Firecracker 10K.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Arbutus Firecracker 10K : Comparing 2020 virtual race time to previous four years\n",
    "\n",
    "This Notebook takes information posted by the Arbutus Firecracker 10K, creates a .csv file for each year, creates data frames from each, calculates average times for runners from 2016-2019, and compares times of the runners of the 2020 virtual race with their average for previous in person racing. \n",
    "\n",
    "There are obvious limitations for this approach. Few hearty souls ran the 2020 race. Runners this year could pick any course, which could vary greatly from the challenging in person race. 2020 times were self reported on the honor system. \n",
    "\n",
    "To run this notebook, use Cell- Run All. The final column ('Actual vs. Virtual') shows the difference between the average and the 2020 time. If the number is positive, it is an improvement/ faster than average. If the time is negative, it is slower than average. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2016 = pd.read_csv('Firecracker10k_2016.csv') #imports .csv's to data frames\n",
    "df_2017 = pd.read_csv('Firecracker10k_2017.csv')\n",
    "df_2018 = pd.read_csv('Firecracker10k_2018.csv')\n",
    "df_2019 = pd.read_csv('Firecracker10k_2019.csv')\n",
    "df_2020 = pd.read_csv('Firecracker10K_2020.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2017['Name'] = df_2017['First '] + ' ' + df_2017['Last '] #add a column for both names\n",
    "df_2020['Name'] = df_2020['First'] + ' ' + df_2020['Last'] #add a column for both names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2016.columns = df_2016.columns.str.replace('Time', '2016 Time') #add year to time column name\n",
    "df_2017.columns = df_2017.columns.str.replace('Time Chip', '2017 Time Chip')\n",
    "df_2018.columns = df_2018.columns.str.replace('Time', '2018 Time')\n",
    "df_2019.columns = df_2019.columns.str.replace('Time', '2019 Time')\n",
    "df_2020.columns = df_2020.columns.str.replace('Time', '2020 Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2016 = df_2016.rename(columns = lambda x: x.strip()) #take out spaces from column names, a bit late in the game\n",
    "df_2017 = df_2017.rename(columns = lambda x: x.strip())\n",
    "df_2018 = df_2018.rename(columns = lambda x: x.strip())\n",
    "df_2019 = df_2019.rename(columns = lambda x: x.strip())\n",
    "df_2020 = df_2020.rename(columns = lambda x: x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge data frames one at a time, keeping the columns I'll need\n",
    "df_20AND19 = pd.merge(left=df_2020[['2020 Time', 'Name']], right=df_2019[['Name', '2019 Time']], on='Name', how='left')\n",
    "df_20TO18 = pd.merge(left=df_20AND19[['2020 Time', 'Name', '2019 Time']], right=df_2018[['Name', '2018 Time']], on='Name', how='left')\n",
    "df_20TO17 = pd.merge(left=df_20TO18[['2020 Time', 'Name', '2019 Time', '2018 Time']], right=df_2017[['Name', '2017 Time Chip']], on='Name', how='left')\n",
    "df_20TO16 = pd.merge(left=df_20TO17[['2020 Time', 'Name', '2019 Time', '2018 Time', '2017 Time Chip']], right=df_2016[['Name', '2016 Time']], on='Name', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_20TO16['2019 Time'] = pd.to_timedelta(df_20TO16['2019 Time']) #changes times from text to time delta\n",
    "df_20TO16['2018 Time'] = pd.to_timedelta(df_20TO16['2018 Time'])\n",
    "df_20TO16['2017 Time Chip'] = pd.to_timedelta(df_20TO16['2017 Time Chip'])\n",
    "df_20TO16['2016 Time'] = pd.to_timedelta(df_20TO16['2016 Time'])\n",
    "df_20TO16['2020 Time'] = pd.to_timedelta(df_20TO16['2020 Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_20TO16['Average'] = df_20TO16[['2019 Time', '2018 Time', '2017 Time Chip', '2016 Time']].mean(axis=1) #create a column for average \n",
    "df_20TO16['Average vs. Virtual'] = df_20TO16['Average']-df_20TO16['2020 Time']#create column to compare average to 2020 time\n",
    "df_20TO16['Average vs. Virtual'] = df_20TO16['Average vs. Virtual']/np.timedelta64(1,'s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>2020 Time</th>\n",
       "      <th>Name</th>\n",
       "      <th>2019 Time</th>\n",
       "      <th>2018 Time</th>\n",
       "      <th>2017 Time Chip</th>\n",
       "      <th>2016 Time</th>\n",
       "      <th>Average</th>\n",
       "      <th>Average vs. Virtual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 days 00:41:41</td>\n",
       "      <td>Chris Colclough</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 days 00:42:26</td>\n",
       "      <td>Phil Lang</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:42:13.900000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:42:13.900000</td>\n",
       "      <td>-12s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0 days 00:44:52</td>\n",
       "      <td>Martin Goode</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:45:28.600000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:45:28.600000</td>\n",
