{
"cells": [
{
"cell_type": "markdown",
"id": "b0ef2009",
"metadata": {},
"source": [
"# df007_snapshot\n",
"Write ROOT data with RDataFrame.\n",
"\n",
"This tutorial shows how to write out datasets in ROOT format using RDataFrame.\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Danilo Piparo (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": "adf770bc",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:29.990217Z",
"iopub.status.busy": "2026-05-19T20:09:29.990060Z",
"iopub.status.idle": "2026-05-19T20:09:30.990216Z",
"shell.execute_reply": "2026-05-19T20:09:30.983414Z"
}
},
"outputs": [],
"source": [
"import ROOT"
]
},
{
"cell_type": "markdown",
"id": "9b18ba47",
"metadata": {},
"source": [
"A simple helper function to fill a test tree: this makes the example stand-alone."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fbaa997a",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:31.008931Z",
"iopub.status.busy": "2026-05-19T20:09:31.008741Z",
"iopub.status.idle": "2026-05-19T20:09:31.136441Z",
"shell.execute_reply": "2026-05-19T20:09:31.133397Z"
}
},
"outputs": [],
"source": [
"def fill_tree(treeName, fileName):\n",
" df = ROOT.RDataFrame(10000)\n",
" df.Define(\"b1\", \"(int) rdfentry_\")\\\n",
" .Define(\"b2\", \"(float) rdfentry_ * rdfentry_\").Snapshot(treeName, fileName)"
]
},
{
"cell_type": "markdown",
"id": "ca667268",
"metadata": {},
"source": [
"We prepare an input tree to run on"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "adac7618",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:31.150557Z",
"iopub.status.busy": "2026-05-19T20:09:31.150419Z",
"iopub.status.idle": "2026-05-19T20:09:32.916914Z",
"shell.execute_reply": "2026-05-19T20:09:32.907435Z"
}
},
"outputs": [],
"source": [
"fileName = \"df007_snapshot_py.root\"\n",
"outFileName = \"df007_snapshot_output_py.root\"\n",
"outFileNameAllColumns = \"df007_snapshot_output_allColumns_py.root\"\n",
"treeName = \"myTree\"\n",
"fill_tree(treeName, fileName)"
]
},
{
"cell_type": "markdown",
"id": "759f1269",
"metadata": {},
"source": [
"We read the tree from the file and create a RDataFrame"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "65ecb341",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:32.919314Z",
"iopub.status.busy": "2026-05-19T20:09:32.919187Z",
"iopub.status.idle": "2026-05-19T20:09:33.033496Z",
"shell.execute_reply": "2026-05-19T20:09:33.033069Z"
}
},
"outputs": [],
"source": [
"d = ROOT.RDataFrame(treeName, fileName)"
]
},
{
"cell_type": "markdown",
"id": "0c6389db",
"metadata": {},
"source": [
"## Select entries\n",
"We now select some entries in the dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "060c7a50",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:33.048352Z",
"iopub.status.busy": "2026-05-19T20:09:33.048214Z",
"iopub.status.idle": "2026-05-19T20:09:33.217623Z",
"shell.execute_reply": "2026-05-19T20:09:33.207016Z"
}
},
"outputs": [],
"source": [
"d_cut = d.Filter(\"b1 % 2 == 0\")"
]
},
{
"cell_type": "markdown",
"id": "0e2386d1",
"metadata": {},
"source": [
"## Enrich the dataset\n",
"Build some temporary columns: we'll write them out"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8a0877e2",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:33.229009Z",
"iopub.status.busy": "2026-05-19T20:09:33.228883Z",
"iopub.status.idle": "2026-05-19T20:09:33.370239Z",
"shell.execute_reply": "2026-05-19T20:09:33.369752Z"
}
},
"outputs": [],
"source": [
"getVector_code ='''\n",
"std::vector getVector (float b2)\n",
"{\n",
" std::vector v;\n",
" for (int i = 0; i < 3; i++) v.push_back(b2*i);\n",
" return v;\n",
"}\n",
"'''\n",
"ROOT.gInterpreter.Declare(getVector_code)\n",
"\n",
"d2 = d_cut.Define(\"b1_square\", \"b1 * b1\") \\\n",
" .Define(\"b2_vector\", \"getVector( b2 )\")"
]
},
{
"cell_type": "markdown",
"id": "3fc25773",
"metadata": {},
"source": [
"## Write it to disk in ROOT format\n",
"We now write to disk a new dataset with one of the variables originally\n",
"present in the tree and the new variables.\n",
"The user can explicitly specify the types of the columns as template\n",
"arguments of the Snapshot method, otherwise they will be automatically\n",
"inferred."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9782e53f",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:33.372094Z",
