tunnel-marathon-2021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tunnel Light Marathon September 2019 : Comparing 2019 virtual race time to previous five years. \n",
    "\n",
    "This Notebook uses a spreadsheet to compare times of finishers of the 2019 race with their publicly avaiable marathon times in other races over the previous 5 years. When I looked for intersting options to add to my \"I might want to run this race someday\" list, I ran across a tantalizing line on findyourmarathon.net about the Tunnel Light Marathon's net downhill is \"never steep enough to be uncomfortable; you just feel like you're having a good day.\"\n",
    "\n",
    "The Notebook adds columns for average times over the previous five years, compares the average to the 2019 finish time for change in time and percentage of change, plots finish order against change in time and percentage, sorts spreadsheet by percentage improvement, and exports to csv. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Place .1</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Place</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>2019 Gun Time</th>\n",
       "      <th>2019 Chip Time</th>\n",
       "      <th>2019 Pace</th>\n",
       "      <th>2018 Time</th>\n",
       "      <th>2017 Time</th>\n",
       "      <th>2016 Time</th>\n",
       "      <th>2015 Time</th>\n",
       "      <th>2014 Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Thomas Danielsen (# 194)</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1 / 16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02:44:35.300</td>\n",
       "      <td>02:44:31.900</td>\n",
       "      <td>6:16/mile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Steven Michalski (# 521)</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>1 / 60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02:45:36.200</td>\n",
       "      <td>02:45:35.100</td>\n",
       "      <td>6:19/mile</td>\n",
       "      <td>03:02:31</td>\n",
       "      <td>02:52:58</td>\n",
       "      <td>02:52:16</td>\n",
       "      <td>02:47:11</td>\n",
       "      <td>02:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Lester Maxwell (# 499)</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>1 / 20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02:46:43.100</td>\n",
       "      <td>02:46:37.100</td>\n",
       "      <td>6:21/mile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Grant Drummond (# 224)</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>1 / 28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02:51:08.700</td>\n",
       "      <td>02:51:07.700</td>\n",
       "      <td>6:31/mile</td>\n",
       "      <td>02:51:01</td>\n",
       "      <td>02:55:46</td>\n",
       "      <td>03:21:02</td>\n",
       "      <td>02:56:10</td>\n",
       "      <td>03:08:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Nick Petrillo (# 26)</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>2 / 16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02:52:34.800</td>\n",
       "      <td>02:52:32.600</td>\n",
       "      <td>6:35/mile</td>\n",
       "      <td>03:04:49</td>\n",
       "      <td>02:57:46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03:03:20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Place                      Name  Gender   Place .1  Age  Age Place  \\\n",
       "0       1  Thomas Danielsen (# 194)       M         1    25    1 / 16   \n",
       "1       2  Steven Michalski (# 521)       M         2    50    1 / 60   \n",
       "2       3    Lester Maxwell (# 499)       M         3    30    1 / 20   \n",
       "3       4    Grant Drummond (# 224)       M         4    42    1 / 28   \n",
       "4       5      Nick Petrillo (# 26)       M         5    28    2 / 16   \n",
       "\n",
       "   Unnamed: 6 2019 Gun Time  2019 Chip Time   2019 Pace 2018 Time  2017 Time   \\\n",
       "0         NaN   02:44:35.300    02:44:31.900  6:16/mile        NaN        NaN   \n",
       "1         NaN   02:45:36.200    02:45:35.100  6:19/mile   03:02:31   02:52:58   \n",
       "2         NaN   02:46:43.100    02:46:37.100  6:21/mile        NaN        NaN   \n",
       "3         NaN   02:51:08.700    02:51:07.700  6:31/mile   02:51:01   02:55:46   \n",
       "4         NaN   02:52:34.800    02:52:32.600  6:35/mile   03:04:49   02:57:46   \n",
       "\n",
       "  2016 Time  2015 Time  2014 Time  \n",
       "0        NaN        NaN       NaN  \n",
       "1   02:52:16   02:47:11  02:53:21  \n",
       "2        NaN        NaN       NaN  \n",
       "3   03:21:02   02:56:10  03:08:36  \n",
       "4        NaN   03:03:20       NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tunnel = pd.read_csv('tunnel.csv')  # imports .csv to data frames\n",
    "display(df_tunnel.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tunnel = df_tunnel.rename(columns = lambda x: x.strip())  # take out spaces from column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_tunnel['2019 Time'] = pd.to_timedelta(df_tunnel['2019 Time']) #changes times from text to time delta\n",
    "df_tunnel['2019 Chip Time'] = pd.to_timedelta(df_tunnel['2019 Chip Time'])\n",
    "df_tunnel['2018 Time'] = pd.to_timedelta(df_tunnel['2018 Time'])\n",
    "df_tunnel['2017 Time'] = pd.to_timedelta(df_tunnel['2017 Time'])\n",
    "df_tunnel['2016 Time'] = pd.to_timedelta(df_tunnel['2016 Time'])\n",
