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df019_Cache.py File Reference


namespace  df019_Cache

Detailed Description

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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 dataframe. 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 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")
ROOT's RDataFrame offers a modern, high-level interface for analysis of data stored in TTree ,...
June 2018
Danilo Piparo (CERN)

Definition in file df019_Cache.py.