{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d69aa7b3",
   "metadata": {},
   "source": [
    "# df032_RDFFromNumpy\n",
    "Read data from Numpy arrays into RDataFrame.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Stefan Wunsch (KIT, CERN)  \n",
    "<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer</a> from the macro found in the ROOT repository  on Tuesday, May 19, 2026 at 08:10 PM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62bd8f58",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:08.838318Z",
     "iopub.status.busy": "2026-05-19T20:10:08.838176Z",
     "iopub.status.idle": "2026-05-19T20:10:09.864018Z",
     "shell.execute_reply": "2026-05-19T20:10:09.863567Z"
    }
   },
   "outputs": [],
   "source": [
    "import ROOT\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0a6bd73",
   "metadata": {},
   "source": [
    "Let's create some data in numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4bb4d0a",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:09.866363Z",
     "iopub.status.busy": "2026-05-19T20:10:09.866159Z",
     "iopub.status.idle": "2026-05-19T20:10:09.984284Z",
     "shell.execute_reply": "2026-05-19T20:10:09.983644Z"
    }
   },
   "outputs": [],
   "source": [
    "x = np.array([1, 2, 3], dtype=np.int32)\n",
    "y = np.array([4, 5, 6], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7fd7b43",
   "metadata": {},
   "source": [
    "Read the data with RDataFrame\n",
    "The column names in the RDataFrame are defined by the keys of the dictionary.\n",
    "Please note that only fundamental types (int, float, ...) are supported and\n",
    "the arrays must have the same length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4fa19ba2",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:10.000444Z",
     "iopub.status.busy": "2026-05-19T20:10:10.000291Z",
     "iopub.status.idle": "2026-05-19T20:10:10.936913Z",
     "shell.execute_reply": "2026-05-19T20:10:10.926365Z"
    }
   },
   "outputs": [],
   "source": [
    "df = ROOT.RDF.FromNumpy({'x': x, 'y': y})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f92e5b",
   "metadata": {},
   "source": [
    "You can now use the RDataFrame as usually, e.g. add a column ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "410d966a",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:10.940397Z",
     "iopub.status.busy": "2026-05-19T20:10:10.940255Z",
     "iopub.status.idle": "2026-05-19T20:10:11.101132Z",
     "shell.execute_reply": "2026-05-19T20:10:11.100648Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.Define('z', 'x + y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c0d7ea6",
   "metadata": {},
   "source": [
    "... or print the content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe574c34",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:11.108350Z",
     "iopub.status.busy": "2026-05-19T20:10:11.108209Z",
     "iopub.status.idle": "2026-05-19T20:10:12.345123Z",
     "shell.execute_reply": "2026-05-19T20:10:12.336649Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+---+----------+----------+\n",
      "| Row | x | y        | z        | \n",
      "+-----+---+----------+----------+\n",
      "| 0   | 1 | 4.000000 | 5.000000 | \n",
      "+-----+---+----------+----------+\n",
      "| 1   | 2 | 5.000000 | 7.000000 | \n",
      "+-----+---+----------+----------+\n",
      "| 2   | 3 | 6.000000 | 9.000000 | \n",
      "+-----+---+----------+----------+\n"
     ]
    }
   ],
   "source": [
    "df.Display().Print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e716db46",
   "metadata": {},
   "source": [
    "... or save the data as a ROOT file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ee8312a",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:12.346727Z",
     "iopub.status.busy": "2026-05-19T20:10:12.346552Z",
     "iopub.status.idle": "2026-05-19T20:10:13.284046Z",
     "shell.execute_reply": "2026-05-19T20:10:13.283348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cppyy.gbl.ROOT.RDF.RResultPtr<ROOT::RDF::RInterface<ROOT::Detail::RDF::RLoopManager> > object at 0x55fe404bdae0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Snapshot('tree', 'df032_RDFFromNumpy.root')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdfd5b4e",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4a199aa",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:13.295212Z",
     "iopub.status.busy": "2026-05-19T20:10:13.295068Z",
     "iopub.status.idle": "2026-05-19T20:10:13.409512Z",
     "shell.execute_reply": "2026-05-19T20:10:13.409137Z"
    }
   },
   "outputs": [],
   "source": [
    "from ROOT import gROOT \n",
    "gROOT.GetListOfCanvases().Draw()"
   ]
  }
 ],
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