    "df_tunnel['2015 Time'] = pd.to_timedelta(df_tunnel['2015 Time'])\n",
    "df_tunnel['2014 Time'] = pd.to_timedelta(df_tunnel['2014 Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tunnel['Average'] = df_tunnel[['2018 Time', '2017 Time', '2016 Time', '2015 Time', '2014 Time']].mean(axis=1)  # find the average \n",
    "df_tunnel['Average'] = pd.to_timedelta(df_tunnel['Average'])  # convert average to time delta\n",
    "df_tunnel['Compare_Average_to_2019'] = df_tunnel['Average'] - df_tunnel['2019 Chip Time']  # create column to compare average to 2019 time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tunnel['Average_Seconds'] = df_tunnel['Average'] / np.timedelta64(1,'s')\n",
    "df_tunnel['Compare_Average_to_2019_Seconds'] = df_tunnel['Compare_Average_to_2019'] / np.timedelta64(1,'s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average_Pretty</th>\n",
       "      <th>Compare_Average_to_2019_Pretty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2h53m39s</td>\n",
       "      <td>8m4s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3h2m31s</td>\n",
       "      <td>11m23s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3h1m58s</td>\n",
       "      <td>9m25s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>6h22m47s</td>\n",
       "      <td>-11m57s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>5h58m3s</td>\n",
       "      <td>-37m0s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>4h57m46s</td>\n",
       "      <td>-1h57m24s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>597 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Average_Pretty Compare_Average_to_2019_Pretty\n",
       "0              NaN                            NaN\n",
       "1         2h53m39s                           8m4s\n",
       "2              NaN                            NaN\n",
       "3          3h2m31s                         11m23s\n",
       "4          3h1m58s                          9m25s\n",
       "..             ...                            ...\n",
       "592       6h22m47s                        -11m57s\n",
       "593        5h58m3s                         -37m0s\n",
       "594            NaN                            NaN\n",
       "595       4h57m46s                      -1h57m24s\n",
       "596            NaN                            NaN\n",
       "\n",
       "[597 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change things to readable eventually \n",
    "def pretty_time_delta(seconds):\n",
    "    if pd.isnull(seconds): \n",
    "        return seconds\n",
    "    sign_string = '-' if seconds < 0 else ''\n",
    "    seconds = abs(int(seconds))\n",
    "    days, seconds = divmod(seconds, 86400)\n",
    "    hours, seconds = divmod(seconds, 3600)\n",
    "    minutes, seconds = divmod(seconds, 60)\n",
    "    if days > 0:\n",
    "        return '%s%dd%dh%dm%ds' % (sign_string, days, hours, minutes, seconds)\n",
    "    elif hours > 0:\n",
    "        return '%s%dh%dm%ds' % (sign_string, hours, minutes, seconds)\n",
    "    elif minutes > 0:\n",
    "        return '%s%dm%ds' % (sign_string, minutes, seconds)\n",
    "    else:\n",
    "        return '%s%ds' % (sign_string, seconds)\n",
    "    \n",
    "df_tunnel['Average_Pretty'] = df_tunnel['Average_Seconds'].apply(pretty_time_delta)\n",
    "df_tunnel['Compare_Average_to_2019_Pretty'] = df_tunnel['Compare_Average_to_2019_Seconds'].apply(pretty_time_delta)\n",
    "df_tunnel_pretty = df_tunnel[[\"Average_Pretty\",\"Compare_Average_to_2019_Pretty\"]]\n",
    "display(df_tunnel_pretty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Place'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tunnel.plot(x='Place', y='Compare_Average_to_2019_Seconds', style='o', legend=None, figsize=(20,10))\n",
    "# compares finish order with the time compared to average time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of runners who improved their time: 242\n",
      "Runners with a previous time: 358\n",
      "percentage of runners with a previous time who improved at 2019 Tunnel Light: 0.6759776536312849\n"
     ]
    }
   ],
   "source": [
    "# find number of runners who beat their average\n",
    "num_improved=(df_tunnel[df_tunnel['Compare_Average_to_2019_Seconds']>0].count()['Compare_Average_to_2019_Seconds'])\n",
    "# find number of runners who had a previous time \n",
    "num_previous_time=(df_tunnel[df_tunnel['Compare_Average_to_2019_Seconds']!='NaN'].count()['Compare_Average_to_2019_Seconds'])\n",
    "# find percentage of runners who beat their average \n",
    "print(f\"Number of runners who improved their time: {num_improved}\")\n",
    "print(f\"Runners with a previous time: {num_previous_time}\")\n",
    "print(f\"percentage of runners with a previous time who improved at 2019 Tunnel Light: {num_improved/num_previous_time}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Percent_Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.648060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.239613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.181499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>-3.123728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>-10.334218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>-39.426301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>597 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Percent_Change\n",
       "0               NaN\n",
       "1          4.648060\n",
