The aim of this page is to demonstrate PyKX functionality that aligns with the approach and principles of the Pandas API for DataFrame interactions. Not all operations supported by Pandas are covered, only the operations on PyKX tables that follow Pandas API conventions. In particular, it focuses on areas where PyKX/q has the potential to provide a performance advantage over the use of Pandas. This advantage may be in the memory footprint of the operations and/or in the execution time of the analytic.
A full breakdown of the the available functionality and examples of its use can be found here.
Covered sections of the Pandas API
Coverage in this instance refers here to functionality covered by the PyKX API for Tables which has equivalent functionality to the methods and attributes supported by the Pandas DataFrame API. This does not cover the functionality supported by Pandas for interactions with Series objects or for reading/writing CSV/JSON files etc.
If there's any functionality you would like to see added to this library, please open an issue here or open a pull request here.