{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "335e32f0-2c5a-4f77-84dd-66c2266e6312",
   "metadata": {},
   "source": [
    "### Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42055a53-b495-4615-bdb4-eaddd9380e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72602"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"aoc1_input.txt\", \"r\") as f:\n",
    "    elves_raw = f.read().split(\"\\n\\n\")\n",
    "    elves = map(lambda x: sum(map(int, x.split(\"\\n\"))), elves_raw)\n",
    "    most_calories = max(elves)\n",
    "\n",
    "most_calories"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4523a59c-06bb-4214-a9ce-143885257bb4",
   "metadata": {},
   "source": [
    "### Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "614216a4-bbe9-40e9-a4b1-108e85946186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "207410"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"aoc1_input.txt\", \"r\") as f:\n",
    "    elves_raw = f.read().split(\"\\n\\n\")\n",
    "    elves = map(lambda x: sum(map(int, x.split(\"\\n\"))), elves_raw)\n",
    "    top_3_calories = sum(sorted(elves)[-3:])\n",
    "\n",
    "top_3_calories"
   ]
  }
 ],
 "metadata": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
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