Data Frame tutorials

These examples show various features of RDataFrame: ROOT's declarative analysis interface.

RDataFrame offers a high level interface for the analysis of data stored in TTrees, CSV files and other data formats.

In addition, multi-threading and other low-level optimisations allow users to exploit all the resources available on their machines transparently.

In a nutshell:

ROOT::EnableImplicitMT(); // Enable ROOT's implicit multi-threading
ROOT::RDataFrame d("myTree", "file_*.root"); // Interface to TTree and TChain
auto histoA = d.Histo1D("Branch_A");  // Book the filling of a histogram
auto histoB = d.Histo1D("Branch_B");  // Book the filling of another histogram
// Data processing is triggered by the next line, which accesses a booked result for the first time
// All booked results are evaluated during the same parallel event loop.
histoA->Draw(); // <-- event loop runs here!
histoB->Draw(); // HistoB has already been filled, no event loop is run here

Explore the examples below or go to RDataFrame's user guide.

Files
file	df000_simple.C
	Simple RDataFrame example in C++.
file	df000_simple.py
	Simple RDataFrame example in Python.
file	df001_introduction.C
	Basic RDataFrame usage.
file	df001_introduction.py
	Basic usage of RDataFrame from python.
file	df002_dataModel.C
	Show how to work with non-flat data models, e.g.
file	df002_dataModel.py
	Show how to work with non-flat data models, e.g.
file	df003_profiles.C
	Use TProfiles with RDataFrame.
file	df003_profiles.py
	Use TProfiles with RDataFrame.
file	df004_cutFlowReport.C
	Display cut/Filter efficiencies with RDataFrame.
file	df004_cutFlowReport.py
	Display cut/Filter efficiencies with RDataFrame.
file	df005_fillAnyObject.C
	Using the generic Fill action.
file	df006_ranges.C
	Use Range to limit the amount of data processed.
file	df006_ranges.py
	Use Range to limit the amount of data processed.
file	df007_snapshot.C
	Write ROOT data with RDataFrame.
file	df007_snapshot.py
	Write ROOT data with RDataFrame.
file	df008_createDataSetFromScratch.C
	Create data from scratch with RDataFrame.
file	df008_createDataSetFromScratch.py
	Create data from scratch with RDataFrame.
file	df009_FromScratchVSTTree.C
	Compare creation of a ROOT dataset with RDataFrame and TTree.
file	df010_trivialDataSource.C
	Use the "trivial data source", an example data source implementation.
file	df010_trivialDataSource.py
	Use the "trivial data source", an example data source implementation.
file	df012_DefinesAndFiltersAsStrings.C
	Use just-in-time-compiled Filters and Defines for quick prototyping.
file	df012_DefinesAndFiltersAsStrings.py
	Use just-in-time-compiled Filters and Defines for quick prototyping.
file	df013_InspectAnalysis.C
	Use callbacks to update a plot and a progress bar during the event loop.
file	df014_CSVDataSource.C
	Process a CSV file with RDataFrame and the CSV data source.
file	df014_CSVDataSource.py
	Process a CSV file with RDataFrame and the CSV data source.
file	df015_LazyDataSource.C
	Use the lazy RDataFrame data source to concatenate computation graphs.
file	df016_vecOps.C
	Process collections in RDataFrame with the help of RVec.
file	df016_vecOps.py
	Process collections in RDataFrame with the help of RVec.
file	df017_vecOpsHEP.C
	Use RVecs to plot the transverse momentum of selected particles.
file	df017_vecOpsHEP.py
	Use RVecs to plot the transverse momentum of selected particles.
file	df018_customActions.C
	Implement a custom action to fill THns.
file	df019_Cache.C
	Cache a processed RDataFrame in memory for further usage.
file	df019_Cache.py
	Cache a processed RDataFrame in memory for further usage.
file	df020_helpers.C
	Show usage of RDataFrame's helper tools, contained in ROOT/RDFHelpers.hxx.
file	df021_createTGraph.C
	Fill a TGraph using RDataFrame.
file	df021_createTGraph.py
	Fill a TGraph using RDataFrame.
file	df022_useKahan.C
	Implement a custom action that evaluates a Kahan sum.
file	df023_aggregate.C
	Use the Aggregate action to specify arbitrary data aggregations.
file	df024_Display.C
	Use the Display action to inspect entry values.
file	df024_Display.py
	Use the Display action to inspect entry values.
file	df025_RNode.C
	Manipulate RDF objects in functions, loops and conditional branches.
file	df026_AsNumpyArrays.py
	Read data from RDataFrame into Numpy arrays.
file	df027_SQliteDependencyOverVersion.C
	Plot the ROOT downloads based on the version reading a remote sqlite3 file.
file	df028_SQliteIPLocation.C
	Plot the location of ROOT downloads reading a remote sqlite3 file.
file	df029_SQlitePlatformDistribution.C
	Use RDataFrame to display data about ROOT downloads.
file	df030_SQliteVersionsOfROOT.C
	Read an sqlite3 databases with RDataFrame and plot statistics on ROOT downloads.
file	df031_Stats.C
	Use the Stats action to extract the statistics of a column.
file	df031_Stats.py
	Use the Stats action to extract the statistics of a column.
file	df032_MakeNumpyDataFrame.py
	Read data from Numpy arrays into RDataFrame.
file	df033_Describe.py
	Get information about the dataframe with the convenience method Describe.
file	df101_h1Analysis.C
	Show how to express ROOT's standard H1 analysis with RDataFrame.
file	df102_NanoAODDimuonAnalysis.C
	Show how NanoAOD files can be processed with RDataFrame.
file	df102_NanoAODDimuonAnalysis.py
	Show how NanoAOD files can be processed with RDataFrame.
file	df103_NanoAODHiggsAnalysis.C
	An example of complex analysis with RDataFrame: reconstructing the Higgs boson.
file	df103_NanoAODHiggsAnalysis.py
	An example of complex analysis with RDataFrame: reconstructing the Higgs boson.
file	df103_NanoAODHiggsAnalysis_python.h
	Header file with functions needed to execute the Python version of the NanoAOD Higgs tutorial.
file	df104_HiggsToTwoPhotons.py
	The Higgs to two photons analysis from the ATLAS Open Data 2020 release, with RDataFrame.
file	df105_WBosonAnalysis.py
	The W boson mass analysis from the ATLAS Open Data release of 2020, with RDataFrame.
file	df106_HiggsToFourLeptons.py
	The Higgs to four lepton analysis from the ATLAS Open Data release of 2020, with RDataFrame.
file	df107_SingleTopAnalysis.py
	A single top analysis using the ATLAS Open Data release of 2020, with RDataFrame.
file	distrdf001_spark_connection.py
	Configure a Spark connection and fill two histograms distributedly.
file	distrdf002_dask_connection.py
	Configure a Dask connection and fill two histograms distributedly.