Richmond Marathon 2018-2019 most improved.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Richmond Marathon Anaysis: Most Improvement from 2018-2019\n",
    "\n",
    "This Notebook takes information posted by the Richmond Marathon, creates a .csv file for each of them, creates data frames from each, and adds columns for race year. Then the data frames are merged based on names appearing in both years race results, filters for results where the age difference is not plausibly the same runner, then sorts by most imporoved time. This process will only recognize times as belonging to the same runner when the race recorded the same name.\n",
    "\n",
    "To run this notebook, upload RichmondMarathon2018.csv and RichmondMarathon2019D.csv, then use Cell- Run All. The output will be a .csv file named Richmond_Improved.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": [],
   "source": [
    "df1 = pd.read_csv('RichmondMarathon2018.csv')  #imports .csv's to data frames\n",
    "df2 = pd.read_csv('RichmondMarathon2019D.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.rename(columns = lambda x: x.strip()) #removes spaces from column names so they can be merged\n",
    "df2 = df2.rename(columns = lambda x: x.strip())\n",
    "df1['Name'] = df1['Name'].str.strip() #erases spaces\n",
    "df2['Name'] = df2['Name'].str.strip()\n",
    "df1['Age'] = df1['Age'].astype(str).str.strip().astype('int64') #erases spaces, changes to int\n",
    "df2['Age'] = df2['Age'].astype(str).str.strip().astype('int64', errors='ignore')\n",
    "df1['Net'] = pd.to_timedelta(df1['Net']) #changes type to time\n",
    "df2['Net'] = pd.to_timedelta(df2['Net'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Race_Year'] = 2018 #adds a column for race year\n",
    "df2['Race_Year'] = 2019\n",
    "df1['Net_In_Hours'] = df1['Net'] / np.timedelta64(1,'h') #adds a column for hours, to make it easier to plot \n",
    "df2['Net_In_Hours'] = df2['Net'] / np.timedelta64(1,'h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "728\n",
      "709\n"
     ]
    }
   ],
   "source": [
    "merged_inner = pd.merge(left=df1, right=df2, left_on='Name', right_on='Name') #merges the data frames based on runner name\n",
    "merged_inner['Age_Difference'] = ((merged_inner['Age_y'].astype('int32')) - (merged_inner['Age_x'].astype('int32')))\n",
    "print(len(merged_inner)) #number of runners both year with the same name \n",
    "merged_inner = merged_inner[merged_inner.Age_Difference >= 0] #filters runners with the same name but aged backwords \n",
    "merged_inner = merged_inner[merged_inner.Age_Difference < 3] #filters runners with the same nave and age difference more than 3\n",
    "print(len(merged_inner)) #number of runners after filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Bib_x</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex_x</th>\n",
       "      <th>Age_x</th>\n",
       "      <th>City, State_x</th>\n",
       "      <th>Net_x</th>\n",
       "      <th>Clock_x</th>\n",
       "      <th>Pace</th>\n",
       "      <th>Race_Year_x</th>\n",
       "      <th>Net_In_Hours_x</th>\n",
       "      <th>Bib_y</th>\n",
       "      <th>Sex_y</th>\n",
       "      <th>Age_y</th>\n",
       "      <th>City, State_y</th>\n",
       "      <th>Net_y</th>\n",
       "      <th>Clock_y</th>\n",
       "      <th>Race_Year_y</th>\n",
       "      <th>Net_In_Hours_y</th>\n",
       "      <th>Age_Difference</th>\n",
       "      <th>Compare_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>935</td>\n",
       "      <td>KEVAUGHN SMITH</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>NEWARK, NJ</td>\n",
       "      <td>06:03:06</td>\n",
       "      <td>06:06:13</td>\n",
       "      <td>13:50 /mi</td>\n",
       "      <td>2018</td>\n",
       "      <td>6.051667</td>\n",
       "      <td>6172</td>\n",