       "2               NaN\n",
       "3          6.239613\n",
       "4          5.181499\n",
       "..              ...\n",
       "592       -3.123728\n",
       "593      -10.334218\n",
       "594             NaN\n",
       "595      -39.426301\n",
       "596             NaN\n",
       "\n",
       "[597 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find percentage of change between average and 2019 \n",
    "df_tunnel['Percent_Change']=(((df_tunnel['Average']-df_tunnel['2019 Chip Time'])/df_tunnel['Average'])*100)\n",
    "df_tunnel_new = df_tunnel[[\"Percent_Change\"]]\n",
    "display(df_tunnel_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Place'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tunnel.plot(x='Place', y='Percent_Change', style='o', legend=None, figsize=(20,10))\n",
    "# compares finish order with percentage change in time compared to average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.491647215603892"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find average percent difference between average and 2019 Tunnel Light Marathon\n",
    "df_tunnel[\"Percent_Change\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "311"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find runner who beat their average by the most time\n",
    "df_tunnel['Compare_Average_to_2019_Seconds'].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "311"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find runner who beat their average by the most percent\n",
    "df_tunnel['Percent_Change'].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Place .1</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Place</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>2019 Gun Time</th>\n",
       "      <th>2019 Chip Time</th>\n",
       "      <th>2019 Pace</th>\n",
       "      <th>...</th>\n",
       "      <th>2016 Time</th>\n",
       "      <th>2015 Time</th>\n",
       "      <th>2014 Time</th>\n",
       "      <th>Average</th>\n",
       "      <th>Compare_Average_to_2019</th>\n",
       "      <th>Average_Seconds</th>\n",
       "      <th>Compare_Average_to_2019_Seconds</th>\n",
       "      <th>Average_Pretty</th>\n",
       "      <th>Compare_Average_to_2019_Pretty</th>\n",
       "      <th>Percent_Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>312</td>\n",
       "      <td>Todd Brown (# 130)</td>\n",
       "      <td>F</td>\n",
       "      <td>202</td>\n",
       "      <td>68</td>\n",
       "      <td>3 / 6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03:58:22.500</td>\n",
       "      <td>0 days 03:58:19.700000</td>\n",
       "      <td>9:05/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 07:23:25</td>\n",
       "      <td>0 days 03:25:05.300000</td>\n",
       "      <td>26605.0</td>\n",
       "      <td>12305.3</td>\n",
       "      <td>7h23m25s</td>\n",
       "      <td>3h25m5s</td>\n",
       "      <td>46.251832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Place                Name Gender  Place .1  Age Age Place  Unnamed: 6  \\\n",
       "311    312  Todd Brown (# 130)      F       202   68     3 / 6         NaN   \n",
       "\n",
       "    2019 Gun Time         2019 Chip Time  2019 Pace  ... 2016 Time 2015 Time  \\\n",
       "311  03:58:22.500 0 days 03:58:19.700000  9:05/mile  ...       NaT       NaT   \n",
       "\n",
       "    2014 Time         Average Compare_Average_to_2019 Average_Seconds  \\\n",
       "311       NaT 0 days 07:23:25  0 days 03:25:05.300000         26605.0   \n",
       "\n",
       "    Compare_Average_to_2019_Seconds  Average_Pretty  \\\n",
       "311                         12305.3        7h23m25s   \n",
       "\n",
       "     Compare_Average_to_2019_Pretty Percent_Change  \n",
       "311                         3h25m5s      46.251832  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display information for the runner who beat their average by the most percent and time\n",
    "df_tunnel.loc[[311]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Place .1</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Place</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>2019 Gun Time</th>\n",
       "      <th>2019 Chip Time</th>\n",
       "      <th>2019 Pace</th>\n",
       "      <th>...</th>\n",
       "      <th>2016 Time</th>\n",
       "      <th>2015 Time</th>\n",
       "      <th>2014 Time</th>\n",
       "      <th>Average</th>\n",
       "      <th>Compare_Average_to_2019</th>\n",
       "      <th>Average_Seconds</th>\n",
       "      <th>Compare_Average_to_2019_Seconds</th>\n",
       "      <th>Average_Pretty</th>\n",
       "      <th>Compare_Average_to_2019_Pretty</th>\n",
       "      <th>Percent_Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Steven Michalski (# 521)</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>1 / 60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02:45:36.200</td>\n",
       "      <td>0 days 02:45:35.100000</td>\n",
       "      <td>6:19/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 02:52:16</td>\n",
       "      <td>0 days 02:47:11</td>\n",
       "      <td>0 days 02:53:21</td>\n",
       "      <td>0 days 02:53:39.400000</td>\n",
       "      <td>0 days 00:08:04.300000</td>\n",
       "      <td>10419.4</td>\n",
       "      <td>484.3</td>\n",
       "      <td>2h53m39s</td>\n",
       "      <td>8m4s</td>\n",
