This tutorial illustrates the basic features of the RDataFrame class, a utility which allows to interact with data stored in TTrees following a functional-chain like approach.
Author: Danilo Piparo
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Sunday, January 19, 2020 at 12:45 AM.
Welcome to JupyROOT 6.19/01
A simple helper function to fill a test tree: this makes the example stand-alone.
def fill_tree(treeName, fileName): tdf = ROOT.ROOT.RDataFrame(10) tdf.Define("b1", "(double) tdfentry_")\ .Define("b2", "(int) tdfentry_ * tdfentry_").Snapshot(treeName, fileName)
We prepare an input tree to run on
fileName = "df001_introduction_py.root" treeName = "myTree" fill_tree(treeName, fileName)
We read the tree from the file and create a RDataFrame, a class that allows us to interact with the data contained in the tree.
RDF = ROOT.ROOT.RDataFrame d = RDF(treeName, fileName)
Operations on the dataframe
We now review some actions which can be performed on the data frame.
All actions but ForEach return a TActionResultPtr
cutb1 = 'b1 < 5.' cutb1b2 = 'b2 % 2 && b1 < 4.'
Count allows to retrieve the number of the entries that passed the
filters. Here we show how the automatic selection of the column kicks
in in case the user specifies none.
entries1 = d.Filter(cutb1) \ .Filter(cutb1b2) \ .Count(); print("%s entries passed all filters" %entries1.GetValue()) entries2 = d.Filter("b1 < 5.").Count(); print("%s entries passed all filters" %entries2.GetValue())
2 entries passed all filters 5 entries passed all filters
These actions allow to retrieve statistical information about the entries
passing the cuts, if any.
b1b2_cut = d.Filter(cutb1b2) minVal = b1b2_cut.Min('b1') maxVal = b1b2_cut.Max('b1') meanVal = b1b2_cut.Mean('b1') nonDefmeanVal = b1b2_cut.Mean("b2") print("The mean is always included between the min and the max: %s <= %s <= %s" %(minVal.GetValue(), meanVal.GetValue(), maxVal.GetValue()))
The mean is always included between the min and the max: 1.0 <= 2.0 <= 3.0
Histo1D action allows to fill an histogram. It returns a TH1F filled
with values of the column that passed the filters. For the most common
types, the type of the values stored in the column is automatically
hist = d.Filter(cutb1).Histo1D('b1') print("Filled h %s times, mean: %s" %(hist.GetEntries(), hist.GetMean()))
Filled h 5.0 times, mean: 2.0
Express your chain of operations with clarity!
We are discussing an example here but it is not hard to imagine much more
complex pipelines of actions acting on data. Those might require code
which is well organised, for example allowing to conditionally add filters
or again to clearly separate filters and actions without the need of
writing the entire pipeline on one line. This can be easily achieved.
We'll show this re-working the
cutb1_result = d.Filter(cutb1); cutb1b2_result = d.Filter(cutb1b2); cutb1_cutb1b2_result = cutb1_result.Filter(cutb1b2)
Now we want to count:
evts_cutb1_result = cutb1_result.Count() evts_cutb1b2_result = cutb1b2_result.Count() evts_cutb1_cutb1b2_result = cutb1_cutb1b2_result.Count() print("Events passing cutb1: %s" %evts_cutb1_result.GetValue()) print("Events passing cutb1b2: %s" %evts_cutb1b2_result.GetValue()) print("Events passing both: %s" %evts_cutb1_cutb1b2_result.GetValue())
Events passing cutb1: 5 Events passing cutb1b2: 2 Events passing both: 2
Calculating quantities starting from existing columns Often, operations need to be carried out on quantities calculated starting from the ones present in the columns. We'll create in this example a third column the values of which are the sum of the b1 and b2 ones, entry by entry. The way in which the new quantity is defined is via a runable. It is important to note two aspects at this point:
entries_sum = d.Define('sum', 'b2 + b1') \ .Filter('sum > 4.2') \ .Count() print(entries_sum.GetValue())