2018/Day4.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Day 4: Repose Record\n",
    "You've sneaked into another supply closet - this time, it's across from the prototype suit manufacturing lab. You need to sneak inside and fix the issues with the suit, but there's a guard stationed outside the lab, so this is as close as you can safely get.\n",
    "\n",
    "As you search the closet for anything that might help, you discover that you're not the first person to want to sneak in. Covering the walls, someone has spent an hour starting every midnight for the past few months secretly observing this guard post! They've been writing down the ID of the one guard on duty that night - the Elves seem to have decided that one guard was enough for the overnight shift - as well as when they fall asleep or wake up while at their post (your puzzle input).\n",
    "\n",
    "For example, consider the following records, which have already been organized into chronological order:\n",
    "```\n",
    "[1518-11-01 00:00] Guard #10 begins shift\n",
    "[1518-11-01 00:05] falls asleep\n",
    "[1518-11-01 00:25] wakes up\n",
    "[1518-11-01 00:30] falls asleep\n",
    "[1518-11-01 00:55] wakes up\n",
    "[1518-11-01 23:58] Guard #99 begins shift\n",
    "[1518-11-02 00:40] falls asleep\n",
    "[1518-11-02 00:50] wakes up\n",
    "[1518-11-03 00:05] Guard #10 begins shift\n",
    "[1518-11-03 00:24] falls asleep\n",
    "[1518-11-03 00:29] wakes up\n",
    "[1518-11-04 00:02] Guard #99 begins shift\n",
    "[1518-11-04 00:36] falls asleep\n",
    "[1518-11-04 00:46] wakes up\n",
    "[1518-11-05 00:03] Guard #99 begins shift\n",
    "[1518-11-05 00:45] falls asleep\n",
    "[1518-11-05 00:55] wakes up\n",
    "```\n",
    "Timestamps are written using year-month-day hour:minute format. The guard falling asleep or waking up is always the one whose shift most recently started. Because all asleep/awake times are during the midnight hour (00:00 - 00:59), only the minute portion (00 - 59) is relevant for those events.\n",
    "\n",
    "Visually, these records show that the guards are asleep at these times:\n",
    "```\n",
    "Date   ID   Minute\n",
    "            000000000011111111112222222222333333333344444444445555555555\n",
    "            012345678901234567890123456789012345678901234567890123456789\n",
    "11-01  #10  .....####################.....#########################.....\n",
    "11-02  #99  ........................................##########..........\n",
    "11-03  #10  ........................#####...............................\n",
    "11-04  #99  ....................................##########..............\n",
    "11-05  #99  .............................................##########.....\n",
    "```\n",
    "The columns are Date, which shows the month-day portion of the relevant day; ID, which shows the guard on duty that day; and Minute, which shows the minutes during which the guard was asleep within the midnight hour. (The Minute column's header shows the minute's ten's digit in the first row and the one's digit in the second row.) Awake is shown as ., and asleep is shown as #.\n",
    "\n",
    "Note that guards count as asleep on the minute they fall asleep, and they count as awake on the minute they wake up. For example, because Guard #10 wakes up at 00:25 on 1518-11-01, minute 25 is marked as awake.\n",
    "\n",
    "If you can figure out the guard most likely to be asleep at a specific time, you might be able to trick that guard into working tonight so you can have the best chance of sneaking in. You have two strategies for choosing the best guard/minute combination.\n",
    "\n",
    "Strategy 1: Find the guard that has the most minutes asleep. What minute does that guard spend asleep the most?\n",
    "\n",
    "In the example above, Guard #10 spent the most minutes asleep, a total of 50 minutes (20+25+5), while Guard #99 only slept for a total of 30 minutes (10+10+10). Guard #10 was asleep most during minute 24 (on two days, whereas any other minute the guard was asleep was only seen on one day).\n",
    "\n",
    "While this example listed the entries in chronological order, your entries are in the order you found them. You'll need to organize them before they can be analyzed.\n",
    "\n",
    "What is the ID of the guard you chose multiplied by the minute you chose?\n",
    "\n",
    "(In the above example, the answer would be `10 * 24 = 240`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll load and split the data. Small trick we use here, as python does not eat 1518. Soooo, we'll ignore millenium and century and hope for the best. Hic sunt dragones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18-06-23 00:43</td>\n",
       "      <td>wakes up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18-06-01 00:26</td>\n",
       "      <td>wakes up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18-08-29 00:02</td>\n",
       "      <td>falls asleep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18-03-06 00:02</td>\n",
       "      <td>Guard #1783 begins shift</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18-04-28 00:01</td>\n",
       "      <td>Guard #1553 begins shift</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Time                    Action\n",
       "0  18-06-23 00:43                  wakes up\n",
       "1  18-06-01 00:26                  wakes up\n",
       "2  18-08-29 00:02              falls asleep\n",
       "3  18-03-06 00:02  Guard #1783 begins shift\n",