       "      <td>4.648060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Grant Drummond (# 224)</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>1 / 28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02:51:08.700</td>\n",
       "      <td>0 days 02:51:07.700000</td>\n",
       "      <td>6:31/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 03:21:02</td>\n",
       "      <td>0 days 02:56:10</td>\n",
       "      <td>0 days 03:08:36</td>\n",
       "      <td>0 days 03:02:31</td>\n",
       "      <td>0 days 00:11:23.300000</td>\n",
       "      <td>10951.0</td>\n",
       "      <td>683.3</td>\n",
       "      <td>3h2m31s</td>\n",
       "      <td>11m23s</td>\n",
       "      <td>6.239613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Gang Qin (# 634)</td>\n",
       "      <td>M</td>\n",
       "      <td>13</td>\n",
       "      <td>55</td>\n",
       "      <td>1 / 43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02:57:23.800</td>\n",
       "      <td>0 days 02:57:21.600000</td>\n",
       "      <td>6:46/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 03:01:35</td>\n",
       "      <td>0 days 03:04:53</td>\n",
       "      <td>0 days 03:08:02</td>\n",
       "      <td>0 days 03:02:56.200000</td>\n",
       "      <td>0 days 00:05:34.600000</td>\n",
       "      <td>10976.2</td>\n",
       "      <td>334.6</td>\n",
       "      <td>3h2m56s</td>\n",
       "      <td>5m34s</td>\n",
       "      <td>3.048414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Joshua Donoghue (# 867)</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "      <td>4 / 25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03:00:20.600</td>\n",
       "      <td>0 days 03:00:16.100000</td>\n",
       "      <td>6:52/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 03:15:00</td>\n",
       "      <td>0 days 03:27:12</td>\n",
       "      <td>0 days 03:34:11</td>\n",
       "      <td>0 days 03:23:29.600000</td>\n",
       "      <td>0 days 00:23:13.500000</td>\n",
       "      <td>12209.6</td>\n",
       "      <td>1393.5</td>\n",
       "      <td>3h23m29s</td>\n",
       "      <td>23m13s</td>\n",
       "      <td>11.413150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>Cathy Cosner (# 179)</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>46</td>\n",
       "      <td>2 / 48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03:04:12</td>\n",
       "      <td>0 days 03:04:07.600000</td>\n",
       "      <td>7:01/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 03:20:23</td>\n",
       "      <td>0 days 03:18:06</td>\n",
       "      <td>0 days 03:24:59</td>\n",
       "      <td>0 days 03:18:07.800000</td>\n",
       "      <td>0 days 00:14:00.200000</td>\n",
       "      <td>11887.8</td>\n",
       "      <td>840.2</td>\n",
       "      <td>3h18m7s</td>\n",
       "      <td>14m0s</td>\n",
       "      <td>7.067750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>545</td>\n",
       "      <td>Pam Lonsdale (# 471)</td>\n",
       "      <td>F</td>\n",
       "      <td>253</td>\n",
       "      <td>64</td>\n",
       "      <td>9 / 15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05:15:07.500</td>\n",
       "      <td>0 days 05:14:40.500000</td>\n",
       "      <td>12:00/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 04:46:56</td>\n",
       "      <td>0 days 04:33:00</td>\n",
       "      <td>0 days 04:56:45</td>\n",
       "      <td>0 days 04:44:54.400000</td>\n",
       "      <td>-1 days +23:30:13.900000</td>\n",
       "      <td>17094.4</td>\n",
       "      <td>-1786.1</td>\n",
       "      <td>4h44m54s</td>\n",
       "      <td>-29m46s</td>\n",
       "      <td>-10.448451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>547</td>\n",
       "      <td>Justin Blanchard (# 106)</td>\n",
       "      <td>F</td>\n",
       "      <td>293</td>\n",
       "      <td>35</td>\n",
       "      <td>25 / 25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05:20:00.600</td>\n",
       "      <td>0 days 05:19:46</td>\n",
       "      <td>12:12/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 04:48:13</td>\n",
       "      <td>0 days 05:40:48</td>\n",
       "      <td>0 days 06:09:24</td>\n",
       "      <td>0 days 05:19:51</td>\n",
       "      <td>0 days 00:00:05</td>\n",
       "      <td>19191.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5h19m51s</td>\n",
       "      <td>5s</td>\n",
       "      <td>0.026054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>549</td>\n",
       "      <td>Chip Bearden (# 93)</td>\n",
       "      <td>F</td>\n",
       "      <td>295</td>\n",
       "      <td>68</td>\n",
       "      <td>6 / 6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05:20:46.400</td>\n",
       "      <td>0 days 05:20:27.500000</td>\n",
       "      <td>12:13/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 06:30:25</td>\n",
       "      <td>0 days 05:29:25</td>\n",
       "      <td>0 days 04:53:55</td>\n",
       "      <td>0 days 05:36:34.800000</td>\n",
       "      <td>0 days 00:16:07.300000</td>\n",
       "      <td>20194.8</td>\n",
       "      <td>967.3</td>\n",
       "      <td>5h36m34s</td>\n",
       "      <td>16m7s</td>\n",
       "      <td>4.789847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>555</td>\n",
       "      <td>Don Keren (# 416)</td>\n",
       "      <td>F</td>\n",
       "      <td>297</td>\n",
