{ "cells": [ { "cell_type": "markdown", "id": "e0c3aa1f", "metadata": {}, "source": [ "# df006_ranges\n", "Use Range to limit the amount of data processed.\n", "\n", "This tutorial shows how to express the concept of ranges when working with the 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": "3c40ef53", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:09:28.089340Z", "iopub.status.busy": "2026-05-19T20:09:28.089210Z", "iopub.status.idle": "2026-05-19T20:09:29.111894Z", "shell.execute_reply": "2026-05-19T20:09:29.111455Z" } }, "outputs": [], "source": [ "import ROOT\n", "\n", "def fill_tree(treeName, fileName):\n", " df = ROOT.RDataFrame(100)\n", " df.Define(\"b1\", \"(int) rdfentry_\")\\\n", " .Define(\"b2\", \"(float) rdfentry_ * rdfentry_\").Snapshot(treeName, fileName)" ] }, { "cell_type": "markdown", "id": "c1068ae2", "metadata": {}, "source": [ "We prepare an input tree to run on" ] }, { "cell_type": "code", "execution_count": 2, "id": "ddb4a643", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:09:29.136446Z", "iopub.status.busy": "2026-05-19T20:09:29.136290Z", "iopub.status.idle": "2026-05-19T20:09:30.897329Z", "shell.execute_reply": "2026-05-19T20:09:30.896961Z" } }, "outputs": [], "source": [ "fileName = \"df006_ranges_py.root\"\n", "treeName = \"myTree\"\n", "\n", "fill_tree(treeName, fileName)" ] }, { "cell_type": "markdown", "id": "bfbd19f4", "metadata": {}, "source": [ "We read the tree from the file and create a RDataFrame." ] }, { "cell_type": "code", "execution_count": 3, "id": "3be6abc6", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:09:30.907254Z", "iopub.status.busy": "2026-05-19T20:09:30.907125Z", "iopub.status.idle": "2026-05-19T20:09:31.038112Z", "shell.execute_reply": "2026-05-19T20:09:31.033720Z" } }, "outputs": [], "source": [ "d = ROOT.RDataFrame(treeName, fileName)" ] }, { "cell_type": "markdown", "id": "27414dd5", "metadata": {}, "source": [ "## Usage of ranges\n", "Now we'll count some entries using ranges" ] }, { "cell_type": "code", "execution_count": 4, "id": "53bf7d79", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:09:31.050121Z", "iopub.status.busy": "2026-05-19T20:09:31.049972Z", "iopub.status.idle": "2026-05-19T20:09:31.673485Z", "shell.execute_reply": "2026-05-19T20:09:31.673054Z" } }, "outputs": [], "source": [ "c_all = d.Count()" ] }, { "cell_type": "markdown", "id": "5180e734", "metadata": {}, "source": [ "This is how you can express a range of the first 30 entries" ] }, { "cell_type": "code", "execution_count": 5, "id": "3a64c612", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:09:31.688476Z", "iopub.status.busy": "2026-05-19T20:09:31.688316Z", "iopub.status.idle": "2026-05-19T20:09:32.385117Z", "shell.execute_reply": "2026-05-19T20:09:32.384621Z" } }, "outputs": [], "source": [ "d_0_30 = d.Range(30) \n", "c_0_30 = d_0_30.Count()" ] }, { "cell_type": "markdown", "id": "d098d866", "metadata": {}, "source": [ "This is how you pick all entries from 15 onwards" ] }, { "cell_type": "code", "execution_count": 6, "id": "65c9451f", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:09:32.397390Z", "iopub.status.busy": "2026-05-19T20:09:32.397239Z", "iopub.status.idle": "2026-05-19T20:09:32.542951Z", "shell.execute_reply": "2026-05-19T20:09:32.542377Z" } }, "outputs": [], "source": [ "d_15_end = d.Range(15, 0)\n", "c_15_end = d_15_end.Count()" ] }, { "cell_type": "markdown", "id": "05824114", "metadata": {}, "source": [ "We can use a stride too, in this case we pick an event every 3 entries" ] }, { "cell_type": "code", "execution_count": 7, "id": "c69d37f2", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:09:32.570244Z", "iopub.status.busy": "2026-05-19T20:09:32.570100Z", "iopub.status.idle": "2026-05-19T20:09:32.674172Z", "shell.execute_reply": "2026-05-19T20:09:32.673059Z" } }, "outputs": [], "source": [ "d_15_end_3 = d.Range(15, 0, 3)\n", "c_15_end_3 = d_15_end_3.Count()" ] }, { "cell_type": "markdown", "id": "df0a2fb8", "metadata": {}, "source": [ "The Range here acts first on the (whole) RDataFrame graph:\n", "Not only actions (like Count) but also filters and new columns can be added to it." ] }, { "cell_type": "code", "execution_count": 8, "id": "67537db0", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:09:32.675700Z", "iopub.status.busy": "2026-05-19T20:09:32.675547Z", "iopub.status.idle": "2026-05-19T20:09:33.206369Z", "shell.execute_reply": "2026-05-19T20:09:33.191424Z" } }, "outputs": [], "source": [ "d_0_50 = d.Range(50)\n", "c_0_50_odd_b1 = d_0_50.Filter(\"1 == b1 % 2\").Count()" ] }, { "cell_type": "markdown", "id": "e4141c11", "metadata": {}, "source": [ "An important thing to notice is that the counts of a filter are relative to the\n", "number of entries a filter \"sees\". Therefore, if a Range depends on a filter,\n", "the Range will act on the entries passing the filter only." ] }, { "cell_type": "code", "execution_count": 9, "id": "d34a1602", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:09:33.226648Z", "iopub.status.busy": "2026-05-19T20:09:33.226481Z", "iopub.status.idle": "2026-05-19T20:09:33.831807Z", "shell.execute_reply": "2026-05-19T20:09:33.831261Z" } }, "outputs": [], "source": [ "c_0_3_after_even_b1 = d.Filter(\"0 == b1 % 2\").Range(0, 3).Count()" ] }, { "cell_type": "markdown", "id": "81e27144", "metadata": {}, "source": [ "Ok, time to wrap up: let's print all counts!" ] }, { "cell_type": "code", "execution_count": 10, "id": "daa945ef", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:09:33.844396Z", "iopub.status.busy": "2026-05-19T20:09:33.844259Z", "iopub.status.idle": "2026-05-19T20:09:34.209204Z", "shell.execute_reply": "2026-05-19T20:09:34.208817Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Usage of ranges:\n", " - All entries: 100\n", " - Entries from 0 to 30: 30\n", " - Entries from 15 onwards: 85\n", " - Entries from 15 onwards in steps of 3: 29\n", " - Entries from 0 to 50, odd only: 25\n", " - First three entries of all even entries: 3\n" ] } ], "source": [ "print(\"Usage of ranges:\")\n", "print(\" - All entries:\", c_all.GetValue())\n", "print(\" - Entries from 0 to 30:\", c_0_30.GetValue())\n", "print(\" - Entries from 15 onwards:\", c_15_end.GetValue())\n", "print(\" - Entries from 15 onwards in steps of 3:\", c_15_end_3.GetValue())\n", "print(\" - Entries from 0 to 50, odd only:\", c_0_50_odd_b1.GetValue())\n", "print(\" - First three entries of all even entries:\", c_0_3_after_even_b1.GetValue())" ] } ], "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", "version": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }