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.

get_available_demos

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

Parameters

• modality: Set this to the string 'sequential' to see all the sequential demo datasets

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

The SDV currently only sequential data that is present in a single table. If you use the 'sequential' modality, the number of tables is always 1.

from sdv.datasets.demo import get_available_demos

get_available_demos(modality='sequential')

dataset_name                    size_MB        num_tables
ArticularyWordRecognition       8.8            1
AtrialFibrillation              0.627          1
BasicMotions                    0.741          1
...                             ...            ...

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 'sequential' to access sequential 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 pandas DataFrame containing the demo data and the metadata is a Metadata object the describes the data.

from sdv.datasets.demo import download_demo

data, metadata = download_demo(
    modality='sequential',
    dataset_name='ArticularyWordRecognition',
    output_folder_name='sdv_demo_datasets/word_data/'
)

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.

load_csvs

Use this method to load any datasets that are stored as CSVs.

Parameters

• (required) folder_name: A string with the name of the folder where the datasets are stored

• read_csv_parameters: A dictionary with any optional parameters needed for loading the CSV data. Set the pandas.read_csv documentation for a full list of options.

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 1 CSV file named 'patient_data.csv'
datasets = load_csvs(folder_name='my_folder/')

patient_object = datasets['patient_data']

Where's the metadata? If you're loading your own datasets, please create and load in your metadata separately. See the Sequential 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.

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 = pd.read_excel('file://localhost/path/to/table.xlsx')

For more options, see the pandas reference.