       "      <td>64</td>\n",
       "      <td>40 / 40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05:27:32.200</td>\n",
       "      <td>0 days 05:27:20.800000</td>\n",
       "      <td>12:29/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 04:10:13</td>\n",
       "      <td>0 days 03:55:32</td>\n",
       "      <td>0 days 03:58:15</td>\n",
       "      <td>0 days 04:16:24</td>\n",
       "      <td>-1 days +22:49:03.200000</td>\n",
       "      <td>15384.0</td>\n",
       "      <td>-4256.8</td>\n",
       "      <td>4h16m24s</td>\n",
       "      <td>-1h10m56s</td>\n",
       "      <td>-27.670307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>593</td>\n",
       "      <td>Claire Carder (# 149)</td>\n",
       "      <td>F</td>\n",
       "      <td>288</td>\n",
       "      <td>66</td>\n",
       "      <td>6 / 6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06:33:27.800</td>\n",
       "      <td>0 days 06:32:57.900000</td>\n",
       "      <td>14:59/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 05:16:58</td>\n",
       "      <td>0 days 04:47:00</td>\n",
       "      <td>0 days 05:06:39</td>\n",
       "      <td>0 days 05:23:33.800000</td>\n",
       "      <td>-1 days +22:50:35.900000</td>\n",
       "      <td>19413.8</td>\n",
       "      <td>-4164.1</td>\n",
       "      <td>5h23m33s</td>\n",
       "      <td>-1h9m24s</td>\n",
       "      <td>-21.449175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Place                      Name Gender  Place .1  Age Age Place  \\\n",
       "1        2  Steven Michalski (# 521)      M         2   50    1 / 60   \n",
       "3        4    Grant Drummond (# 224)      M         4   42    1 / 28   \n",
       "12      13          Gang Qin (# 634)      M        13   55    1 / 43   \n",
       "19      20   Joshua Donoghue (# 867)      M        20   39    4 / 25   \n",
       "29      30      Cathy Cosner (# 179)      F         3   46    2 / 48   \n",
       "..     ...                       ...    ...       ...  ...       ...   \n",
       "543    545      Pam Lonsdale (# 471)      F       253   64    9 / 15   \n",
       "545    547  Justin Blanchard (# 106)      F       293   35   25 / 25   \n",
       "547    549       Chip Bearden (# 93)      F       295   68     6 / 6   \n",
       "553    555         Don Keren (# 416)      F       297   64   40 / 40   \n",
       "591    593     Claire Carder (# 149)      F       288   66     6 / 6   \n",
       "\n",
       "     Unnamed: 6 2019 Gun Time         2019 Chip Time   2019 Pace  ...  \\\n",
       "1           NaN  02:45:36.200 0 days 02:45:35.100000   6:19/mile  ...   \n",
       "3           NaN  02:51:08.700 0 days 02:51:07.700000   6:31/mile  ...   \n",
       "12          NaN  02:57:23.800 0 days 02:57:21.600000   6:46/mile  ...   \n",
       "19          NaN  03:00:20.600 0 days 03:00:16.100000   6:52/mile  ...   \n",
       "29          NaN      03:04:12 0 days 03:04:07.600000   7:01/mile  ...   \n",
       "..          ...           ...                    ...         ...  ...   \n",
       "543         NaN  05:15:07.500 0 days 05:14:40.500000  12:00/mile  ...   \n",
       "545         NaN  05:20:00.600        0 days 05:19:46  12:12/mile  ...   \n",
       "547         NaN  05:20:46.400 0 days 05:20:27.500000  12:13/mile  ...   \n",
       "553         NaN  05:27:32.200 0 days 05:27:20.800000  12:29/mile  ...   \n",
       "591         NaN  06:33:27.800 0 days 06:32:57.900000  14:59/mile  ...   \n",
       "\n",
       "          2016 Time       2015 Time       2014 Time                Average  \\\n",
       "1   0 days 02:52:16 0 days 02:47:11 0 days 02:53:21 0 days 02:53:39.400000   \n",
       "3   0 days 03:21:02 0 days 02:56:10 0 days 03:08:36        0 days 03:02:31   \n",
       "12  0 days 03:01:35 0 days 03:04:53 0 days 03:08:02 0 days 03:02:56.200000   \n",
       "19  0 days 03:15:00 0 days 03:27:12 0 days 03:34:11 0 days 03:23:29.600000   \n",
       "29  0 days 03:20:23 0 days 03:18:06 0 days 03:24:59 0 days 03:18:07.800000   \n",
       "..              ...             ...             ...                    ...   \n",
       "543 0 days 04:46:56 0 days 04:33:00 0 days 04:56:45 0 days 04:44:54.400000   \n",
       "545 0 days 04:48:13 0 days 05:40:48 0 days 06:09:24        0 days 05:19:51   \n",
       "547 0 days 06:30:25 0 days 05:29:25 0 days 04:53:55 0 days 05:36:34.800000   \n",
       "553 0 days 04:10:13 0 days 03:55:32 0 days 03:58:15        0 days 04:16:24   \n",
       "591 0 days 05:16:58 0 days 04:47:00 0 days 05:06:39 0 days 05:23:33.800000   \n",
       "\n",
       "     Compare_Average_to_2019 Average_Seconds Compare_Average_to_2019_Seconds  \\\n",
       "1     0 days 00:08:04.300000         10419.4                           484.3   \n",
       "3     0 days 00:11:23.300000         10951.0                           683.3   \n",
       "12    0 days 00:05:34.600000         10976.2                           334.6   \n",
       "19    0 days 00:23:13.500000         12209.6                          1393.5   \n",
       "29    0 days 00:14:00.200000         11887.8                           840.2   \n",
       "..                       ...             ...                             ...   \n",
       "543 -1 days +23:30:13.900000         17094.4                         -1786.1   \n",
       "545          0 days 00:00:05         19191.0                             5.0   \n",
