ROOT's TDataFrame offers a high level interface for analyses of data stored in TTrees.

In addition, multi-threading and other low-level optimisations allow users to exploit all the resources available on their machines completely transparently.
Skip to the class reference or keep reading for the user guide.

In a nutshell:

ROOT::EnableImplicitMT(); // Tell ROOT you want to go parallel
ROOT::Experimental::TDataFrame d("myTree", "file.root"); // Interface to TTree and TChain
auto myHisto = d.Histo1D("Branch_A"); // This happens in parallel!
myHisto->Draw();

Calculations are expressed in terms of a type-safe functional chain of actions and transformations, TDataFrame takes care of their execution. The implementation automatically puts in place several low level optimisations such as multi-thread parallelisation and caching. The namespace containing the TDataFrame is ROOT::Experimental. This signals the fact that the interfaces may evolve in time.

Introduction

Users define their analysis as a sequence of operations to be performed on the data-frame object; the framework takes care of the management of the loop over entries as well as low-level details such as I/O and parallelisation. TDataFrame provides methods to perform most common operations required by ROOT analyses; at the same time, users can just as easily specify custom code that will be executed in the event loop.

TDataFrame is built with a modular and flexible workflow in mind, summarised as follows:

build a data-frame object by specifying your data-set
apply a series of transformations to your data
1. filter (e.g. apply some cuts) or
2. define a new column (e.g. the result of an expensive computation on branches)
apply actions to the transformed data to produce results (e.g. fill a histogram)

The following table shows how analyses based on TTreeReader and TTree::Draw translate to TDataFrame. Follow the crash course to discover more idiomatic and flexible ways to express analyses with TDataFrame.

TTreeReader	ROOT::Experimental::TDataFrame
TTreeReader reader("myTree", file); TTreeReaderValue<A_t> a(reader, "A"); TTreeReaderValue<B_t> b(reader, "B"); TTreeReaderValue<C_t> c(reader, "C"); while(reader.Next()) { if(IsGoodEvent(a, b, c)) DoStuff(a, b, c); }	ROOT::Experimental::TDataFrame d("myTree", file, {"A", "B", "C"}); d.Filter(IsGoodEvent).Foreach(DoStuff);
TTree::Draw	ROOT::Experimental::TDataFrame
TTree t = static_cast<TTree>( file->Get("myTree") ); t->Draw("var", "var > 2");	ROOT::Experimental::TDataFrame d("myTree", file); auto h = d.Filter("var > 2").Histo1D("var");

Crash course

All snippets of code presented in the crash course can be executed in the ROOT interpreter. Simply precede them with

using namespace ROOT::Experimental; // TDataFrame's namespace

which is omitted for brevity. The terms "column" and "branch" are used interchangeably.

Creating a TDataFrame

TDataFrame's constructor is where the user specifies the dataset and, optionally, a default set of columns that operations should work with. Here are the most common methods to construct a TDataFrame object:

// single file -- all ctors are equivalent
TDataFrame d1("treeName", "file.root");
TFile *f = TFile::Open("file.root");
TDataFrame d2("treeName", f); // same as TTreeReader
TTree *t = nullptr;
f.GetObject("treeName", t);
TDataFrame d3(*t); // TTreeReader takes a pointer, TDF takes a reference
// multiple files -- all ctors are equivalent
TDataFrame d3("myTree", {"file1.root", "file2.root"});
std::vector<std::string> files = {"file1.root", "file2.root"};
TDataFrame d3("myTree", files);
TDataFrame d4("myTree", "file*.root"); // see TRegexp's documentation for a list of valid regexes
TChain chain("myTree");
chain.Add("file1.root");
chain.Add("file2.root");
TDataFrame d3(chain);

Additionally, users can construct a TDataFrame specifying just an integer number. This is the number of "events" that will be generated by this TDataFrame.

TDataFrame d(10); // a TDF with 10 entries (and no columns/branches, for now)

d.Foreach([] { static int i = 0; std::cout << i++ << std::endl; }); // silly example usage: count to ten

This is useful to generate simple data-sets on the fly: the contents of each event can be specified via the Define transformation (explained below). For example, we have used this method to generate Pythia events (with a Define transformation) and write them to disk in parallel (with the Snapshot action).

Programmatically get the list of column names

The list of column names available in the dataset can be obtained with the GetColumnsNames method:

TDataFrame d("myTree", "file.root");
auto colNames = d.GetColumnNames();
for (auto &&colName : colNames) {
   std::cout << colName << std::endl;
   }

Filling a histogram

Let's now tackle a very common task, filling a histogram:

// Fill a TH1D with the "MET" branch
TDataFrame d("myTree", "file.root");
auto h = d.Histo1D("MET");
h->Draw();

The first line creates a TDataFrame associated to the TTree "myTree". This tree has a branch named "MET".

Histo1D is an action; it returns a smart pointer (a TResultProxy to be precise) to a TH1D histogram filled with the MET of all events. If the quantity stored in the branch is a collection (e.g. a vector or an array), the histogram is filled with its elements.

You can use the objects returned by actions as if they were pointers to the desired results. There are many other possible actions, and all their results are wrapped in smart pointers; we'll see why in a minute.