       "      <td>M</td>\n",
       "      <td>42</td>\n",
       "      <td>NEWARK, NJ</td>\n",
       "      <td>04:15:08</td>\n",
       "      <td>04:16:48</td>\n",
       "      <td>2019</td>\n",
       "      <td>4.252222</td>\n",
       "      <td>1</td>\n",
       "      <td>-1 days +22:12:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8517</td>\n",
       "      <td>ALLEN MA</td>\n",
       "      <td>M</td>\n",
       "      <td>49</td>\n",
       "      <td>GLEN ALLEN, VA</td>\n",
       "      <td>04:57:31</td>\n",
       "      <td>04:59:07</td>\n",
       "      <td>11:20 /mi</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.958611</td>\n",
       "      <td>3152</td>\n",
       "      <td>M</td>\n",
       "      <td>50</td>\n",
       "      <td>GLEN ALLEN, VA</td>\n",
       "      <td>03:27:27</td>\n",
       "      <td>03:30:34</td>\n",
       "      <td>2019</td>\n",
       "      <td>3.457500</td>\n",
       "      <td>1</td>\n",
       "      <td>-1 days +22:29:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>2000</td>\n",
       "      <td>SAMUEL LIAN</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>HENRICO, VA</td>\n",
       "      <td>05:54:02</td>\n",
       "      <td>05:55:11</td>\n",
       "      <td>13:30 /mi</td>\n",
       "      <td>2018</td>\n",
       "      <td>5.900556</td>\n",
       "      <td>5880</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "      <td>HENRICO, VA</td>\n",
       "      <td>04:29:03</td>\n",
       "      <td>04:33:47</td>\n",
       "      <td>2019</td>\n",
       "      <td>4.484167</td>\n",
       "      <td>1</td>\n",
       "      <td>-1 days +22:35:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>4630</td>\n",
       "      <td>PATRICK BOYD</td>\n",
       "      <td>M</td>\n",
       "      <td>52</td>\n",
       "      <td>VIRGINIA BEACH, VA</td>\n",
       "      <td>06:23:20</td>\n",
       "      <td>06:30:18</td>\n",
       "      <td>14:37 /mi</td>\n",
       "      <td>2018</td>\n",
       "      <td>6.388889</td>\n",
       "      <td>912</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>VIRGINIA BEACH, VA</td>\n",
       "      <td>05:00:20</td>\n",
       "      <td>05:04:31</td>\n",
       "      <td>2019</td>\n",
       "      <td>5.005556</td>\n",
       "      <td>1</td>\n",
       "      <td>-1 days +22:37:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>7302</td>\n",
       "      <td>VICTORIA HEBERT</td>\n",
       "      <td>F</td>\n",
       "      <td>24</td>\n",
       "      <td>LYNCHBURG, VA</td>\n",
       "      <td>06:34:33</td>\n",
       "      <td>06:44:55</td>\n",
       "      <td>15:02 /mi</td>\n",
       "      <td>2018</td>\n",
       "      <td>6.575833</td>\n",
       "      <td>5425</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>LYNCHBURG, VA</td>\n",
       "      <td>05:17:21</td>\n",
       "      <td>05:19:27</td>\n",
       "      <td>2019</td>\n",
       "      <td>5.289167</td>\n",
       "      <td>1</td>\n",
       "      <td>-1 days +22:42:48</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>2049</td>\n",
       "      <td>LISA CURTIN</td>\n",
       "      <td>F</td>\n",
       "      <td>38</td>\n",
       "      <td>MOSELEY, VA</td>\n",
       "      <td>03:47:12</td>\n",
       "      <td>03:48:56</td>\n",
       "      <td>8:39 /mi</td>\n",
       "      <td>2018</td>\n",
       "      <td>3.786667</td>\n",
       "      <td>4382</td>\n",
       "      <td>F</td>\n",
       "      <td>39</td>\n",
       "      <td>REISTERSTOWN, MD</td>\n",
       "      <td>05:22:27</td>\n",
       "      <td>05:27:53</td>\n",
       "      <td>2019</td>\n",
       "      <td>5.374167</td>\n",
       "      <td>1</td>\n",
       "      <td>01:35:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>5357</td>\n",
       "      <td>SIVASUBRAMANIAN SIVARAJ</td>\n",