       "547   0 days 00:16:07.300000         20194.8                           967.3   \n",
       "553 -1 days +22:49:03.200000         15384.0                         -4256.8   \n",
       "591 -1 days +22:50:35.900000         19413.8                         -4164.1   \n",
       "\n",
       "     Average_Pretty  Compare_Average_to_2019_Pretty Percent_Change  \n",
       "1          2h53m39s                            8m4s       4.648060  \n",
       "3           3h2m31s                          11m23s       6.239613  \n",
       "12          3h2m56s                           5m34s       3.048414  \n",
       "19         3h23m29s                          23m13s      11.413150  \n",
       "29          3h18m7s                           14m0s       7.067750  \n",
       "..              ...                             ...            ...  \n",
       "543        4h44m54s                         -29m46s     -10.448451  \n",
       "545        5h19m51s                              5s       0.026054  \n",
       "547        5h36m34s                           16m7s       4.789847  \n",
       "553        4h16m24s                       -1h10m56s     -27.670307  \n",
       "591        5h23m33s                        -1h9m24s     -21.449175  \n",
       "\n",
       "[82 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filter for runners with marathon times every year\n",
    "df_tunnel_filtered = df_tunnel.dropna(subset=['2018 Time','2017 Time','2016 Time','2015 Time','2014 Time'])\n",
    "display(df_tunnel_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Place .1</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age Place</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>2019 Gun Time</th>\n",
       "      <th>2019 Chip Time</th>\n",
       "      <th>2019 Pace</th>\n",
       "      <th>...</th>\n",
       "      <th>2016 Time</th>\n",
       "      <th>2015 Time</th>\n",
       "      <th>2014 Time</th>\n",
       "      <th>Average</th>\n",
       "      <th>Compare_Average_to_2019</th>\n",
       "      <th>Average_Seconds</th>\n",
       "      <th>Compare_Average_to_2019_Seconds</th>\n",
       "      <th>Average_Pretty</th>\n",
       "      <th>Compare_Average_to_2019_Pretty</th>\n",
       "      <th>Percent_Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>Jun Chea (# 158)</td>\n",
       "      <td>M</td>\n",
       "      <td>71</td>\n",
       "      <td>49</td>\n",
       "      <td>17 / 57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03:17:59.400</td>\n",
       "      <td>0 days 03:17:51.800000</td>\n",
       "      <td>7:33/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 03:46:41</td>\n",
       "      <td>0 days 03:52:49</td>\n",
       "      <td>0 days 03:41:06</td>\n",
       "      <td>0 days 04:01:48.400000</td>\n",
       "      <td>0 days 00:43:56.600000</td>\n",
       "      <td>14508.4</td>\n",
       "      <td>2636.6</td>\n",
       "      <td>4h1m48s</td>\n",
       "      <td>43m56s</td>\n",
       "      <td>18.172921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>281</td>\n",
       "      <td>Lawrence Barclay (# 81)</td>\n",
       "      <td>F</td>\n",
       "      <td>187</td>\n",
       "      <td>64</td>\n",
       "      <td>19 / 40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03:52:06.700</td>\n",
       "      <td>0 days 03:51:16.300000</td>\n",
       "      <td>8:49/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 04:45:49</td>\n",
       "      <td>0 days 04:35:57</td>\n",
       "      <td>0 days 04:07:00</td>\n",
       "      <td>0 days 04:38:44.400000</td>\n",
       "      <td>0 days 00:47:28.100000</td>\n",
       "      <td>16724.4</td>\n",
       "      <td>2848.1</td>\n",
       "      <td>4h38m44s</td>\n",
       "      <td>47m28s</td>\n",
       "      <td>17.029609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>Terence Baptiste (# 80)</td>\n",
       "      <td>M</td>\n",
       "      <td>58</td>\n",
       "      <td>51</td>\n",
       "      <td>4 / 60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03:14:52</td>\n",
       "      <td>0 days 03:14:45.600000</td>\n",
       "      <td>7:26/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 03:56:57</td>\n",
       "      <td>0 days 03:59:56</td>\n",
       "      <td>0 days 04:09:53</td>\n",
       "      <td>0 days 03:53:43.200000</td>\n",
       "      <td>0 days 00:38:57.600000</td>\n",
       "      <td>14023.2</td>\n",
       "      <td>2337.6</td>\n",
       "      <td>3h53m43s</td>\n",
       "      <td>38m57s</td>\n",
       "      <td>16.669519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>208</td>\n",
       "      <td>Alan Bull (# 133)</td>\n",
       "      <td>F</td>\n",
       "      <td>146</td>\n",
       "      <td>56</td>\n",
       "      <td>23 / 43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03:41:22.500</td>\n",
       "      <td>0 days 03:41:00.500000</td>\n",
       "      <td>8:26/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 04:39:49</td>\n",
       "      <td>0 days 04:27:43</td>\n",
       "      <td>0 days 04:44:29</td>\n",
       "      <td>0 days 04:23:07.600000</td>\n",
       "      <td>0 days 00:42:07.100000</td>\n",
       "      <td>15787.6</td>\n",
       "      <td>2527.1</td>\n",
       "      <td>4h23m7s</td>\n",
       "      <td>42m7s</td>\n",
       "      <td>16.006866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>Winston Trice (# 785)</td>\n",
