{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e5ccd4ae",
   "metadata": {},
   "source": [
    "# df035_RDFFromPandas\n",
    "Read data from Pandas Data Frame into RDataFrame.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Pere Mato (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": "6bdc7dbd",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:15.716879Z",
     "iopub.status.busy": "2026-05-19T20:10:15.716739Z",
     "iopub.status.idle": "2026-05-19T20:10:16.949164Z",
     "shell.execute_reply": "2026-05-19T20:10:16.948424Z"
    }
   },
   "outputs": [],
   "source": [
    "import ROOT\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d0f2a5b",
   "metadata": {},
   "source": [
    "Let's create some data in a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7dfe2d4b",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:16.954223Z",
     "iopub.status.busy": "2026-05-19T20:10:16.954028Z",
     "iopub.status.idle": "2026-05-19T20:10:17.070874Z",
     "shell.execute_reply": "2026-05-19T20:10:17.070132Z"
    }
   },
   "outputs": [],
   "source": [
    "pdf = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e446b13",
   "metadata": {},
   "source": [
    "Convert the Pandas DataFrame to RDataFrame\n",
    "The column names are directly copied to the RDF \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": "7eae53ca",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:17.072331Z",
     "iopub.status.busy": "2026-05-19T20:10:17.072201Z",
     "iopub.status.idle": "2026-05-19T20:10:18.007988Z",
     "shell.execute_reply": "2026-05-19T20:10:18.007161Z"
    }
   },
   "outputs": [],
   "source": [
    "df = ROOT.RDF.FromPandas(pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25c3a539",
   "metadata": {},
   "source": [
    "You can now use the RDataFrame as usually, e.g. add a column ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "771795e0",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:18.012230Z",
     "iopub.status.busy": "2026-05-19T20:10:18.012092Z",
     "iopub.status.idle": "2026-05-19T20:10:18.177092Z",
     "shell.execute_reply": "2026-05-19T20:10:18.175754Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.Define('z', 'x + y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99eac7a",
   "metadata": {},
   "source": [
    "... or print the content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4631e38a",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:18.179249Z",
     "iopub.status.busy": "2026-05-19T20:10:18.179046Z",
     "iopub.status.idle": "2026-05-19T20:10:19.330686Z",
     "shell.execute_reply": "2026-05-19T20:10:19.329528Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+---+---+---+\n",
      "| Row | x | y | z | \n",
      "+-----+---+---+---+\n",
      "| 0   | 1 | 4 | 5 | \n",
      "+-----+---+---+---+\n",
      "| 1   | 2 | 5 | 7 | \n",
      "+-----+---+---+---+\n",
      "| 2   | 3 | 6 | 9 | \n",
      "+-----+---+---+---+\n"
     ]
    }
   ],
   "source": [
    "df.Display().Print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc9fcfc",
   "metadata": {},
   "source": [
    "... or save the data as a ROOT file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4df639bd",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:19.340268Z",
     "iopub.status.busy": "2026-05-19T20:10:19.340129Z",
     "iopub.status.idle": "2026-05-19T20:10:20.320152Z",
     "shell.execute_reply": "2026-05-19T20:10:20.319385Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cppyy.gbl.ROOT.RDF.RResultPtr<ROOT::RDF::RInterface<ROOT::Detail::RDF::RLoopManager> > object at 0x5622094aebf0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Snapshot('tree', 'df035_RDFFromPandas.root')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714d65d9",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5293580",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:10:20.331309Z",
     "iopub.status.busy": "2026-05-19T20:10:20.331167Z",
     "iopub.status.idle": "2026-05-19T20:10:20.447436Z",
     "shell.execute_reply": "2026-05-19T20:10:20.442895Z"
    }
   },
   "outputs": [],
   "source": [
    "from ROOT import gROOT \n",
    "gROOT.GetListOfCanvases().Draw()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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   "file_extension": ".py",
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