Applying a filter

Let's say we want to cut over the value of branch "MET" and count how many events pass this cut. This is one way to do it:

TDataFrame d("myTree", "file.root");
auto c = d.Filter("MET > 4.").Count();
std::cout << *c << std::endl;

The filter string (which must contain a valid c++ expression) is applied to the specified branches for each event; the name and types of the columns are inferred automatically. The string expression is required to return a bool which signals whether the event passes the filter (true) or not (false).

You can think of your data as "flowing" through the chain of calls, being transformed, filtered and finally used to perform actions. Multiple Filter calls can be chained one after another.

Using string filters is nice for simple things, but they are limited to specifying the equivalent of a single return statement or the body of a lambda, so it's cumbersome to use strings with more complex filters. They also add a small runtime overhead, as ROOT needs to just-in-time compile the string into C++ code. When more freedom is required or runtime performance is very important, a C++ callable can be specified instead (a lambda in the following snippet, but it can be any kind of function or even a functor class), together with a list of branch names. This snippet is analogous to the one above:

TDataFrame d("myTree", "file.root");
auto metCut = [](double x) { return x > 4.; }; // a c++11 lambda function checking "x > 4"
auto c = d.Filter(metCut, {"MET"}).Count();
std::cout << *c << std::endl;

An example of a more complex filter expressed as a string containing C++ code is shown below

TDataFrame d("myTree", "file.root");
auto df = d.Define("p", "std::array<double, 4> p{px, py, pz, E}; return p;")
           .Filter("double p2 = 0.0; for (auto&& x : p) p2 += x*x; return sqrt(p2) < 10.0;");

The code snippet above defines a column p that is a fixed-size array using the component column names and then filters on its magnitude by looping over its elements. It must be noted that the usage of strings to define columns like the one above is a major advantage when using PyROOT. However, only constants and data coming from other columns in the dataset can be involved in the code passed as a string. Local variables and functions cannot be used, since the interpreter will not know how to find them. When capturing local state is necessary, a C++ callable can be used.

More information on filters and how to use them to automatically generate cutflow reports can be found below.

Defining custom columns

Let's now consider the case in which "myTree" contains two quantities "x" and "y", but our analysis relies on a derived quantity z = sqrt(x*x + y*y). Using the Define transformation, we can create a new column in the data-set containing the variable "z":

TDataFrame d("myTree", "file.root");
auto sqrtSum = [](double x, double y) { return sqrt(x*x + y*y); };
auto zMean = d.Define("z", sqrtSum, {"x","y"}).Mean("z");
std::cout << *zMean << std::endl;

Define creates the variable "z" by applying sqrtSum to "x" and "y". Later in the chain of calls we refer to variables created with Define as if they were actual tree branches/columns, but they are evaluated on demand, at most once per event. As with filters, Define calls can be chained with other transformations to create multiple custom columns. Define and Filter transformations can be concatenated and intermixed at will.

As with filters, it is possible to specify new columns as string expressions. This snippet is analogous to the one above:

TDataFrame d("myTree", "file.root");
auto zMean = d.Define("z", "sqrt(x*x + y*y)").Mean("z");
std::cout << *zMean << std::endl;

Again the names of the branches used in the expression and their types are inferred automatically. The string must be valid c++ and is just-in-time compiled by the ROOT interpreter, cling – the process has a small runtime overhead.

Previously, when showing the different ways a TDataFrame can be created, we showed a constructor that only takes a number of entries a parameter. In the following example we show how to combine such an "empty" TDataFrame with Define transformations to create a data-set on the fly. We then save the generated data on disk using the Snapshot action.

TDataFrame d(100); // a TDF that will generate 100 entries (currently empty)
int x = -1;
auto d_with_columns = d.Define("x", [&x] { return ++x; })
                       .Define("xx", [&x] { return x*x; });
d_with_columns.Snapshot("myNewTree", "newfile.root");

This example is slightly more advanced than what we have seen so far: for starters, it makes use of lambda captures (a simple way to make external variables available inside the body of c++ lambdas) to act on the same variable x from both Define transformations. Secondly we have stored the transformed data-frame in a variable. This is always possible: at each point of the transformation chain, users can store the status of the data-frame for further use (more on this below).

You can read more about defining new columns here.

A graph composed of two branches, one starting with a filter and one with a define. The end point of a branch is always an action.

Running on a range of entries

It is sometimes necessary to limit the processing of the dataset to a range of entries. For this reason, the TDataFrame offers the concept of ranges as a node of the TDataFrame chain of transformations; this means that filters, columns and actions can be concatenated to and intermixed with Ranges. If a range is specified after a filter, the range will act exclusively on the entries passing the filter – it will not even count the other entries! The same goes for a Range hanging from another Range. Here are some commented examples:

TDataFrame d("myTree", "file.root");
// Here we store a data-frame that loops over only the first 30 entries in a variable
auto d30 = d.Range(30);
// This is how you pick all entries from 15 onwards
auto d15on = d.Range(15, 0);
// We can specify a stride too, in this case we pick an event every 3
auto d15each3 = d.Range(0, 15, 3);

Note that ranges are not available when multi-threading is enabled. More information on ranges is available here.

Executing multiple actions in the same event loop

As a final example let us apply two different cuts on branch "MET" and fill two different histograms with the "pt\_v" of the filtered events. By now, you should be able to easily understand what's happening:

TDataFrame d("treeName", "file.root");
auto h1 = d.Filter("MET > 10").Histo1D("pt_v");
auto h2 = d.Histo1D("pt_v");
h1->Draw();       // event loop is run once here
h2->Draw("SAME"); // no need to run the event loop again

TDataFrame executes all above actions by running the event-loop only once. The trick is that actions are not executed at the moment they are called, but they are lazy, i.e. delayed until the moment one of their results is accessed through the smart pointer. At that time, the event loop is triggered and all results are produced simultaneously.