       "      <td>M</td>\n",
       "      <td>51</td>\n",
       "      <td>46</td>\n",
       "      <td>10 / 57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03:13:39.300</td>\n",
       "      <td>0 days 03:13:25.700000</td>\n",
       "      <td>7:22/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 03:30:43</td>\n",
       "      <td>0 days 04:06:12</td>\n",
       "      <td>0 days 04:32:54</td>\n",
       "      <td>0 days 03:49:13.200000</td>\n",
       "      <td>0 days 00:35:47.500000</td>\n",
       "      <td>13753.2</td>\n",
       "      <td>2147.5</td>\n",
       "      <td>3h49m13s</td>\n",
       "      <td>35m47s</td>\n",
       "      <td>15.614548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>501</td>\n",
       "      <td>Janet Green (# 310)</td>\n",
       "      <td>F</td>\n",
       "      <td>224</td>\n",
       "      <td>65</td>\n",
       "      <td>2 / 6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04:48:41.900</td>\n",
       "      <td>0 days 04:48:39.100000</td>\n",
       "      <td>11:01/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 04:04:52</td>\n",
       "      <td>0 days 04:02:24</td>\n",
       "      <td>0 days 03:53:13</td>\n",
       "      <td>0 days 04:01:28.600000</td>\n",
       "      <td>-1 days +23:12:49.500000</td>\n",
       "      <td>14488.6</td>\n",
       "      <td>-2830.5</td>\n",
       "      <td>4h1m28s</td>\n",
       "      <td>-47m10s</td>\n",
       "      <td>-19.536049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>593</td>\n",
       "      <td>Claire Carder (# 149)</td>\n",
       "      <td>F</td>\n",
       "      <td>288</td>\n",
       "      <td>66</td>\n",
       "      <td>6 / 6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06:33:27.800</td>\n",
       "      <td>0 days 06:32:57.900000</td>\n",
       "      <td>14:59/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 05:16:58</td>\n",
       "      <td>0 days 04:47:00</td>\n",
       "      <td>0 days 05:06:39</td>\n",
       "      <td>0 days 05:23:33.800000</td>\n",
       "      <td>-1 days +22:50:35.900000</td>\n",
       "      <td>19413.8</td>\n",
       "      <td>-4164.1</td>\n",
       "      <td>5h23m33s</td>\n",
       "      <td>-1h9m24s</td>\n",
       "      <td>-21.449175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>456</td>\n",
       "      <td>Larry Trimmer (# 786)</td>\n",
       "      <td>F</td>\n",
       "      <td>265</td>\n",
       "      <td>49</td>\n",
       "      <td>51 / 57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04:33:58.800</td>\n",
       "      <td>0 days 04:33:45.800000</td>\n",
       "      <td>10:26/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 03:16:04</td>\n",
       "      <td>0 days 03:40:49</td>\n",
       "      <td>0 days 03:39:45</td>\n",
       "      <td>0 days 03:34:48.400000</td>\n",
       "      <td>-1 days +23:01:02.600000</td>\n",
       "      <td>12888.4</td>\n",
       "      <td>-3537.4</td>\n",
       "      <td>3h34m48s</td>\n",
       "      <td>-58m57s</td>\n",
       "      <td>-27.446386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>555</td>\n",
       "      <td>Don Keren (# 416)</td>\n",
       "      <td>F</td>\n",
       "      <td>297</td>\n",
       "      <td>64</td>\n",
       "      <td>40 / 40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05:27:32.200</td>\n",
       "      <td>0 days 05:27:20.800000</td>\n",
       "      <td>12:29/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 04:10:13</td>\n",
       "      <td>0 days 03:55:32</td>\n",
       "      <td>0 days 03:58:15</td>\n",
       "      <td>0 days 04:16:24</td>\n",
       "      <td>-1 days +22:49:03.200000</td>\n",
       "      <td>15384.0</td>\n",
       "      <td>-4256.8</td>\n",
       "      <td>4h16m24s</td>\n",
       "      <td>-1h10m56s</td>\n",
       "      <td>-27.670307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>529</td>\n",
       "      <td>Thomas Perri (# 618)</td>\n",
       "      <td>F</td>\n",
       "      <td>286</td>\n",
       "      <td>58</td>\n",
       "      <td>42 / 43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05:00:05</td>\n",
       "      <td>0 days 04:59:38.700000</td>\n",
       "      <td>11:26/mile</td>\n",
       "      <td>...</td>\n",
       "      <td>0 days 03:55:41</td>\n",
       "      <td>0 days 03:47:10</td>\n",
       "      <td>0 days 03:43:10</td>\n",
       "      <td>0 days 03:51:32</td>\n",
       "      <td>-1 days +22:51:53.300000</td>\n",
       "      <td>13892.0</td>\n",
       "      <td>-4086.7</td>\n",
       "      <td>3h51m32s</td>\n",
       "      <td>-1h8m6s</td>\n",
       "      <td>-29.417650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Place                     Name Gender  Place .1  Age Age Place  \\\n",
       "81      82         Jun Chea (# 158)      M        71   49   17 / 57   \n",
       "280    281  Lawrence Barclay (# 81)      F       187   64   19 / 40   \n",
       "65      66  Terence Baptiste (# 80)      M        58   51    4 / 60   \n",
       "207    208        Alan Bull (# 133)      F       146   56   23 / 43   \n",
       "57      58    Winston Trice (# 785)      M        51   46   10 / 57   \n",
       "..     ...                      ...    ...       ...  ...       ...   \n",
       "499    501      Janet Green (# 310)      F       224   65     2 / 6   \n",
