RDataFrame

RDataFrame is a high-level data analysis interface provided by the ROOT framework. It offers a declarative, dataframe-style API for processing data stored in ROOT TTrees, enabling concise pipelines and scalable performance for large datasets.

The API emphasizes lazy evaluation and automatic parallelization. Users compose a sequence of operations such as filtering, transforming columns, and performing aggregations. The actual data processing is executed only when a terminal action is invoked, allowing ROOT to optimize and parallelize work across available cores.

Key concepts and operations include filters to select events, Define to create new columns or derived quantities, Map and Foreach for per-entry processing, and Snapshot to write results back to a TTree. RDataFrame also provides built-in support for histograms and common aggregations like sums, means, counts, and more complex reductions. The framework is designed to integrate smoothly with ROOT I/O and performance-optimized data structures.

Under the hood, RDataFrame constructs an execution graph representing the pipeline and generates efficient code that can run in multi-threaded environments. This approach reduces the need for hand-written loops and promotes reproducible analyses. The system handles missing values and diverse data types commonly found in ROOT trees while aiming to minimize memory usage and maximize throughput through just-in-time code generation and vectorized processing where possible.

RDataFrame is part of ROOT’s data-analysis toolkit and is accessible from C++ and via PyROOT in Python, reflecting ROOT’s emphasis on interoperability and performance. It is widely used for scalable data exploration, quick prototyping of analysis ideas, and production-grade processing of large physics datasets.