It is therefore good practice to declare all your transformations and actions before accessing their results, allowing TDataFrame to run the loop once and produce all results in one go.

Going parallel

Let's say we would like to run the previous examples in parallel on several cores, dividing events fairly between cores. The only modification required to the snippets would be the addition of this line before constructing the main data-frame object:

ROOT::EnableImplicitMT();

Simple as that. More details are given below.

More features

Here is a list of the most important features that have been omitted in the "Crash course" for brevity. You don't need to read all these to start using TDataFrame, but they are useful to save typing time and runtime.

Treatment of columns holding collections

When using TDataFrame to read data from a ROOT file, users can specify that the type of a branch is TArrayBranch<T> to indicate the branch is a c-style array, an STL array or any other collection type associated to a contiguous storage in memory.

Column values of type TArrayBranch<T> perform no copy of the underlying array data, it's in some sense a view, and offer a minimal array-like interface to access the array elements: either via square brackets, or with range-based for loops.

The TArrayBranch<T> type signals to TDataFrame that a special behaviour needs to be adopted when snapshotting a dataset on disk. Indeed, if columns which are variable size C arrays are treated via the TArrayBranch<T>, TDataFrame will correctly persistify them - if anything else is adopted, for example std::span, only the first element of the array will be written.

Callbacks

Acting on a TResultProxy, it is possible to register a callback that TDataFrame will execute "everyNEvents" on a partial result.

The callback must be a callable that takes a reference to the result type as argument and returns nothing. TDataFrame, acting as a full fledged data processing framework, will invoke registered callbacks passing partial action results as arguments to them (e.g. a histogram filled with a part of the selected events).

Callbacks can be used e.g. to inspect partial results of the analysis while the event loop is running. For example one can draw an up-to-date version of a result histogram every 100 entries like this:

auto h = tdf.Histo1D("x");
TCanvas c("c","x hist");
h.OnPartialResult(100, [&c](TH1D &h_) { c.cd(); h_.Draw(); c.Update(); });
h->Draw(); // event loop runs here, this `Draw` is executed after the event loop is finished

Default branch lists

When constructing a TDataFrame object, it is possible to specify a default column list for your analysis, in the usual form of a list of strings representing branch/column names. The default column list will be used as a fallback whenever a list specific to the transformation/action is not present. TDataFrame will take as many of these columns as needed, ignoring trailing extra names if present.

// use "b1" and "b2" as default branches
TDataFrame d1("myTree", "file.root", {"b1","b2"});
auto h = d1.Filter([](int b1, int b2) { return b1 > b2; }) // will act on "b1" and "b2"
           .Histo1D(); // will act on "b1"
// just one default branch this time
TDataFrame d2("myTree", "file.root", {"b1"});
auto min = d2.Filter([](double b2) { return b2 > 0; }, {"b2"}) // we can still specify non-default branch lists
             .Min(); // returns the minimum value of "b1" for the filtered entries

Branch type guessing and explicit declaration of branch types

C++ is a statically typed language: all types must be known at compile-time. This includes the types of the TTree branches we want to work on. For filters, temporary columns and some of the actions, branch types are deduced from the signature of the relevant filter function/temporary column expression/action function:

// here b1 is deduced to be `int` and b2 to be `double`

dataFrame.Filter([](int x, double y) { return x > 0 && y < 0.; }, {"b1", "b2"});

If we specify an incorrect type for one of the branches, an exception with an informative message will be thrown at runtime, when the branch value is actually read from the TTree: TDataFrame detects type mismatches. The same would happen if we swapped the order of "b1" and "b2" in the branch list passed to Filter.

Certain actions, on the other hand, do not take a function as argument (e.g. Histo1D), so we cannot deduce the type of the branch at compile-time. In this case **TDataFrame infers the type of the branch** from the TTree itself. This is why we never needed to specify the branch types for all actions in the above snippets.

When the branch type is not a common one such as int, double, char or float it is nonetheless good practice to specify it as a template parameter to the action itself, like this:

dataFrame.Histo1D("b1"); // OK, the type of "b1" is deduced at runtime

dataFrame.Min<MyNumber_t>("myObject"); // OK, "myObject" is deduced to be of type `MyNumber_t`

Deducing types at runtime requires the just-in-time compilation of the relevant actions, which has a small runtime overhead, so specifying the type of the columns as template parameters to the action is good practice when performance is a goal.

Generic actions

TDataFrame strives to offer a comprehensive set of standard actions that can be performed on each event. At the same time, it allows users to execute arbitrary code (i.e. a generic action) inside the event loop through the Foreach and ForeachSlot actions.

Foreach(f, columnList) takes a function f (lambda expression, free function, functor...) and a list of columns, and executes f on those columns for each event. The function passed must return nothing (i.e. void). It can be used to perform actions that are not already available in the interface. For example, the following snippet evaluates the root mean square of column "b":

// Single-thread evaluation of RMS of column "b" using Foreach
double sumSq = 0.;
unsigned int n = 0;
TDataFrame d("bTree", bFilePtr);
d.Foreach([&sumSq, &n](double b) { ++n; sumSq += b*b; }, {"b"});
std::cout << "rms of b: " << std::sqrt(sumSq / n) << std::endl;

When executing on multiple threads, users are responsible for the thread-safety of the expression passed to Foreach. The code above would need to employ some resource protection mechanism to ensure non-concurrent writing of rms; but this is probably too much head-scratch for such a simple operation.

