write_resource_batch

Warning

🚧 Sprout is still in active development and evolving quickly, so the documentation and functionality may not work as described and could undergo substantial changes 🚧

write_resource_batch(
    data: pl.DataFrame,
    resource_properties: ResourceProperties,
    package_path: Path | None = None,
)

Write the tidied data into the resource’s batch data folder.

Use this function to write the original data that is in a tidy Polars DataFrame to the resource folder provided by the path property in the resource_properties. The function saves the DataFrame as file with a timestamped, unique name, as a backup. See the design docs for an explanation of this batch file. Data is always checked against the resource_properties before it is written to the batch folder.

Parameters

data : pl.DataFrame: A Polars DataFrame object with the data to write to the batch folder.
resource_properties : ResourceProperties: The properties object for the specific resource. Use read_properties() to read the properties for the resource.
package_path : Path | None = None: The path to the data package root folder (where datapackage.json is located). Defaults to the current working directory.

Returns

Path: The path to the written Parquet resource file.

Raises

ExceptionGroup[CheckError]: If the resource properties are incorrect.
ValueError: If column names in the data are incorrect.
ExceptionGroup[ValueError]: If data types in the data are incorrect.

Examples

import seedcase_sprout as sp

with sp.ExamplePackage():
    resource_properties = sp.read_properties().resources[0]
    sp.write_resource_batch(
        data=sp.example_data(),
        resource_properties=resource_properties,
    )

PosixPath('/tmp/tmpbmx02dnu/example-package/resources/example-resource/batch/2025-06-26T013020Z-8af571f5-5c34-43f8-84fb-155681dd9be9.parquet')