Abstract
In recent years, RDataFrame, ROOT’s high-level interface for data analysis and processing, has seen widespread adoption on the part of HEP physicists. Much of this success is due to RDataFrame’s ergonomic programming model that enables the implementation of common analysis tasks more easily than previous APIs, without compromising on application performance. Nonetheless, RDataFrame’s interfaces have been further improved by the recent addition of several major HEP-oriented features: in this contribution we will introduce for instance a dedicated syntax to define systematic variations, per-data-sample call-backs useful to define quantities that vary on a per-sample basis, simplifications of collection operations and the injection of just-in-time-compiled Python functions in the optimized C++ event loop.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