ForeachSlot can help in this situation. It is an alternative version of Foreach for which the function takes an additional parameter besides the columns it should be applied to: an unsigned int slot parameter, where slot is a number indicating which thread (0, 1, 2 , ..., poolSize - 1) the function is being run in. We can take advantage of ForeachSlot to evaluate a thread-safe root mean square of branch "b":

// Thread-safe evaluation of RMS of branch "b" using ForeachSlot
ROOT::EnableImplicitMT();
const unsigned int nSlots = ROOT::GetImplicitMTPoolSize();
std::vector<double> sumSqs(nSlots, 0.);
std::vector<unsigned int> ns(nSlots, 0);
TDataFrame d("bTree", bFilePtr);
d.ForeachSlot([&sumSqs, &ns](unsigned int slot, double b) { sumSqs[slot] += b*b; ns[slot] += 1; }, {"b"});
double sumSq = std::accumulate(sumSqs.begin(), sumSqs.end(), 0.); // sum all squares
unsigned int n = std::accumulate(ns.begin(), ns.end(), 0); // sum all counts
std::cout << "rms of b: " << std::sqrt(sumSq / n) << std::endl;

You see how we created one double variable for each thread in the pool, and later merged their results via std::accumulate.

Friend trees

Friend trees are supported by TDataFrame. In order to deal with friend trees with TDataFrame, the user is required to build the tree and its friends and instantiate a TDataFrame with it. Two caveats are presents when using jitted Defines and Filters: 1) the only columns which can be used in the strings passed to the aforementioned transformations are the top level branches of the friend trees. 2) the "friend columns" cannot be written with the notation involving a dot. For example, if a tree is created like this:

TTree t([...]);
TTree ft([...]);
t.AddFriend(t,"myFriend");

in order to access a certain column col of the tree ft, it will be necessary to alias it before. To continue the example:

TDataFrame d(t);
d.Alias("myFriend_MyCol", "myFriend.MyCol");
auto f = d.Filter("myFriend_MyCol == 42");

Reading file formats different from ROOT's

TDataFrame can be interfaced with TDataSources. The TDataSource interface defines an API that TDataFrame can use to read arbitrary data formats.

A concrete TDataSource implementation (i.e. a class that inherits from TDataSource and implements all of its pure methods) provides an adaptor that TDataFrame can leverage to read any kind of tabular data formats. TDataFrame calls into TDataSource to retrieve information about the data, retrieve (thread-local) readers or "cursors" for selected columns and to advance the readers to the desired data entry. Some predefined TDataSources are natively provided by ROOT such as the TCsvDS which allows to read comma separated files:

auto tdf = ROOT::Experimental::TDF::MakeCsvDataFrame("MuRun2010B.csv");
auto filteredEvents =
   tdf.Filter("Q1 * Q2 == -1")
      .Define("m", "sqrt(pow(E1 + E2, 2) - (pow(px1 + px2, 2) + pow(py1 + py2, 2) + pow(pz1 + pz2, 2)))");
auto h = filteredEvents.Histo1D("m");
h->Draw();

Call graphs (storing and reusing sets of transformations)

Sets of transformations can be stored as variables** and reused multiple times to create call graphs in which several paths of filtering/creation of columns are executed simultaneously; we often refer to this as "storing the state of the chain".

This feature can be used, for example, to create a temporary column once and use it in several subsequent filters or actions, or to apply a strict filter to the data-set before executing several other transformations and actions, effectively reducing the amount of events processed.

Let's try to make this clearer with a commented example:

// build the data-frame and specify a default column list
TDataFrame d(treeName, filePtr, {"var1", "var2", "var3"});
// apply a cut and save the state of the chain
auto filtered = d.Filter(myBigCut);
// plot branch "var1" at this point of the chain
auto h1 = filtered.Histo1D("var1");
// create a new branch "vec" with a vector extracted from a complex object (only for filtered entries)
// and save the state of the chain
auto newBranchFiltered = filtered.Define("vec", [](const Obj& o) { return o.getVector(); }, {"obj"});
// apply a cut and fill a histogram with "vec"
auto h2 = newBranchFiltered.Filter(cut1).Histo1D("vec");
// apply a different cut and fill a new histogram
auto h3 = newBranchFiltered.Filter(cut2).Histo1D("vec");
// Inspect results
h2->Draw(); // first access to an action result: run event-loop!
h3->Draw("SAME"); // event loop does not need to be run again here..
std::cout << "Entries in h1: " << h1->GetEntries() << std::endl; // ..or here

TDataFrame detects when several actions use the same filter or the same temporary column, and only evaluates each filter or temporary column once per event, regardless of how many times that result is used down the call graph. Objects read from each column are built once and never copied, for maximum efficiency. When "upstream" filters are not passed, subsequent filters, temporary column expressions and actions are not evaluated, so it might be advisable to put the strictest filters first in the chain.

Transformations

Filters

A filter is defined through a call to Filter(f, columnList). f can be a function, a lambda expression, a functor class, or any other callable object. It must return a bool signalling whether the event has passed the selection (true) or not (false). It must perform "read-only" actions on the columns, and should not have side-effects (e.g. modification of an external or static variable) to ensure correct results when implicit multi-threading is active.