       "      <td>36s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0 days 00:45:23</td>\n",
       "      <td>Sigurd Knippengard</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 days 00:46:18</td>\n",
       "      <td>Pamela Jock</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0 days 00:47:48</td>\n",
       "      <td>Brent Smith</td>\n",
       "      <td>0 days 00:57:18</td>\n",
       "      <td>0 days 01:04:14</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 01:00:46</td>\n",
       "      <td>12m58s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0 days 00:47:49</td>\n",
       "      <td>Dennis Albright</td>\n",
       "      <td>0 days 00:44:56</td>\n",
       "      <td>0 days 00:46:30</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:45:43</td>\n",
       "      <td>-2m6s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0 days 00:50:12</td>\n",
       "      <td>Aggie Wojdon</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:53:24</td>\n",
       "      <td>0 days 00:53:24</td>\n",
       "      <td>3m12s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0 days 00:52:53</td>\n",
       "      <td>Robert Roa</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:48:08</td>\n",
       "      <td>0 days 00:43:57.600000</td>\n",
       "      <td>0 days 00:45:26</td>\n",
       "      <td>0 days 00:45:50.533333333</td>\n",
       "      <td>-7m2s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0 days 00:56:18</td>\n",
       "      <td>Kari Whitmer</td>\n",
       "      <td>0 days 00:57:23</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:57:23</td>\n",
       "      <td>1m5s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0 days 00:56:42</td>\n",
       "      <td>Sherry Stick</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:42:05</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:42:05</td>\n",
       "      <td>-14m37s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0 days 00:56:42</td>\n",
       "      <td>Becky Rhodes</td>\n",
       "      <td>0 days 00:47:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:47:00</td>\n",
       "      <td>-9m42s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0 days 00:56:42</td>\n",
       "      <td>Maura Knestout</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0 days 00:58:07</td>\n",
       "      <td>TJ McLaughlin</td>\n",
       "      <td>0 days 01:01:52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 01:01:52</td>\n",
       "      <td>3m45s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0 days 01:01:15</td>\n",
       "      <td>Connie Colclough</td>\n",
       "      <td>0 days 01:02:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 01:02:00</td>\n",
       "      <td>45s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0 days 01:01:33</td>\n",
       "      <td>David Hopkins</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:56:22</td>\n",
       "      <td>0 days 00:54:35.300000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:55:28.650000</td>\n",
       "      <td>-6m4s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0 days 01:02:17</td>\n",
       "      <td>Natalie Burrowes</td>\n",
       "      <td>0 days 00:56:53</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:56:53</td>\n",
       "      <td>-5m24s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0 days 01:03:07</td>\n",
       "      <td>Georgina Woiak</td>\n",
       "      <td>0 days 01:02:20</td>\n",
       "      <td>0 days 01:01:37</td>\n",
       "      <td>0 days 00:51:27</td>\n",
       "      <td>0 days 00:55:39</td>\n",
       "      <td>0 days 00:57:45.750000</td>\n",
       "      <td>-5m21s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0 days 01:03:43</td>\n",
       "      <td>Buddy Weber</td>\n",
       "      <td>0 days 00:59:05</td>\n",
       "      <td>0 days 00:53:16</td>\n",
       "      <td>0 days 00:51:07.800000</td>\n",
       "      <td>0 days 00:55:40</td>\n",
       "      <td>0 days 00:54:47.200000</td>\n",
       "      <td>-8m55s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0 days 01:04:31</td>\n",
       "      <td>Marc Hermstein</td>\n",
       "      <td>0 days 00:46:05</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:46:05</td>\n",
       "      <td>-18m26s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0 days 01:04:33</td>\n",
       "      <td>John Stanmore</td>\n",
       "      <td>0 days 00:47:18</td>\n",
       "      <td>0 days 00:51:20</td>\n",
       "      <td>0 days 00:46:45.100000</td>\n",
       "      <td>0 days 00:49:21</td>\n",
       "      <td>0 days 00:48:41.025000</td>\n",
       "      <td>-15m51s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0 days 01:04:36</td>\n",
       "      <td>Erik Shumaker</td>\n",
       "      <td>0 days 01:03:07</td>\n",
       "      <td>0 days 01:12:32</td>\n",
       "      <td>0 days 00:57:58.700000</td>\n",
       "      <td>0 days 00:55:36</td>\n",