       "4  18-04-28 00:01  Guard #1553 begins shift"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"day4.txt\") as f:\n",
    "    content = f.readlines()\n",
    "content = pd.DataFrame([x[3:].strip().split(\"] \") for x in content], columns=[\"Time\", \"Action\"])\n",
    "content.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything's better with datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-06-23 00:43:00</td>\n",
       "      <td>wakes up</td>\n",
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       "      <th>1</th>\n",
       "      <td>2018-06-01 00:26:00</td>\n",
       "      <td>wakes up</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-08-29 00:02:00</td>\n",
       "      <td>falls asleep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-06 00:02:00</td>\n",
       "      <td>Guard #1783 begins shift</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-28 00:01:00</td>\n",
       "      <td>Guard #1553 begins shift</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                 Time                    Action\n",
       "0 2018-06-23 00:43:00                  wakes up\n",
       "1 2018-06-01 00:26:00                  wakes up\n",
       "2 2018-08-29 00:02:00              falls asleep\n",
       "3 2018-03-06 00:02:00  Guard #1783 begins shift\n",
       "4 2018-04-28 00:01:00  Guard #1553 begins shift"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content['Time'] = pd.to_datetime(content['Time'], format='%y-%m-%d %H:%M')\n",
    "content.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And some sorting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>2018-02-20 23:47:00</td>\n",
       "      <td>Guard #101 begins shift</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>2018-02-21 00:03:00</td>\n",
       "      <td>falls asleep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>2018-02-21 00:45:00</td>\n",
       "      <td>wakes up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>2018-02-22 00:02:00</td>\n",
       "      <td>Guard #827 begins shift</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2018-02-22 00:38:00</td>\n",
       "      <td>falls asleep</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   Time                   Action\n",
       "625 2018-02-20 23:47:00  Guard #101 begins shift\n",
       "390 2018-02-21 00:03:00             falls asleep\n",
       "684 2018-02-21 00:45:00                 wakes up\n",
       "487 2018-02-22 00:02:00  Guard #827 begins shift\n",
       "238 2018-02-22 00:38:00             falls asleep"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content = content.sort_values(\"Time\")\n",
    "content.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'll push some characters around to get the Guard ID out. That makes it much better. Also, we'll do a nice difference, so we can get a time difference to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Action</th>\n",
       "      <th>Guard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>625</th>\n",
       "      <td>2018-02-20 23:47:00</td>\n",
       "      <td>Guard #101 begins shift</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>2018-02-21 00:03:00</td>\n",
       "      <td>falls asleep</td>\n",
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       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>2018-02-21 00:45:00</td>\n",
       "      <td>wakes up</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>2018-02-22 00:02:00</td>\n",
       "      <td>Guard #827 begins shift</td>\n",
       "      <td>827</td>\n",
       "    </tr>\n",
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       "      <th>238</th>\n",
       "      <td>2018-02-22 00:38:00</td>\n",
       "      <td>falls asleep</td>\n",
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      ],
      "text/plain": [
       "                   Time                   Action  Guard\n",
       "625 2018-02-20 23:47:00  Guard #101 begins shift    101\n",
       "390 2018-02-21 00:03:00             falls asleep    101\n",
       "684 2018-02-21 00:45:00                 wakes up    101\n",
       "487 2018-02-22 00:02:00  Guard #827 begins shift    827\n",
       "238 2018-02-22 00:38:00             falls asleep    827"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guard = []\n",
    "for x in content[\"Action\"]:\n",
    "    q = x.split(' ')[1]\n",
    "    if '#' in q:\n",
    "        p = int(q[1:])\n",
    "    guard.append(p)\n",
    "content[\"Guard\"] = guard\n",
    "content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Action</th>\n",
       "      <th>Guard</th>\n",
       "      <th>Time_Diff</th>\n",
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       "      <th>625</th>\n",
       "      <td>2018-02-20 23:47:00</td>\n",
       "      <td>Guard #101 begins shift</td>\n",
       "      <td>101</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>2018-02-21 00:03:00</td>\n",
       "      <td>falls asleep</td>\n",
       "      <td>101</td>\n",
       "      <td>00:16:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>2018-02-21 00:45:00</td>\n",
       "      <td>wakes up</td>\n",
       "      <td>101</td>\n",
       "      <td>00:42:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>2018-02-22 00:02:00</td>\n",
       "      <td>Guard #827 begins shift</td>\n",
       "      <td>827</td>\n",
       "      <td>23:17:00</td>\n",
       "    </tr>\n",
