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On this page
  • Demo Data
  • get_available_demos
  • download_demo
  • Loading your own (local) datasets
  • load_csvs
  • Do you have data in other formats?
  1. Single Table Data
  2. Data Preparation

Loading Data

PreviousData PreparationNextCreating Metadata

Last updated 7 months ago

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 'single_table' to see all the single table demo datasets

Returns A 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

get_available_demos(modality='single_table')
dataset_name        size_MB        num_tables
adult               3.6            1
alarm               4.6            1
census              141.2          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 'single_table' to access single 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).

from sdv.datasets.demo import download_demo

data, metadata = download_demo(
    modality='single_table',
    dataset_name='fake_hotel_guests'
)

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

from sdv.datasets.local import load_csvs

# assume that my_folder contains a CSV file named 'guests.csv'
datasets = load_csvs(
    folder_name='my_folder/',
    read_csv_parameters={
        'skipinitialspace': True,
        'encoding': 'utf_32'
    })

# the data is available under the file name
data = datasets['guests']

Where's the metadata? If you're loading your own datasets, please create and load in your metadata separately. See the Metadata guide for more details.

Do you have data in other formats?

import pandas as pd

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

The data is a containing the demo data and the metadata is a object the describes the data.

read_csv_parameters: A dictionary with additional parameters to use when reading the CSVs. The keys are any of the parameter names of the 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 containing the data.

The SDV uses the for data manipulation and synthesizing. If your data is in any other format, load it in as a object to use in the SDV.

Pandas offers many methods to load in different types of data. For example: , or .

For more options, see the .

pandas DataFrame
pandas DataFrame
Metadata
pands.read_csv
pandas DataFrame
pandas library
pandas.DataFrame
Excel file
SQL table
JSON string
pandas reference