'license_key'`. *This is currently only available to internal, DataCebo users.*

**Output** A tuple `(data, metadata)`.

The `data` is a dictionary that maps each table name to a pandas DataFrame containing the demo data for that table. The `metadata` is a [Metadata](https://docs.sdv.dev/sdv/concepts/metadata) object the describes the data.

```python
from sdv.datasets.demo import download_demo

data, metadata = download_demo(
    modality='multi_table',
    dataset_name='fake_hotels'
)

guests_table = data['guests']
hotels_table = data['hotels']
```

### get\_source

Some datasets have a source file that describes where the dataset comes from. This can include information like a URL, citations for the original publication, and other information that tracks the dataset's provenance. Use this function to get all this source information.

**Parameters**

* (required) `modality`: Set this to the string `'multi_table'` to  access multi table demo data
* (required) `dataset_name`: A string with the name of the demo dataset. You can use any of the dataset names from the `get_available_demo` method.
* `output_filepath`: A string with the name of a file path. If provided, this method will create the file and write the source information to the file, in addition to returning it.
  * (default) `None`: Do not save the source information into a file. The contents will still be returned so that you can print it out and read it.
* *\[private]* `s3_bucket_name`: Additional, private demo datasets may be available in other buckets. If you have been given access to one of these buckets, you can input the name as a string. *This is currently only available to internal, DataCebo users.*
* *\[private]* `credentials`: Provide a dictionary with the SDV credentials in order to access the bucket. The expected keys are `'username'` and `'license_key'`. *This is currently only available to internal, DataCebo users.*

**Output** A string containing the contents of the source information. You can print it out to read it. *(If no source information is available for a dataset, the function returns `None` and no file will be written.)*

```python
from sdv.datasets.demo import get_source

source_text = get_source(
    modality='multi_table',
    dataset_name='Bupa',
    output_filepath='Bupa_source.txt'
)
print(source_text)
```

```
Source URL: https://archive.ics.uci.edu/dataset/60/liver+disorders
License name: Creative Commons Attribution 4.0 International

Citations:
[1] Liver Disorders [Dataset]. (2016). UCI Machine Learning Repository. https://doi.org/10.24432/C54G67.
```

### get\_readme

Some datasets have a README file that describes more information about what the dataset means. This could include explanations for naming conventions used in the dataset, mappings for ID codes, or business logic. Use this function to get the README (if it exists).

{% hint style="info" %}
**README information is coming soon!** At this time, SDV demo datasets do not contain any README information. If you're looking for more information about the dataset, we recommend [getting the source](#get_source). From there, you'll be able to navigate to any URLs or contact the original authors as needed.  
{% endhint %}

**Parameters**

* (required) `modality`: Set this to the string `'multi_table'` to  access multi table demo data
* (required) `dataset_name`: A string with the name of the demo dataset. You can use any of the dataset names from the `get_available_demo` method.
* `output_filepath`: A string with the name of a file path. If provided, this method will create the file and write the README information to the file, in addition to returning it.
  * (default) `None`: Do not save the README information into a file. The contents will still be returned so that you can print it out and read it.
* *\[private]* `s3_bucket_name`: Additional, private demo datasets may be available in other buckets. If you have been given access to one of these buckets, you can input the name as a string. *This is currently only available to internal, DataCebo users.*
* *\[private]* `credentials`: Provide a dictionary with the SDV credentials in order to access the bucket. The expected keys are `'username'` and `'license_key'`. *This is currently only available to internal, DataCebo users.*

**Output** A string containing the contents of the README information. You can print it out to read it. *(If no README information is available for a dataset, the function returns `None` and no file will be written.)*