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1## \file
2## \ingroup tutorial_dataframe
3## \notebook
4## Read data from RDataFrame into Numpy arrays.
6## \macro_code
7## \macro_output
9## \date December 2018
10## \author Stefan Wunsch (KIT, CERN)
12import ROOT
13from sys import exit
15# Let's create a simple dataframe with ten rows and two columns
16df = ROOT.RDataFrame(10) \
17 .Define("x", "(int)rdfentry_") \
18 .Define("y", "1.f/(1.f+rdfentry_)")
20# Next, we want to access the data from Python as Numpy arrays. To do so, the
21# content of the dataframe is converted using the AsNumpy method. The returned
22# object is a dictionary with the column names as keys and 1D numpy arrays with
23# the content as values.
24npy = df.AsNumpy()
25print("Read-out of the full RDataFrame:\n{}\n".format(npy))
27# Since reading out data to memory is expensive, always try to read-out only what
28# is needed for your analysis. You can use all RDataFrame features to reduce your
29# dataset, e.g., the Filter transformation. Furthermore, you can can pass to the
30# AsNumpy method a whitelist of column names with the option `columns` or a blacklist
31# with column names with the option `exclude`.
32df2 = df.Filter("x>5")
33npy2 = df2.AsNumpy()
34print("Read-out of the filtered RDataFrame:\n{}\n".format(npy2))
36npy3 = df2.AsNumpy(columns=["x"])
37print("Read-out of the filtered RDataFrame with the columns option:\n{}\n".format(npy3))
39npy4 = df2.AsNumpy(exclude=["x"])
40print("Read-out of the filtered RDataFrame with the exclude option:\n{}\n".format(npy4))
42# You can read-out all objects from ROOT files since these are wrapped by PyROOT
43# in the Python world. However, be aware that objects other than fundamental types,
44# such as complex C++ objects and not int or float, are costly to read-out.
46// Inject the C++ class CustomObject in the C++ runtime.
47class CustomObject {
49 int x = 42;
51// Create a function that returns such an object. This is called to fill the dataframe.
52CustomObject fill_object() { return CustomObject(); }
55df3 = df.Define("custom_object", "fill_object()")
56npy5 = df3.AsNumpy()
57print("Read-out of C++ objects:\n{}\n".format(npy5["custom_object"]))
58print("Access to all methods and data members of the C++ object:\nObject: {}\nAccess data member: custom_object.x = {}\n".format(
59 repr(npy5["custom_object"][0]), npy5["custom_object"][0].x))
61# Note that you can pass the object returned by AsNumpy directly to pandas.DataFrame
62# including any complex C++ object that may be read-out.
64 import pandas
66 print("Please install the pandas package to run this section of the tutorial.")
67 exit(1)
69df = pandas.DataFrame(npy5)
70print("Content of the ROOT.RDataFrame as pandas.DataFrame:\n{}\n".format(df))
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ROOT's RDataFrame offers a modern, high-level interface for analysis of data stored in TTree ,...