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Reference Guide
df019_Cache.py File Reference



Detailed Description

View in nbviewer Open in SWAN Cache a processed RDataFrame in memory for further usage.

This tutorial shows how the content of a data frame can be cached in memory in form of a data frame. The content of the columns is stored in memory in contiguous slabs of memory and is "ready to use", i.e. no ROOT IO operation is performed.

Creating a cached data frame storing all of its content deserialised and uncompressed in memory is particularly useful when dealing with datasets of a moderate size (small enough to fit the RAM) over which several explorative loops need to be performed at as fast as possible. In addition, caching can be useful when no file on disk needs to be created as a side effect of checkpointing part of the analysis.

All steps in the caching are lazy, i.e. the cached data frame is actually filled only when the event loop is triggered on it.

import ROOT
import os
# We create a data frame on top of the hsimple example
hsimplePath = os.path.join(str(ROOT.gROOT.GetTutorialDir().Data()), "hsimple.root")
df = ROOT.RDataFrame("ntuple", hsimplePath)
# We apply a simple cut and define a new column
df_cut = df.Filter("py > 0.f")\
.Define("px_plus_py", "px + py")
# We cache the content of the dataset. Nothing has happened yet: the work to accomplish
# has been described.
df_cached = df_cut.Cache()
h = df_cached.Histo1D("px_plus_py")
# Now the event loop on the cached dataset is triggered by accessing the histogram.
# This event triggers the loop on the `df` data frame lazily.
c = ROOT.TCanvas()
print("Saved figure to df019_Cache.png")
June 2018
Danilo Piparo (CERN)

Definition in file df019_Cache.py.

ROOT's RDataFrame offers a high level interface for analyses of data stored in TTree,...
Definition: RDataFrame.hxx:42