       "      <td>M</td>\n",
       "      <td>35</td>\n",
       "      <td>CHANTILLY, VA</td>\n",
       "      <td>04:34:58</td>\n",
       "      <td>04:36:55</td>\n",
       "      <td>10:29 /mi</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.582778</td>\n",
       "      <td>1006</td>\n",
       "      <td>M</td>\n",
       "      <td>36</td>\n",
       "      <td>CHANTILLY, VA</td>\n",
       "      <td>06:27:41</td>\n",
       "      <td>06:29:54</td>\n",
       "      <td>2019</td>\n",
       "      <td>6.461389</td>\n",
       "      <td>1</td>\n",
       "      <td>01:52:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>4747</td>\n",
       "      <td>KIM ENGLEMAN</td>\n",
       "      <td>F</td>\n",
       "      <td>46</td>\n",
       "      <td>RICHMOND, VA</td>\n",
       "      <td>04:52:40</td>\n",
       "      <td>04:59:22</td>\n",
       "      <td>11:09 /mi</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.877778</td>\n",
       "      <td>2855</td>\n",
       "      <td>F</td>\n",
       "      <td>47</td>\n",
       "      <td>RICHMOND, VA</td>\n",
       "      <td>06:45:59</td>\n",
       "      <td>06:54:03</td>\n",
       "      <td>2019</td>\n",
       "      <td>6.766389</td>\n",
       "      <td>1</td>\n",
       "      <td>01:53:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>6660</td>\n",
       "      <td>DAWN WALKER</td>\n",
       "      <td>F</td>\n",
       "      <td>47</td>\n",
       "      <td>RICHMOND, VA</td>\n",
       "      <td>04:50:20</td>\n",
       "      <td>04:57:16</td>\n",
       "      <td>11:04 /mi</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.838889</td>\n",
       "      <td>2856</td>\n",
       "      <td>F</td>\n",
       "      <td>48</td>\n",
       "      <td>RICHMOND, VA</td>\n",
       "      <td>07:17:50</td>\n",
       "      <td>07:24:50</td>\n",
       "      <td>2019</td>\n",
       "      <td>7.297222</td>\n",
       "      <td>1</td>\n",
       "      <td>02:27:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1499</td>\n",
       "      <td>DYLAN GIBBS</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>RICHMOND, VA</td>\n",
       "      <td>03:32:25</td>\n",
       "      <td>03:33:03</td>\n",
       "      <td>8:06 /mi</td>\n",
       "      <td>2018</td>\n",
       "      <td>3.540278</td>\n",
       "      <td>968</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>RICHMOND, VA</td>\n",
       "      <td>06:57:27</td>\n",
       "      <td>07:08:57</td>\n",
       "      <td>2019</td>\n",
       "      <td>6.957500</td>\n",
       "      <td>1</td>\n",
       "      <td>03:25:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>709 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Bib_x                     Name Sex_x  Age_x       City, State_x    Net_x  \\\n",
       "419    935           KEVAUGHN SMITH     M     41          NEWARK, NJ 06:03:06   \n",
       "18    8517                 ALLEN MA     M     49      GLEN ALLEN, VA 04:57:31   \n",
       "610   2000              SAMUEL LIAN     M     25         HENRICO, VA 05:54:02   \n",
       "559   4630             PATRICK BOYD     M     52  VIRGINIA BEACH, VA 06:23:20   \n",
       "702   7302          VICTORIA HEBERT     F     24       LYNCHBURG, VA 06:34:33   \n",
       "..     ...                      ...   ...    ...                 ...      ...   \n",
       "462   2049              LISA CURTIN     F     38         MOSELEY, VA 03:47:12   \n",
       "648   5357  SIVASUBRAMANIAN SIVARAJ     M     35       CHANTILLY, VA 04:34:58   \n",
       "428   4747             KIM ENGLEMAN     F     46        RICHMOND, VA 04:52:40   \n",
       "173   6660              DAWN WALKER     F     47        RICHMOND, VA 04:50:20   \n",
       "196   1499              DYLAN GIBBS     M     19        RICHMOND, VA 03:32:25   \n",
       "\n",
