# SDGym Datasets¶

SDGym uses SDV datasets to benchmark the Synthesizers which are in three data modalities:

• Single Table Datasets: Datasets that contain only one table with no inter-row dependencies.

• Multi Table Datasets: Datasets that contain more than one table, potentially with relationships between them.

• Time Series Datasets: Datasets that contain a single table that represents sequences of rows.

## Dataset Format¶

The SDV Datasets are comprised of two elements:

• A metadata.json file which describes the data found in the dataset. This file follows the SDV Metadata schema

• A collection of CSV files stored in a format which can be loaded by the pandas.read_csv function without any additional arguments than the csv path.

## Using the datasets¶

All the datasets can also be found for download inside the sdv-datasets S3 bucket in the form of a .zip file that contains both the metadata.json and the CSV file collection.

In order to load these datasets in the same format as they will be passed to your synthesizer you can use the sdgym.load_dataset function passing the name of the dataset to load.

In this example, we will load the adult dataset:

In [1]: from sdgym.datasets import load_dataset



This will read the metadata.json file and return it as a sdv.Metadata instance.

In [3]: metadata
Out[3]:
relationships:


Afterwards, you can load the tables from the dataset passing the loaded metadata to the sdgym.load_tables function:

In [4]: from sdgym.datasets import load_tables



This will return a dict containing the tables loaded as pandas.DataFrames.

In [6]: tables
Out[6]:
{'adult':        age  workclass  fnlwgt     education  education-num  ... capital-gain capital-loss hours-per-week native-country  label
0       27    Private  177119  Some-college             10  ...            0            0             44  United-States  <=50K
1       27    Private  216481     Bachelors             13  ...            0            0             40  United-States  <=50K
2       25    Private  256263    Assoc-acdm             12  ...            0            0             40  United-States  <=50K
3       46    Private  147640       5th-6th              3  ...            0         1902             40  United-States  <=50K
4       45    Private  172822          11th              7  ...            0         2824             76  United-States   >50K
...    ...        ...     ...           ...            ...  ...          ...          ...            ...            ...    ...
32556   43  Local-gov   33331       Masters             14  ...            0            0             40  United-States   >50K
32557   44    Private   98466          10th              6  ...            0            0             35  United-States  <=50K
32558   23    Private   45317  Some-college             10  ...            0            0             40  United-States  <=50K
32559   45  Local-gov  215862     Doctorate             16  ...         7688            0             45  United-States   >50K
32560   25    Private  186925  Some-college             10  ...         2597            0             48  United-States  <=50K

[32561 rows x 15 columns]}


## Getting the list of all the datasets¶

If you want to obtain the list of all the available datasets you can use the sdgym.datasets.get_available_datasets function:

In [7]: from sdgym.datasets import get_available_datasets

In [8]: get_available_datasets()
Out[8]:
name      size
0                 Accidents_v1  44717026
1    ArticularyWordRecognition   1928334
2           Atherosclerosis_v1    521308
3           AtrialFibrillation    111036
4        AustralianFootball_v1   3500419
..                         ...       ...
99      student_placements_pii     11602
100                  trains_v1      1772
101              university_v1      3226
102                    walmart   3566966
103                   world_v1    110291

[104 rows x 2 columns]