TDataFrame only evaluates filters when necessary: if multiple filters are chained one after another, they are executed in order and the first one returning false causes the event to be discarded and triggers the processing of the next entry. If multiple actions or transformations depend on the same filter, that filter is not executed multiple times for each entry: after the first access it simply serves a cached result.

Named filters and cutflow reports

An optional string parameter name can be passed to the Filter method to create a named filter. Named filters work as usual, but also keep track of how many entries they accept and reject.

Statistics are retrieved through a call to the Report method:

when Report is called on the main TDataFrame object, it prints stats for all named filters declared up to that point
when called on a specific node (e.g. the result of a Define or Filter), it prints stats for all named filters in the section of the chain between the main TDataFrame and that node (included).

Stats are printed in the same order as named filters have been added to the graph, and refer to the latest event-loop that has been run using the relevant TDataFrame. If Report is called before the event-loop has been run at least once, a run is triggered.

Ranges

When TDataFrame is not being used in a multi-thread environment (i.e. no call to EnableImplicitMT was made), Range transformations are available. These act very much like filters but instead of basing their decision on a filter expression, they rely on start,stop and stride parameters.

start: number of entries that will be skipped before starting processing again
stop: maximum number of entries that will be processed
stride: only process one entry every stride entries

The actual number of entries processed downstream of a Range node will be (stop - start)/stride (or less if less entries than that are available).

Note that ranges act "locally", not based on the global entry count: Range(10,50) means "skip the first 10 entries that reach this node*, let the next 40 entries pass, then stop processing". If a range node hangs from a filter node, and the range has a start parameter of 10, that means the range will skip the first 10 entries that pass the preceding filter.

Ranges allow "early quitting": if all branches of execution of a functional graph reached their stop value of processed entries, the event-loop is immediately interrupted. This is useful for debugging and quick data explorations.

Custom columns

Custom columns are created by invoking Define(name, f, columnList). As usual, f can be any callable object (function, lambda expression, functor class...); it takes the values of the columns listed in columnList (a list of strings) as parameters, in the same order as they are listed in columnList. f must return the value that will be assigned to the temporary column.

A new variable is created called name, accessible as if it was contained in the dataset from subsequent transformations/actions.

Use cases include:

caching the results of complex calculations for easy and efficient multiple access
extraction of quantities of interest from complex objects
branch aliasing, i.e. changing the name of a branch

An exception is thrown if the name of the new column/branch is already in use for another branch in the TTree.

It is also possible to specify the quantity to be stored in the new temporary column as a C++ expression with the method Define(name, expression). For example this invocation

tdf.Define("pt", "sqrt(px*px + py*py)");

will create a new column called "pt" the value of which is calculated starting from the columns px and py. The system builds a just-in-time compiled function starting from the expression after having deduced the list of necessary branches from the names of the variables specified by the user.

Custom columns as function of slot and entry number

It is possible to create custom columns also as a function of the processing slot and entry numbers. The methods that can be invoked are:

DefineSlot(name, f, columnList). In this case the callable f has this signature R(unsigned int, T1, T2, ...): the first parameter is the slot number which ranges from 0 to ROOT::GetImplicitMTPoolSize() - 1.
DefineSlotEntry(name, f, columnList). In this case the callable f has this signature R(unsigned int, ULong64_t, T1, T2, ...): the first parameter is the slot number while the second one the number of the entry being processed.

Actions

Instant and lazy actions

Actions can be instant or lazy. Instant actions are executed as soon as they are called, while lazy actions are executed whenever the object they return is accessed for the first time. As a rule of thumb, actions with a return value are lazy, the others are instant.

Overview

Here is a quick overview of what actions are present and what they do. Each one is described in more detail in the reference guide.

In the following, whenever we say an action "returns" something, we always mean it returns a smart pointer to it. Also note that all actions are only executed for events that pass all preceding filters.

Lazy actions	Description
Count	Return the number of events processed.
Fill	Fill a user-defined object with the values of the specified branches, as if by calling `Obj.Fill(branch1, branch2, ...).
Histo{1D,2D,3D}	Fill a {one,two,three}-dimensional histogram with the processed branch values.
Max	Return the maximum of processed branch values. If the type of the column is inferred, the return type is `double`, the type of the column otherwise.
Mean	Return the mean of processed branch values. If the type of the column is inferred, the return type is `double`, the type of the column otherwise.
Min	Return the minimum of processed branch values. If the type of the column is inferred, the return type is `double`, the type of the column otherwise.
Profile{1D,2D}	Fill a {one,two}-dimensional profile with the branch values that passed all filters.
Reduce	Reduce (e.g. sum, merge) entries using the function (lambda, functor...) passed as argument. The function must have signature `T(T,T)` where `T` is the type of the branch. Return the final result of the reduction operation. An optional parameter allows initialization of the result object to non-default values.
Take	Extract a column from the dataset as a collection of values. If the type of the column is a C-style array, the type stored in the return container is a `std::vector<T>` to guarantee the lifetime of the data involved.

