{
"cells": [
{
"cell_type": "markdown",
"id": "d2b93495",
"metadata": {},
"source": [
"# df014_CSVDataSource\n",
"Process a CSV file with RDataFrame and the CSV data source.\n",
"\n",
"This tutorial illustrates how use the RDataFrame in combination with a\n",
"RDataSource. In this case we use a TCsvDS. This data source allows to read\n",
"a CSV file from a RDataFrame.\n",
"As a result of running this tutorial, we will produce plots of the dimuon\n",
"spectrum starting from a subset of the CMS collision events of Run2010B.\n",
"Dataset Reference:\n",
"McCauley, T. (2014). Dimuon event information derived from the Run2010B\n",
"public Mu dataset. CERN Open Data Portal.\n",
"DOI: [10.7483/OPENDATA.CMS.CB8H.MFFA](http://opendata.cern.ch/record/700).\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Enric Tejedor (CERN) \n",
"This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, May 19, 2026 at 08:09 PM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8d5e21e9",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:41.514369Z",
"iopub.status.busy": "2026-05-19T20:09:41.514227Z",
"iopub.status.idle": "2026-05-19T20:09:42.462108Z",
"shell.execute_reply": "2026-05-19T20:09:42.461099Z"
}
},
"outputs": [],
"source": [
"import ROOT"
]
},
{
"cell_type": "markdown",
"id": "2e54bc9e",
"metadata": {},
"source": [
"Let's first create a RDF that will read from the remote CSV file.\n",
"The types of the columns will be automatically inferred."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "585fcd01",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:42.463844Z",
"iopub.status.busy": "2026-05-19T20:09:42.463696Z",
"iopub.status.idle": "2026-05-19T20:09:43.075757Z",
"shell.execute_reply": "2026-05-19T20:09:43.075237Z"
}
},
"outputs": [],
"source": [
"fileUrl = \"http://root.cern/files/tutorials/df014_CsvDataSource_MuRun2010B.csv\"\n",
"df = ROOT.RDF.FromCSV(fileUrl)"
]
},
{
"cell_type": "markdown",
"id": "021f90e0",
"metadata": {},
"source": [
"Now we will apply a first filter based on two columns of the CSV,\n",
"and we will define a new column that will contain the invariant mass.\n",
"Note how the new invariant mass column is defined from several other\n",
"columns that already existed in the CSV file."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9ebe405d",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:43.080310Z",
"iopub.status.busy": "2026-05-19T20:09:43.080188Z",
"iopub.status.idle": "2026-05-19T20:09:43.295830Z",
"shell.execute_reply": "2026-05-19T20:09:43.295285Z"
}
},
"outputs": [],
"source": [
"filteredEvents = df.Filter(\"Q1 * Q2 == -1\") \\\n",
" .Define(\"m\", \"sqrt(pow(E1 + E2, 2) - (pow(px1 + px2, 2) + pow(py1 + py2, 2) + pow(pz1 + pz2, 2)))\")"
]
},
{
"cell_type": "markdown",
"id": "586b0649",
"metadata": {},
"source": [
"Next we create a histogram to hold the invariant mass values and we draw it."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "71cd4cc0",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:43.314401Z",
"iopub.status.busy": "2026-05-19T20:09:43.314258Z",
"iopub.status.idle": "2026-05-19T20:09:46.182904Z",
"shell.execute_reply": "2026-05-19T20:09:46.181941Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file df014_invMass.png has been created\n"
]
}
],
"source": [
"invMass = filteredEvents.Histo1D((\"invMass\", \"CMS Opendata: #mu#mu mass;#mu#mu mass [GeV];Events\", 512, 2, 110), \"m\")\n",
"\n",
"c = ROOT.TCanvas()\n",
"c.SetLogx()\n",
"c.SetLogy()\n",
"invMass.Draw()\n",
"c.SaveAs(\"df014_invMass.png\")"
]
},
{
"cell_type": "markdown",
"id": "4348b38c",
"metadata": {},
"source": [
"We will now produce a plot also for the J/Psi particle. We will plot\n",
"on the same canvas the full spectrum and the zoom in on the J/psi particle.\n",
"First we will create the full spectrum histogram from the invariant mass\n",
"column, using a different histogram model than before."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f3073135",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:46.184682Z",
"iopub.status.busy": "2026-05-19T20:09:46.184523Z",
"iopub.status.idle": "2026-05-19T20:09:46.288487Z",
"shell.execute_reply": "2026-05-19T20:09:46.288016Z"
}
},
"outputs": [],
"source": [
"fullSpectrum = filteredEvents.Histo1D((\"Spectrum\", \"Subset of CMS Run 2010B;#mu#mu mass [GeV];Events\", 1024, 2, 110), \"m\")"
]
},
{
"cell_type": "markdown",
"id": "a68ed3a9",
"metadata": {},
"source": [
"Next we will create the histogram for the J/psi particle, applying first\n",
"the corresponding cut."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2d10b629",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:46.291267Z",
"iopub.status.busy": "2026-05-19T20:09:46.291144Z",
"iopub.status.idle": "2026-05-19T20:09:46.410938Z",
"shell.execute_reply": "2026-05-19T20:09:46.410545Z"
}
},
"outputs": [],
"source": [
"jpsiLow = 2.95\n",
"jpsiHigh = 3.25\n",
"jpsiCut = 'm < %s && m > %s' % (jpsiHigh, jpsiLow)\n",
"jpsi = filteredEvents.Filter(jpsiCut) \\\n",
" .Histo1D((\"jpsi\", \"Subset of CMS Run 2010B: J/#psi window;#mu#mu mass [GeV];Events\", 128, jpsiLow, jpsiHigh), \"m\")"
]
},
{
"cell_type": "markdown",
"id": "b98fa322",
"metadata": {},
"source": [
"Finally we draw the two histograms side by side."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1a94ef22",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:46.414884Z",
"iopub.status.busy": "2026-05-19T20:09:46.414753Z",
"iopub.status.idle": "2026-05-19T20:09:47.843173Z",
"shell.execute_reply": "2026-05-19T20:09:47.842337Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file df014_jpsi.png has been created\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved figures to df014_*.png\n"
]
}
],
"source": [
"dualCanvas = ROOT.TCanvas(\"DualCanvas\", \"DualCanvas\", 800, 512)\n",
"dualCanvas.Divide(2, 1)\n",
"leftPad = dualCanvas.cd(1)\n",
"leftPad.SetLogx()\n",
"leftPad.SetLogy()\n",
"fullSpectrum.Draw(\"Hist\")\n",
"dualCanvas.cd(2)\n",
"jpsi.SetMarkerStyle(20)\n",
"jpsi.Draw(\"HistP\")\n",
"dualCanvas.SaveAs(\"df014_jpsi.png\")\n",
"\n",
"print(\"Saved figures to df014_*.png\")"
]
},
{
"cell_type": "markdown",
"id": "847a7cd0",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e14f4334",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:47.844561Z",
"iopub.status.busy": "2026-05-19T20:09:47.844435Z",
"iopub.status.idle": "2026-05-19T20:09:48.036904Z",
"shell.execute_reply": "2026-05-19T20:09:48.036405Z"
}
},
"outputs": [
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"source": [
"from ROOT import gROOT \n",
"gROOT.GetListOfCanvases().Draw()"
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}
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