       "      <td>0 days 01:02:18.425000</td>\n",
       "      <td>-2m17s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0 days 01:04:36</td>\n",
       "      <td>Jason Carpenter</td>\n",
       "      <td>0 days 01:04:50</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 01:04:50</td>\n",
       "      <td>14s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0 days 01:04:46</td>\n",
       "      <td>Susan Kim</td>\n",
       "      <td>0 days 01:00:30</td>\n",
       "      <td>0 days 01:04:15</td>\n",
       "      <td>0 days 00:55:56.600000</td>\n",
       "      <td>0 days 00:59:26</td>\n",
       "      <td>0 days 01:00:01.900000</td>\n",
       "      <td>-4m44s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0 days 01:05:57</td>\n",
       "      <td>Wiebke Hannigan</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0 days 01:10:35</td>\n",
       "      <td>Sisou Armstrong</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0 days 01:13:38</td>\n",
       "      <td>Michelle Macurak</td>\n",
       "      <td>0 days 01:14:31</td>\n",
       "      <td>0 days 01:07:26</td>\n",
       "      <td>0 days 01:00:54.400000</td>\n",
       "      <td>0 days 00:55:57</td>\n",
       "      <td>0 days 01:04:42.100000</td>\n",
       "      <td>-8m55s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0 days 01:14:00</td>\n",
       "      <td>Jane Linde</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0 days 01:17:35</td>\n",
       "      <td>Elizabeth Brock</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0 days 01:21:24</td>\n",
       "      <td>Gina Painter</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0 days 01:24:35</td>\n",
       "      <td>Tom Green</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0 days 01:36:14</td>\n",
       "      <td>Katherine Bourland</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0 days 02:05:00</td>\n",
       "      <td>Arleen Dinneen</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0 days 02:30:00</td>\n",
       "      <td>Toks Fashola-Walker</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2020 Time                 Name       2019 Time       2018 Time  \\\n",
       "0  0 days 00:41:41      Chris Colclough             NaT             NaT   \n",
       "1  0 days 00:42:26            Phil Lang             NaT             NaT   \n",
       "2  0 days 00:44:52         Martin Goode             NaT             NaT   \n",
       "3  0 days 00:45:23   Sigurd Knippengard             NaT             NaT   \n",
       "4  0 days 00:46:18          Pamela Jock             NaT             NaT   \n",
       "5  0 days 00:47:48          Brent Smith 0 days 00:57:18 0 days 01:04:14   \n",
       "6  0 days 00:47:49      Dennis Albright 0 days 00:44:56 0 days 00:46:30   \n",
       "7  0 days 00:50:12         Aggie Wojdon             NaT             NaT   \n",
       "8  0 days 00:52:53           Robert Roa             NaT 0 days 00:48:08   \n",
       "9  0 days 00:56:18         Kari Whitmer 0 days 00:57:23             NaT   \n",
       "10 0 days 00:56:42         Sherry Stick             NaT 0 days 00:42:05   \n",
       "11 0 days 00:56:42         Becky Rhodes 0 days 00:47:00             NaT   \n",
       "12 0 days 00:56:42       Maura Knestout             NaT             NaT   \n",
       "13 0 days 00:58:07        TJ McLaughlin 0 days 01:01:52             NaT   \n",
       "14 0 days 01:01:15     Connie Colclough 0 days 01:02:00             NaT   \n",
       "15 0 days 01:01:33        David Hopkins             NaT 0 days 00:56:22   \n",
       "16 0 days 01:02:17     Natalie Burrowes 0 days 00:56:53             NaT   \n",
       "17 0 days 01:03:07       Georgina Woiak 0 days 01:02:20 0 days 01:01:37   \n",
       "18 0 days 01:03:43          Buddy Weber 0 days 00:59:05 0 days 00:53:16   \n",
       "19 0 days 01:04:31       Marc Hermstein 0 days 00:46:05             NaT   \n",
       "20 0 days 01:04:33        John Stanmore 0 days 00:47:18 0 days 00:51:20   \n",
       "21 0 days 01:04:36        Erik Shumaker 0 days 01:03:07 0 days 01:12:32   \n",
       "22 0 days 01:04:36      Jason Carpenter 0 days 01:04:50             NaT   \n",
       "23 0 days 01:04:46            Susan Kim 0 days 01:00:30 0 days 01:04:15   \n",
       "24 0 days 01:05:57      Wiebke Hannigan             NaT             NaT   \n",
       "25 0 days 01:10:35      Sisou Armstrong             NaT             NaT   \n",
       "26 0 days 01:13:38     Michelle Macurak 0 days 01:14:31 0 days 01:07:26   \n",
       "27 0 days 01:14:00           Jane Linde             NaT             NaT   \n",
       "28 0 days 01:17:35      Elizabeth Brock             NaT             NaT   \n",
       "29 0 days 01:21:24         Gina Painter             NaT             NaT   \n",
       "30 0 days 01:24:35            Tom Green             NaT             NaT   \n",
       "31 0 days 01:36:14   Katherine Bourland             NaT             NaT   \n",