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       "      <th>238</th>\n",
       "      <td>2018-02-22 00:38:00</td>\n",
       "      <td>falls asleep</td>\n",
       "      <td>827</td>\n",
       "      <td>00:36:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   Time                   Action  Guard Time_Diff\n",
       "625 2018-02-20 23:47:00  Guard #101 begins shift    101       NaT\n",
       "390 2018-02-21 00:03:00             falls asleep    101  00:16:00\n",
       "684 2018-02-21 00:45:00                 wakes up    101  00:42:00\n",
       "487 2018-02-22 00:02:00  Guard #827 begins shift    827  23:17:00\n",
       "238 2018-02-22 00:38:00             falls asleep    827  00:36:00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content[\"Time_Diff\"] = content[\"Time\"]-content[\"Time\"].shift(1)\n",
    "content.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to try out Pivot Tables!\n",
    "\n",
    "We can let Pandas just figure out the sums for all the Guards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>05:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2383</th>\n",
       "      <td>02:39:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>06:19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2857</th>\n",
       "      <td>03:21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>04:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>03:38:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Time_Diff\n",
       "Guard          \n",
       "101    04:34:00\n",
       "283    07:40:00\n",
       "349    04:15:00\n",
       "449    07:35:00\n",
       "691    07:29:00\n",
       "751    03:17:00\n",
       "827    06:05:00\n",
       "1193   05:30:00\n",
       "1553   07:13:00\n",
       "1669   04:04:00\n",
       "1723   03:35:00\n",
       "1783   04:56:00\n",
       "1973   03:16:00\n",
       "2207   06:25:00\n",
       "2273   05:15:00\n",
       "2383   02:39:00\n",
       "2411   06:19:00\n",
       "2857   03:21:00\n",
       "3023   04:36:00\n",
       "3499   03:38:00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = content[content[\"Action\"] == \"wakes up\"].drop([\"Time\",\"Action\"],axis=1)\n",
    "table = pd.pivot_table(df, values='Time_Diff', columns=['Guard'], aggfunc=np.sum).T\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time_Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guard</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2383</th>\n",
       "      <td>02:39:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>03:16:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>03:17:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2857</th>\n",
       "      <td>03:21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>03:35:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>03:38:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>04:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>04:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>04:34:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>04:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1783</th>\n",
       "      <td>04:56:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>05:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>05:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>06:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>06:19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>06:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>07:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>07:29:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>07:35:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>07:40:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Time_Diff\n",
       "Guard          \n",
       "2383   02:39:00\n",
       "1973   03:16:00\n",
       "751    03:17:00\n",
       "2857   03:21:00\n",
       "1723   03:35:00\n",
       "3499   03:38:00\n",
       "1669   04:04:00\n",
       "349    04:15:00\n",
       "101    04:34:00\n",
       "3023   04:36:00\n",
       "1783   04:56:00\n",
       "2273   05:15:00\n",
       "1193   05:30:00\n",
       "827    06:05:00\n",
       "2411   06:19:00\n",
       "2207   06:25:00\n",
       "1553   07:13:00\n",
       "691    07:29:00\n",
       "449    07:35:00\n",
       "283    07:40:00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.sort_values('Time_Diff')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hello Mister 283. We have to have a talk. That job is amazing 8 hours of sleeping on the job. So we'll extract a table with the sleepy wakey times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\tools\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Action</th>\n",
       "      <th>Guard</th>\n",
       "      <th>Time_Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>2018-03-08 00:09:00</td>\n",
       "      <td>falls asleep</td>\n",
       "      <td>283</td>\n",
       "      <td>00:09:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>2018-03-08 00:57:00</td>\n",
       "      <td>wakes up</td>\n",
       "      <td>283</td>\n",
       "      <td>00:48:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2018-03-28 00:43:00</td>\n",
       "      <td>falls asleep</td>\n",
       "      <td>283</td>\n",
       "      <td>00:40:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2018-03-28 00:59:00</td>\n",
       "      <td>wakes up</td>\n",
       "      <td>283</td>\n",
       "      <td>00:16:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>2018-03-30 00:31:00</td>\n",
