Featuretools is an open-source Python library for automated feature engineering. It excels at transforming temporal and relational datasets into feature matrices suitable for machine learning. The library, currently at version 1.31.0, is actively maintained by Alteryx and follows a frequent release cadence, often introducing new features and improvements.
pip install featuretoolsVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to load a multi-table dataset into an EntitySet, define a target dataframe, and then use Deep Feature Synthesis (DFS) to automatically generate a rich set of features. It utilizes built-in aggregation and transform primitives to create new meaningful features for a machine learning task.
Convert Dask or PySpark DataFrames to pandas DataFrames before creating an EntitySet. For Dask, use `.compute()` to get a pandas DataFrame.
All CLI functionalities must now be performed programmatically within Python scripts.
Refer to the 'Transitioning to Featuretools Version 1.0' guide for detailed migration steps. Key changes include using `Woodwork DataFrames` and accessing column metadata via the `.ww` accessor on DataFrames within an EntitySet.
Install Dask with `pip install "featuretools[dask]"` or `pip install dask[dataframe]` before running parallel computations.