Instant actions	Description
Foreach	Execute a user-defined function on each entry. Users are responsible for the thread-safety of this lambda when executing with implicit multi-threading enabled.
ForeachSlot	Same as `Foreach`, but the user-defined function must take an extra `unsigned int slot` as its first parameter. `slot` will take a different value, `0` to `nThreads - 1`, for each thread of execution. This is meant as a helper in writing thread-safe `Foreach` actions when using `TDataFrame` after `ROOT::EnableImplicitMT()`. `ForeachSlot` works just as well with single-thread execution: in that case `slot` will always be `0`.
Snapshot	Writes processed data-set to disk, in a new `TTree` and `TFile`. Custom columns can be saved as well, filtered entries are not saved. Users can specify which columns to save (default is all). Snapshot, by default, overwrites the output file if it already exists.
Cache	Caches in contiguous memory columns' entries. Custom columns can be cached as well, filtered entries are not cached. Users can specify which columns to save (default is all).

Queries	Description
Report	This is not properly an action, since when `Report` is called it does not book an operation to be performed on each entry. Instead, it interrogates the data-frame directly to print a cutflow report, i.e. statistics on how many entries have been accepted and rejected by the filters. See the section on named filters for a more detailed explanation.

Parallel execution

As pointed out before in this document, TDataFrame can transparently perform multi-threaded event loops to speed up the execution of its actions. Users only have to call ROOT::EnableImplicitMT() before constructing the TDataFrame object to indicate that it should take advantage of a pool of worker threads. Each worker thread processes a distinct subset of entries, and their partial results are merged before returning the final values to the user.

Thread safety

Filter and Define transformations should be inherently thread-safe: they have no side-effects and are not dependent on global state. Most Filter/Define functions will in fact be pure in the functional programming sense. All actions are built to be thread-safe with the exception of Foreach, in which case users are responsible of thread-safety, see here.

Definition at line 39 of file TDataFrame.hxx.

Public Member Functions
	TDataFrame (std::string_view treeName, std::string_view filenameglob, const ColumnNames_t &defaultBranches={})
	Build the dataframe. More...

	TDataFrame (std::string_view treename, const std::vector< std::string > &filenames, const ColumnNames_t &defaultBranches={})
	Build the dataframe. More...

	TDataFrame (std::string_view treeName, ::TDirectory *dirPtr, const ColumnNames_t &defaultBranches={})

	TDataFrame (TTree &tree, const ColumnNames_t &defaultBranches={})
	Build the dataframe. More...

	TDataFrame (ULong64_t numEntries)
	Build a dataframe that generates numEntries entries. More...

	TDataFrame (std::unique_ptr< TDataSource >, const ColumnNames_t &defaultBranches={})
	Build dataframe associated to datasource. More...

Public Member Functions inherited from ROOT::Experimental::TDF::TInterface< TDFDetail::TLoopManager >
	TInterface (const TInterface &)=default
	Copy-ctor for TInterface. More...

	TInterface (TInterface &&)=default
	Move-ctor for TInterface. More...

TInterface< TDFDetail::TLoopManager >	Alias (std::string_view alias, std::string_view columnName)
	Allow to refer to a column with a different name. More...

TInterface< TLoopManager >	Cache (const ColumnNames_t &columnList)
	Save selected columns in memory. More...

TInterface< TLoopManager >	Cache (const ColumnNames_t &columnList)
	Save selected columns in memory. More...

TInterface< TLoopManager >	Cache (std::string_view columnNameRegexp="")
	Save selected columns in memory. More...

TResultProxy< ULong64_t >	Count ()
	Return the number of entries processed (lazy action) More...

TInterface< TDFDetail::TLoopManager >	Define (std::string_view name, F expression, const ColumnNames_t &columns={})
	Creates a custom column. More...

TInterfaceJittedDefine	Define (std::string_view name, std::string_view expression)
	Creates a custom column. More...

TInterface< TDFDetail::TLoopManager >	DefineSlot (std::string_view name, F expression, const ColumnNames_t &columns={})
	Creates a custom column with a value dependent on the processing slot. More...

TInterface< TDFDetail::TLoopManager >	DefineSlotEntry (std::string_view name, F expression, const ColumnNames_t &columns={})
	Creates a custom column with a value dependent on the processing slot and the current entry. More...

TResultProxy< T >	Fill (T &&model, const ColumnNames_t &columnList)
	Return an object of type T on which `T::Fill` will be called once per event (lazy action) More...

TResultProxy< T >	Fill (T &&model, const ColumnNames_t &bl)
	Return an object of type T on which `T::Fill` will be called once per event (lazy action) More...

TInterface< TDFDetail::TFilter< F, TDFDetail::TLoopManager > >	Filter (F f, const ColumnNames_t &columns={}, std::string_view name="")
	Append a filter to the call graph. More...

TInterface< TDFDetail::TFilter< F, TDFDetail::TLoopManager > >	Filter (F f, std::string_view name)
	Append a filter to the call graph. More...

TInterface< TDFDetail::TFilter< F, TDFDetail::TLoopManager > >	Filter (F f, const std::initializer_list< std::string > &columns)
	Append a filter to the call graph. More...

TInterface< TFilterBase >	Filter (std::string_view expression, std::string_view name="")
	Append a filter to the call graph. More...

void	Foreach (F f, const ColumnNames_t &columns={})
	Execute a user-defined function on each entry (instant action) More...

void	ForeachSlot (F f, const ColumnNames_t &columns={})
	Execute a user-defined function requiring a processing slot index on each entry (instant action) More...

ColumnNames_t	GetColumnNames ()
	Returns the names of the available columns. More...