       "      <td>falls asleep</td>\n",
       "      <td>283</td>\n",
       "      <td>00:30:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Time        Action  Guard Time_Diff\n",
       "479 2018-03-08 00:09:00  falls asleep    283  00:09:00\n",
       "895 2018-03-08 00:57:00      wakes up    283  00:48:00\n",
       "55  2018-03-28 00:43:00  falls asleep    283  00:40:00\n",
       "130 2018-03-28 00:59:00      wakes up    283  00:16:00\n",
       "550 2018-03-30 00:31:00  falls asleep    283  00:30:00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleepy_guard = content[content[\"Guard\"] == 283]\n",
    "sleepy_guard = sleepy_guard[~content[\"Action\"].str.startswith(\"Guard\")]\n",
    "sleepy_guard.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sooo, iterate over rows and just add that jazz up and throw in a plot for good measure!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x150de07e898>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8x+/uJ929NvK6AzgC6LZ1Ara/WE9edhZ/8O4lQYciIgH4zbQNU9vdk5DhnGZW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150ddeeef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counter = np.zeros(60)\n",
    "flippy = True\n",
    "for rows in sleepy_guard[\"Time\"]:\n",
    "    if flippy:\n",
    "        start = rows.minute\n",
    "    else:\n",
    "        counter[start:rows.minute] += 1\n",
    "    flippy = np.invert(flippy)\n",
    "plt.plot(counter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12169"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "43*283"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part Two\n",
    "Strategy 2: Of all guards, which guard is most frequently asleep on the same minute?\n",
    "\n",
    "In the example above, Guard #99 spent minute 45 asleep more than any other guard or minute - three times in total. (In all other cases, any guard spent any minute asleep at most twice.)\n",
    "\n",
    "What is the ID of the guard you chose multiplied by the minute you chose? (In the above example, the answer would be 99 * 45 = 4455.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "guards = list(pd.unique(content[\"Guard\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\tools\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x150de28e940>,\n",
       " <matplotlib.lines.Line2D at 0x150de28eb00>,\n",
       " <matplotlib.lines.Line2D at 0x150de28ecf8>,\n",
       " <matplotlib.lines.Line2D at 0x150de28eef0>,\n",
       " <matplotlib.lines.Line2D at 0x150de296128>,\n",
       " <matplotlib.lines.Line2D at 0x150de296320>,\n",
       " <matplotlib.lines.Line2D at 0x150de296518>,\n",
       " <matplotlib.lines.Line2D at 0x150de296710>,\n",
       " <matplotlib.lines.Line2D at 0x150de296908>,\n",
       " <matplotlib.lines.Line2D at 0x150de296b00>,\n",
       " <matplotlib.lines.Line2D at 0x150de240ef0>,\n",
       " <matplotlib.lines.Line2D at 0x150de296eb8>,\n",
       " <matplotlib.lines.Line2D at 0x150de29c0f0>,\n",
       " <matplotlib.lines.Line2D at 0x150de29c2e8>,\n",
       " <matplotlib.lines.Line2D at 0x150de29c4e0>,\n",
       " <matplotlib.lines.Line2D at 0x150de29c6d8>,\n",
       " <matplotlib.lines.Line2D at 0x150de29c8d0>,\n",
       " <matplotlib.lines.Line2D at 0x150de29cac8>,\n",
       " <matplotlib.lines.Line2D at 0x150de29ccc0>,\n",
       " <matplotlib.lines.Line2D at 0x150de29ceb8>,\n",
       " <matplotlib.lines.Line2D at 0x150de2a10f0>,\n",
       " <matplotlib.lines.Line2D at 0x150de2a12e8>,\n",
       " <matplotlib.lines.Line2D at 0x150de2a14e0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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56IyfH64EJiVmyzKCwe5ztjCLIvwKZwVFmzeh0evJu3J4X/MZecuJz8ii6K3X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6JcaSclciF+rQeChonbmTwHLvjXsQNbdDPYUnDsfJYO02w2BJJIVhUaHEXEN90Hg+sw2RYTPdEY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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150ddeee358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counter = np.zeros([60,23])\n",
    "row_count = 0\n",
    "\n",
    "sleep_content = content[~content[\"Action\"].str.startswith(\"Guard\")]\n",
    "\n",
    "for guard in guards:\n",
    "    flippy = True\n",
    "    all_guard = sleep_content[content[\"Guard\"] == guard]\n",
    "    for rows in all_guard[\"Time\"]:\n",
    "        if flippy:\n",
    "            start = rows.minute\n",
    "        else:\n",
    "            counter[start:rows.minute,row_count] += 1\n",
    "        flippy = np.invert(flippy)\n",
    "    row_count += 1\n",
    "plt.plot(counter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x150de4aa470>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2va9CELzLlj9L46seikR5lsV6sgJMyAowISug2QyKhQueO9BdjTTP7HDdjxm3prg2FJmx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\nyAowISvAhKwAE7ICTMgKMCEr4H/qL2P2dmQO+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150de1faa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(counter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly... No. Nothing is clear from this one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " np.unravel_index(np.argmax(counter, axis=None), counter.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "449"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guards[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16164"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "36*449"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}