       "591    593    Claire Carder (# 149)      F       288   66     6 / 6   \n",
       "454    456    Larry Trimmer (# 786)      F       265   49   51 / 57   \n",
       "553    555        Don Keren (# 416)      F       297   64   40 / 40   \n",
       "527    529     Thomas Perri (# 618)      F       286   58   42 / 43   \n",
       "\n",
       "     Unnamed: 6 2019 Gun Time         2019 Chip Time   2019 Pace  ...  \\\n",
       "81          NaN  03:17:59.400 0 days 03:17:51.800000   7:33/mile  ...   \n",
       "280         NaN  03:52:06.700 0 days 03:51:16.300000   8:49/mile  ...   \n",
       "65          NaN      03:14:52 0 days 03:14:45.600000   7:26/mile  ...   \n",
       "207         NaN  03:41:22.500 0 days 03:41:00.500000   8:26/mile  ...   \n",
       "57          NaN  03:13:39.300 0 days 03:13:25.700000   7:22/mile  ...   \n",
       "..          ...           ...                    ...         ...  ...   \n",
       "499         NaN  04:48:41.900 0 days 04:48:39.100000  11:01/mile  ...   \n",
       "591         NaN  06:33:27.800 0 days 06:32:57.900000  14:59/mile  ...   \n",
       "454         NaN  04:33:58.800 0 days 04:33:45.800000  10:26/mile  ...   \n",
       "553         NaN  05:27:32.200 0 days 05:27:20.800000  12:29/mile  ...   \n",
       "527         NaN      05:00:05 0 days 04:59:38.700000  11:26/mile  ...   \n",
       "\n",
       "          2016 Time       2015 Time       2014 Time                Average  \\\n",
       "81  0 days 03:46:41 0 days 03:52:49 0 days 03:41:06 0 days 04:01:48.400000   \n",
       "280 0 days 04:45:49 0 days 04:35:57 0 days 04:07:00 0 days 04:38:44.400000   \n",
       "65  0 days 03:56:57 0 days 03:59:56 0 days 04:09:53 0 days 03:53:43.200000   \n",
       "207 0 days 04:39:49 0 days 04:27:43 0 days 04:44:29 0 days 04:23:07.600000   \n",
       "57  0 days 03:30:43 0 days 04:06:12 0 days 04:32:54 0 days 03:49:13.200000   \n",
       "..              ...             ...             ...                    ...   \n",
       "499 0 days 04:04:52 0 days 04:02:24 0 days 03:53:13 0 days 04:01:28.600000   \n",
       "591 0 days 05:16:58 0 days 04:47:00 0 days 05:06:39 0 days 05:23:33.800000   \n",
       "454 0 days 03:16:04 0 days 03:40:49 0 days 03:39:45 0 days 03:34:48.400000   \n",
       "553 0 days 04:10:13 0 days 03:55:32 0 days 03:58:15        0 days 04:16:24   \n",
       "527 0 days 03:55:41 0 days 03:47:10 0 days 03:43:10        0 days 03:51:32   \n",
       "\n",
       "     Compare_Average_to_2019 Average_Seconds Compare_Average_to_2019_Seconds  \\\n",
       "81    0 days 00:43:56.600000         14508.4                          2636.6   \n",
       "280   0 days 00:47:28.100000         16724.4                          2848.1   \n",
       "65    0 days 00:38:57.600000         14023.2                          2337.6   \n",
       "207   0 days 00:42:07.100000         15787.6                          2527.1   \n",
       "57    0 days 00:35:47.500000         13753.2                          2147.5   \n",
       "..                       ...             ...                             ...   \n",
       "499 -1 days +23:12:49.500000         14488.6                         -2830.5   \n",
       "591 -1 days +22:50:35.900000         19413.8                         -4164.1   \n",
       "454 -1 days +23:01:02.600000         12888.4                         -3537.4   \n",
       "553 -1 days +22:49:03.200000         15384.0                         -4256.8   \n",
       "527 -1 days +22:51:53.300000         13892.0                         -4086.7   \n",
       "\n",
       "     Average_Pretty  Compare_Average_to_2019_Pretty Percent_Change  \n",
       "81          4h1m48s                          43m56s      18.172921  \n",
       "280        4h38m44s                          47m28s      17.029609  \n",
       "65         3h53m43s                          38m57s      16.669519  \n",
       "207         4h23m7s                           42m7s      16.006866  \n",
       "57         3h49m13s                          35m47s      15.614548  \n",
       "..              ...                             ...            ...  \n",
       "499         4h1m28s                         -47m10s     -19.536049  \n",
       "591        5h23m33s                        -1h9m24s     -21.449175  \n",
       "454        3h34m48s                         -58m57s     -27.446386  \n",
       "553        4h16m24s                       -1h10m56s     -27.670307  \n",
       "527        3h51m32s                         -1h8m6s     -29.417650  \n",
       "\n",
       "[82 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sort runners with yearly times by percent change \n",
    "df_tunnel_filtered_sorted = (df_tunnel_filtered.sort_values('Percent_Change', ascending=False))\n",
    "display(df_tunnel_filtered_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tunnel_filtered_sorted.to_csv('tunnel_filtered_sorted.csv')  # outputs .csv version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Click here to download: <a href='./tunnel_filtered_sorted.csv' target='_blank'>./tunnel_filtered_sorted.csv</a><br>"
      ],
      "text/plain": [
       "/work/tunnel_filtered_sorted.csv"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import FileLink\n",
    "FileLink('./tunnel_filtered_sorted.csv', result_html_prefix=\"Click here to download: \")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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