TResultProxy<::TH1D >	Histo1D (const TH1DModel &model={"", "", 128u, 0., 0.}, std::string_view vName="")
	Fill and return a one-dimensional histogram with the values of a column (lazy action) More...

TResultProxy<::TH1D >	Histo1D (std::string_view vName)

TResultProxy<::TH1D >	Histo1D (const TH1DModel &model, std::string_view vName, std::string_view wName)
	Fill and return a one-dimensional histogram with the weighted values of a column (lazy action) More...

TResultProxy<::TH1D >	Histo1D (std::string_view vName, std::string_view wName)
	Fill and return a one-dimensional histogram with the weighted values of a column (lazy action) More...

TResultProxy<::TH1D >	Histo1D (const TH1DModel &model={"", "", 128u, 0., 0.})
	Fill and return a one-dimensional histogram with the weighted values of a column (lazy action) More...

TResultProxy<::TH2D >	Histo2D (const TH2DModel &model, std::string_view v1Name="", std::string_view v2Name="")
	Fill and return a two-dimensional histogram (lazy action) More...

TResultProxy<::TH2D >	Histo2D (const TH2DModel &model, std::string_view v1Name, std::string_view v2Name, std::string_view wName)
	Fill and return a weighted two-dimensional histogram (lazy action) More...

TResultProxy<::TH2D >	Histo2D (const TH2DModel &model)

TResultProxy<::TH3D >	Histo3D (const TH3DModel &model, std::string_view v1Name="", std::string_view v2Name="", std::string_view v3Name="")
	Fill and return a three-dimensional histogram (lazy action) More...

TResultProxy<::TH3D >	Histo3D (const TH3DModel &model, std::string_view v1Name, std::string_view v2Name, std::string_view v3Name, std::string_view wName)
	Fill and return a three-dimensional histogram (lazy action) More...

TResultProxy<::TH3D >	Histo3D (const TH3DModel &model)

TResultProxy< TDFDetail::MaxReturnType_t< T > >	Max (std::string_view columnName="")
	Return the maximum of processed column values (lazy action) More...

TResultProxy< double >	Mean (std::string_view columnName="")
	Return the mean of processed column values (lazy action) More...

TResultProxy< TDFDetail::MinReturnType_t< T > >	Min (std::string_view columnName="")
	Return the minimum of processed column values (lazy action) More...

TInterface &	operator= (const TInterface &)=default
	Copy-assignment operator for TInterface. More...

TResultProxy<::TProfile >	Profile1D (const TProfile1DModel &model, std::string_view v1Name="", std::string_view v2Name="")
	Fill and return a one-dimensional profile (lazy action) More...

TResultProxy<::TProfile >	Profile1D (const TProfile1DModel &model, std::string_view v1Name, std::string_view v2Name, std::string_view wName)
	Fill and return a one-dimensional profile (lazy action) More...

TResultProxy<::TProfile >	Profile1D (const TProfile1DModel &model)

TResultProxy<::TProfile2D >	Profile2D (const TProfile2DModel &model, std::string_view v1Name="", std::string_view v2Name="", std::string_view v3Name="")
	Fill and return a two-dimensional profile (lazy action) More...

TResultProxy<::TProfile2D >	Profile2D (const TProfile2DModel &model, std::string_view v1Name, std::string_view v2Name, std::string_view v3Name, std::string_view wName)
	Fill and return a two-dimensional profile (lazy action) More...

TResultProxy<::TProfile2D >	Profile2D (const TProfile2DModel &model)

TInterface< TDFDetail::TRange< TDFDetail::TLoopManager > >	Range (unsigned int start, unsigned int stop, unsigned int stride=1)
	Creates a node that filters entries based on range. More...

TInterface< TDFDetail::TRange< TDFDetail::TLoopManager > >	Range (unsigned int stop)
	Creates a node that filters entries based on range. More...

TResultProxy< T >	Reduce (F f, std::string_view columnName="")
	Execute a user-defined reduce operation on the values of a column. More...

TResultProxy< T >	Reduce (F f, std::string_view columnName, const T &redIdentity)
	Execute a user-defined reduce operation on the values of a column. More...

void	Report ()
	Print filtering statistics on screen. More...

TInterface< TLoopManager >	Snapshot (std::string_view treename, std::string_view filename, const ColumnNames_t &columnList, const TSnapshotOptions &options=TSnapshotOptions())
	Save selected columns to disk, in a new TTree `treename` in file `filename`. More...

TInterface< TLoopManager >	Snapshot (std::string_view treename, std::string_view filename, const ColumnNames_t &columnList, const TSnapshotOptions &options=TSnapshotOptions())
	Save selected columns to disk, in a new TTree `treename` in file `filename`. More...

TInterface< TLoopManager >	Snapshot (std::string_view treename, std::string_view filename, std::string_view columnNameRegexp="", const TSnapshotOptions &options=TSnapshotOptions())
	Save selected columns to disk, in a new TTree `treename` in file `filename`. More...

TResultProxy< TDFDetail::SumReturnType_t< T > >	Sum (std::string_view columnName="", const TDFDetail::SumReturnType_t< T > &initValue=TDFDetail::SumReturnType_t< T >{})
	Return the sum of processed column values (lazy action) More...

TResultProxy< typename TDFDetail::ColType_t< T, COLL > >	Take (std::string_view column="")
	Return a collection of values of a column (lazy action, returns a std::vector by default) More...

