{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monthly Revenue Analysis\n",
    "\n",
    "A short example notebook: load a small revenue table, compute the total, and ",
    "find the best month. Free to use for testing notebook parsers and converters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],\n",
    "    'revenue': [42, 48, 55, 51, 63, 70],\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total revenue: 329\n"
     ]
    }
   ],
   "source": [
    "print('Total revenue:', df['revenue'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Jun'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['revenue'].idxmax(), 'month']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "June was the strongest month, and the half-year total was 329 (thousand USD)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}