Loading Data

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.
The demo module accesses the SDV's public dataset repository. These methods require an internet connection.


Use this method to get information about all the available demos in the SDV's public dataset repository.
  • modality: Set this to the string 'multi_table' to see all the multi table demo datasets
Returns A pandas DataFrame object containing the name of the dataset, its size (in MB) and the number of tables it contains.
from sdv.datasets.demo import get_available_demos
dataset_name size_MB num_tables
Accidents_v1 172.3 3
airbnb-simplified 371.5 2
Atherosclerosis_v1 2.9 4
... ... ...


Use this method to download a demo dataset from the SDV's public dataset repository.
  • (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.
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 MultiTableMetadata object the describes the data.
from sdv.datasets.demo import download_demo
data, metadata = download_demo(
guests_table = data['guests']
hotels_table = data['hotels']

Loading your own local datasets

A local dataset is a dataset that you have already downloaded onto your computer. These do not require any internet connectivity to access.


Use this method to load any datasets that are stored as CSVs.
  • (required) folder_name: A string with the name of the folder where the datasets are stored
  • read_csv_parameters: A dictionary with additional parameters to use when reading the CSVs. The keys are any of the parameter names of the pands.read_csv function and the values are your inputs.
Returns A dictionary that contains all the CSV data found in the folder. The key is the name of the file (without the .csv suffix) and the value is a pandas DataFrame containing the data.
from sdv.datasets.local import load_csvs
# assume that my_folder contains many CSV files
datasets = load_csvs(
'skipinitialspace': True,
'encoding': 'utf_32'
# the data is available under the file name
guests_table = datasets['guests']
hotels_table = datasets['guests']
Where's the metadata? If you're loading your own datasets, please create and load in your metadata separately. See the Multi Table Metadata API guide for more details.

Do you have data in other formats?

The SDV uses the pandas library for data manipulation and synthesizing. If your data is in any other format, load it in as a pandas.DataFrame object to use in the SDV. For multi table data, make sure you format your data as a dictionary, mapping each table name to a different DataFrame object.
multi_table_data = {
'table_name_1': <pandas.DataFrame>,
'table_name_2': <pandas.DataFrame>,
Pandas offers many methods to load in different types of data. For example: Excel file, SQL table or JSON string.
import pandas as pd
data_table_1 = pd.read_excel('file://localhost/path/to/table_1.xlsx')
data_table_2 = pd.read_excel('file://localhost/path/to/table_2.xlsx')
For more options, see the pandas reference.
Copyright (c) 2023, DataCebo, Inc.