{
"cells": [
{
"cell_type": "markdown",
"id": "5c6fb7a0",
"metadata": {},
"source": [
"# df019_Cache\n",
"Cache a processed RDataFrame in memory for further usage.\n",
"\n",
"This tutorial shows how the content of a data frame can be cached in memory\n",
"in form of a dataframe. The content of the columns is stored in memory in\n",
"contiguous slabs of memory and is \"ready to use\", i.e. no ROOT IO operation\n",
"is performed.\n",
"\n",
"Creating a cached data frame storing all of its content deserialised and uncompressed\n",
"in memory is particularly useful when dealing with datasets of a moderate size\n",
"(small enough to fit the RAM) over which several explorative loops need to be\n",
"performed as fast as possible. In addition, caching can be useful when no file\n",
"on disk needs to be created as a side effect of checkpointing part of the analysis.\n",
"\n",
"All steps in the caching are lazy, i.e. the cached data frame is actually filled\n",
"only when the event loop is triggered on it.\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": "4477aa2c",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:52.538422Z",
"iopub.status.busy": "2026-05-19T20:09:52.538254Z",
"iopub.status.idle": "2026-05-19T20:09:53.547401Z",
"shell.execute_reply": "2026-05-19T20:09:53.547030Z"
}
},
"outputs": [],
"source": [
"import ROOT\n",
"import os"
]
},
{
"cell_type": "markdown",
"id": "a4df9015",
"metadata": {},
"source": [
"We create a data frame on top of the hsimple example."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "119217af",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:53.558528Z",
"iopub.status.busy": "2026-05-19T20:09:53.558372Z",
"iopub.status.idle": "2026-05-19T20:09:54.115575Z",
"shell.execute_reply": "2026-05-19T20:09:54.115205Z"
}
},
"outputs": [],
"source": [
"hsimplePath = os.path.join(str(ROOT.gROOT.GetTutorialDir().Data()), \"hsimple.root\")\n",
"df = ROOT.RDataFrame(\"ntuple\", hsimplePath)"
]
},
{
"cell_type": "markdown",
"id": "934e3572",
"metadata": {},
"source": [
"We apply a simple cut and define a new column."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8f685876",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:54.124129Z",
"iopub.status.busy": "2026-05-19T20:09:54.123997Z",
"iopub.status.idle": "2026-05-19T20:09:54.348932Z",
"shell.execute_reply": "2026-05-19T20:09:54.337422Z"
}
},
"outputs": [],
"source": [
"df_cut = df.Filter(\"py > 0.f\")\\\n",
" .Define(\"px_plus_py\", \"px + py\")"
]
},
{
"cell_type": "markdown",
"id": "34364c0d",
"metadata": {},
"source": [
"We cache the content of the dataset. Nothing has happened yet: the work to accomplish\n",
"has been described."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "58ff3592",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:54.370384Z",
"iopub.status.busy": "2026-05-19T20:09:54.370234Z",
"iopub.status.idle": "2026-05-19T20:09:56.693920Z",
"shell.execute_reply": "2026-05-19T20:09:56.676428Z"
}
},
"outputs": [],
"source": [
"df_cached = df_cut.Cache()\n",
"\n",
"h = df_cached.Histo1D(\"px_plus_py\")"
]
},
{
"cell_type": "markdown",
"id": "d82664ee",
"metadata": {},
"source": [
"Now the event loop on the cached dataset is triggered by accessing the histogram.\n",
"This event triggers the loop on the `df` data frame lazily."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e614611e",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:56.705207Z",
"iopub.status.busy": "2026-05-19T20:09:56.705055Z",
"iopub.status.idle": "2026-05-19T20:09:57.594867Z",
"shell.execute_reply": "2026-05-19T20:09:57.589396Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file df019_Cache.png has been created\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved figure to df019_Cache.png\n"
]
}
],
"source": [
"c = ROOT.TCanvas()\n",
"h.Draw()\n",
"c.SaveAs(\"df019_Cache.png\")\n",
"\n",
"print(\"Saved figure to df019_Cache.png\")"
]
},
{
"cell_type": "markdown",
"id": "adac1c00",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9cfe3af6",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:57.607237Z",
"iopub.status.busy": "2026-05-19T20:09:57.607101Z",
"iopub.status.idle": "2026-05-19T20:09:57.792244Z",
"shell.execute_reply": "2026-05-19T20:09:57.791726Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
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"\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": {
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},
"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
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