       "      Clock_x       Pace  Race_Year_x  Net_In_Hours_x  Bib_y Sex_y Age_y  \\\n",
       "419  06:06:13  13:50 /mi         2018        6.051667   6172     M    42   \n",
       "18   04:59:07  11:20 /mi         2018        4.958611   3152     M    50   \n",
       "610  05:55:11  13:30 /mi         2018        5.900556   5880     M    26   \n",
       "559  06:30:18  14:37 /mi         2018        6.388889    912     M    53   \n",
       "702  06:44:55  15:02 /mi         2018        6.575833   5425     F    25   \n",
       "..        ...        ...          ...             ...    ...   ...   ...   \n",
       "462  03:48:56   8:39 /mi         2018        3.786667   4382     F    39   \n",
       "648  04:36:55  10:29 /mi         2018        4.582778   1006     M    36   \n",
       "428  04:59:22  11:09 /mi         2018        4.877778   2855     F    47   \n",
       "173  04:57:16  11:04 /mi         2018        4.838889   2856     F    48   \n",
       "196  03:33:03   8:06 /mi         2018        3.540278    968     M    20   \n",
       "\n",
       "          City, State_y    Net_y   Clock_y  Race_Year_y  Net_In_Hours_y  \\\n",
       "419          NEWARK, NJ 04:15:08  04:16:48         2019        4.252222   \n",
       "18       GLEN ALLEN, VA 03:27:27  03:30:34         2019        3.457500   \n",
       "610         HENRICO, VA 04:29:03  04:33:47         2019        4.484167   \n",
       "559  VIRGINIA BEACH, VA 05:00:20  05:04:31         2019        5.005556   \n",
       "702       LYNCHBURG, VA 05:17:21  05:19:27         2019        5.289167   \n",
       "..                  ...      ...       ...          ...             ...   \n",
       "462    REISTERSTOWN, MD 05:22:27  05:27:53         2019        5.374167   \n",
       "648       CHANTILLY, VA 06:27:41  06:29:54         2019        6.461389   \n",
       "428        RICHMOND, VA 06:45:59  06:54:03         2019        6.766389   \n",
       "173        RICHMOND, VA 07:17:50  07:24:50         2019        7.297222   \n",
       "196        RICHMOND, VA 06:57:27  07:08:57         2019        6.957500   \n",
       "\n",
       "     Age_Difference     Compare_Years  \n",
       "419               1 -1 days +22:12:02  \n",
       "18                1 -1 days +22:29:56  \n",
       "610               1 -1 days +22:35:01  \n",
       "559               1 -1 days +22:37:00  \n",
       "702               1 -1 days +22:42:48  \n",
       "..              ...               ...  \n",
       "462               1          01:35:15  \n",
       "648               1          01:52:43  \n",
       "428               1          01:53:19  \n",
       "173               1          02:27:30  \n",
       "196               1          03:25:02  \n",
       "\n",
       "[709 rows x 20 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_inner['Compare_Years']=pd.Series(merged_inner['Net_y']-merged_inner['Net_x']) #makes a column for differnce in race time\n",
    "most_improved = merged_inner[merged_inner.Age_Difference <3].sort_values('Compare_Years') #sorts field by change in race time\n",
    "display(most_improved)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_improved.plot(x='Net_In_Hours_x', y='Net_In_Hours_y', style='o', legend=None, figsize=(20,10)) # plots 2018 time on x axis and 2019 on y axis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_improved.to_csv('Richmond_Improved.csv')  # outputs .csv version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Click here to download: <a href='./Richmond_Improved.csv' target='_blank'>./Richmond_Improved.csv</a><br>"
      ],
      "text/plain": [
       "/work/Richmond_Improved.csv"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import FileLink\n",
    "FileLink('./Richmond_Improved.csv', result_html_prefix=\"Click here to download: \")"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}