Private Types
using	ColumnNames_t = TDFDetail::ColumnNames_t

using	TDataSource = ROOT::Experimental::TDF::TDataSource

Additional Inherited Members
Protected Member Functions inherited from ROOT::Experimental::TDF::TInterface< TDFDetail::TLoopManager >
	TInterface (const std::shared_ptr< TDFDetail::TLoopManager > &proxied, const std::weak_ptr< TLoopManager > &impl, const ColumnNames_t &validColumns, TDataSource *ds)

	TInterface (const std::shared_ptr< TDFDetail::TLoopManager > &proxied)
	Only enabled when building a TInterface<TLoopManager> More...

std::shared_ptr< TLoopManager >	GetDataFrameChecked ()
	Get the TLoopManager if reachable. If not, throw. More...

const std::shared_ptr< TDFDetail::TLoopManager > &	GetProxiedPtr () const

#include <ROOT/TDataFrame.hxx>

Inheritance diagram for ROOT::Experimental::TDataFrame:

[legend]

Member Typedef Documentation

◆ ColumnNames_t

using ROOT::Experimental::TDataFrame::ColumnNames_t = TDFDetail::ColumnNames_t

private

Definition at line 40 of file TDataFrame.hxx.

◆ TDataSource

using ROOT::Experimental::TDataFrame::TDataSource = ROOT::Experimental::TDF::TDataSource

private

Definition at line 41 of file TDataFrame.hxx.

Constructor & Destructor Documentation

◆ TDataFrame() [1/6]

TDataFrame::TDataFrame	(	std::string_view	treeName,
		std::string_view	filenameglob,
		const ColumnNames_t &	defaultBranches = `{}`
	)

Build the dataframe.

Parameters

[in]	treeName	Name of the tree contained in the directory
[in]	filenameglob	TDirectory where the tree is stored, e.g. a TFile.
[in]	defaultBranches	Collection of default branches.

The default branches are looked at in case no branch is specified in the booking of actions or transformations. See TInterface for the documentation of the methods available.

Definition at line 682 of file TDataFrame.cxx.

◆ TDataFrame() [2/6]

TDataFrame::TDataFrame	(	std::string_view	treeName,
		const std::vector< std::string > &	filenames,
		const ColumnNames_t &	defaultBranches = `{}`
	)

Build the dataframe.

Parameters

[in]	treeName	Name of the tree contained in the directory
[in]	filenames	Collection of file names
[in]	defaultBranches	Collection of default branches.

The default branches are looked at in case no branch is specified in the booking of actions or transformations. See TInterface for the documentation of the methods available.

Definition at line 700 of file TDataFrame.cxx.

◆ TDataFrame() [3/6]

ROOT::Experimental::TDataFrame::TDataFrame	(	std::string_view	treeName,
		::TDirectory *	dirPtr,
		const ColumnNames_t &	defaultBranches = `{}`
	)

◆ TDataFrame() [4/6]

TDataFrame::TDataFrame	(	TTree &	tree,
		const ColumnNames_t &	defaultBranches = `{}`
	)

Build the dataframe.

Parameters

[in]	tree	The tree or chain to be studied.
[in]	defaultBranches	Collection of default column names to fall back to when none is specified.

The default branches are looked at in case no branch is specified in the booking of actions or transformations. See TInterface for the documentation of the methods available.

Definition at line 720 of file TDataFrame.cxx.

◆ TDataFrame() [5/6]

TDataFrame::TDataFrame ( ULong64_t numEntries )

Build a dataframe that generates numEntries entries.

Parameters

[in] numEntries The number of entries to generate.

An empty-source dataframe constructed with a number of entries will generate those entries on the fly when some action is triggered, and it will do so for all the previously-defined temporary branches.

Definition at line 732 of file TDataFrame.cxx.

◆ TDataFrame() [6/6]

TDataFrame::TDataFrame	(	std::unique_ptr< TDataSource >	ds,
		const ColumnNames_t &	defaultBranches = `{}`
	)

Build dataframe associated to datasource.

Parameters

[in]	ds	The data-source object.
[in]	defaultBranches	Collection of default column names to fall back to when none is specified.

A dataframe associated to a datasource will query it to access column values.

Definition at line 743 of file TDataFrame.cxx.

Libraries for ROOT::Experimental::TDataFrame:

[legend]

Table of Contents

Introduction

Crash course

Creating a TDataFrame

Programmatically get the list of column names

Filling a histogram

Applying a filter

Defining custom columns

Running on a range of entries

Executing multiple actions in the same event loop

Going parallel

More features

Treatment of columns holding collections

Callbacks

Default branch lists

Branch type guessing and explicit declaration of branch types

Generic actions

Friend trees

Reading file formats different from ROOT's

Call graphs (storing and reusing sets of transformations)

Transformations

Filters

Named filters and cutflow reports

Ranges

Custom columns

Custom columns as function of slot and entry number

Actions

Instant and lazy actions

Overview

Parallel execution

Thread safety

Public Member Functions

Private Types

Additional Inherited Members

Member Typedef Documentation

◆ ColumnNames_t

◆ TDataSource

Constructor & Destructor Documentation

◆ TDataFrame() [1/6]

◆ TDataFrame() [2/6]

◆ TDataFrame() [3/6]

◆ TDataFrame() [4/6]

◆ TDataFrame() [5/6]

◆ TDataFrame() [6/6]