The battery-data-toolkit, battdat, creates consistently-formatted collections of battery data. The library has three main purposes:

1. Storing battery data in standardized formats. battdat stores data in HDF5 or Parquet files which include extensive metadata.
2. Interfacing battery data with the PyData ecosystem. The core data model, BatteryDataset, is built atop Pandas DataFrames.
3. Providing standard implementations of common analysis techniques. battdat implements functions which ensure quality or perform common analyses.

Install battdat with pip: pip install battery-data-toolkit

Find the documentation at: https://rovi-org.github.io/battery-data-toolkit/

The motivation and funding for this project came from the Rapid Operational Validation Initiative (ROVI) sponsored by the Office of Electricity. The focus of ROVI is "to greatly reduce time required for emerging energy storage technologies to go from lab to market by developing new tools that will accelerate the testing and validation process needed to ensure commercial success." If interested, you can read more about ROVI here.