Which decorator should you use to define a Transform in Foundry that processes input dataframes and outputs multiple datasets?

To define a Transform in Foundry that processes input dataframes and outputs multiple datasets, using the @transform decorator is the appropriate choice. This decorator allows you to create a function that processes given input data and produces multiple outputs, making it ideal for complex data transformations that require handling more than one output dataset.

The @transform decorator is versatile and is specifically designed to accommodate a variety of data transformation scenarios. It effectively maps input dataframes to multiple output datasets, providing the necessary framework to manage transformations and output types in an organized manner.

The other decorators, while they may serve specific use cases, are not suited for handling multiple outputs in the same way. For example, @transform_df typically deals with single dataframe transformations, limiting its usage. @transform_pandas might focus more on operations specific to pandas DataFrames, which may not directly relate to the broader use-case of multiple outputs. @transform_file is often used for file operations that may not involve direct transformations of dataframes.

Thus, for the requirement of processing input dataframes and generating multiple datasets, the @transform decorator stands out as the ideal solution.