       "32 0 days 02:05:00       Arleen Dinneen             NaT             NaT   \n",
       "33 0 days 02:30:00  Toks Fashola-Walker             NaT             NaT   \n",
       "\n",
       "           2017 Time Chip       2016 Time                   Average  \\\n",
       "0                     NaT             NaT                       NaT   \n",
       "1  0 days 00:42:13.900000             NaT    0 days 00:42:13.900000   \n",
       "2  0 days 00:45:28.600000             NaT    0 days 00:45:28.600000   \n",
       "3                     NaT             NaT                       NaT   \n",
       "4                     NaT             NaT                       NaT   \n",
       "5                     NaT             NaT           0 days 01:00:46   \n",
       "6                     NaT             NaT           0 days 00:45:43   \n",
       "7                     NaT 0 days 00:53:24           0 days 00:53:24   \n",
       "8  0 days 00:43:57.600000 0 days 00:45:26 0 days 00:45:50.533333333   \n",
       "9                     NaT             NaT           0 days 00:57:23   \n",
       "10                    NaT             NaT           0 days 00:42:05   \n",
       "11                    NaT             NaT           0 days 00:47:00   \n",
       "12                    NaT             NaT                       NaT   \n",
       "13                    NaT             NaT           0 days 01:01:52   \n",
       "14                    NaT             NaT           0 days 01:02:00   \n",
       "15 0 days 00:54:35.300000             NaT    0 days 00:55:28.650000   \n",
       "16                    NaT             NaT           0 days 00:56:53   \n",
       "17        0 days 00:51:27 0 days 00:55:39    0 days 00:57:45.750000   \n",
       "18 0 days 00:51:07.800000 0 days 00:55:40    0 days 00:54:47.200000   \n",
       "19                    NaT             NaT           0 days 00:46:05   \n",
       "20 0 days 00:46:45.100000 0 days 00:49:21    0 days 00:48:41.025000   \n",
       "21 0 days 00:57:58.700000 0 days 00:55:36    0 days 01:02:18.425000   \n",
       "22                    NaT             NaT           0 days 01:04:50   \n",
       "23 0 days 00:55:56.600000 0 days 00:59:26    0 days 01:00:01.900000   \n",
       "24                    NaT             NaT                       NaT   \n",
       "25                    NaT             NaT                       NaT   \n",
       "26 0 days 01:00:54.400000 0 days 00:55:57    0 days 01:04:42.100000   \n",
       "27                    NaT             NaT                       NaT   \n",
       "28                    NaT             NaT                       NaT   \n",
       "29                    NaT             NaT                       NaT   \n",
       "30                    NaT             NaT                       NaT   \n",
       "31                    NaT             NaT                       NaT   \n",
       "32                    NaT             NaT                       NaT   \n",
       "33                    NaT             NaT                       NaT   \n",
       "\n",
       "   Average vs. Virtual  \n",
       "0                  NaN  \n",
       "1                 -12s  \n",
       "2                  36s  \n",
       "3                  NaN  \n",
       "4                  NaN  \n",
       "5               12m58s  \n",
       "6                -2m6s  \n",
       "7                3m12s  \n",
       "8                -7m2s  \n",
       "9                 1m5s  \n",
       "10             -14m37s  \n",
       "11              -9m42s  \n",
       "12                 NaN  \n",
       "13               3m45s  \n",
       "14                 45s  \n",
       "15               -6m4s  \n",
       "16              -5m24s  \n",
       "17              -5m21s  \n",
       "18              -8m55s  \n",
       "19             -18m26s  \n",
       "20             -15m51s  \n",
       "21              -2m17s  \n",
       "22                 14s  \n",
       "23              -4m44s  \n",
       "24                 NaN  \n",
       "25                 NaN  \n",
       "26              -8m55s  \n",
       "27                 NaN  \n",
       "28                 NaN  \n",
       "29                 NaN  \n",
       "30                 NaN  \n",
       "31                 NaN  \n",
       "32                 NaN  \n",
       "33                 NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#try to make the result in the final column make sense to people \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_20TO16['Average vs. Virtual'] = df_20TO16['Average vs. Virtual'].apply(pretty_time_delta)\n",
    "display(df_20TO16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f881eb86a90>"
      ]
     },
     "execution_count": 12,
     "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_20TO16.plot(x='2020 Time', y='Average', style='o', legend=None, figsize=(20,10))"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}