"iopub.status.busy": "2026-05-19T20:09:33.371981Z",
"iopub.status.idle": "2026-05-19T20:09:34.401806Z",
"shell.execute_reply": "2026-05-19T20:09:34.380467Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
" > object at 0x557141898260>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d2.Snapshot(treeName, outFileName, \\\n",
" [\"b1\", \"b1_square\", \"b2_vector\"])"
]
},
{
"cell_type": "markdown",
"id": "3a893dad",
"metadata": {},
"source": [
"Open the new file and list the columns of the tree"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e0cdafb0",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:34.415187Z",
"iopub.status.busy": "2026-05-19T20:09:34.415039Z",
"iopub.status.idle": "2026-05-19T20:09:34.582907Z",
"shell.execute_reply": "2026-05-19T20:09:34.582582Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"These are the columns b1, b1_square and b2_vector:\n",
"Branch: b1\n",
"Branch: b1_square\n",
"Branch: b2_vector\n"
]
}
],
"source": [
"f1 = ROOT.TFile(outFileName)\n",
"t = f1[treeName]\n",
"print(\"These are the columns b1, b1_square and b2_vector:\")\n",
"for branch in t.GetListOfBranches():\n",
" print(\"Branch: %s\" %branch.GetName())\n",
"\n",
"f1.Close()"
]
},
{
"cell_type": "markdown",
"id": "8e8eadaa",
"metadata": {},
"source": [
"We are not forced to write the full set of column names. We can also\n",
"specify a regular expression for that. In case nothing is specified, all\n",
"columns are persistified."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4c667432",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:34.584693Z",
"iopub.status.busy": "2026-05-19T20:09:34.584556Z",
"iopub.status.idle": "2026-05-19T20:09:34.847076Z",
"shell.execute_reply": "2026-05-19T20:09:34.846429Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
" > object at 0x5571487fe0a0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d2.Snapshot(treeName, outFileNameAllColumns)"
]
},
{
"cell_type": "markdown",
"id": "e64bf44f",
"metadata": {},
"source": [
"Open the new file and list the columns of the tree"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "86d32ed6",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:34.850152Z",
"iopub.status.busy": "2026-05-19T20:09:34.850027Z",
"iopub.status.idle": "2026-05-19T20:09:34.954843Z",
"shell.execute_reply": "2026-05-19T20:09:34.954014Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"These are all the columns available to this dataframe:\n",
"Branch: b1_square\n",
"Branch: b2_vector\n",
"Branch: b1\n",
"Branch: b2\n"
]
}
],
"source": [
"f2 = ROOT.TFile(outFileNameAllColumns)\n",
"t = f2[treeName]\n",
"print(\"These are all the columns available to this dataframe:\")\n",
"for branch in t.GetListOfBranches():\n",
" print(\"Branch: %s\" %branch.GetName())\n",
"\n",
"f2.Close()"
]
},
{
"cell_type": "markdown",
"id": "7b0d57e3",
"metadata": {},
"source": [
"We can also get a fresh RDataFrame out of the snapshot and restart the\n",
"analysis chain from it."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "540847a3",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:34.956267Z",
"iopub.status.busy": "2026-05-19T20:09:34.956135Z",
"iopub.status.idle": "2026-05-19T20:09:36.025645Z",
"shell.execute_reply": "2026-05-19T20:09:36.024810Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file df007_snapshot.png has been created\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved figure to df007_snapshot.png\n"
]
}
],
"source": [
"snapshot_df = d2.Snapshot(treeName, outFileName, [\"b1_square\"]);\n",
"h = snapshot_df.Histo1D(\"b1_square\")\n",
"\n",
"c = ROOT.TCanvas()\n",
"h.Draw()\n",
"c.SaveAs(\"df007_snapshot.png\")\n",
"\n",
"print(\"Saved figure to df007_snapshot.png\")"
]
},
{
"cell_type": "markdown",
"id": "c79b1edb",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "623c03e5",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:36.027115Z",
"iopub.status.busy": "2026-05-19T20:09:36.026980Z",
"iopub.status.idle": "2026-05-19T20:09:36.201123Z",
"shell.execute_reply": "2026-05-19T20:09:36.200299Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ROOT import gROOT \n",
"gROOT.GetListOfCanvases().Draw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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