Logo ROOT  
Reference Guide
df026_AsNumpyArrays.py
Go to the documentation of this file.
1## \file
2## \ingroup tutorial_dataframe
3## \notebook
4## Read data from RDataFrame into Numpy arrays.
5##
6## \macro_code
7## \macro_output
8##
9## \date December 2018
10## \author Stefan Wunsch (KIT, CERN)
11
12import ROOT
13from sys import exit
14
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_)")
19
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))
26
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))
35
36npy3 = df2.AsNumpy(columns=["x"])
37print("Read-out of the filtered RDataFrame with the columns option:\n{}\n".format(npy3))
38
39npy4 = df2.AsNumpy(exclude=["x"])
40print("Read-out of the filtered RDataFrame with the exclude option:\n{}\n".format(npy4))
41
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.
45ROOT.gInterpreter.Declare("""
46// Inject the C++ class CustomObject in the C++ runtime.
47class CustomObject {
48public:
49 int x = 42;
50};
51// Create a function that returns such an object. This is called to fill the dataframe.
52CustomObject fill_object() { return CustomObject(); }
53""")
54
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))
60
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.
63try:
64 import pandas
65except:
66 print("Please install the pandas package to run this section of the tutorial.")
67 exit(1)
68
69df = pandas.DataFrame(npy5)
70print("Content of the ROOT.RDataFrame as pandas.DataFrame:\n{}\n".format(df))
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t format
ROOT's RDataFrame offers a modern, high-level interface for analysis of data stored in TTree ,...
Definition: RDataFrame.hxx:41